is a case study non experimental

Case Study vs. Experiment

What's the difference.

Case studies and experiments are both research methods used in various fields to gather data and draw conclusions. However, they differ in their approach and purpose. A case study involves in-depth analysis of a particular individual, group, or situation, aiming to provide a detailed understanding of a specific phenomenon. On the other hand, an experiment involves manipulating variables and observing the effects on a sample population, aiming to establish cause-and-effect relationships. While case studies provide rich qualitative data, experiments provide quantitative data that can be statistically analyzed. Ultimately, the choice between these methods depends on the research question and the desired outcomes.

Further Detail

Introduction.

When conducting research, there are various methods available to gather data and analyze phenomena. Two commonly used approaches are case study and experiment. While both methods aim to provide insights and answers to research questions, they differ in their design, implementation, and the type of data they generate. In this article, we will explore the attributes of case study and experiment, highlighting their strengths and limitations.

A case study is an in-depth investigation of a particular individual, group, or phenomenon. It involves collecting and analyzing detailed information from multiple sources, such as interviews, observations, documents, and archival records. Case studies are often used in social sciences, psychology, and business research to gain a deep understanding of complex and unique situations.

One of the key attributes of a case study is its ability to provide rich and detailed data. Researchers can gather a wide range of information, allowing for a comprehensive analysis of the case. This depth of data enables researchers to explore complex relationships, identify patterns, and generate new hypotheses.

Furthermore, case studies are particularly useful when studying rare or unique phenomena. Since they focus on specific cases, they can provide valuable insights into situations that are not easily replicated or observed in controlled experiments. This attribute makes case studies highly relevant in fields where generalizability is not the primary goal.

However, it is important to note that case studies have limitations. Due to their qualitative nature, the findings may lack generalizability to broader populations or contexts. The small sample size and the subjective interpretation of data can also introduce bias. Additionally, case studies are time-consuming and resource-intensive, requiring extensive data collection and analysis.

An experiment is a research method that involves manipulating variables and measuring their effects on outcomes. It aims to establish cause-and-effect relationships by controlling and manipulating independent variables while keeping other factors constant. Experiments are commonly used in natural sciences, psychology, and medicine to test hypotheses and determine the impact of specific interventions or treatments.

One of the key attributes of an experiment is its ability to establish causal relationships. By controlling variables and randomly assigning participants to different conditions, researchers can confidently attribute any observed effects to the manipulated variables. This attribute allows for strong internal validity, making experiments a powerful tool for drawing causal conclusions.

Moreover, experiments often provide quantitative data, allowing for statistical analysis and objective comparisons. This attribute enhances the precision and replicability of findings, enabling researchers to draw more robust conclusions. The ability to replicate experiments also contributes to the cumulative nature of scientific knowledge.

However, experiments also have limitations. They are often conducted in controlled laboratory settings, which may limit the generalizability of findings to real-world contexts. Ethical considerations may also restrict the manipulation of certain variables or the use of certain interventions. Additionally, experiments can be time-consuming and costly, especially when involving large sample sizes or long-term follow-ups.

While case studies and experiments have distinct attributes, they can complement each other in research. Case studies provide in-depth insights and a rich understanding of complex phenomena, while experiments offer controlled conditions and the ability to establish causal relationships. By combining these methods, researchers can gain a more comprehensive understanding of the research question at hand.

When deciding between case study and experiment, researchers should consider the nature of their research question, the available resources, and the desired level of control and generalizability. Case studies are particularly suitable when exploring unique or rare phenomena, aiming for depth rather than breadth, and when resources allow for extensive data collection and analysis. On the other hand, experiments are ideal for establishing causal relationships, testing specific hypotheses, and when control over variables is crucial.

In conclusion, case study and experiment are two valuable research methods with their own attributes and limitations. Both approaches contribute to the advancement of knowledge in various fields, and their selection depends on the research question, available resources, and desired outcomes. By understanding the strengths and weaknesses of each method, researchers can make informed decisions and conduct rigorous and impactful research.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

6.1 Overview of Non-Experimental Research

Learning objectives.

• Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
• Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

Non-experimental research  is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).

Most researchers in psychology consider the distinction between experimental and non-experimental research to be an extremely important one. This is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. As we will see, however, this inability to make causal conclusions does not mean that non-experimental research is less important than experimental research.

When to Use Non-Experimental Research

As we saw in the last chapter , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:

• the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
• the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
• the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
• the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach will suffice. But the two approaches can also be used to address the same research question in complementary ways. For example, Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into three broad categories: cross-sectional research, correlational research, and observational research. 

First, cross-sectional research  involves comparing two or more pre-existing groups of people. What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a cross-sectional study because the researcher did not manipulate the students’ nationalities. As another example, if we wanted to compare the memory test performance of a group of cannabis users with a group of non-users, this would be considered a cross-sectional study because for ethical and practical reasons we would not be able to randomly assign participants to the cannabis user and non-user groups. Rather we would need to compare these pre-existing groups which could introduce a selection bias (the groups may differ in other ways that affect their responses on the dependent variable). For instance, cannabis users are more likely to use more alcohol and other drugs and these differences may account for differences in the dependent variable across groups, rather than cannabis use per se.

Cross-sectional designs are commonly used by developmental psychologists who study aging and by researchers interested in sex differences. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect) rather than a direct effect of age. For this reason, longitudinal studies in which one group of people is followed as they age offer a superior means of studying the effects of aging. Once again, cross-sectional designs are also commonly used to study sex differences. Since researchers cannot practically or ethically manipulate the sex of their participants they must rely on cross-sectional designs to compare groups of men and women on different outcomes (e.g., verbal ability, substance use, depression). Using these designs researchers have discovered that men are more likely than women to suffer from substance abuse problems while women are more likely than men to suffer from depression. But, using this design it is unclear what is causing these differences. So, using this design it is unclear whether these differences are due to environmental factors like socialization or biological factors like hormones?

When researchers use a participant characteristic to create groups (nationality, cannabis use, age, sex), the independent variable is usually referred to as an experimenter-selected independent variable (as opposed to the experimenter-manipulated independent variables used in experimental research). Figure 6.1 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a cross-sectional study because it is unclear whether the independent variable was manipulated by the researcher or simply selected by the researcher. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then the independent variable was experimenter-manipulated and it is a true experiment. If the researcher simply asked participants whether they made daily to-do lists or not, then the independent variable it is experimenter-selected and the study is cross-sectional. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a cross-sectional study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead. Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed. The crucial point is that what defines a study as experimental or cross-sectional l is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 6.1  Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Second, the most common type of non-experimental research conducted in Psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.  More specifically, in correlational research , the researcher measures two continuous variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related. Correlational research is very similar to cross-sectional research, and sometimes these terms are used interchangeably. The distinction that will be made in this book is that, rather than comparing two or more pre-existing groups of people as is done with cross-sectional research, correlational research involves correlating two continuous variables (groups are not formed and compared).

Third,   observational research  is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In  qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable.  Figure 6.2  shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) is in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Figure 6.2 Internal Validity of Correlation, Quasi-Experimental, and Experimental Studies. Experiments are generally high in internal validity, quasi-experiments lower, and correlation studies lower still.

Notice also in  Figure 6.2  that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Key Takeaways

• Non-experimental research is research that lacks the manipulation of an independent variable.
• There are two broad types of non-experimental research. Correlational research that focuses on statistical relationships between variables that are measured but not manipulated, and observational research in which participants are observed and their behavior is recorded without the researcher interfering or manipulating any variables.
• In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.
• A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
• A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
• A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
• A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
• Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

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Chapter 7: Nonexperimental Research

Overview of Nonexperimental Research

Learning Objectives

• Define nonexperimental research, distinguish it clearly from experimental research, and give several examples.
• Explain when a researcher might choose to conduct nonexperimental research as opposed to experimental research.

What Is Nonexperimental Research?

Nonexperimental research  is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are  not . But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This distinction is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this inability does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense.

When to Use Nonexperimental Research

As we saw in  Chapter 6 , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable and randomly assign participants to conditions or to orders of conditions. It stands to reason, therefore, that nonexperimental research is appropriate—even necessary—when these conditions are not met. There are many ways in which preferring nonexperimental research can be the case.

• The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
• The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?).
• The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?).
• The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and nonexperimental approaches is generally dictated by the nature of the research question. If it is about a causal relationship and involves an independent variable that can be manipulated, the experimental approach is typically preferred. Otherwise, the nonexperimental approach is preferred. But the two approaches can also be used to address the same research question in complementary ways. For example, nonexperimental studies establishing that there is a relationship between watching violent television and aggressive behaviour have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] . Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [2] .

Types of Nonexperimental Research

Nonexperimental research falls into three broad categories: single-variable research, correlational and quasi-experimental research, and qualitative research. First, research can be nonexperimental because it focuses on a single variable rather than a statistical relationship between two variables. Although there is no widely shared term for this kind of research, we will call it  single-variable research . Milgram’s original obedience study was nonexperimental in this way. He was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of single-variable research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the research asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.)

As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This detail is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their self-esteem. But this design would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied  compares  with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied thereby introducing another variable.

Research can also be nonexperimental because it focuses on a statistical relationship between two variables but does not include the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both. This kind of research takes two basic forms: correlational research and quasi-experimental research. In correlational research , the researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them. A research methods student who finds out whether each of several middle-school students has been bullied and then measures each student’s self-esteem is conducting correlational research. In  quasi-experimental research , the researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions. For example, a researcher might start an antibullying program (a kind of treatment) at one school and compare the incidence of bullying at that school with the incidence at a similar school that has no antibullying program.

The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In  qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256). [3] Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable.  Figure 7.1  shows how experimental, quasi-experimental, and correlational research vary in terms of internal validity. Experimental research tends to be highest because it addresses the directionality and third-variable problems through manipulation and the control of extraneous variables through random assignment. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Correlational research is lowest because it fails to address either problem. If the average score on the dependent variable differs across levels of the independent variable, it  could  be that the independent variable is responsible, but there are other interpretations. In some situations, the direction of causality could be reversed. In others, there could be a third variable that is causing differences in both the independent and dependent variables. Quasi-experimental research is in the middle because the manipulation of the independent variable addresses some problems, but the lack of random assignment and experimental control fails to address others. Imagine, for example, that a researcher finds two similar schools, starts an antibullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” There is no directionality problem because clearly the number of bullying incidents did not determine which school got the program. However, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying.

Notice also in  Figure 7.1  that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in  Chapter 5.

Key Takeaways

• Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both.
• There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables. Correlational and quasi-experimental research focus on a statistical relationship but lack manipulation or random assignment. Qualitative research focuses on broader research questions, typically involves collecting large amounts of data from a small number of participants, and analyses the data nonstatistically.
• In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.

Discussion: For each of the following studies, decide which type of research design it is and explain why.

• A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
• A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
• A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
• A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

Research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

Research that focuses on a single variable rather than a statistical relationship between two variables.

The researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them.

The researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Non-experimental research: What it is, overview & advantages

Non-experimental research is the type of research that lacks an independent variable. Instead, the researcher observes the context in which the phenomenon occurs and analyzes it to obtain information.

Unlike experimental research , where the variables are held constant, non-experimental research happens during the study when the researcher cannot control, manipulate or alter the subjects but relies on interpretation or observations to conclude.

This means that the method must not rely on correlations, surveys , or case studies and cannot demonstrate an actual cause and effect relationship.

Characteristics of non-experimental research

Some of the essential characteristics of non-experimental research are necessary for the final results. Let’s talk about them to identify the most critical parts of them.

• Most studies are based on events that occurred previously and are analyzed later.
• In this method, controlled experiments are not performed for reasons such as ethics or morality.
• No study samples are created; on the contrary, the samples or participants already exist and develop in their environment.
• The researcher does not intervene directly in the environment of the sample.
• This method studies the phenomena exactly as they occurred.

Types of non-experimental research

Non-experimental research can take the following forms:

Cross-sectional research : Cross-sectional research is used to observe and analyze the exact time of the research to cover various study groups or samples. This type of research is divided into:

• Descriptive: When values are observed where one or more variables are presented.
• Causal: It is responsible for explaining the reasons and relationship that exists between variables in a given time.

Longitudinal research: In a longitudinal study , researchers aim to analyze the changes and development of the relationships between variables over time. Longitudinal research can be divided into:

• Trend: When they study the changes faced by the study group in general.
• Group evolution: When the study group is a smaller sample.
• Panel: It is in charge of analyzing individual and group changes to discover the factor that produces them.

LEARN ABOUT: Quasi-experimental Research

When to use non-experimental research

Non-experimental research can be applied in the following ways:

• When the research question may be about one variable rather than a statistical relationship about two variables.
• There is a non-causal statistical relationship between variables in the research question.
• The research question has a causal research relationship, but the independent variable cannot be manipulated.
• In exploratory or broad research where a particular experience is confronted.

Advantages and disadvantages

Some advantages of non-experimental research are:

• It is very flexible during the research process
• The cause of the phenomenon is known, and the effect it has is investigated.
• The researcher can define the characteristics of the study group.

Among the disadvantages of non-experimental research are:

• The groups are not representative of the entire population.
• Errors in the methodology may occur, leading to research biases .

Non-experimental research is based on the observation of phenomena in their natural environment. In this way, they can be studied later to reach a conclusion.

Difference between experimental and non-experimental research

Experimental research involves changing variables and randomly assigning conditions to participants. As it can determine the cause, experimental research designs are used for research in medicine, biology, and social science. 

Experimental research designs have strict standards for control and establishing validity. Although they may need many resources, they can lead to very interesting results.

Non-experimental research, on the other hand, is usually descriptive or correlational without any explicit changes done by the researcher. You simply describe the situation as it is, or describe a relationship between variables. Without any control, it is difficult to determine causal effects. The validity remains a concern in this type of research. However, it’s’ more regarding the measurements instead of the effects.

LEARN MORE: Descriptive Research vs Correlational Research

Whether you should choose experimental research or non-experimental research design depends on your goals and resources. If you need any help with how to conduct research and collect relevant data, or have queries regarding the best approach for your research goals, contact us today! You can create an account with our survey software and avail of 88+ features including dashboard and reporting for free.

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Non-Experimental Research

28 Overview of Non-Experimental Research

Learning objectives.

• Define non-experimental research, distinguish it clearly from experimental research, and give several examples.
• Explain when a researcher might choose to conduct non-experimental research as opposed to experimental research.

What Is Non-Experimental Research?

Non-experimental research  is research that lacks the manipulation of an independent variable. Rather than manipulating an independent variable, researchers conducting non-experimental research simply measure variables as they naturally occur (in the lab or real world).

Most researchers in psychology consider the distinction between experimental and non-experimental research to be an extremely important one. This is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, non-experimental research generally cannot. As we will see, however, this inability to make causal conclusions does not mean that non-experimental research is less important than experimental research. It is simply used in cases where experimental research is not able to be carried out.

When to Use Non-Experimental Research

As we saw in the last chapter , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable. It stands to reason, therefore, that non-experimental research is appropriate—even necessary—when these conditions are not met. There are many times in which non-experimental research is preferred, including when:

• the research question or hypothesis relates to a single variable rather than a statistical relationship between two variables (e.g., how accurate are people’s first impressions?).
• the research question pertains to a non-causal statistical relationship between variables (e.g., is there a correlation between verbal intelligence and mathematical intelligence?).
• the research question is about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions for practical or ethical reasons (e.g., does damage to a person’s hippocampus impair the formation of long-term memory traces?).
• the research question is broad and exploratory, or is about what it is like to have a particular experience (e.g., what is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and non-experimental approaches is generally dictated by the nature of the research question. Recall the three goals of science are to describe, to predict, and to explain. If the goal is to explain and the research question pertains to causal relationships, then the experimental approach is typically preferred. If the goal is to describe or to predict, a non-experimental approach is appropriate. But the two approaches can also be used to address the same research question in complementary ways. For example, in Milgram’s original (non-experimental) obedience study, he was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. However,  Milgram subsequently conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [1] .

Types of Non-Experimental Research

Non-experimental research falls into two broad categories: correlational research and observational research. 

The most common type of non-experimental research conducted in psychology is correlational research. Correlational research is considered non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable. More specifically, in correlational research , the researcher measures two variables with little or no attempt to control extraneous variables and then assesses the relationship between them. As an example, a researcher interested in the relationship between self-esteem and school achievement could collect data on students’ self-esteem and their GPAs to see if the two variables are statistically related.

Observational research  is non-experimental because it focuses on making observations of behavior in a natural or laboratory setting without manipulating anything. Milgram’s original obedience study was non-experimental in this way. He was primarily interested in the extent to which participants obeyed the researcher when he told them to shock the confederate and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of observational research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the researchers asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories).

Cross-Sectional, Longitudinal, and Cross-Sequential Studies

When psychologists wish to study change over time (for example, when developmental psychologists wish to study aging) they usually take one of three non-experimental approaches: cross-sectional, longitudinal, or cross-sequential. Cross-sectional studies involve comparing two or more pre-existing groups of people (e.g., children at different stages of development). What makes this approach non-experimental is that there is no manipulation of an independent variable and no random assignment of participants to groups. Using this design, developmental psychologists compare groups of people of different ages (e.g., young adults spanning from 18-25 years of age versus older adults spanning 60-75 years of age) on various dependent variables (e.g., memory, depression, life satisfaction). Of course, the primary limitation of using this design to study the effects of aging is that differences between the groups other than age may account for differences in the dependent variable. For instance, differences between the groups may reflect the generation that people come from (a cohort effect ) rather than a direct effect of age. For this reason, longitudinal studies , in which one group of people is followed over time as they age, offer a superior means of studying the effects of aging. However, longitudinal studies are by definition more time consuming and so require a much greater investment on the part of the researcher and the participants. A third approach, known as cross-sequential studies , combines elements of both cross-sectional and longitudinal studies. Rather than measuring differences between people in different age groups or following the same people over a long period of time, researchers adopting this approach choose a smaller period of time during which they follow people in different age groups. For example, they might measure changes over a ten year period among participants who at the start of the study fall into the following age groups: 20 years old, 30 years old, 40 years old, 50 years old, and 60 years old. This design is advantageous because the researcher reaps the immediate benefits of being able to compare the age groups after the first assessment. Further, by following the different age groups over time they can subsequently determine whether the original differences they found across the age groups are due to true age effects or cohort effects.

The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. But as you will learn in this chapter, many observational research studies are more qualitative in nature. In  qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s observational study of the experience of people in psychiatric wards was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semi-public room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [2] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable.  Figure 6.1 shows how experimental, quasi-experimental, and non-experimental (correlational) research vary in terms of internal validity. Experimental research tends to be highest in internal validity because the use of manipulation (of the independent variable) and control (of extraneous variables) help to rule out alternative explanations for the observed relationships. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Non-experimental (correlational) research is lowest in internal validity because these designs fail to use manipulation or control. Quasi-experimental research (which will be described in more detail in a subsequent chapter) falls in the middle because it contains some, but not all, of the features of a true experiment. For instance, it may fail to use random assignment to assign participants to groups or fail to use counterbalancing to control for potential order effects. Imagine, for example, that a researcher finds two similar schools, starts an anti-bullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” While a comparison is being made with a control condition, the inability to randomly assign children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying (e.g., there may be a selection effect).

Notice also in  Figure 6.1 that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational (non-experimental) studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well-designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

• Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

A research that lacks the manipulation of an independent variable.

Research that is non-experimental because it focuses on the statistical relationship between two variables but does not include the manipulation of an independent variable.

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

Studies that involve comparing two or more pre-existing groups of people (e.g., children at different stages of development).

Differences between the groups may reflect the generation that people come from rather than a direct effect of age.

Studies in which one group of people are followed over time as they age.

Studies in which researchers follow people in different age groups in a smaller period of time.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Experimental Vs Non-Experimental Research: 15 Key Differences

There is a general misconception around research that once the research is non-experimental, then it is non-scientific, making it more important to understand what experimental and experimental research entails. Experimental research is the most common type of research, which a lot of people refer to as scientific research. 

Non experimental research, on the other hand, is easily used to classify research that is not experimental. It clearly differs from experimental research, and as such has different use cases. 

In this article, we will be explaining these differences in detail so as to ensure proper identification during the research process.

What is Experimental Research?  

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables of the research subject(s) and measuring the effect of this manipulation on the subject. It is known for the fact that it allows the manipulation of control variables. 

This research method is widely used in various physical and social science fields, even though it may be quite difficult to execute. Within the information field, they are much more common in information systems research than in library and information management research.

Experimental research is usually undertaken when the goal of the research is to trace cause-and-effect relationships between defined variables. However, the type of experimental research chosen has a significant influence on the results of the experiment.

Therefore bringing us to the different types of experimental research. There are 3 main types of experimental research, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research

Pre-experimental research is the simplest form of research, and is carried out by observing a group or groups of dependent variables after the treatment of an independent variable which is presumed to cause change on the group(s). It is further divided into three types.

• One-shot case study research 
• One-group pretest-posttest research 
• Static-group comparison

Quasi-experimental Research

The Quasi type of experimental research is similar to true experimental research, but uses carefully selected rather than randomized subjects. The following are examples of quasi-experimental research:

• Time series 
• No equivalent control group design
• Counterbalanced design.

True Experimental Research

True experimental research is the most accurate type,  and may simply be called experimental research. It manipulates a control group towards a group of randomly selected subjects and records the effect of this manipulation.

True experimental research can be further classified into the following groups:

• The posttest-only control group 
• The pretest-posttest control group 
• Solomon four-group 

Pros of True Experimental Research

• Researchers can have control over variables.
• It can be combined with other research methods.
• The research process is usually well structured.
• It provides specific conclusions.
• The results of experimental research can be easily duplicated.

Cons of True Experimental Research

• It is highly prone to human error.
• Exerting control over extraneous variables may lead to the personal bias of the researcher.
• It is time-consuming.
• It is expensive. 
• Manipulating control variables may have ethical implications.
• It produces artificial results.

What is Non-Experimental Research?  

Non-experimental research is the type of research that does not involve the manipulation of control or independent variable. In non-experimental research, researchers measure variables as they naturally occur without any further manipulation.

This type of research is used when the researcher has no specific research question about a causal relationship between 2 different variables, and manipulation of the independent variable is impossible. They are also used when:

• subjects cannot be randomly assigned to conditions.
• the research subject is about a causal relationship but the independent variable cannot be manipulated.
• the research is broad and exploratory
• the research pertains to a non-causal relationship between variables.
• limited information can be accessed about the research subject.

There are 3 main types of non-experimental research , namely; cross-sectional research, correlation research, and observational research.

Cross-sectional Research

Cross-sectional research involves the comparison of two or more pre-existing groups of people under the same criteria. This approach is classified as non-experimental because the groups are not randomly selected and the independent variable is not manipulated.

For example, an academic institution may want to reward its first-class students with a scholarship for their academic excellence. Therefore, each faculty places students in the eligible and ineligible group according to their class of degree.

In this case, the student’s class of degree cannot be manipulated to qualify him or her for a scholarship because it is an unethical thing to do. Therefore, the placement is cross-sectional.

Correlational Research

Correlational type of research compares the statistical relationship between two variables .Correlational research is classified as non-experimental because it does not manipulate the independent variables.

For example, a researcher may wish to investigate the relationship between the class of family students come from and their grades in school. A questionnaire may be given to students to know the average income of their family, then compare it with CGPAs. 

The researcher will discover whether these two factors are positively correlated, negatively corrected, or have zero correlation at the end of the research.

Observational Research

Observational research focuses on observing the behavior of a research subject in a natural or laboratory setting. It is classified as non-experimental because it does not involve the manipulation of independent variables.

A good example of observational research is an investigation of the crowd effect or psychology in a particular group of people. Imagine a situation where there are 2 ATMs at a place, and only one of the ATMs is filled with a queue, while the other is abandoned.

The crowd effect infers that the majority of newcomers will also abandon the other ATM.

You will notice that each of these non-experimental research is descriptive in nature. It then suffices to say that descriptive research is an example of non-experimental research.

Pros of Observational Research

• The research process is very close to a real-life situation.
• It does not allow for the manipulation of variables due to ethical reasons.
• Human characteristics are not subject to experimental manipulation.

Cons of Observational Research

• The groups may be dissimilar and nonhomogeneous because they are not randomly selected, affecting the authenticity and generalizability of the study results.
• The results obtained cannot be absolutely clear and error-free.

What Are The Differences Between Experimental and Non-Experimental Research?    

• Definitions

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables and measuring their defect on the dependent variables, while non-experimental research is the type of research that does not involve the manipulation of control variables.

The main distinction in these 2 types of research is their attitude towards the manipulation of control variables. Experimental allows for the manipulation of control variables while non-experimental research doesn’t.

 Examples of experimental research are laboratory experiments that involve mixing different chemical elements together to see the effect of one element on the other while non-experimental research examples are investigations into the characteristics of different chemical elements.

Consider a researcher carrying out a laboratory test to determine the effect of adding Nitrogen gas to Hydrogen gas. It may be discovered that using the Haber process, one can create Nitrogen gas.

Non-experimental research may further be carried out on Ammonia, to determine its characteristics, behaviour, and nature.

There are 3 types of experimental research, namely; experimental research, quasi-experimental research, and true experimental research. Although also 3 in number, non-experimental research can be classified into cross-sectional research, correlational research, and observational research.

The different types of experimental research are further divided into different parts, while non-experimental research types are not further divided. Clearly, these divisions are not the same in experimental and non-experimental research.

• Characteristics

Experimental research is usually quantitative, controlled, and multivariable. Non-experimental research can be both quantitative and qualitative , has an uncontrolled variable, and also a cross-sectional research problem.

The characteristics of experimental research are the direct opposite of that of non-experimental research. The most distinct characteristic element is the ability to control or manipulate independent variables in experimental research and not in non-experimental research. 

In experimental research, a level of control is usually exerted on extraneous variables, therefore tampering with the natural research setting. Experimental research settings are usually more natural with no tampering with the extraneous variables.

• Data Collection/Tools

  The data used during experimental research is collected through observational study, simulations, and surveys while non-experimental data is collected through observations, surveys, and case studies. The main distinction between these data collection tools is case studies and simulations.

Even at that, similar tools are used differently. For example, an observational study may be used during a laboratory experiment that tests how the effect of a control variable manifests over a period of time in experimental research. 

However, when used in non-experimental research, data is collected based on the researcher’s discretion and not through a clear scientific reaction. In this case, we see a difference in the level of objectivity. 

The goal of experimental research is to measure the causes and effects of variables present in research, while non-experimental research provides very little to no information about causal agents.

Experimental research answers the question of why something is happening. This is quite different in non-experimental research, as they are more descriptive in nature with the end goal being to describe what .

 Experimental research is mostly used to make scientific innovations and find major solutions to problems while non-experimental research is used to define subject characteristics, measure data trends, compare situations and validate existing conditions.

For example, if experimental research results in an innovative discovery or solution, non-experimental research will be conducted to validate this discovery. This research is done for a period of time in order to properly study the subject of research.

Experimental research process is usually well structured and as such produces results with very little to no errors, while non-experimental research helps to create real-life related experiments. There are a lot more advantages of experimental and non-experimental research , with the absence of each of these advantages in the other leaving it at a disadvantage.

For example, the lack of a random selection process in non-experimental research leads to the inability to arrive at a generalizable result. Similarly, the ability to manipulate control variables in experimental research may lead to the personal bias of the researcher.

• Disadvantage

 Experimental research is highly prone to human error while the major disadvantage of non-experimental research is that the results obtained cannot be absolutely clear and error-free. In the long run, the error obtained due to human error may affect the results of the experimental research.

Some other disadvantages of experimental research include the following; extraneous variables cannot always be controlled, human responses can be difficult to measure, and participants may also cause bias.

  In experimental research, researchers can control and manipulate control variables, while in non-experimental research, researchers cannot manipulate these variables. This cannot be done due to ethical reasons. 

For example, when promoting employees due to how well they did in their annual performance review, it will be unethical to manipulate the results of the performance review (independent variable). That way, we can get impartial results of those who deserve a promotion and those who don’t.

Experimental researchers may also decide to eliminate extraneous variables so as to have enough control over the research process. Once again, this is something that cannot be done in non-experimental research because it relates more to real-life situations.

Experimental research is carried out in an unnatural setting because most of the factors that influence the setting are controlled while the non-experimental research setting remains natural and uncontrolled. One of the things usually tampered with during research is extraneous variables.

In a bid to get a perfect and well-structured research process and results, researchers sometimes eliminate extraneous variables. Although sometimes seen as insignificant, the elimination of these variables may affect the research results.

Consider the optimization problem whose aim is to minimize the cost of production of a car, with the constraints being the number of workers and the number of hours they spend working per day. 

In this problem, extraneous variables like machine failure rates or accidents are eliminated. In the long run, these things may occur and may invalidate the result.

• Cause-Effect Relationship

The relationship between cause and effect is established in experimental research while it cannot be established in non-experimental research. Rather than establish a cause-effect relationship, non-experimental research focuses on providing descriptive results.

Although it acknowledges the causal variable and its effect on the dependent variables, it does not measure how or the extent to which these dependent variables change. It, however, observes these changes, compares the changes in 2 variables, and describes them.

Experimental research does not compare variables while non-experimental research does. It compares 2 variables and describes the relationship between them.

The relationship between these variables can be positively correlated, negatively correlated or not correlated at all. For example, consider a case whereby the subject of research is a drum, and the control or independent variable is the drumstick.

Experimental research will measure the effect of hitting the drumstick on the drum, where the result of this research will be sound. That is, when you hit a drumstick on a drum, it makes a sound.

Non-experimental research, on the other hand, will investigate the correlation between how hard the drum is hit and the loudness of the sound that comes out. That is, if the sound will be higher with a harder bang, lower with a harder bang, or will remain the same no matter how hard we hit the drum.

• Quantitativeness

Experimental research is a quantitative research method while non-experimental research can be both quantitative and qualitative depending on the time and the situation where it is been used. An example of a non-experimental quantitative research method is correlational research .

Researchers use it to correlate two or more variables using mathematical analysis methods. The original patterns, relationships, and trends between variables are observed, then the impact of one of these variables on the other is recorded along with how it changes the relationship between the two variables.

Observational research is an example of non-experimental research, which is classified as a qualitative research method.

• Cross-section

Experimental research is usually single-sectional while non-experimental research is cross-sectional. That is, when evaluating the research subjects in experimental research, each group is evaluated as an entity.

For example, let us consider a medical research process investigating the prevalence of breast cancer in a certain community. In this community, we will find people of different ages, ethnicities, and social backgrounds. 

If a significant amount of women from a particular age are found to be more prone to have the disease, the researcher can conduct further studies to understand the reason behind it. A further study into this will be experimental and the subject won’t be a cross-sectional group. 

A lot of researchers consider the distinction between experimental and non-experimental research to be an extremely important one. This is partly due to the fact that experimental research can accommodate the manipulation of independent variables, which is something non-experimental research can not.

Therefore, as a researcher who is interested in using any one of experimental and non-experimental research, it is important to understand the distinction between these two. This helps in deciding which method is better for carrying out particular research. 

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Planning and Conducting Clinical Research: The Whole Process

Boon-how chew.

1 Family Medicine, Universiti Putra Malaysia, Serdang, MYS

The goal of this review was to present the essential steps in the entire process of clinical research. Research should begin with an educated idea arising from a clinical practice issue. A research topic rooted in a clinical problem provides the motivation for the completion of the research and relevancy for affecting medical practice changes and improvements. The research idea is further informed through a systematic literature review, clarified into a conceptual framework, and defined into an answerable research question. Engagement with clinical experts, experienced researchers, relevant stakeholders of the research topic, and even patients can enhance the research question’s relevance, feasibility, and efficiency. Clinical research can be completed in two major steps: study designing and study reporting. Three study designs should be planned in sequence and iterated until properly refined: theoretical design, data collection design, and statistical analysis design. The design of data collection could be further categorized into three facets: experimental or non-experimental, sampling or census, and time features of the variables to be studied. The ultimate aims of research reporting are to present findings succinctly and timely. Concise, explicit, and complete reporting are the guiding principles in clinical studies reporting.

Introduction and background

Medical and clinical research can be classified in many different ways. Probably, most people are familiar with basic (laboratory) research, clinical research, healthcare (services) research, health systems (policy) research, and educational research. Clinical research in this review refers to scientific research related to clinical practices. There are many ways a clinical research's findings can become invalid or less impactful including ignorance of previous similar studies, a paucity of similar studies, poor study design and implementation, low test agent efficacy, no predetermined statistical analysis, insufficient reporting, bias, and conflicts of interest [ 1 - 4 ]. Scientific, ethical, and moral decadence among researchers can be due to incognizant criteria in academic promotion and remuneration and too many forced studies by amateurs and students for the sake of research without adequate training or guidance [ 2 , 5 - 6 ]. This article will review the proper methods to conduct medical research from the planning stage to submission for publication (Table ​ (Table1 1 ).

a Feasibility and efficiency are considered during the refinement of the research question and adhered to during data collection.

Epidemiologic studies in clinical and medical fields focus on the effect of a determinant on an outcome [ 7 ]. Measurement errors that happen systematically give rise to biases leading to invalid study results, whereas random measurement errors will cause imprecise reporting of effects. Precision can usually be increased with an increased sample size provided biases are avoided or trivialized. Otherwise, the increased precision will aggravate the biases. Because epidemiologic, clinical research focuses on measurement, measurement errors are addressed throughout the research process. Obtaining the most accurate estimate of a treatment effect constitutes the whole business of epidemiologic research in clinical practice. This is greatly facilitated by clinical expertise and current scientific knowledge of the research topic. Current scientific knowledge is acquired through literature reviews or in collaboration with an expert clinician. Collaboration and consultation with an expert clinician should also include input from the target population to confirm the relevance of the research question. The novelty of a research topic is less important than the clinical applicability of the topic. Researchers need to acquire appropriate writing and reporting skills from the beginning of their careers, and these skills should improve with persistent use and regular reviewing of published journal articles. A published clinical research study stands on solid scientific ground to inform clinical practice given the article has passed through proper peer-reviews, revision, and content improvement.

Systematic literature reviews

Systematic literature reviews of published papers will inform authors of the existing clinical evidence on a research topic. This is an important step to reduce wasted efforts and evaluate the planned study [ 8 ]. Conducting a systematic literature review is a well-known important step before embarking on a new study [ 9 ]. A rigorously performed and cautiously interpreted systematic review that includes in-process trials can inform researchers of several factors [ 10 ]. Reviewing the literature will inform the choice of recruitment methods, outcome measures, questionnaires, intervention details, and statistical strategies – useful information to increase the study’s relevance, value, and power. A good review of previous studies will also provide evidence of the effects of an intervention that may or may not be worthwhile; this would suggest either no further studies are warranted or that further study of the intervention is needed. A review can also inform whether a larger and better study is preferable to an additional small study. Reviews of previously published work may yield few studies or low-quality evidence from small or poorly designed studies on certain intervention or observation; this may encourage or discourage further research or prompt consideration of a first clinical trial.

Conceptual framework

The result of a literature review should include identifying a working conceptual framework to clarify the nature of the research problem, questions, and designs, and even guide the latter discussion of the findings and development of possible solutions. Conceptual frameworks represent ways of thinking about a problem or how complex things work the way they do [ 11 ]. Different frameworks will emphasize different variables and outcomes, and their inter-relatedness. Each framework highlights or emphasizes different aspects of a problem or research question. Often, any single conceptual framework presents only a partial view of reality [ 11 ]. Furthermore, each framework magnifies certain elements of the problem. Therefore, a thorough literature search is warranted for authors to avoid repeating the same research endeavors or mistakes. It may also help them find relevant conceptual frameworks including those that are outside one’s specialty or system. 

Conceptual frameworks can come from theories with well-organized principles and propositions that have been confirmed by observations or experiments. Conceptual frameworks can also come from models derived from theories, observations or sets of concepts or even evidence-based best practices derived from past studies [ 11 ].

Researchers convey their assumptions of the associations of the variables explicitly in the conceptual framework to connect the research to the literature. After selecting a single conceptual framework or a combination of a few frameworks, a clinical study can be completed in two fundamental steps: study design and study report. Three study designs should be planned in sequence and iterated until satisfaction: the theoretical design, data collection design, and statistical analysis design [ 7 ]. 

Study designs

Theoretical Design

Theoretical design is the next important step in the research process after a literature review and conceptual framework identification. While the theoretical design is a crucial step in research planning, it is often dealt with lightly because of the more alluring second step (data collection design). In the theoretical design phase, a research question is designed to address a clinical problem, which involves an informed understanding based on the literature review and effective collaboration with the right experts and clinicians. A well-developed research question will have an initial hypothesis of the possible relationship between the explanatory variable/exposure and the outcome. This will inform the nature of the study design, be it qualitative or quantitative, primary or secondary, and non-causal or causal (Figure ​ (Figure1 1 ).

A study is qualitative if the research question aims to explore, understand, describe, discover or generate reasons underlying certain phenomena. Qualitative studies usually focus on a process to determine how and why things happen [ 12 ]. Quantitative studies use deductive reasoning, and numerical statistical quantification of the association between groups on data often gathered during experiments [ 13 ]. A primary clinical study is an original study gathering a new set of patient-level data. Secondary research draws on the existing available data and pooling them into a larger database to generate a wider perspective or a more powerful conclusion. Non-causal or descriptive research aims to identify the determinants or associated factors for the outcome or health condition, without regard for causal relationships. Causal research is an exploration of the determinants of an outcome while mitigating confounding variables. Table ​ Table2 2 shows examples of non-causal (e.g., diagnostic and prognostic) and causal (e.g., intervention and etiologic) clinical studies. Concordance between the research question, its aim, and the choice of theoretical design will provide a strong foundation and the right direction for the research process and path. 

A problem in clinical epidemiology is phrased in a mathematical relationship below, where the outcome is a function of the determinant (D) conditional on the extraneous determinants (ED) or more commonly known as the confounding factors [ 7 ]:

For non-causal research, Outcome = f (D1, D2…Dn) For causal research, Outcome = f (D | ED)

A fine research question is composed of at least three components: 1) an outcome or a health condition, 2) determinant/s or associated factors to the outcome, and 3) the domain. The outcome and the determinants have to be clearly conceptualized and operationalized as measurable variables (Table ​ (Table3; 3 ; PICOT [ 14 ] and FINER [ 15 ]). The study domain is the theoretical source population from which the study population will be sampled, similar to the wording on a drug package insert that reads, “use this medication (study results) in people with this disease” [ 7 ].

The interpretation of study results as they apply to wider populations is known as generalization, and generalization can either be statistical or made using scientific inferences [ 16 ]. Generalization supported by statistical inferences is seen in studies on disease prevalence where the sample population is representative of the source population. By contrast, generalizations made using scientific inferences are not bound by the representativeness of the sample in the study; rather, the generalization should be plausible from the underlying scientific mechanisms as long as the study design is valid and nonbiased. Scientific inferences and generalizations are usually the aims of causal studies. 

Confounding: Confounding is a situation where true effects are obscured or confused [ 7 , 16 ]. Confounding variables or confounders affect the validity of a study’s outcomes and should be prevented or mitigated in the planning stages and further managed in the analytical stages. Confounders are also known as extraneous determinants in epidemiology due to their inherent and simultaneous relationships to both the determinant and outcome (Figure ​ (Figure2), 2 ), which are usually one-determinant-to-one outcome in causal clinical studies. The known confounders are also called observed confounders. These can be minimized using randomization, restriction, or a matching strategy. Residual confounding has occurred in a causal relationship when identified confounders were not measured accurately. Unobserved confounding occurs when the confounding effect is present as a variable or factor not observed or yet defined and, thus, not measured in the study. Age and gender are almost universal confounders followed by ethnicity and socio-economic status.

Confounders have three main characteristics. They are a potential risk factor for the disease, associated with the determinant of interest, and should not be an intermediate variable between the determinant and the outcome or a precursor to the determinant. For example, a sedentary lifestyle is a cause for acute coronary syndrome (ACS), and smoking could be a confounder but not cardiorespiratory unfitness (which is an intermediate factor between a sedentary lifestyle and ACS). For patients with ACS, not having a pair of sports shoes is not a confounder – it is a correlate for the sedentary lifestyle. Similarly, depression would be a precursor, not a confounder.

Sample size consideration: Sample size calculation provides the required number of participants to be recruited in a new study to detect true differences in the target population if they exist. Sample size calculation is based on three facets: an estimated difference in group sizes, the probability of α (Type I) and β (Type II) errors chosen based on the nature of the treatment or intervention, and the estimated variability (interval data) or proportion of the outcome (nominal data) [ 17 - 18 ]. The clinically important effect sizes are determined based on expert consensus or patients’ perception of benefit. Value and economic consideration have increasingly been included in sample size estimations. Sample size and the degree to which the sample represents the target population affect the accuracy and generalization of a study’s reported effects. 

Pilot study: Pilot studies assess the feasibility of the proposed research procedures on small sample size. Pilot studies test the efficiency of participant recruitment with minimal practice or service interruptions. Pilot studies should not be conducted to obtain a projected effect size for a larger study population because, in a typical pilot study, the sample size is small, leading to a large standard error of that effect size. This leads to bias when projected for a large population. In the case of underestimation, this could lead to inappropriately terminating the full-scale study. As the small pilot study is equally prone to bias of overestimation of the effect size, this would lead to an underpowered study and a failed full-scale study [ 19 ]. 

The Design of Data Collection

The “perfect” study design in the theoretical phase now faces the practical and realistic challenges of feasibility. This is the step where different methods for data collection are considered, with one selected as the most appropriate based on the theoretical design along with feasibility and efficiency. The goal of this stage is to achieve the highest possible validity with the lowest risk of biases given available resources and existing constraints. 

In causal research, data on the outcome and determinants are collected with utmost accuracy via a strict protocol to maximize validity and precision. The validity of an instrument is defined as the degree of fidelity of the instrument, measuring what it is intended to measure, that is, the results of the measurement correlate with the true state of an occurrence. Another widely used word for validity is accuracy. Internal validity refers to the degree of accuracy of a study’s results to its own study sample. Internal validity is influenced by the study designs, whereas the external validity refers to the applicability of a study’s result in other populations. External validity is also known as generalizability and expresses the validity of assuming the similarity and comparability between the study population and the other populations. Reliability of an instrument denotes the extent of agreeableness of the results of repeated measurements of an occurrence by that instrument at a different time, by different investigators or in a different setting. Other terms that are used for reliability include reproducibility and precision. Preventing confounders by identifying and including them in data collection will allow statistical adjustment in the later analyses. In descriptive research, outcomes must be confirmed with a referent standard, and the determinants should be as valid as those found in real clinical practice.

Common designs for data collection include cross-sectional, case-control, cohort, and randomized controlled trials (RCTs). Many other modern epidemiology study designs are based on these classical study designs such as nested case-control, case-crossover, case-control without control, and stepwise wedge clustered RCTs. A cross-sectional study is typically a snapshot of the study population, and an RCT is almost always a prospective study. Case-control and cohort studies can be retrospective or prospective in data collection. The nested case-control design differs from the traditional case-control design in that it is “nested” in a well-defined cohort from which information on the cohorts can be obtained. This design also satisfies the assumption that cases and controls represent random samples of the same study base. Table ​ Table4 4 provides examples of these data collection designs.

Additional aspects in data collection: No single design of data collection for any research question as stated in the theoretical design will be perfect in actual conduct. This is because of myriad issues facing the investigators such as the dynamic clinical practices, constraints of time and budget, the urgency for an answer to the research question, and the ethical integrity of the proposed experiment. Therefore, feasibility and efficiency without sacrificing validity and precision are important considerations in data collection design. Therefore, data collection design requires additional consideration in the following three aspects: experimental/non-experimental, sampling, and timing [ 7 ]:

Experimental or non-experimental: Non-experimental research (i.e., “observational”), in contrast to experimental, involves data collection of the study participants in their natural or real-world environments. Non-experimental researches are usually the diagnostic and prognostic studies with cross-sectional in data collection. The pinnacle of non-experimental research is the comparative effectiveness study, which is grouped with other non-experimental study designs such as cross-sectional, case-control, and cohort studies [ 20 ]. It is also known as the benchmarking-controlled trials because of the element of peer comparison (using comparable groups) in interpreting the outcome effects [ 20 ]. Experimental study designs are characterized by an intervention on a selected group of the study population in a controlled environment, and often in the presence of a similar group of the study population to act as a comparison group who receive no intervention (i.e., the control group). Thus, the widely known RCT is classified as an experimental design in data collection. An experimental study design without randomization is referred to as a quasi-experimental study. Experimental studies try to determine the efficacy of a new intervention on a specified population. Table ​ Table5 5 presents the advantages and disadvantages of experimental and non-experimental studies [ 21 ].

a May be an issue in cross-sectional studies that require a long recall to the past such as dietary patterns, antenatal events, and life experiences during childhood.

Once an intervention yields a proven effect in an experimental study, non-experimental and quasi-experimental studies can be used to determine the intervention’s effect in a wider population and within real-world settings and clinical practices. Pragmatic or comparative effectiveness are the usual designs used for data collection in these situations [ 22 ].

Sampling/census: Census is a data collection on the whole source population (i.e., the study population is the source population). This is possible when the defined population is restricted to a given geographical area. A cohort study uses the census method in data collection. An ecologic study is a cohort study that collects summary measures of the study population instead of individual patient data. However, many studies sample from the source population and infer the results of the study to the source population for feasibility and efficiency because adequate sampling provides similar results to the census of the whole population. Important aspects of sampling in research planning are sample size and representation of the population. Sample size calculation accounts for the number of participants needed to be in the study to discover the actual association between the determinant and outcome. Sample size calculation relies on the primary objective or outcome of interest and is informed by the estimated possible differences or effect size from previous similar studies. Therefore, the sample size is a scientific estimation for the design of the planned study.

A sampling of participants or cases in a study can represent the study population and the larger population of patients in that disease space, but only in prevalence, diagnostic, and prognostic studies. Etiologic and interventional studies do not share this same level of representation. A cross-sectional study design is common for determining disease prevalence in the population. Cross-sectional studies can also determine the referent ranges of variables in the population and measure change over time (e.g., repeated cross-sectional studies). Besides being cost- and time-efficient, cross-sectional studies have no loss to follow-up; recall bias; learning effect on the participant; or variability over time in equipment, measurement, and technician. A cross-sectional design for an etiologic study is possible when the determinants do not change with time (e.g., gender, ethnicity, genetic traits, and blood groups). 

In etiologic research, comparability between the exposed and the non-exposed groups is more important than sample representation. Comparability between these two groups will provide an accurate estimate of the effect of the exposure (risk factor) on the outcome (disease) and enable valid inference of the causal relation to the domain (the theoretical population). In a case-control study, a sampling of the control group should be taken from the same study population (study base), have similar profiles to the cases (matching) but do not have the outcome seen in the cases. Matching important factors minimizes the confounding of the factors and increases statistical efficiency by ensuring similar numbers of cases and controls in confounders’ strata [ 23 - 24 ]. Nonetheless, perfect matching is neither necessary nor achievable in a case-control study because a partial match could achieve most of the benefits of the perfect match regarding a more precise estimate of odds ratio than statistical control of confounding in unmatched designs [ 25 - 26 ]. Moreover, perfect or full matching can lead to an underestimation of the point estimates [ 27 - 28 ].

Time feature: The timing of data collection for the determinant and outcome characterizes the types of studies. A cross-sectional study has the axis of time zero (T = 0) for both the determinant and the outcome, which separates it from all other types of research that have time for the outcome T > 0. Retrospective or prospective studies refer to the direction of data collection. In retrospective studies, information on the determinant and outcome have been collected or recorded before. In prospective studies, this information will be collected in the future. These terms should not be used to describe the relationship between the determinant and the outcome in etiologic studies. Time of exposure to the determinant, the time of induction, and the time at risk for the outcome are important aspects to understand. Time at risk is the period of time exposed to the determinant risk factors. Time of induction is the time from the sufficient exposure to the risk or causal factors to the occurrence of a disease. The latent period is when the occurrence of a disease without manifestation of the disease such as in “silence” diseases for example cancers, hypertension and type 2 diabetes mellitus which is detected from screening practices. Figure ​ Figure3 3 illustrates the time features of a variable. Variable timing is important for accurate data capture. 

The Design of Statistical Analysis

Statistical analysis of epidemiologic data provides the estimate of effects after correcting for biases (e.g., confounding factors) measures the variability in the data from random errors or chance [ 7 , 16 , 29 ]. An effect estimate gives the size of an association between the studied variables or the level of effectiveness of an intervention. This quantitative result allows for comparison and assessment of the usefulness and significance of the association or the intervention between studies. This significance must be interpreted with a statistical model and an appropriate study design. Random errors could arise in the study resulting from unexplained personal choices by the participants. Random error is, therefore, when values or units of measurement between variables change in non-concerted or non-directional manner. Conversely, when these values or units of measurement between variables change in a concerted or directional manner, we note a significant relationship as shown by statistical significance. 

Variability: Researchers almost always collect the needed data through a sampling of subjects/participants from a population instead of a census. The process of sampling or multiple sampling in different geographical regions or over different periods contributes to varied information due to the random inclusion of different participants and chance occurrence. This sampling variation becomes the focus of statistics when communicating the degree and intensity of variation in the sampled data and the level of inference in the population. Sampling variation can be influenced profoundly by the total number of participants and the width of differences of the measured variable (standard deviation). Hence, the characteristics of the participants, measurements and sample size are all important factors in planning a study.

Statistical strategy: Statistical strategy is usually determined based on the theoretical and data collection designs. Use of a prespecified statistical strategy (including the decision to dichotomize any continuous data at certain cut-points, sub-group analysis or sensitive analyses) is recommended in the study proposal (i.e., protocol) to prevent data dredging and data-driven reports that predispose to bias. The nature of the study hypothesis also dictates whether directional (one-tailed) or non-directional (two-tailed) significance tests are conducted. In most studies, two-sided tests are used except in specific instances when unidirectional hypotheses may be appropriate (e.g., in superiority or non-inferiority trials). While data exploration is discouraged, epidemiological research is, by nature of its objectives, statistical research. Hence, it is acceptable to report the presence of persistent associations between any variables with plausible underlying mechanisms during the exploration of the data. The statistical methods used to produce the results should be explicitly explained. Many different statistical tests are used to handle various kinds of data appropriately (e.g., interval vs discrete), and/or the various distribution of the data (e.g., normally distributed or skewed). For additional details on statistical explanations and underlying concepts of statistical tests, readers are recommended the references as cited in this sentence [ 30 - 31 ]. 

Steps in statistical analyses: Statistical analysis begins with checking for data entry errors. Duplicates are eliminated, and proper units should be confirmed. Extremely low, high or suspicious values are confirmed from the source data again. If this is not possible, this is better classified as a missing value. However, if the unverified suspicious data are not obviously wrong, they should be further examined as an outlier in the analysis. The data checking and cleaning enables the analyst to establish a connection with the raw data and to anticipate possible results from further analyses. This initial step involves descriptive statistics that analyze central tendency (i.e., mode, median, and mean) and dispersion (i.e., (minimum, maximum, range, quartiles, absolute deviation, variance, and standard deviation) of the data. Certain graphical plotting such as scatter plot, a box-whiskers plot, histogram or normal Q-Q plot are helpful at this stage to verify data normality in distribution. See Figure ​ Figure4 4 for the statistical tests available for analyses of different types of data.

Once data characteristics are ascertained, further statistical tests are selected. The analytical strategy sometimes involves the transformation of the data distribution for the selected tests (e.g., log, natural log, exponential, quadratic) or for checking the robustness of the association between the determinants and their outcomes. This step is also referred to as inferential statistics whereby the results are about hypothesis testing and generalization to the wider population that the study’s sampled participants represent. The last statistical step is checking whether the statistical analyses fulfill the assumptions of that particular statistical test and model to avoid violation and misleading results. These assumptions include evaluating normality, variance homogeneity, and residuals included in the final statistical model. Other statistical values such as Akaike information criterion, variance inflation factor/tolerance, and R2 are also considered when choosing the best-fitted models. Transforming raw data could be done, or a higher level of statistical analyses can be used (e.g., generalized linear models and mixed-effect modeling). Successful statistical analysis allows conclusions of the study to fit the data. 

Bayesian and Frequentist statistical frameworks: Most of the current clinical research reporting is based on the frequentist approach and hypotheses testing p values and confidence intervals. The frequentist approach assumes the acquired data are random, attained by random sampling, through randomized experiments or influences, and with random errors. The distribution of the data (its point estimate and confident interval) infers a true parameter in the real population. The major conceptual difference between Bayesian statistics and frequentist statistics is that in Bayesian statistics, the parameter (i.e., the studied variable in the population) is random and the data acquired is real (true or fix). Therefore, the Bayesian approach provides a probability interval for the parameter. The studied parameter is random because it could vary and be affected by prior beliefs, experience or evidence of plausibility. In the Bayesian statistical approach, this prior belief or available knowledge is quantified into a probability distribution and incorporated into the acquired data to get the results (i.e., the posterior distribution). This uses mathematical theory of Bayes’ Theorem to “turn around” conditional probabilities.

The goal of research reporting is to present findings succinctly and timely via conference proceedings or journal publication. Concise and explicit language use, with all the necessary details to enable replication and judgment of the study applicability, are the guiding principles in clinical studies reporting.

Writing for Reporting

Medical writing is very much a technical chore that accommodates little artistic expression. Research reporting in medicine and health sciences emphasize clear and standardized reporting, eschewing adjectives and adverbs extensively used in popular literature. Regularly reviewing published journal articles can familiarize authors with proper reporting styles and help enhance writing skills. Authors should familiarize themselves with standard, concise, and appropriate rhetoric for the intended audience, which includes consideration for journal reviewers, editors, and referees. However, proper language can be somewhat subjective. While each publication may have varying requirements for submission, the technical requirements for formatting an article are usually available via author or submission guidelines provided by the target journal. 

Research reports for publication often contain a title, abstract, introduction, methods, results, discussion, and conclusions section, and authors may want to write each section in sequence. However, best practices indicate the abstract and title should be written last. Authors may find that when writing one section of the report, ideas come to mind that pertains to other sections, so careful note taking is encouraged. One effective approach is to organize and write the result section first, followed by the discussion and conclusions sections. Once these are drafted, write the introduction, abstract, and the title of the report. Regardless of the sequence of writing, the author should begin with a clear and relevant research question to guide the statistical analyses, result interpretation, and discussion. The study findings can be a motivator to propel the author through the writing process, and the conclusions can help the author draft a focused introduction.

Writing for Publication

Specific recommendations on effective medical writing and table generation are available [ 32 ]. One such resource is Effective Medical Writing: The Write Way to Get Published, which is an updated collection of medical writing articles previously published in the Singapore Medical Journal [ 33 ]. The British Medical Journal’s Statistics Notes series also elucidates common and important statistical concepts and usages in clinical studies. Writing guides are also available from individual professional societies, journals, or publishers such as Chest (American College of Physicians) medical writing tips, PLoS Reporting guidelines collection, Springer’s Journal Author Academy, and SAGE’s Research methods [ 34 - 37 ]. Standardized research reporting guidelines often come in the form of checklists and flow diagrams. Table ​ Table6 6 presents a list of reporting guidelines. A full compilation of these guidelines is available at the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network website [ 38 ] which aims to improve the reliability and value of medical literature by promoting transparent and accurate reporting of research studies. Publication of the trial protocol in a publicly available database is almost compulsory for publication of the full report in many potential journals.

Graphics and Tables

Graphics and tables should emphasize salient features of the underlying data and should coherently summarize large quantities of information. Although graphics provide a break from dense prose, authors must not forget that these illustrations should be scientifically informative, not decorative. The titles for graphics and tables should be clear, informative, provide the sample size, and use minimal font weight and formatting only to distinguish headings, data entry or to highlight certain results. Provide a consistent number of decimal points for the numerical results, and with no more than four for the P value. Most journals prefer cell-delineated tables created using the table function in word processing or spreadsheet programs. Some journals require specific table formatting such as the absence or presence of intermediate horizontal lines between cells.

Decisions of authorship are both sensitive and important and should be made at an early stage by the study’s stakeholders. Guidelines and journals’ instructions to authors abound with authorship qualifications. The guideline on authorship by the International Committee of Medical Journal Editors is widely known and provides a standard used by many medical and clinical journals [ 39 ]. Generally, authors are those who have made major contributions to the design, conduct, and analysis of the study, and who provided critical readings of the manuscript (if not involved directly in manuscript writing). 

Picking a target journal for submission

Once a report has been written and revised, the authors should select a relevant target journal for submission. Authors should avoid predatory journals—publications that do not aim to advance science and disseminate quality research. These journals focus on commercial gain in medical and clinical publishing. Two good resources for authors during journal selection are Think-Check-Submit and the defunct Beall's List of Predatory Publishers and Journals (now archived and maintained by an anonymous third-party) [ 40 , 41 ]. Alternatively, reputable journal indexes such as Thomson Reuters Journal Citation Reports, SCOPUS, MedLine, PubMed, EMBASE, EBSCO Publishing's Electronic Databases are available areas to start the search for an appropriate target journal. Authors should review the journals’ names, aims/scope, and recently published articles to determine the kind of research each journal accepts for publication. Open-access journals almost always charge article publication fees, while subscription-based journals tend to publish without author fees and instead rely on subscription or access fees for the full text of published articles.

Conclusions

Conducting a valid clinical research requires consideration of theoretical study design, data collection design, and statistical analysis design. Proper study design implementation and quality control during data collection ensures high-quality data analysis and can mitigate bias and confounders during statistical analysis and data interpretation. Clear, effective study reporting facilitates dissemination, appreciation, and adoption, and allows the researchers to affect real-world change in clinical practices and care models. Neutral or absence of findings in a clinical study are as important as positive or negative findings. Valid studies, even when they report an absence of expected results, still inform scientific communities of the nature of a certain treatment or intervention, and this contributes to future research, systematic reviews, and meta-analyses. Reporting a study adequately and comprehensively is important for accuracy, transparency, and reproducibility of the scientific work as well as informing readers.

Acknowledgments

The author would like to thank Universiti Putra Malaysia and the Ministry of Higher Education, Malaysia for their support in sponsoring the Ph.D. study and living allowances for Boon-How Chew.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The materials presented in this paper is being organized by the author into a book.

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Understanding Research Designs and External Scientific Evidence  

• Evidence-Based Practice
• How to Search for Evidence in Communication Sciences and Disorders
• Understanding Research Designs and External Scientific Evidence
• Bias, Appraisal Tools, and Levels of Evidence
• Statistics Refresher

External scientific evidence is a component of evidence-based practice (EBP) and refers to sources outside of everyday clinical practice. This page discusses 

• primary research ;
• secondary research ; and
• matching your clinical question to a type of evidence .

Primary Research

Primary research  pertains to individual studies attempting to answer a specific research question using raw data collected by the researcher(s). In experimental studies, the investigator manipulates one or more variables to compare those that received the manipulated condition to those that did not. In qualitative and observational/non-experimental studies, although there is no experimental manipulation, they may involve a comparison group. 

Primary research can be conducted 

• retrospectively  (i.e., researchers collect data on the study participants’ past) or 
• prospectively  (i.e., researchers follow study participants over time and collect data to capture change). 

Here are some common types of study designs: 

Experimental Study Designs 

• Randomized controlled trial (RCT)  – Participants are randomly assigned to either the control group or an experimental group. Researchers compare outcomes from each group to determine whether the intervention caused any change.
• Controlled trial  – A study involving non-randomized groups (i.e., experimental, comparison/control), which helps determine the effects of the intervention.
• Single-subject designs  – Also known as  single-case experimental designs , this type of experimental design allows researchers to closely examine specific changes in each participant. Each participant serves as their own control (i.e., compared to themselves) and researchers measure the outcome or dependent variable repeatedly across phases (e.g., baseline phase, intervention phase, withdraw phase). There are many variations  of a single-subject design study.
• Cross-over trial  – This is a study in which participants first receive one type of treatment and then researchers switch them to a different type of treatment.

Observational/Non-Experimental Study Designs

• Cohort  – A  cohort  is an observational design study, possibly including a control group, in which researchers follow participants over time to determine the factors leading to different outcomes. Cohort studies can be retrospective or prospective.
• Case–control  – This retrospective, observational study identifies an outcome of interest and compares a sample of people with that outcome ( case ) and a sample of people without that outcome ( control ). This design enables researchers to determine possible differences of previous exposures, experiences, and risk factors—any of which could explain their different outcomes. 
• Cross-sectional  – This is a study of a single sample at one point in time to understand the relationships among variables in the sample.
• Case study  – A  case study  is an uncontrolled, observational study of events and outcomes in a single case.
• Case series  – A description of uncontrolled, non-experimental events and outcomes for a series of similar cases who receive the same intervention or have the same outcome.

The chart below can help you better understand the features of the study designs commonly seen in audiology and speech-language pathology research.

Note:  Y=yes; S=sometimes.

Secondary Research

Secondary research , also called  synthesized research , combines the findings from primary research studies and provides conclusions about that body of evidence. Below are three common types of synthesized research, which are also found on the ASHA Evidence Maps :  

Systematic Reviews

Systematic reviews use systematic methods to search for and compile a body of evidence to answer a research or clinical question about the efficacy/effectiveness of an assessment or treatment approach. Typically, studies included in a systematic review have met predetermined eligibility and quality criteria (e.g., studies must be experimental designs). The systematic review then provides qualitative conclusions based on the included studies.

Well-done systematic reviews offer greater transparency because they provide details about their inclusion/exclusion process. They also typically assess each study for its methodological quality and level of evidence. Using transparent methods reduces bias and increases the confidence of the findings and conclusions of the research. Systematic reviews can provide a synopsis of the state of the evidence about a given clinical topic.

Meta-Analyses

Meta-analyses use systematic and statistical methods to answer a research or clinical question about a specific assessment or treatment approach. Like systematic reviews, included primary studies must meet predetermined eligibility and quality criteria. The meta-analyses provide quantitative conclusions (e.g., pooled effect size, confidence interval) to determine the overall treatment effect or effect size across studies. The additional statistical measures can provide a better picture of the clinical significance.

Clinical Practice Guidelines

Clinical practice guidelines are systematically developed statements created by a group of subject matter experts to provide a comprehensive overview of a disorder, detail the benefits and harms of specific assessment and treatment approaches, and optimize delivery of services. Guidelines grade recommendations based on the quality and amount of available evidence and classify them as either of the following two types of recommendations:

• Evidence-based recommendations : A systematic review of the evidence informs the group of experts and their recommendations.
• Consensus-based recommendations : These recommendations are based on a summary of expert opinions. 

Matching Your Clinical Question to a Type of Evidence

Your clinical question  determines the study design (e.g., randomized controlled trials, single-subject design) needed to address your question and impacts your search for evidence . Systematic reviews and meta-analyses should also include the study designs with the highest likelihood of answering your clinical question.

Keep in mind that if you are looking for research on a newer treatment or assessment approach, you may only find early-stage research designs, such as case studies and case series. These designs may provide preliminary evidence but cannot demonstrate the efficacy of the newer approaches.  

Quality Control

Once you find study designs appropriate for your clinical question, you need to determine the methodological quality of the primary or secondary studies. There are different methods and checklists to appraise the methodological quality of primary and synthesized research.

See below to find out which study design best addresses your clinical question:

For Screening/Diagnosis Questions

Assess accuracy in differentiating clients with or without a condition.

Example question:   Is an auditory brainstem response or an otoacoustic emissions screening more accurate in identifying newborns with hearing loss?

Preferred Study Design(s): Prospective, blind comparison to reference standard

Other Study Design(s): Cross-sectional

For Treatment/Service Delivery  Questions

Determine the efficacy of an intervention.

Example question:   What is the most effective treatment to improve cognition in adults with traumatic brain injury?

Preferred Study Design(s): Randomized controlled trial (RCT)

Other Study Design(s): Controlled trial (non-randomized) Single-subject/single-case experimental design

For Etiology Questions

Identify causes or risk factors of a condition.

Example question: What are the risk factors for speech and language disorders?

Preferred Study Design(s): Cohort

Other Study Design(s): Case–contro Case series

For Quality of Life/Perspective  Questions

Obtain and assess clients’ opinions and experiences.

Example question: How do parents feel about implementing parent-mediated interventions?

Preferred Study Design(s): Qualitative studies (e.g., case study, case series)

Other Study Design(s): Not Applicable

For Prognosis  Questions

Predict client’s likelihood of outcomes over time due to factors other than intervention.

Example question:   What is the prognosis of a child with autism spectrum disorder?

Other Study Design(s): Case–control Case series

For Cost Q uestions

Compare cost of treatments, tests, and other factors due to the disorder.

Example question:   What is the cost of care for individuals with dysphagia requiring a feeding tube compared to those requiring diet modification?

Preferred Study Design(s): Economic analysis

For Prevention Q uestions

Identify factors to reduce likelihood of a disorder.

Example question:   What are some strategies to prevent hearing loss?

Preferred Study Design(s): Randomized control trial

Other Study Design(s): Controlled trial (non-randomized) Cohort Case-control

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Overview of Nonexperimental Research

Learning objectives.

• Define nonexperimental research, distinguish it clearly from experimental research, and give several examples.
• Explain when a researcher might choose to conduct nonexperimental research as opposed to experimental research.

What Is Nonexperimental Research?

Nonexperimental research  is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both.

In a sense, it is unfair to define this large and diverse set of approaches collectively by what they are  not . But doing so reflects the fact that most researchers in psychology consider the distinction between experimental and nonexperimental research to be an extremely important one. This distinction is because although experimental research can provide strong evidence that changes in an independent variable cause differences in a dependent variable, nonexperimental research generally cannot. As we will see, however, this inability does not mean that nonexperimental research is less important than experimental research or inferior to it in any general sense.

When to Use Nonexperimental Research

As we saw in  Chapter 6 , experimental research is appropriate when the researcher has a specific research question or hypothesis about a causal relationship between two variables—and it is possible, feasible, and ethical to manipulate the independent variable and randomly assign participants to conditions or to orders of conditions. It stands to reason, therefore, that nonexperimental research is appropriate—even necessary—when these conditions are not met. There are many ways in which preferring nonexperimental research can be the case.

• The research question or hypothesis can be about a single variable rather than a statistical relationship between two variables (e.g., How accurate are people’s first impressions?).
• The research question can be about a noncausal statistical relationship between variables (e.g., Is there a correlation between verbal intelligence and mathematical intelligence?).
• The research question can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does damage to a person’s hippocampus impair the formation of long-term memory traces?).
• The research question can be broad and exploratory, or it can be about what it is like to have a particular experience (e.g., What is it like to be a working mother diagnosed with depression?).

Again, the choice between the experimental and nonexperimental approaches is generally dictated by the nature of the research question. If it is about a causal relationship and involves an independent variable that can be manipulated, the experimental approach is typically preferred. Otherwise, the nonexperimental approach is preferred. But the two approaches can also be used to address the same research question in complementary ways. For example, nonexperimental studies establishing that there is a relationship between watching violent television and aggressive behaviour have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] . Similarly, after his original study, Milgram conducted experiments to explore the factors that affect obedience. He manipulated several independent variables, such as the distance between the experimenter and the participant, the participant and the confederate, and the location of the study (Milgram, 1974) [2] .

Types of Nonexperimental Research

Nonexperimental research falls into three broad categories: single-variable research, correlational and quasi-experimental research, and qualitative research. First, research can be nonexperimental because it focuses on a single variable rather than a statistical relationship between two variables. Although there is no widely shared term for this kind of research, we will call it  single-variable research . Milgram’s original obedience study was nonexperimental in this way. He was primarily interested in one variable—the extent to which participants obeyed the researcher when he told them to shock the confederate—and he observed all participants performing the same task under the same conditions. The study by Loftus and Pickrell described at the beginning of this chapter is also a good example of single-variable research. The variable was whether participants “remembered” having experienced mildly traumatic childhood events (e.g., getting lost in a shopping mall) that they had not actually experienced but that the research asked them about repeatedly. In this particular study, nearly a third of the participants “remembered” at least one event. (As with Milgram’s original study, this study inspired several later experiments on the factors that affect false memories.)

As these examples make clear, single-variable research can answer interesting and important questions. What it cannot do, however, is answer questions about statistical relationships between variables. This detail is a point that beginning researchers sometimes miss. Imagine, for example, a group of research methods students interested in the relationship between children’s being the victim of bullying and the children’s self-esteem. The first thing that is likely to occur to these researchers is to obtain a sample of middle-school students who have been bullied and then to measure their self-esteem. But this design would be a single-variable study with self-esteem as the only variable. Although it would tell the researchers something about the self-esteem of children who have been bullied, it would not tell them what they really want to know, which is how the self-esteem of children who have been bullied  compares  with the self-esteem of children who have not. Is it lower? Is it the same? Could it even be higher? To answer this question, their sample would also have to include middle-school students who have not been bullied thereby introducing another variable.

Research can also be nonexperimental because it focuses on a statistical relationship between two variables but does not include the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both. This kind of research takes two basic forms: correlational research and quasi-experimental research. In correlational research , the researcher measures the two variables of interest with little or no attempt to control extraneous variables and then assesses the relationship between them. A research methods student who finds out whether each of several middle-school students has been bullied and then measures each student’s self-esteem is conducting correlational research. In  quasi-experimental research , the researcher manipulates an independent variable but does not randomly assign participants to conditions or orders of conditions. For example, a researcher might start an antibullying program (a kind of treatment) at one school and compare the incidence of bullying at that school with the incidence at a similar school that has no antibullying program.

The final way in which research can be nonexperimental is that it can be qualitative. The types of research we have discussed so far are all quantitative, referring to the fact that the data consist of numbers that are analyzed using statistical techniques. In  qualitative research , the data are usually nonnumerical and therefore cannot be analyzed using statistical techniques. Rosenhan’s study of the experience of people in a psychiatric ward was primarily qualitative. The data were the notes taken by the “pseudopatients”—the people pretending to have heard voices—along with their hospital records. Rosenhan’s analysis consists mainly of a written description of the experiences of the pseudopatients, supported by several concrete examples. To illustrate the hospital staff’s tendency to “depersonalize” their patients, he noted, “Upon being admitted, I and other pseudopatients took the initial physical examinations in a semipublic room, where staff members went about their own business as if we were not there” (Rosenhan, 1973, p. 256) [3] . Qualitative data has a separate set of analysis tools depending on the research question. For example, thematic analysis would focus on themes that emerge in the data or conversation analysis would focus on the way the words were said in an interview or focus group.

Internal Validity Revisited

Recall that internal validity is the extent to which the design of a study supports the conclusion that changes in the independent variable caused any observed differences in the dependent variable.  Figure 7.1  shows how experimental, quasi-experimental, and correlational research vary in terms of internal validity. Experimental research tends to be highest because it addresses the directionality and third-variable problems through manipulation and the control of extraneous variables through random assignment. If the average score on the dependent variable in an experiment differs across conditions, it is quite likely that the independent variable is responsible for that difference. Correlational research is lowest because it fails to address either problem. If the average score on the dependent variable differs across levels of the independent variable, it  could  be that the independent variable is responsible, but there are other interpretations. In some situations, the direction of causality could be reversed. In others, there could be a third variable that is causing differences in both the independent and dependent variables. Quasi-experimental research is in the middle because the manipulation of the independent variable addresses some problems, but the lack of random assignment and experimental control fails to address others. Imagine, for example, that a researcher finds two similar schools, starts an antibullying program in one, and then finds fewer bullying incidents in that “treatment school” than in the “control school.” There is no directionality problem because clearly the number of bullying incidents did not determine which school got the program. However, the lack of random assignment of children to schools could still mean that students in the treatment school differed from students in the control school in some other way that could explain the difference in bullying.

Notice also in  Figure 7.1  that there is some overlap in the internal validity of experiments, quasi-experiments, and correlational studies. For example, a poorly designed experiment that includes many confounding variables can be lower in internal validity than a well designed quasi-experiment with no obvious confounding variables. Internal validity is also only one of several validities that one might consider, as noted in Chapter 5.

Key Takeaways

• Nonexperimental research is research that lacks the manipulation of an independent variable, control of extraneous variables through random assignment, or both.
• There are three broad types of nonexperimental research. Single-variable research focuses on a single variable rather than a relationship between variables. Correlational and quasi-experimental research focus on a statistical relationship but lack manipulation or random assignment. Qualitative research focuses on broader research questions, typically involves collecting large amounts of data from a small number of participants, and analyses the data nonstatistically.
• In general, experimental research is high in internal validity, correlational research is low in internal validity, and quasi-experimental research is in between.
• A researcher conducts detailed interviews with unmarried teenage fathers to learn about how they feel and what they think about their role as fathers and summarizes their feelings in a written narrative.
• A researcher measures the impulsivity of a large sample of drivers and looks at the statistical relationship between this variable and the number of traffic tickets the drivers have received.
• A researcher randomly assigns patients with low back pain either to a treatment involving hypnosis or to a treatment involving exercise. She then measures their level of low back pain after 3 months.
• A college instructor gives weekly quizzes to students in one section of his course but no weekly quizzes to students in another section to see whether this has an effect on their test performance.
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Milgram, S. (1974). Obedience to authority: An experimental view . New York, NY: Harper & Row. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵

Research Methods in Psychology Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Methods

• Research Process
• Research Design & Method

Qualitative vs. Quantiative

Correlational vs. experimental, empirical vs. non-empirical.

• Survey Research
• Survey & Interview Data Analysis
• Resources for Research
• Ethical Considerations in Research

Qualitative Research gathers data about lived experiences, emotions or behaviors, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorized through statistical analysis. It assists with uncovering patterns or relationships, and for making generalizations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Correlational Research cannot determine causal relationships. Instead they examine relationships between variables.

Experimental Research can establish causal relationship and variables can be manipulated.

Empirical Studies are based on evidence. The data is collected through experimentation or observation.

Non-empirical Studies do not require researchers to collect first-hand data.

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Human Subjects Office

Medical terms in lay language.

Please use these descriptions in place of medical jargon in consent documents, recruitment materials and other study documents. Note: These terms are not the only acceptable plain language alternatives for these vocabulary words.

This glossary of terms is derived from a list copyrighted by the University of Kentucky, Office of Research Integrity (1990).

For clinical research-specific definitions, see also the Clinical Research Glossary developed by the Multi-Regional Clinical Trials (MRCT) Center of Brigham and Women’s Hospital and Harvard  and the Clinical Data Interchange Standards Consortium (CDISC) .

Alternative Lay Language for Medical Terms for use in Informed Consent Documents

A   B   C   D   E   F   G   H   I  J  K   L   M   N   O   P   Q   R   S   T   U   V   W  X  Y  Z

ABDOMEN/ABDOMINAL body cavity below diaphragm that contains stomach, intestines, liver and other organs ABSORB take up fluids, take in ACIDOSIS condition when blood contains more acid than normal ACUITY clearness, keenness, esp. of vision and airways ACUTE new, recent, sudden, urgent ADENOPATHY swollen lymph nodes (glands) ADJUVANT helpful, assisting, aiding, supportive ADJUVANT TREATMENT added treatment (usually to a standard treatment) ANTIBIOTIC drug that kills bacteria and other germs ANTIMICROBIAL drug that kills bacteria and other germs ANTIRETROVIRAL drug that works against the growth of certain viruses ADVERSE EFFECT side effect, bad reaction, unwanted response ALLERGIC REACTION rash, hives, swelling, trouble breathing AMBULATE/AMBULATION/AMBULATORY walk, able to walk ANAPHYLAXIS serious, potentially life-threatening allergic reaction ANEMIA decreased red blood cells; low red cell blood count ANESTHETIC a drug or agent used to decrease the feeling of pain, or eliminate the feeling of pain by putting you to sleep ANGINA pain resulting from not enough blood flowing to the heart ANGINA PECTORIS pain resulting from not enough blood flowing to the heart ANOREXIA disorder in which person will not eat; lack of appetite ANTECUBITAL related to the inner side of the forearm ANTIBODY protein made in the body in response to foreign substance ANTICONVULSANT drug used to prevent seizures ANTILIPEMIC a drug that lowers fat levels in the blood ANTITUSSIVE a drug used to relieve coughing ARRHYTHMIA abnormal heartbeat; any change from the normal heartbeat ASPIRATION fluid entering the lungs, such as after vomiting ASSAY lab test ASSESS to learn about, measure, evaluate, look at ASTHMA lung disease associated with tightening of air passages, making breathing difficult ASYMPTOMATIC without symptoms AXILLA armpit

BENIGN not malignant, without serious consequences BID twice a day BINDING/BOUND carried by, to make stick together, transported BIOAVAILABILITY the extent to which a drug or other substance becomes available to the body BLOOD PROFILE series of blood tests BOLUS a large amount given all at once BONE MASS the amount of calcium and other minerals in a given amount of bone BRADYARRHYTHMIAS slow, irregular heartbeats BRADYCARDIA slow heartbeat BRONCHOSPASM breathing distress caused by narrowing of the airways

CARCINOGENIC cancer-causing CARCINOMA type of cancer CARDIAC related to the heart CARDIOVERSION return to normal heartbeat by electric shock CATHETER a tube for withdrawing or giving fluids CATHETER a tube placed near the spinal cord and used for anesthesia (indwelling epidural) during surgery CENTRAL NERVOUS SYSTEM (CNS) brain and spinal cord CEREBRAL TRAUMA damage to the brain CESSATION stopping CHD coronary heart disease CHEMOTHERAPY treatment of disease, usually cancer, by chemical agents CHRONIC continuing for a long time, ongoing CLINICAL pertaining to medical care CLINICAL TRIAL an experiment involving human subjects COMA unconscious state COMPLETE RESPONSE total disappearance of disease CONGENITAL present before birth CONJUNCTIVITIS redness and irritation of the thin membrane that covers the eye CONSOLIDATION PHASE treatment phase intended to make a remission permanent (follows induction phase) CONTROLLED TRIAL research study in which the experimental treatment or procedure is compared to a standard (control) treatment or procedure COOPERATIVE GROUP association of multiple institutions to perform clinical trials CORONARY related to the blood vessels that supply the heart, or to the heart itself CT SCAN (CAT) computerized series of x-rays (computerized tomography) CULTURE test for infection, or for organisms that could cause infection CUMULATIVE added together from the beginning CUTANEOUS relating to the skin CVA stroke (cerebrovascular accident)

DERMATOLOGIC pertaining to the skin DIASTOLIC lower number in a blood pressure reading DISTAL toward the end, away from the center of the body DIURETIC "water pill" or drug that causes increase in urination DOPPLER device using sound waves to diagnose or test DOUBLE BLIND study in which neither investigators nor subjects know what drug or treatment the subject is receiving DYSFUNCTION state of improper function DYSPLASIA abnormal cells

ECHOCARDIOGRAM sound wave test of the heart EDEMA excess fluid collecting in tissue EEG electric brain wave tracing (electroencephalogram) EFFICACY effectiveness ELECTROCARDIOGRAM electrical tracing of the heartbeat (ECG or EKG) ELECTROLYTE IMBALANCE an imbalance of minerals in the blood EMESIS vomiting EMPIRIC based on experience ENDOSCOPIC EXAMINATION viewing an  internal part of the body with a lighted tube  ENTERAL by way of the intestines EPIDURAL outside the spinal cord ERADICATE get rid of (such as disease) Page 2 of 7 EVALUATED, ASSESSED examined for a medical condition EXPEDITED REVIEW rapid review of a protocol by the IRB Chair without full committee approval, permitted with certain low-risk research studies EXTERNAL outside the body EXTRAVASATE to leak outside of a planned area, such as out of a blood vessel

FDA U.S. Food and Drug Administration, the branch of federal government that approves new drugs FIBROUS having many fibers, such as scar tissue FIBRILLATION irregular beat of the heart or other muscle

GENERAL ANESTHESIA pain prevention by giving drugs to cause loss of consciousness, as during surgery GESTATIONAL pertaining to pregnancy

HEMATOCRIT amount of red blood cells in the blood HEMATOMA a bruise, a black and blue mark HEMODYNAMIC MEASURING blood flow HEMOLYSIS breakdown in red blood cells HEPARIN LOCK needle placed in the arm with blood thinner to keep the blood from clotting HEPATOMA cancer or tumor of the liver HERITABLE DISEASE can be transmitted to one’s offspring, resulting in damage to future children HISTOPATHOLOGIC pertaining to the disease status of body tissues or cells HOLTER MONITOR a portable machine for recording heart beats HYPERCALCEMIA high blood calcium level HYPERKALEMIA high blood potassium level HYPERNATREMIA high blood sodium level HYPERTENSION high blood pressure HYPOCALCEMIA low blood calcium level HYPOKALEMIA low blood potassium level HYPONATREMIA low blood sodium level HYPOTENSION low blood pressure HYPOXEMIA a decrease of oxygen in the blood HYPOXIA a decrease of oxygen reaching body tissues HYSTERECTOMY surgical removal of the uterus, ovaries (female sex glands), or both uterus and ovaries

IATROGENIC caused by a physician or by treatment IDE investigational device exemption, the license to test an unapproved new medical device IDIOPATHIC of unknown cause IMMUNITY defense against, protection from IMMUNOGLOBIN a protein that makes antibodies IMMUNOSUPPRESSIVE drug which works against the body's immune (protective) response, often used in transplantation and diseases caused by immune system malfunction IMMUNOTHERAPY giving of drugs to help the body's immune (protective) system; usually used to destroy cancer cells IMPAIRED FUNCTION abnormal function IMPLANTED placed in the body IND investigational new drug, the license to test an unapproved new drug INDUCTION PHASE beginning phase or stage of a treatment INDURATION hardening INDWELLING remaining in a given location, such as a catheter INFARCT death of tissue due to lack of blood supply INFECTIOUS DISEASE transmitted from one person to the next INFLAMMATION swelling that is generally painful, red, and warm INFUSION slow injection of a substance into the body, usually into the blood by means of a catheter INGESTION eating; taking by mouth INTERFERON drug which acts against viruses; antiviral agent INTERMITTENT occurring (regularly or irregularly) between two time points; repeatedly stopping, then starting again INTERNAL within the body INTERIOR inside of the body INTRAMUSCULAR into the muscle; within the muscle INTRAPERITONEAL into the abdominal cavity INTRATHECAL into the spinal fluid INTRAVENOUS (IV) through the vein INTRAVESICAL in the bladder INTUBATE the placement of a tube into the airway INVASIVE PROCEDURE puncturing, opening, or cutting the skin INVESTIGATIONAL NEW DRUG (IND) a new drug that has not been approved by the FDA INVESTIGATIONAL METHOD a treatment method which has not been proven to be beneficial or has not been accepted as standard care ISCHEMIA decreased oxygen in a tissue (usually because of decreased blood flow)

LAPAROTOMY surgical procedure in which an incision is made in the abdominal wall to enable a doctor to look at the organs inside LESION wound or injury; a diseased patch of skin LETHARGY sleepiness, tiredness LEUKOPENIA low white blood cell count LIPID fat LIPID CONTENT fat content in the blood LIPID PROFILE (PANEL) fat and cholesterol levels in the blood LOCAL ANESTHESIA creation of insensitivity to pain in a small, local area of the body, usually by injection of numbing drugs LOCALIZED restricted to one area, limited to one area LUMEN the cavity of an organ or tube (e.g., blood vessel) LYMPHANGIOGRAPHY an x-ray of the lymph nodes or tissues after injecting dye into lymph vessels (e.g., in feet) LYMPHOCYTE a type of white blood cell important in immunity (protection) against infection LYMPHOMA a cancer of the lymph nodes (or tissues)

MALAISE a vague feeling of bodily discomfort, feeling badly MALFUNCTION condition in which something is not functioning properly MALIGNANCY cancer or other progressively enlarging and spreading tumor, usually fatal if not successfully treated MEDULLABLASTOMA a type of brain tumor MEGALOBLASTOSIS change in red blood cells METABOLIZE process of breaking down substances in the cells to obtain energy METASTASIS spread of cancer cells from one part of the body to another METRONIDAZOLE drug used to treat infections caused by parasites (invading organisms that take up living in the body) or other causes of anaerobic infection (not requiring oxygen to survive) MI myocardial infarction, heart attack MINIMAL slight MINIMIZE reduce as much as possible Page 4 of 7 MONITOR check on; keep track of; watch carefully MOBILITY ease of movement MORBIDITY undesired result or complication MORTALITY death MOTILITY the ability to move MRI magnetic resonance imaging, diagnostic pictures of the inside of the body, created using magnetic rather than x-ray energy MUCOSA, MUCOUS MEMBRANE moist lining of digestive, respiratory, reproductive, and urinary tracts MYALGIA muscle aches MYOCARDIAL pertaining to the heart muscle MYOCARDIAL INFARCTION heart attack

NASOGASTRIC TUBE placed in the nose, reaching to the stomach NCI the National Cancer Institute NECROSIS death of tissue NEOPLASIA/NEOPLASM tumor, may be benign or malignant NEUROBLASTOMA a cancer of nerve tissue NEUROLOGICAL pertaining to the nervous system NEUTROPENIA decrease in the main part of the white blood cells NIH the National Institutes of Health NONINVASIVE not breaking, cutting, or entering the skin NOSOCOMIAL acquired in the hospital

OCCLUSION closing; blockage; obstruction ONCOLOGY the study of tumors or cancer OPHTHALMIC pertaining to the eye OPTIMAL best, most favorable or desirable ORAL ADMINISTRATION by mouth ORTHOPEDIC pertaining to the bones OSTEOPETROSIS rare bone disorder characterized by dense bone OSTEOPOROSIS softening of the bones OVARIES female sex glands

PARENTERAL given by injection PATENCY condition of being open PATHOGENESIS development of a disease or unhealthy condition PERCUTANEOUS through the skin PERIPHERAL not central PER OS (PO) by mouth PHARMACOKINETICS the study of the way the body absorbs, distributes, and gets rid of a drug PHASE I first phase of study of a new drug in humans to determine action, safety, and proper dosing PHASE II second phase of study of a new drug in humans, intended to gather information about safety and effectiveness of the drug for certain uses PHASE III large-scale studies to confirm and expand information on safety and effectiveness of new drug for certain uses, and to study common side effects PHASE IV studies done after the drug is approved by the FDA, especially to compare it to standard care or to try it for new uses PHLEBITIS irritation or inflammation of the vein PLACEBO an inactive substance; a pill/liquid that contains no medicine PLACEBO EFFECT improvement seen with giving subjects a placebo, though it contains no active drug/treatment PLATELETS small particles in the blood that help with clotting POTENTIAL possible POTENTIATE increase or multiply the effect of a drug or toxin (poison) by giving another drug or toxin at the same time (sometimes an unintentional result) POTENTIATOR an agent that helps another agent work better PRENATAL before birth PROPHYLAXIS a drug given to prevent disease or infection PER OS (PO) by mouth PRN as needed PROGNOSIS outlook, probable outcomes PRONE lying on the stomach PROSPECTIVE STUDY following patients forward in time PROSTHESIS artificial part, most often limbs, such as arms or legs PROTOCOL plan of study PROXIMAL closer to the center of the body, away from the end PULMONARY pertaining to the lungs

QD every day; daily QID four times a day

RADIATION THERAPY x-ray or cobalt treatment RANDOM by chance (like the flip of a coin) RANDOMIZATION chance selection RBC red blood cell RECOMBINANT formation of new combinations of genes RECONSTITUTION putting back together the original parts or elements RECUR happen again REFRACTORY not responding to treatment REGENERATION re-growth of a structure or of lost tissue REGIMEN pattern of giving treatment RELAPSE the return of a disease REMISSION disappearance of evidence of cancer or other disease RENAL pertaining to the kidneys REPLICABLE possible to duplicate RESECT remove or cut out surgically RETROSPECTIVE STUDY looking back over past experience

SARCOMA a type of cancer SEDATIVE a drug to calm or make less anxious SEMINOMA a type of testicular cancer (found in the male sex glands) SEQUENTIALLY in a row, in order SOMNOLENCE sleepiness SPIROMETER an instrument to measure the amount of air taken into and exhaled from the lungs STAGING an evaluation of the extent of the disease STANDARD OF CARE a treatment plan that the majority of the medical community would accept as appropriate STENOSIS narrowing of a duct, tube, or one of the blood vessels in the heart STOMATITIS mouth sores, inflammation of the mouth STRATIFY arrange in groups for analysis of results (e.g., stratify by age, sex, etc.) STUPOR stunned state in which it is difficult to get a response or the attention of the subject SUBCLAVIAN under the collarbone SUBCUTANEOUS under the skin SUPINE lying on the back SUPPORTIVE CARE general medical care aimed at symptoms, not intended to improve or cure underlying disease SYMPTOMATIC having symptoms SYNDROME a condition characterized by a set of symptoms SYSTOLIC top number in blood pressure; pressure during active contraction of the heart

TERATOGENIC capable of causing malformations in a fetus (developing baby still inside the mother’s body) TESTES/TESTICLES male sex glands THROMBOSIS clotting THROMBUS blood clot TID three times a day TITRATION a method for deciding on the strength of a drug or solution; gradually increasing the dose T-LYMPHOCYTES type of white blood cells TOPICAL on the surface TOPICAL ANESTHETIC applied to a certain area of the skin and reducing pain only in the area to which applied TOXICITY side effects or undesirable effects of a drug or treatment TRANSDERMAL through the skin TRANSIENTLY temporarily TRAUMA injury; wound TREADMILL walking machine used to test heart function

UPTAKE absorbing and taking in of a substance by living tissue

VALVULOPLASTY plastic repair of a valve, especially a heart valve VARICES enlarged veins VASOSPASM narrowing of the blood vessels VECTOR a carrier that can transmit disease-causing microorganisms (germs and viruses) VENIPUNCTURE needle stick, blood draw, entering the skin with a needle VERTICAL TRANSMISSION spread of disease

WBC white blood cell

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Title: real-time energy measurement for non-intrusive well-being monitoring of elderly people -- a case study.

Abstract: This article presents a case study demonstrating a non-intrusive method for the well-being monitoring of elderly people. It is based on our real-time energy measurement system, which uses tiny beacons attached to electricity meters. Four participants aged 67-82 years took part in our study. We observed their electric power consumption for approx. a month, and then we analyzed them, taking into account the participants' notes on their activities. We created typical daily usage profiles for each participant and used anomaly detection to find unusual energy consumption. We found out that real-time energy measurement can give significant insight into someone's daily activities and, consequently, bring invaluable information to caregivers about the well-being of an elderly person, while being discreet and entirely non-intrusive.

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Effects of a Remote Multimodal Intervention Involving Diet, Walking Program, and Breathing Exercise on Quality of Life Among Newly Diagnosed People with Multiple Sclerosis: A Quasi-Experimental Non-Inferiority Pilot Study

Affiliations.

• 1 Department of Internal Medicine, University of Iowa, Iowa City, IA, USA.
• 2 Department of Epidemiology, University of Iowa, Iowa City, IA, USA.
• 3 Institute for Clinical and Translational Science, University of Iowa, Iowa City, IA, USA.
• 4 Department of Psychiatry and the Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA.
• 5 Department of Neurology, University of Iowa Hospital and Clinics, Iowa City, IA, USA.
• PMID: 38222092
• PMCID: PMC10787513
• DOI: 10.2147/DNND.S441738

Background: Interventions involving diet, physical activity, and breathing exercises are shown to be beneficial in managing both fatigue and quality of life (QoL) related to MS; however, the impact of such interventions among people newly diagnosed with clinically isolated syndrome (CIS) or relapsing-remitting multiple sclerosis (RRMS) who decline disease-modifying therapies (DMTs) is unknown.

Methods: A 12-month prospective quasi-experimental non-inferiority trial recruited people newly diagnosed with CIS or RRMS who voluntarily declined DMTs (health behavior group; HB, n = 29) or followed standard of care (SOC, n = 15). Participants in the HB group were remotely coached on the study diet, moderate-intensity walking, and breathing exercises. All participants completed questionnaires validated to assess MS symptoms, including perceived mental and physical QoL (MSQOL54); fatigue (Fatigue Severity Scale, FSS; and Modified Fatigue Impact Scale, MFIS); mood (Hospital Anxiety and Depression Scale, HADS); and cognitive function (Perceived Deficits Questionnaire, PDQ).

Results: During the 12 months, the HB group experienced improvement in scores for mental QoL (MSQOL54 - Mental, 0.24, 95% CI 0.01, 0.47; p = 0.04), fatigue (Total MFIS, -7.26, 95% CI -13.3,-1.18; p = 0.02), and perceived cognitive function (Total PDQ, PDQ-Attention, PDQ-Promemory, and PDQ-Planning, p ≤ 0.03 for all). A between-group difference was observed only for PDQ-Planning (p = 0.048). Non-inferiority analysis revealed that the 12-month changes in means for the HB group were not worse than those for the SOC group with respect to fatigue (FSS, p = 0.02), mood (HDS-Anxiety, p = 0.02; HADS-Depression, p < 0.0001), physical QoL (MSQOL54 - Physical, p = 0.02), or cognitive dysfunction (Total PDQ, p = 0.01).

Conclusion: The multimodal lifestyle intervention for individuals newly diagnosed with CIS or RRMS, who voluntarily decline DMTs, did not yield patient-reported outcomes worse than those observed in the SOC group regarding perceived mental quality of life, mood, fatigue, and cognitive function.

Trial registration: clinicaltrials.gov identifier: NCT04009005 .

Keywords: mindfulness-based breathing; modified paleolithic diet; multiple sclerosis; physical activity; quasi-experimental.

© 2024 Saxby et al.

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Conflict of interest statement

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Conflict of Interest Disclosures: Dr Terry Wahls personally follows and promotes the Wahls™ diet. She has equity interest in the following companies: Terry Wahls LLC; TZ Press LLC; The Wahls Institute, PLC; FBB Biomed Inc.; Levels Health Inc., and the website http://www.terrywahls.com. She also owns the copyright to the books Minding My Mitochondria (2nd Edition) and The Wahls Protocol, The Wahls Protocol Cooking for Life, and the trademarks The Wahls Protocol® and Wahls™ diet, Wahls Paleo™ diet, and Wahls Paleo Plus™ diets. She has completed grant funding from the National Multiple Sclerosis Society for the Dietary Approaches to Treating Multiple Sclerosis Related Fatigue Study. She has financial relationships with Vibrant America LLC, Standard Process Inc., MasterHealth Technologies Inc., Foogal Inc., and the Institute for Functional Medicine Inc. She receives royalty payments from Penguin Random House. Dr Wahls has conflict-of-interest management plans in place with the University of Iowa and the Iowa City Veteran’s Affairs Medical Center. This study received funding from Dr Terry Wahls LLC. The funder had the following involvement with the study: study design, the writing of this article, and the decision to submit it for publication. Also, Dr Tyler Titcomb received Fellowship fund that supported me for this study from the Carter Chapman Shreve Family Foundation, during the conduct of the study. All authors declare no other competing interests.

Diagram illustrating participant recruitment and…

Diagram illustrating participant recruitment and study flow for health behaviors (HB) intervention and…

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Use-Wear Analysis of Obsidian and Other Volcanic Rocks: An Experimental Approach to Working Plant Resources

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• Published: 29 June 2024

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• Idaira Brito-Abrante   ORCID: orcid.org/0000-0001-8491-9679 1 &
• Amelia Rodríguez-Rodríguez   ORCID: orcid.org/0000-0001-7112-2441 1  

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This experimental study aims to contribute to functional analysis research on tools which specifically served to work wood and non-woody plants. They were made of obsidian and other volcanic rocks (basalt, trachyte, and phonolite) characterised by an amorphous matrix and phenocrysts of different number and size. In spite of prior functional analysis research resorting to these raw materials, there remain gaps in our understanding of specific activities. The work thus focused on working different types of wood from the Canary Island as well as on harvesting cereals. It is likewise centred on craftwork, especially regarding certain rarely studied contact materials such as palm leaves and rushes. The results reveal use-wear differences stemming from working woody and non-woody plants with both obsidian and other volcanic rocks. A special attention was given to the identification and description of the different features depending on the raw materials and the characteristics of their knapped surfaces. Identifying the combination of attributes has been essential to attain more accurate diagnostics. There are limits to each of the types of raw materials. The surfaces of obsidian are easier to observe and allow more specific identifications. In turn, the heterogeneous surfaces of volcanic rocks with phenocrysts that require more to time to develop diagnostic traces render use-wear amongst these types of rocks more difficult to observe. It is possible to distinguish longitudinal and transversal actions between woody and non-woody plants on every rock. Actions related to basketry, such us splitting and scraping, are more complicated to identify. The state of the worked plant (dry or fresh) and the time of use are key factors to consider in each case.

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Introduction

Working plants with lithic tools, either to process food or to manufacture architectural features, tools and other artefacts, has been a fundamental human activity since the earliest times (Alfaro, 1980 ; Shick, 1989 ; Stordeur, 1989 ; Zohary et al., 2012 ). However, the archaeological record offers little evidence serving to identify the processes of obtaining and transforming these materials. One of the main reasons for this is the lack of preservation of organic materials. The exceptions are wetland or extremely dry contexts. Yet even waterlogged and desiccated objects are not devoid of issues such as deformation, bacterial activity and pests (Abdel-Azeem et al., 2019 ; Blanchette, 2000 ; Piqué, 2006 ). Wood, especially its most resistant species, survives better than non-woody plants. This explains why these objects have benefited from more scientific contributions (e.g. Bosch et al., 2005 ; Caruso-Fermé et al., 2023 ; López-Bultó et al., 2020 ; Piqué, 2000 ; Vidal-Matutano et al., 2020 ) compared to non-wood plants (e.g. Herrero-Otal et al., 2023 ; Mineo et al., 2023 ; Palomo et al., 2013 ; Wendrich & Holdaway, 2018 ). Therefore, delving in depth into the actions affecting wood through the study of the lithic tools serving to work it can yield relevant data as to the conditioning of the workspaces, craftwork technological processes and storage strategies (Gutiérrez-Cuenca et al., 2017 ; Martín-Seijo & Panagiotakopulu, 2022 ; Vidal-Matutano et al., 2021 ).

Traces of these ‘hidden technologies’ (Hurcombe, 2014 ) can be identified, in part, through use-wear analyses applied to lithic (and at times even bone) tools, as these tend to better withstand the passage of time. This discipline has traditionally focused on siliceous rocks and their applications in working plants (Semenov, 1964 ; Keeley, 1980 ; Vaughan, 1985 ; González & Ibáñez, 1994 ; Hardy & Garufi, 1998 ; Bencomo & Jardón, 2023 ). Yet certain Palaeolithic research has confirmed the use of other types of volcanic rock tools for these tasks (Bello-Alonso et al., 2020 ; Panera et al., 2019 ; Plisson, 1982 ; Rios-Garaizar et al., 2018 ). Obsidian, for example, has been the subject of very compelling analyses in various chronological and cultural contexts, especially concerning its role in the origins of agriculture (Anderson-Gerfaud, 1990 ; Anderson-Gerfaud & Formenti, 1994 ; Astruc, 2022 ). However, much more research has focused on the role of flint in harvesting wild and domestic species in the Middle East (Anderson-Gerfaud, 1981 , 1988 ; Ibáñez et al., 2007 , 2008 ; Pichon et al., 2021 ).

In this sense, the Canary Islands, particularly the Indigenous Period (1st–fifteenth centuries AD), offers ample archaeological evidence (due to desiccation) of the exploitation of plants with lithic tools. The numerous botanical objects serving as models to reproduce through experimental work are made of wood, cultivated plants and wild plant fibres (Galván Santos, 1980 ; Morales et al., 2014 ; Vidal-Matutano et al., 2020 ). The Canarian archipelago was colonised, according to genetic, epigraphic, and material culture data, by groups of North African Berber/Amazigh origin (Hagenblad & Morales, 2020 ; Maca-Meyer et al., 2004 ; Mora Aguiar, 2021 ; Springer Bunk, 2019 ). Radiocarbon dating’s coupled with material evidence bear witness also to the presence of Romanised settlers on the Islet of Lobos at around the turn of the Era (Del Arco-Aguilar et al., 2020 ). Genetic evidence points to an East–West dynamic of occupation marked by subsequent inter-island contacts for an undetermined period (Fregel et al., 2019 , 2021 ; Serrano et al., 2023 ; Santana Cabrera et al., in press). This was followed by an extended period of isolation from the African continent and between the islands themselves until the arrival of Europeans in the fourteenth century AD (Alberto Barroso et al., 2019 , 2020 ; Morales, 2006 ; Pardo-Gordó et al., 2022 ; Velasco-Vázquez et al., 2020 , 2021 ). These groups survived their isolation by adopting self-sufficient agro-pastoral economies based on cultivating cereals and legumes as well as on collecting endemic species (Henríquez-Valido, 2022 ; Morales et al., 2023 ) and raising livestock (goats, sheep and pigs). Their diet was complemented by ichthyo and malacofauna (Castellano-Alonso et al., 2018 ; Rodríguez Santana, 1994 ; Rodríguez-Rodríguez et al., 2021 ).

The way of life of the different islanders was greatly conditioned by the absence of metal ores in the archipelago. They adapted to this shortfall by knapping tools from obsidian and especially from volcanic rocks such as basalt, trachyte and phonolite (Lacave Hernández et al., 2023 ; Rodríguez-Rodríguez, 1993 , 1998 ). The study of their material culture has traditionally been oriented towards identifying the technological processes to procure and transform various raw materials into products. These include investigations on plants whose findings have paved the way for experimental programmes designed according to different specific contexts. These have led to initial advances in identifying the technology linked to woodworking (Diego Cuscoy, 1968 ; Rivas Martínez et al., 1993 ; Vidal-Matutano et al., 2020 , 2021 ) and, to a lesser extent, that of non-woody plants and the processes of their procurement, manufacture and use (Galván Santos, 1980 ; Morales et al., 2014 ).

Functional lithic industry research in the Canary Islands has until recently been limited, as noted above, to tools serving to obtain and transform plants. Initial research highlights their use in tasks of cereal grinding and woodworking specifically on the islands of La Palma, Tenerife and Gran Canaria (Naranjo-Mayor & Rodríguez-Rodríguez, 2015 ; Rodríguez-Rodríguez, 1993 , 1998 ). However, there is a need to develop more robust experimental frameworks and extend the focus to a wider range of archaeological contexts. The development of archaeobotany and the documentation of working marks on wooden tools and certain plant fibres has given rise to new hypotheses regarding the role of lithic tools in harvesting cereals, woodworking and plant craftwork (Morales et al., 2014 ; Vidal-Matutano et al., 2020 , 2021 ). A response to new questions requires improving experimental collections resorting to obsidian and especially in the other volcanic rocks.

This study has specifically attempted to offer a complete record of the transformation of plants based on experimentation, ethnography and archaeological research. The experiments were carried out with lithic tools similar in typology and raw material to those collected on archaeological sites. The use-wear results are presented according to the different types of volcanic raw materials, obsidian on the one hand, and on the other hand other eruptive rocks, in particular those most often used in the Canary Islands (basalts, trachytes and phonolites). Although the group are characterised by different compositions, texture and hardness, they all share a similar structure, notably an amorphous matrix with phenocrysts, which conditions the development of wear and the nature of traces. The experimentation described here therefore pursues two main objectives: (1) identify and characterise the attributes of the lithic raw materials according to use-wear traces deriving from working vegetal materials and (2) delve into the technological aspects linked to the procurement, preservation and manufacture of wood and plant fibres.

Materials and Methods

The experimental programme was designed to identify the use-wear on volcanic tools (obsidian and other volcanic rocks) after working vegetal materials. The tasks include harvesting cereals, working other non-woody plants (e.g. rushes) and carrying out actions to process palm leaves and wooden branches. The work likewise undertook steps linked to manufacture artefacts of wood and plant fibre. It was inspired by observations of the exceptionally well-preserved Canarian archaeological finds (Fig.  1 a, d, g) often bearing traces of different technological actions (Fig.  2 a, d, g, j). The study likewise borrowed from ethnographic research and, at the methodological level, it adopted the registration protocols established by previous use-wear analyses, notably those by Hurcombe ( 1992 ), Clemente-Conte ( 1997 ), Rodríguez-Rodríguez ( 1998 ), Kononenko ( 2011 ), Huidobro ( 2018 ) and Walton ( 2019 ).

Archaeological plant remains and different experimental tasks. a Archaeological woodworking, transversal actions on wood ( Pinus canariensis ) discovered at the funerary site Cruz de la Esquina, Gran Canaria, (no. 287); b experimental scraping of dry wood ( Pinus canariensis ) with a basalt flake (exp: 232); c experimental scraping of fresh wood ( Pinus canariensis ) with a basalt flake (exp: 216); d archaeological woodworking, chopping action of wood ( Pinus canariensis ) from the funerary site Cruz de la Esquina, Gran Canaria, (no. 285); e experimental splitting of dry wood ( Ilex canariensis ) by indirect percussion using a basalt flake (exp: 219); f experimental splitting of fresh wood ( Ilex canariensis ) by indirect percussion with a basalt flake (exp: 153); g archaeological remains of cut grass stems from the funerary site Cruz de la Esquina, Gran Canaria (no. 213); h experimental harvesting of ripe cereals ( Hordeum vulgare ) with a basalt sickle; i experimental reaping of fresh rush ( Juncus acutus ) with a basalt sickle

Archaeological plant remains linked to the craft and experimentation carried out. a Archaeological remains of a cut palm leaf ( Phoenix canariensis ) at the petiole from the granary of Cruz de la Esquina, Gran Canaria (no. 1075); b experimental sawing of the petiole of fresh palm leaves ( Phoenix canariensis ) with an obsidian flake (exp: 281); c experimental cutting of dry palm leaflets ( Phoenix canariensis ) with a basalt flake (exp: 275); d archaeological fragment of a rush ( Juncus sp.) textile of type 1 from the funerary site of Cruz de la Esquina, Gran Canaria (no. 243); e experimental splitting of a remoistened rush ( Juncus acutus ) with a basalt flake (exp: 293); f experimental scraping of the pulp of a remoistened rush ( Juncus acutus ) with an obsidian flake (exp: 300); g archaeological twisted cord ( Juncus sp.) from the funerary site of Cruz de la Esquina, Gran Canaria (no. 17–6); h experimental splitting of remoistened palm leaflets ( Phoenix canariensis ) with a basalt flake (no. 307); i experimental twisted cord of palm leaflets ( Phoenix canariensis ); j archaeological rush mat ( Juncus sp.) of type 1 from the funerary site of Cruz de la Esquina, Gran Canaria (no. 1086); k-l experimental reconstruction of a funerary rush ( Juncus acutus ) bundle of type 1 from Gran Canaria

The Experimental Programme

The experimental tools, when possible, were manufactured with the same raw materials and following the technological processes and typologies identified at sites elsewhere in the Canary Islands. The knapping techniques consisted mainly of direct percussion with hard hammers (stone) and to a much lesser extent soft tools (wood). Indirect percussion has been only identified to date for the manufacture of querns. It consists of combining picks as intermediate tools with wood mallets. Obsidian, especially the small fragments, were also knapped using the bipolar technique on an anvil (Galván Santos & Hernández Gómez, 1996 ; Galván Santos et al., 1987 ; Lacave Hernández et al., 2023 ; Naranjo-Mayor & Rodríguez-Rodríguez, 2015 ; Rodríguez-Rodríguez, 1993 , 1998 ). The knapping methods were intended mainly to produce flakes. Retouched flakes are not abundant and there are also few uni- or bifacial tools and picks.

Obsidian is only located on three islands of the Canary Islands. Here, we resorted to three varieties. The first is black phonolite marked at times by brown bands from the El Tabonal de los Guanches lava flow, located on the northwestern area of the Island of Tenerife. It is semi-translucent revealing under light honey-green colours. Its knapped fractures vary from very fine to coarse (Hernández Gómez, 2006 ) (Fig.  3 a). The second and third types of obsidians are from two areas of Gran Canaria. Ignimbrite obsidian comes from the Hogarzales-El Cedro mine complex located to the west of the island. It is a trachytic glass which is opaque and coloured with different tones from green to blue-grey. Its aspect after knapping varies also from very fine to a slightly coarse (Fig.  3 b). The third is an ignimbritic, phonolitic obsidian from the south of the island. This type, collected in open areas such as ravines and slopes, is black, semi-translucent, green under light and when knapped reveals a very fine fracture (Martín Rodríguez et al., 2001 ) (Fig.  3 c, d).

Photographs (100x) illustrating the variation of the topography of the ventral surface of unused obsidian tools. a Phonolite obsidian from Tenerife; b trachyte obsidian from the mines of Hogarzales-El Cedro of Gran Canaria; c, d phonolite obsidian from Gran Canaria

The volcanic rocks serving for the experimentation, present on every island of the archipelago, are of the type most often found in Canarian archaeological sites: basalt, trachyte and phonolite. All share an amorphous matrix containing phenocrysts (plagioclase, olivines, pyroxenes…) which vary in size to naked eye from large to cryptocrystallines. Consequently, to the naked eye they reveal different knapped surfaces ranging from very coarse to fine (Fig.  4 a, b). Basalts are often black, phonolites grey and trachytes green to brown. The experimental tools were knapped mainly from cobbles of different size from beaches and ravines of La Palma, Tenerife and Gran Canaria. A few exceptions consist of fragments of trachyte from the Hogarzales-El Cedro mines, which bear finer knapped surfaces than the others (Fig.  4 c, d).

Photographs (100X) illustrating the variation of topography of the ventral surface of unused phenocryst volcanic rocks. a Basalt; b phonolite; c, d trachyte with phenocryst of varying size

Most of the experimental tools (88,82%) consisted of flakes. The few remaining consisted of blades (9,32%), picks (1,24%) and knapped pebbles (0,62%) (Fig.  5 ). Experimental work, except for the harvesting of cereals and rushes, was carried out with unhafted tools as the morphology of the archaeological examples do not lend themselves to hafting and no hafts have been identified in archaeological record. There are nonetheless references amongst written narrative sources dating to the period of contact between the native and European populations suggesting the use of goat horns and wooden handles for certain tools (Gómez Escudero [1629–1694] in Morales Padrón, 2008 : 374).

Lithic tools used in the experimental programme: 1. Basalt pick to chop wood (Fig. 31); 2. Basalt flake to saw wood (a: Fig. 23, b: Fig. 24); 3. Basalt flake to saw palm petioles (a: Fig. 28, b: Fig. 29); 4. Basalt flake to saw wood (a: Fig. 25, b: Fig. 24); 5. Obsidian flake to saw wood (Fig. 7); 6. Obsidian flake to saw wood (a: Fig. 8, b: Fig. 9); 7. Obsidian retouched flake to saw wood (Fig. 6); 8. Obsidian lame to split palm leaves (Fig. 21); 9. Basalt flake to scrape wood (Fig. 30); 10. Obsidian retouched flake to scrape wood (a: Fig. 15, b: Fig. 14); 11. Obsidian flake to split wood (a: Fig. 18, b: Fig. 19); 12. Obsidian blade to harvest cereal (Fig. 11); 13. Trachyte flake to harvest cereal (Fig. 26); 14. Basalt flake to harvest cereal (Fig. 27); 15. Obsidian flake to harvest rushes (Fig. 11); 16. Obsidian blade to split/scrape rushes (Fig. 20); 17. Trachyte flake to scrape rushes (Fig. 32); 18. Obsidian retouched flake to saw palm petioles (a: Fig. 12, b: Fig. 13); 19. Obsidian flake to split rushes (a: Fig. 16, b: Fig. 17); 20. Trachyte flake to split palm leaves (Fig. 34)

The experimentation comprised a total of 161 tools (82 obsidian; 79 different volcanic rocks) as listed in the spreadsheets (Supplementary file 1 ).

The stone tools serving for the wood experimental work consisted of 38 obsidian and 28 volcanic rocks. They are for the most part unretouched flakes followed by a few retouched cases, a pick and a bifacial pebble. The wood used for the experimentation included both endemic and foreign species, mostly known from the archaeological record (Machado Yanes, 1996 ; Vidal-Matutano et al., 2020 ) (Table  1 ). Following ethnographic examples, the wood consisted of both dry and fresh pieces. As the experimentation aimed at fashioning both roughouts and finished artefacts, the tasks included sawing, scraping and chopping and, to a lesser extent, splitting and drilling (Fig.  1 b, c, e, f). These tasks required different timeframes ranging from 2 to 120 min (see Supplementary data 1 ).

The second set of experiments intended to shed light on working non-woody plants. The harvesting of ripe cereals involved 17 obsidian and 18 volcanic experimental tools. Fresh rushes in turn, were worked with 9 obsidian and 9 volcanic rocks (Table  1 ). Straight or curved three-piece hafted sickles served to reap the cereals and rushes, an action carried out at ground level (Fig.  1 h). Rushes, in turn, were cut in bundles about 10 cm from the ground (Fig.  1 i). The time involved in this work ranged from 4 to 180 min.

The third set of experiments involving 18 obsidian and 24 volcanic rock tools was oriented towards preparing fibres to manufacture artefacts (cordage, baskets and textiles). Rushes and palm leaves were collected whilst fresh. Then they were dried and finally remoistened for further work (Table  1 ). Different types of unhafted flakes of several sizes served to cut the bases of the palm leaves (Fig.  2 b, c), split the rush stems, scrape their interior to remove the pulp and render the fibre more pliable (Fig.  2 e, f) and split the palm leaves to make twisted rope (Fig.  2 h, i). A funerary mat made of type 1 fabric (according to the typology of Galván, 1980 ) required the plant fibres to be braided and joined manually prior to cutting off the elements protruding from the cords and the ends (Fig.  2 k, l). In these cases the time of use varied from 5 to 60 min.

The method also included a systematic recording of the weather conditions and the type of soil as they (e.g. dry and hard) could have an influence on the use-wear. Another key element recorded was of the time required by each action. All variables were then registered in specific forms and accompanied, especially during the final stages of the tasks, by photographs and videos.

The experimental tools were cleaned with water and neutral soap before placing them in an EMAG Emmi-H30 ultrasonic tank containing demineralised water, soap and pure acetone [C₃H₆O] during intervals of 20 and 25 min. In the cases where this process did not suffice to remove the residues, the procedure was repeated by adding pure alcohol (96%).

Tools and Laboratory Protocols

The stone tools were examined with three different types of microscopes, notably a Nikon SMZ1000 binocular magnifying glass (henceforth BMG) with a magnification of 8 × to 80 × to observe the macro traces. A Nikon Labophot-2 metallographic microscope (henceforth MM) with a magnification of 100 × to 400 × to view the micro traces and a Nikon Eclipse MA100 inverted microscope with a magnification of 50 × to 400 × to view the microtraces on the larger pieces. Photographs were taken with a DS-Fi2 camera at various heights controlled manually with NIS-Elements software (version 4.30). The images were compiled with the Helicon Focus 6 focus stacking programme prior to adding scales with Photoshop CS4.

Criteria of Observation

The comprehensive study of the traces of use-wear on obsidian and volcanic rocks resorted to criteria defined by prior research (Mansur-Franchomme, 1986 ; Cotterell & Kamminga, 1987 ; Hurcombe, 1992 ; González & Ibáñez, 1994 ; Rodríguez-Rodríguez, 1993 , 1998 ; Kononenko, 2011 ; Clemente-Conte et al., 2015 ; Huidobro, 2018 ). A descriptive protocol was likewise designed to highlight the responses of each type of lithic raw material to identical tasks. As noted, the other volcanic rocks (basalt, trachyte and phonolite) of the Canary Islands are characterised by coarse surfaces due to the presence of varying degrees of phenocrysts (some can be classified as cryptocrystallines). Canarian obsidian is also characterised by a variety of surfaces, with that of Tenerife slightly coarser than that of Gran Canaria (Figs. 3 and 4 ). These different factors lead to uneven wear and development of the traces of use influencing on their distribution and development. This study resorted to the descriptive conventions of both glass and phenocrysts. Previous research has ratified these conventions in the case of other volcanic rocks (Bello-Alonso et al., 2020 ; Clemente-Conte, 1997 ; Clemente-Conte et al., 2015 ; Huidobro, 2018 ). This is a particularly important aspect when comparing the similarities and differences in the surfaces of the different raw materials when working identical plants in a similar way. It is nonetheless possible to define the following series of general characteristics for each of the two main lithic materials.

The traces of use-wear on the stone tools observed with a binocular magnifying glass (8–80x) are the following:

Scars. Location (ventral or dorsal, unifacial, bifacial or alternate); distribution (continuous or discontinuous); disposition (isolated, in chain or overlapping); orientation (perpendicular or oblique); morphology (crescent, trapezoidal, semicircular or irregular); termination (feather, step, hinge or multiple) (Kamminga et al., 1979 ; González & Ibáñez, 1994 ; Huidobro, 2018 ).

Rounding of the edge. Location, distribution and intensity (low, medium or high) (Huidobro, 2018 ).

Abrasion. Location (edge, surface or both), distribution and intensity.

Striae. Location, distribution, disposition (grouped or isolated); orientation (parallel, perpendicular, oblique or random); size (short 1 mm, medium 3 mm or large > 4 mm).

The traces of use-wear observed through a metallographic microscope (50-400X) are the following:

Abrasion. Location, distribution, disposition (marginal, (patches); moderate (abrasion zones), orientation (parallel, perpendicular/oblique or indeterminate); intensity (low, medium, high) (Clemente-Conte et al., 2015 ).

Striations. Location, distribution, disposition, orientation, depth (shallow or deep), length (short (< 2 μm), medium or long (> 2 μm); type of edge (straight or irregular); type of striations (rough-bottomed; sleek; flaked; intermittent; comet-shaped; fern-like) (Mansur-Franchomme, 1982; Hurcombe, 1992 ; Huidobro, 2018 ).

Polish. Location, microtopographic distribution: poor development (smooth only along the edge, lancets or depressions), medium development (when the polish almost totally modifies the surface); high development (when the polished surface is almost totally modified); texture (very smooth, smooth or rough); morphology (smooth, bumpy or undulated). Variables such as the degree of the linkage of the polish ( trame ) were not considered due to the difficulty in detecting them on each of the two rock types.

The results of the study are presented first according to the type of raw material (obsidian or other volcanic rocks) followed by the nature of the worked material (wood and non-wood plants) and the types of actions. The study considers that the physical and mechanical characteristics of each type of tool can react differently to the same type of action. These results focus on the more diagnostic criteria of the materials. The remaining attributes of use-wear are shown in the following spreadsheets (see Supplementary files 2 and 3 ). It is essential to bear in mind that the use-wear descriptions from woodworking were in its fresh and dry state and are highlighted only if they reveal differences.

Longitudinal Actions with Obsidian Tools

The experimental sawing of dry and fresh woody plants involved 13 obsidian tools. BMG observations reveal continuous, alternating and overlapping scars, disposed along the edge in a chain. Most are semicircular with feather terminations. The edge reveals abrasion with a discontinuous distribution. MM observations, in turn, reveal some bifacial, discontinuous and marginal traces along the edge, notably abrasion of low intensity restricted to the edge. The striations are also bifacial, parallel to the edge, and continuous in the case of working fresh wood but discontinuous when the wood is dry. The most common type reveals irregular edges and is rough-bottomed and fern-like (Figs. 6 and 7 ). The distribution of the polish linked to working dry wood is medium developed, appearing only along the ridges or on the protruding areas. The polish visible from working fresh wood, although not continuous, extends over a wide surface occupying areas adjacent to the most prominent relief. Its texture is smooth and the morphology is bumpy (Figs. 8 and 9 ).

Exp 132. Obsidian: sawing dry wood ( Viburnum rugosum ) for 20′ (200x): edge scarring, sleek and rough-bottomed striations, discontinuous smooth and bumpy polish mainly along the ridge

Exp 104. Obsidian: sawing dry wood ( Ilex canariensis ) for 60′ (200x): edge scarring, discontinuous abrasion, fern-like striations, discontinuous smooth and bumpy polish

Exp 105. Obsidian: sawing fresh wood ( Ilex canariensis ) for 20′ (200x): abrasion along the edge, intermittent, sleek and rough-bottomed striations, continuous smooth and bumpy polish

Exp. 105. Obsidian: sawing fresh wood ( Ilex canariensis ) for 20′ (200x): abrasion along the edge, sleek and fern-like striations, continuous smooth polish

Ripe Cereals

The harvesting of ripe cereals was carried out with 17 obsidian pieces. BMG analyses reveal bifacial, continuously distributed scars in chain and an overlapping disposition. Although most often semicircular, there are a few irregular cases with feather terminations. The rounding is bifacial and continuously distributed. Furthermore, there are cases marked by discontinuously distributed bifacial, dense and parallel striae along the edge. MM observations reveal discontinuously distributed bifacial areas of abrasion. Their disposition is moderate and characterised by a medium to high intensity. The striations are bifacial, continuously distributed and parallel to the edge, and characterised by straight and irregular edges, flaked and comet-shaped types. There arealso intermittent and comet-shaped cases. The polish is distributed throughout the surface’s microtopography adjacent to the edge, even slightly to the interior, and reveals a very smooth texture and flat morphology (Fig.  10 ).

Exp. 211. Obsidian: cereal harvesting ( Hordeum vulgare ) for 120′ (200x): continuous rounding along the edge, sleek, flaked and comet-shaped striations, continuous smooth and flat polish

Fresh Rushes

Nine obsidian tools served to reap green rushes. Their scars under BMG magnification are bifacial, distributed continuously in chain and an overlapping disposition. Their semicircular shape is marked mostly by step terminations and crescent morphology. The abrasion is on the edge and surface with discontinuous distribution. Also visible are bifacial and continuous isolated striae, oriented parallel to the edge. MM observations, in turn, reveal discontinuously distributed marginal bifacial abrasion of medium intensity. The striations are very abundant, bifacial and parallel. The most common have irregular edges and are rough-bottomed and sleek. The polish covers most of the edge, although it is denser along the protruding areas and in the interior of certain scars. Its texture is smooth and its morphology undulated (Fig.  11 ).

Exp. 202. Obsidian: reaping fresh rush ( Juncus acutus ) for 40 ′ (200x): abrasion along the edge, rough-bottomed and sleek striations, continuous smooth and undulated polish

Working fresh palm leaves required sawing the base of each leaflet to separate it from the rachis. This involved four pieces of obsidian. Under BMG magnification, the scars are alternating with a discontinuous distribution and an isolated or overlapping disposition. Although most often crescent-shaped, they also appear at times semicircular with step terminations. The rounding is distributed discontinuously on the edge. Abrasion under the MM is bifacial, continuously distributed, arranged marginally and marked by a low intensity. Striae are abundant, bifacial and parallel to the edge and continuously distributed both far and near the edge. Although most often intermittent, they also reveal sleek with straight edges. The polish is bifacial, continuously distributed along the edge, practically modifying the obsidian's microtopography. Its texture is smooth and its morphology is bumpy (Figs. 12 and 13 ).

Exp. 280. Obsidian: sawing the petiole of fresh palm leaves ( Phoenix canariensis ) for 20′ (200x): continuous rounding along the edge, sleek and intermittent striations, continuous smooth and bumpy polish

Transversal Actions with Obsidian Tools

Scraping both fresh and dry wood was undertaken with 14 obsidian flakes. The location and distribution of the traces when observed both by BMG and MM follow the angle of contact of the tool (positive, negative or perpendicular). Amongst the 14 pieces, 9 worked coupe positive . In this case, most of the traces are on the ventral face. The rest worked coupe negative and no traces were observed on the ventral face. The scars thus, on the whole, respond to the obsidian’s working angle. Most are unifacial, ventral or dorsal depending on the position of the attack face. They are distributed discontinuously along the edge and disposed in chain with semicircular and irregular morphology and feather or step terminations. The abrasion is continuous, on the edge and surface on the attack face. Abrasion under MM is bifacial with a marginal distribution and arranged discontinuously, although revealing a greater development along the face of contact. Its intensity is mainly medium. The location of the striations also depends on the conductive face, although their distribution in both dry and wet conditions is discontinuous. Although most commonly presenting a transversal orientation, there are also cases oblique with respect to the edge. The most representative type bears an irregular edge and is rough-bottomed, whereas a lesser number bear sleek striations. The polish is poorly distributed, appearing only on the upper areas of the microtopography. Its texture is smooth, and its morphology is slightly flattish and bumpy (Figs. 14 and 15 ).

Exp. 120. Obsidian: scraping dry wood ( Morella faya ) for 60′ (200x): abrasion along the edge, sleek and rough-bottomed striations, discontinuous smooth and flat polish

Exp. 120. Obsidian: scraping dry wood ( Morella faya ) for 60′ (200x): discontinuous abrasion along the edge, sleek striations, discontinuous smooth and bumpy polish

Remoistened Rushes

Scraping the interior of remoistened rush stems (subsequent to their splitting after drying) was carried out with three obsidian tools. All pieces have an acute angle. Therefore, the attack and contact faces were interchangeable. The BMG reveals a chain of discontinuously distributed bifacial scars. Although most are crescent-shaped, there are also semicircular cases with feather and step terminations. The abrasion is on the edge with a continuous distribution. Striae under MM magnification are bifacial with an irregular location and distribution. The most common type has straight edges and is sleek even if there are a few cases with irregular rough-bottomed. The polish is medium developed, especially along the upper areas of the microtopography such as the ridges. Its texture is very smooth and its morphology slightly flat (Figs. 16 and 17 ).

Exp. 292. Obsidian: scraping and splitting remoistened rush ( Juncus acutus ) for 10 ′ (200x): continuous abrasion along the edge, sleek striations, discontinuous smooth and flat polish

Splitting with Obsidian Tools

The experiments of splitting both fresh and dried wood were carried out with 11 obsidian tools. BMG observations reveal abundant bifacial overlapping scars distributed continuously along the edge. Most are crescent and semicircular morphology with hinge terminations. The abrasion is discontinuously distributed along the edge and the surface. Abrasion under MM is bifacial although it can reveal a greater development on one edge rather than on the other (depending on the working angle). The distribution is marginal and arranged discontinuously, although revealing a greater development along the face of contact. Its intensity is mainly medium. The striae are discontinuously distributed, bifacial with transversal and oblique orientations. Although most types of striations present irregular edges and are rough-bottomed, a few, by contrast, have straight edges and are sleek. No polish was observed (Figs. 18 and 19 ).

Exp. 44. Obsidian: splitting dry wood ( Laurus novocanariensis ) for 15′ (200x): discontinuous abrasion on the surface and rough-bottomed striations

Exp. 44. Obsidian: splitting dry wood ( Laurus novocanariensis ) for 22′ (200x): discontinuous abrasion along the edge and surface, sleek and rough-bottomed striations

The action of splitting remoistened rushes involved four obsidian tools. The scars visible through BMG magnification are bifacial, continuously distributed, overlapping and disposed in chain. Most are of irregularly morphology with step and hinge terminations. The abrasion is discontinuously distributed along the edge and the surface. MM views are more difficult to interpret as there is no evidence of clear use-wear. The scarce striae are bifacial, discontinuously distributed along the edge with both transversal and oblique orientations. Most have straight edges and are sleek. Otherwise, it is not possible to observe any polish (Fig.  20 ).

Exp. 297. Obsidian: splitting remoistened rushes ( Juncus acutus ) for 40′ (200x): scarring, discontinuous abrasion along the edge, sleek striations

Remoistened Palm

Splitting remoistened palm was carried out with eight obsidian fragments. The scars under the BMG are discontinuously distributed along the edge. Their disposition is in chain and they are most often of crescent morphology (in spite of some semicircular cases with feather terminations). The abrasion is discontinuously distributed along the edge. MM magnification reveals alternating discontinuously distributed striae very near the edge with both parallel and transversal orientations. They most commonly are straight edged and sleek. The polish, distributed alternately in the upper areas of the microtopography, is smooth with a bumpy morphology (Fig.  21 ).

Exp. 324. Obsidian: splitting remoistened palm leaves ( Phoenix canariensis ) for 20′ (200x): discontinuous abrasion along the edge, sleek striations, discontinuous smooth and bumpy polish

Other Volcanic Rocks

Longitudinal actions with other volcanic rock tools.

Experimental sawing of both fresh and dry wood involved 12 tools. These pieces under BMG magnification reveal continuously distributed, alternating scars, both in chain and overlapping (in the case of dry wood). They are mostly of crescent or trapezoidal morphology with feather and hinge terminations. The abrasion is discontinuously distributed along the edge and the surface. MM observations reveal discontinuously distributed bifacial, marginal abrasion. Striations are along the amorphous matrix along the edge with parallel orientations. The most common type has an irregular edge and is rough-bottomed (Figs. 22 and 23 ). The microtopography of the bifacial polish is smooth in texture and flat in morphology. It only occupies the upper areas of the amorphous matrix. Sleek striations and flat polished surfaces can be observed on the phenocrysts (Figs. 24 and 25 ).

Exp. 144. Basalt: sawing dry wood ( Juniperus turbinata ) for 25′ (200x): discontinuous abrasion on the surface, rough-bottomed striations, discontinuous smooth and flat polish on the amorphous matrix

Exp. 144. Basalt: sawing dry wood ( Juniperus turbinata ) for 25′ (200x): discontinuous abrasion on the surface and discontinuous smooth and flat polish on the amorphous matrix

Exp. 155. Basalt: sawing fresh wood (Dracaena draco ) for 15′ (200x): discontinuous abrasion on the surface and discontinuous smooth and flat polish on both the amorphous matrix and the phenocrysts

Exp. 155. Basalt: sawing fresh wood ( Dracaena draco ) for 15′ (200x): discontinuous abrasion on the surface, sleek striations and discontinuous smooth and flat polish on the phenocrysts

Harvesting with the other volcanic rocks involved 18 tools. Under the BMG the scars are bifacial, distributed continuously and disposed in chain. Although most are crescent-shaped, there are also cases of semicircular morphology with feather terminations. The rounding is bifacial and continuous along the edge. Striae, at times visible, are bifacial, continuously distributed and arranged following a dense parallel orientation. Under the MM the abundant striae are also bifacial, continuously distributed and oriented parallel to the edge. Most are characterised by straight edges and sleek or irregular rough-bottoms. There are likewise some intermittent cases. Polish is visible on the microtopography along most of the edge. Its distribution, when the experimentation endured less than 60 min, is partially developed along both the low and upper areas. In the experiments exceeding 180 min it is greatly distributed along almost the entire edge. Its texture is very smooth with a flat morphology and a few micropits. Striae and flat polished surfaces can be observed along most of the phenocrysts of the tools having worked for several hours (Fig.  26 ).

Exp. 354. Trachyte: harvesting ripe cereals ( Hordeum vulgare ) for 180′ (200x): continuous rounding along the edge, sleek and rough-bottomed striations, continuous smooth and flat polish on the amorphous matrix and on the phenocrysts

Nine volcanic rock tools served to reap green rushes. BMG observations reveal alternating scars, mostly of crescent morphology, distributed continuously and disposed either in chain or overlapping. Abrasion is continuously distributed along the edge and the surfaces. Striae are visible in certain cases. They are bifacial, dense and oriented parallel to the edge. MM magnification reveals a moderate continuously distributed bifacial abrasion. Striations are very abundant along the amorphous matrix. They are bifacial and continuously distributed parallel to the edge. Most reveal an irregular edge and are rough-bottomed or intermittent (although there are some cases with straight edges and sleek). The polish is bifacial and extensive, modifying part of the amorphous matrix. It is located on the edge and on the surfaces. Its smooth texture and morphology are bumpy marked by micropits and abrasion. The phenocrysts of these tools only feature polish (Fig.  27 ).

Exp. 153. Basalt: harvesting fresh rushes ( Juncus acutus ) for 60′ (200x): discontinuous abrasion along the edge, rough-bottomed and intermittent striations, discontinuous smooth and bumpy polish

The sawing of fresh palm leaflets was undertaken by four tools. BMG observations evidence continuously distributed, alternating scars, most often of crescent morphology, disposed either in chain or isolated in other areas of the edge. Abrasion is discontinuously distribution along the edge and surface. MM observations, in turn, reveal a marginal discontinuous and alternating abrasion. The striae are bifacial, located along the amorphous matrix. Their distribution is discontinuous and, although mostly parallel, can be oblique. The most common type has an irregular edge and are rough-bottomed and sleek. Their phenocrysts are marked by very few striae bearing the same characteristics. The polish is bifacial and very slightly distributed amongst certain areas of the microtopography. It is usually of smooth texture, of bumpy morphology and devoid of micropits. It is very difficult to observe traces on the phenocrysts as their surfaces are completely worn by abrasion (Figs. 28 and 29 ).

Exp. 145. Basalt: sawing fresh palm ( Phoenix canariensis ) for 20′, discontinuous abrasion on the surface, sleek striations, discontinuous smooth and bumpy polish

Exp. 145. Basalt: sawing fresh palm ( Phoenix canariensis ) for 20′, discontinuous abrasion on the surface and discontinuous smooth and bumpy polish

Transversal and Chopping Actions with Volcanic Rock Tools

The scrapping of both dry and fresh wood was undertaken with 12 pieces. Here, the BMG observations evidence scars in a variety of locations depending on the working angle and the edge of the tool. Although they are at times bifacial, in other cases with positive angles they are on the face of attack. Most are distributed discontinuously and are overlapping. Although predominantly semicircular with step termination, a lesser number are crescent-shaped. Abrasion is distributed discontinuously along the edge and surfaces. MM observations indicate that abrasion is bifacial, marginal and discontinuously distributed. Striae are scarce in both states of the wood. Their location, away from the edge, depends on the face of attack. They are distributed discontinuously with a varied orientation (predominantly transversal). The most common bear irregular edges and have rough-bottom. Striae on phenocrysts are only visible at 400x. The orientation varies depending on the working angle. The most common have straight edges and are sleek. The polish is poorly developed and only found along the upper areas of the microtopography of the amorphous matrix. Their texture is smooth with a flat morphology in the case of fresh wood and only smooth when the wood is dry. Areas of polish are only observed on the edges of the phenocrysts with bumpy morphology (Fig.  30 ).

Exp. 149. Basalt: scraping dry wood ( Juniperus turbinata ) for 45′, discontinuous striations and discontinuous smooth and bumpy polish on the crystal

The chopping of fresh wood was carried out with a bifacial knapped pebble and two picks. The traces on the picks are located on the active apexes and on the pebble along the entire knapped ridge. BMG observations of the knapped pebble reveal bifacial scars mostly along the upper face. Their distribution along the edge is discontinuous, disposed in chain and slightly overlapping. Although most often semicircular, there are also cases of trapezoidal and crescent morphology with feather and hinge terminations. A slight discontinuous abrasion is visible along the protruding areas. In the case of the picks, the traces visible by BMG, mainly bifacial scars, are specific to the apexes. Their distribution is continuous, clustered and overlapping. Their morphology is irregular, with step termination. The abrasion is distributed discontinuously along the edge and the surfaces. MM reveals bifacial abrasion that is marginal and discontinuous. Randomly oriented striae are visible along the surface of some restricted polished areas far from the edge. The most common bear irregular edges and rough-bottoms. Striae are not visible on the phenocrysts. Polish is poorly developed in patches. Its texture is smooth with a bumpy morphology (Fig.  31 ).

Exp. 375. Basalt: chopping fresh wood ( Pinus canariensis ) for 120′, discontinuous abrasion on surface, rough-bottomed striations, discontinuous smooth and bumpy polish

The inner part of the remoistened rushes was scraped with four volcanic rock pieces. The scars deriving from these actions under BMG magnification are bifacial, predominantly crescent-shaped, discontinuously distributed, overlapping and isolated in certain areas. Abrasion is discontinuously distributed along the edge. The development of use-wear as viewed under the MM is difficult to ascertain. Few continuously distributed, bifacial, highly variable oriented striae are far from the edge. The most common striae have irregular edges and rough-bottoms. Otherwise, it is not possible to observe any polish (Fig.  32 ).

Exp. 290. Trachyte: scraping remoistened rushes ( Juncus acutus ) for 20′, rough-bottomed striations on the phenocrysts

Splitting and Drilling with Volcanic Rock Tools

The single drilling experiment carried out with a basalt tool on dry wood yielded highly visible wear. BMG observations reveal no scars as the apex is completely rounded. There are striae, predominantly transversal, densely arranged over the entire surface. The striae under MM magnification are highly visible on the amorphous matrix. They are arranged along the distal area, with a continuous distribution and random orientation. The most common type has irregular edges, sleek with rough-bottoms. Polish, visible on the surface of the amorphous matrix, is developed on the upper areas of the microtopography. Its texture is smooth and its morphology slightly bumpy. No traces are visible on the phenocrysts (Fig.  33 ).

Exp. 160. Basalt: drilling dry wood ( Ilex canariensis ) for 15′, discontinuous sleek striations, discontinuous smooth and bumpy polish of the microtopography

The wedging of fresh wood was carried out with two volcanic rock flakes. Both bear complete bifacial scars disposed in chain with very irregular shapes and step terminations. The numerous chipping scars render it impossible to view the other use-wear, which explains why we did not include photographs.

Splitting rushes were undertaken with five volcanic rocks. Under BMG, they display discontinuously distributed and overlapping bifacial scars. Although most are crescent-shaped, some are semicircular with feather terminations. MM observations do not evidence any use-wear.

The splitting of remoistened palm leaves involved 10 volcanic rocks. BMG reveals continuously distributed bifacial scars, most often crescent shaped, and disposed in chain. Abrasion is discontinuously distributed along the edge. MM observations reveal bifacial striae near the edge which are distributed discontinuously with random orientation. The predominant type has an irregular edge and is rough-bottomed. The bifacial polish is poorly distributed which explains why it can only be observed near the edge along the upper areas of the microtopography. Its texture is smooth and its morphology is bumpy. The pattern of polish on the phenocrysts is very similar to that visible on the amorphous matrix (Fig.  34 ).

Exp. 306. Trachyte: splitting remoistened palm leaves ( Phoenix canariensis ) for 20′, discontinuous striations and bumpy polish on the crystals

This article is focused on wear visible on two main categories of volcanic effusive elements: obsidians and other volcanic rocks. Within each of the two categories it is possible to observe variability of use-wear. Firstly, there is the relationship between the topography of the knapped surfaces, the distribution of features and the time of use for the action. As mentioned previously, the surfaces of obsidians from Tenerife and Gran Canaria vary from the very fine to slightly rough. For example, the phonolite obsidian from Tenerife is the roughest which in terms of traces of identical actions yield less rounding, less scarring and less striae. Although their polish is usually more irregularly distributed, their characteristics resemble those of the phonolite and trachyte obsidians from Gran Canaria. The experimentation with obsidian from Tenerife was in general more resistant, even in the case of woodworking. It has been stressed that obsidian has physical properties which differ from other fine knapped fracture rocks such as flint (Astruc, 2022 ; Hurcombe, 1992 ; Kononenko, 2011 ). This is a key notion when studying use-wear. Obsidian is in fact more brittle that flint and its edges are more easily abraded. It is also particularly prone to surface alteration by chemical and, to a lesser extent, mechanical transformation (Hurcombe, 1992 ). As wear has a greater effect on the upper features of the microtopography, one must keep in mind that the rougher the surface, the more discontinuous the attributes. Polish is less visible as the natural surface of obsidian reflects light. In addition, attributes such as extension or morphology are less accessible and others as the degree of linkage ( trame ) of the polish is not recognisable.

In the case of the other volcanic rocks, the heterogeneous composition of their amorphous matrix and the variability of their phenocrysts in terms of size and quantity renders is difficult to observe the use-wear. The greater amount of crystals the less chance of identifying traces such as striae or polish even at high magnification (400X). The development of these traces in the cases of rocks with an amorphous matrix is very uneven. Abrasion (and sometimes polish) together with striations can only be observed on the upper areas of the surfaces. As in other amorphous crystalline rocks, the difficulty in locating and describing them complicates observations (Clemente-Conte, 1997 ; Clemente-Conte et al., 2015 ; Huidobro, 2018 ). It is key to take into account that the completely irregular microtopography of this type of raw material yielding discontinuous wear, segmented striations and polish in patches or spots, reduces visibility and the possibility of diagnosis.

This discussion will thus delve into how to distinguish the different types of worked plants according associations of attributes. To do this, one must first describe those specific to obsidian before turning to those of the remaining volcanic rock types.

Obsidian Experimental Tools

Contact of this rock with woody and siliceous plants yields different types of wear and different distribution. Woodworking creates abrasion along the edge, fern-like striations and discontinuously distributed polish of bumpy morphology. Contact with siliceous plants leads to rounding, sleek striations, and a polish with smooth morphology. In spite of this, there are certain minute differences (that will be explained later) that allow to identify certain plants. It is important to mention that woody and non-woody plants can be differentiated by means of associations of attributes. Moreover, their distribution is also influenced by the condition (fresh, dry or intentionally re-moistened) of the plant and its hardness. Hardness mainly influences not only the development of striae (distribution and morphology) but also other attributes such as polish (Table  2 ). In general, it is possible to distinguish between hard and soft plant material, but a more detailed interpretation such as whether it is in a dry or fresh state is more difficult to advance. The mode of action in general also yields different types of traces such as striations or polish. It is possible to observe, in the case of woody plants, a greater number of longitudinal than transversal actions. This is potentially due to the working angle as in scraping the angle of the cutting edge and the angle of work significantly influence the distribution of the traces (notably striae and polish). Siliceous plants also yield more attributes in the case of longitudinal tasks such as harvesting or cutting. Another influence in these cases is the silica content and the time of use.

Woodworking with obsidian yields characteristic use-wear. Scarring and abrasion are concentrated along the edge. Striations can be near the edge attached to the areas of abrasion, on the surface and inside certain scars. They are mostly fern-like and rough-bottomed. Striations are linked to polished areas. Polish is discontinuously distributed along the microtopography of the surface and reveals a smooth texture and bumpy morphology. Diagnosing woodworking is possible subsequent to at least 20 min of work. Distinguishing the state of the wood (hardness and degree of humidity) was not possible in the current experimental programme due to the fact that scarring cannot always be linked to these conditions, and striae and polish are similar in all cases.

Cereal harvesting yields specific types of use-wear on obsidian. Soft plants rich in silica yield a high degree of rounding of the edge. Striations are located near the edge, on whole surfaces and inside scars. The most common are comet-shaped or sleek. Polish is invasive along the edge and adjacent surfaces and completely modifies the microtopography of the surface of the tool. It has a smooth texture and flat morphology. The invasiveness of the wear is a criterion serving to identify cereals as opposed to other silica-rich plants. Their interpretation is possible even when the experimentation was relatively short (e.g. 10 min).

Rushes although siliceous are harder than cereals yielding more developed scarring and abrasion on the edge and surface. Striations are limited to the surfaces situated near the edge. The rough-bottomed type is the most common. Polish also develops along the edge with a smooth texture and an undulated morphology. It is not possible to distinguish this plant when in a dry state as these traces do not develop before 60 min of use. Hence, working time greatly influences on the level of interpretation.

Palms are also siliceous plants whose differences in hardness can be observed when processing the stem and the leaves. The clearest traces on the obsidian tools are those stemming from the cutting the stem. Scarring and rounding are also common along the edge. Striations are near the edge and on the surface, although not often inside the scars. The most common are intermittent. Polish near the edge is not as invasive as in the case of cereals. It has a smooth texture and bumpy morphology. These types of striations and the polish located the near the edge are characteristic of the cutting of palm stems. These criteria can be identified subsequent to working lasting at least 20 min. Splitting palm leaves, in turn, does not yield developed attributes meaning it is much more difficult to identify the matter of contact. It is also not possible to differentiate whether the plant was in a fresh or remoistened state.

A broad comparative framework is available to contrast the findings of the experimentation we carried out with obsidian tools. Yet it is necessary to note that it is not possible in the case of woodworking to offer specific comparisons because most obsidian study variables correspond to either hard or soft states whilst in our case the observations correspond to fresh/dry states. Hence the state of the wood from different contexts is an important factor to take into consideration. In addition, this is the first experiment conducted on fresh palm and remoistened palm and rushes.

Similar patterns to those observed here have been described in the case of woodworking. For example, Rodríguez-Rodríguez ( 1998 ) observed scarring while Kononenko ( 2011 ) has cited rough-bottomed striations. Huidobro ( 2018 ) also reported a poorly developed distribution of polish and Walton ( 2019 ) has described what is labelled as bumpy morphology. However, there are certain discrepancies such as the observation of rounding by Hurcombe ( 1992 ) and the presence of intermittent striae by Kononenko ( 2011 ) and Huidobro ( 2018 ). Although these characteristics were not identified in our experimentation, we did observe abrasion along the edge and fern-like striations. These may be due to the difference of the species of wood and their humidity or degree of hardness.

The traces stemming from harvesting cereal is also the subject of previous research. Rodriguez-Rodriguez ( 1998 ) and Astruc ( 2012 ) observed moderate rounding. Hurcombe ( 1992 ) identified long striae both near and far from the edge, most often sleek, rough-bottomed or intermittent. Smooth and flat-shaped polish, in turn, is regular and very invasive along the edge, attributes likewise have observed in the experimentation. Moreover, cereals with a high silica content will yield similarly distributed attributes on all types of obsidian.

The results of our experiments in harvesting green rushes coincided with the microscopic observations by Hurcombe ( 1992 ) in that they reveal well-developed striae both along the tool's edge and the interior of the scars that are predominately rough-bottomed (in spite of intermittent or comet-shaped cases). Differences with this earlier study relate to the distribution of the regular polish and the bumpy morphology which in this case are undulated. It is possible that this may be due to the characteristics of the knapped surface of the obsidian.

Other Experimental Volcanic Rock Tools

Basalt, trachyte and phonolite, the other volcanic rocks serving for this experimentation, reveal use-wear features subsequent to working wood and other siliceous plants that differ from those on the obsidian tools. These differences affect in particular the amorphous matrix and the phenocrysts. Woodworking yields surface abrasion, rough-bottomed striations and discontinuously distributed polish of flat morphology. Contact with siliceous plants, in turn, gives rise to rounding or abrasion, depending on the plant. Sleek or intermittent striations and polish with bumpy morphology are more common. As these traces require more time to appear on the surfaces of these rocks than obsidian, their diagnosis is more arduous (Table  3 ). It must also be stressed that these rocks yield a lesser variety of striations and polish than obsidian which likewise renders it more difficult to identify the worked plant. Therefore, identifying the type of plant is complicated in the case of scraping, even amongst those that are silica-rich. Although the state of the plants and their hardness also have an influence on these rocks, these conditions are not easy to discern.

Woodworking yields scarring along the edge. Abrasion, for example, is visible along both the edge and the amorphous matrix surface. Striations, most commonly rough-bottomed, are near the edge, linked to the polished areas. Polish is discontinuously distributed through the microtopography of the surface. Its texture is smooth and its morphology flat. A diagnosis of wood is possible only for the most part after a lapse of at least 20 min. However, distinguishing the state of the wood (hardness and humidity) was not possible in our experimental programme.

Cereal harvesting also has specific traces due to their high silica content. The actions yield rounded edges, and striations are visible over all the polished surface. The most common are sleek or rough-bottomed. Polish appears close to the edge and adjacent surfaces. It modifies part of the microtopography (amorphous matrix) yielding a smooth texture and a flat morphology. In this case, interpretation is only possible after 120 min of use, a timeframe which is much longer time than the case of obsidian tools.

Rushes, also siliceous plants, are harder than cereals which produces more developed scarring. Abrasion appears along the edge and the surface of the amorphous matrix. Striations, rough-bottomed and intermittent, are limited to surfaces near the edge. Polish marked by a smooth texture and bumpy morphology develops near the edge. These characteristics are common to green and remoistened rushes when worked with a transversal motion. These traces only appear after 60 min, as in the case of obsidian.

It is possible, in the case of palm, to observe differences in the processing of stem and leaves. However, the most readable traces are those linked to cutting green stems. Scars appear along the edge, and abrasion is on the edge and surface. Striae, usually rough-bottomed, are located near the edge of the amorphous matrix. Polish, in turn, develops far from the edge and is less invasive than in the case of cereals. Its texture is smooth and a bumpy morphology. These traces only appear on the pieces after at least 20 min of use.

There are relatively few use-wear studies related to other volcanic rocks (compared to research on obsidians). Worth highlighting is the research on woodworking by Clemente-Conte (1994) and Bello-Alonso et al. ( 2020 ) that also observed scarring as well as short striations on or near the polished areas. Huidobro ( 2018 ) also reported poorly developed polish whilst Richards ( 1988 ) described smooth pitted polish in patches. Plisson ( 1985 ) also cited short striations and marginal polish distributed in spots. Although our observations do at time concur with their findings, our experiments did yield different results. For example, we did not observe rounding along the edge (Richards, 1988 ) or a polish of bumpy morphology (Huidobro, 2018 ) but abrasion along the edge and surface, and a flat polish. It remains to be seen if these differences are due to the particularities of the raw material or the nature of wood.

The harvesting of cereals and rushes has been described by Richards ( 1988 ) and Clemente-Conte (1994). The descriptions of Richards ( 1988 ) coincide with our observation in that rounding and striations are associated with a smooth polish. Clemente-Conte (1994) did not discriminate between rushes and cereals. His study notes intermittent scars and striations associated with a polish of irregular morphology, attributes that coincide with our experimentation with rushes. They can be distinguished traces linked to mature cereals by the presence of smooth grooves and a polished of flat morphology. These differences could be due to the characteristics of the volcanic raw material and the composition of their phenocrysts.

This systematic experimental study is an attempt to interpret the use-wear stemming from working plants with stone tools from the Indigenous Period of the Canary Islands. Although the characteristics of the raw materials serving to manufacture volcanic tools may differ slightly from site to site, there remain, as in the case of other lithic raw materials, enough similarities to compare the use-wear produced by the different contact materials. Because this work is experimental, some results may not match those of the archaeological record. This is not only due to the great variability of working conditions but also to post-depositional alterations. One of the issues of this study has been the reproduction of certain craft techniques that to date have not received sufficient attention. We have identified several problems linked to these tasks. It was not simple, for example, to discern use-wear from working fresh or remoistened plants. Furthermore, we experimented with two types of volcanic rocks yielding very different types of use-wear, notably glassy obsidian, where the traces are easier to observe, and rough volcanic rocks with phenocrysts where observations are arduous. In any case, the patterns of use-wear are essential to identify not only each type of worked plant but the motion or gesture used. This study has attempted to offer an overview of the different diagnostic features, as well as highlight the limits imposed by each type of volcanic material. This study thus offers new data as to the treatment of different plant fibres such as rushes and palms and in particular those related to working plants with coarse-grained volcanic rocks.

Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

This study was funded by the project PID2020-117496GB-I00 of the Ministerio de Ciencia e Innovación (Spain) and FEDER. Our sincere thanks go to specialists from the Tarha Research Group for their assistance and go to the farmers who offered us access to their land to carry out the experiments, and to the artisans who shared their materials and knowledge. We likewise acknowledge Dr. Laurence Astruc for testing and discussing our hypotheses. Idaira Brito-Abrante is a beneficiary of a PhD grant from the Agencia Canaria de Investigación, Innovación y Sociedad de la Información de la Consejería de Universidades, Ciencia e Innovación y Cultura and Fondo Social Europeo Plus (FSE+) Programa Operativo Integrado de Canarias 2021-2027, Eje 3 Tema Prioritario 74 (85%).

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study was funded by the project PID2020-117496 GB-I00 of the Ministerio de Ciencia e Innovación (Spain) and FEDER. Idaira Brito-Abrante is a beneficiary of a PhD grant from the Agencia Canaria de Investigación, Innovación y Sociedad de la Información de la Consejería de Universidades, Ciencia e Innovación y Cultura and Fondo Social Europeo Plus (FSE +) Programa Operativo Integrado de Canarias 2021–2027, Eje 3 Tema Prioritario 74 (85%).

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Brito-Abrante, I., Rodríguez-Rodríguez, A. Use-Wear Analysis of Obsidian and Other Volcanic Rocks: An Experimental Approach to Working Plant Resources. J Archaeol Method Theory (2024). https://doi.org/10.1007/s10816-024-09659-4

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Non-Hermitian Delocalization in a Two-Dimensional Photonic Quasicrystal

Zhaoyang zhang, shun liang, ismaël septembre, jiawei yu, yongping huang, maochang liu, yanpeng zhang, min xiao, guillaume malpuech, and dmitry solnyshkov, phys. rev. lett. 132 , 263801 – published 27 june 2024.

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Theoretical and experimental studies suggest that both Hermitian and non-Hermitian quasicrystals show localization due to the fractal spectrum and to the transition to diffusive bands via exceptional points, respectively. Here, we present an experimental study of a dodecagonal photonic quasicrystal based on electromagnetically induced transparency in a Rb vapor cell. First, we observe the suppression of the wave packet expansion in the Hermitian case. We then discover a new regime, where increasing the non-Hermiticity leads to delocalization, demonstrating that the behavior in non-Hermitian quasicrystals is richer than previously thought.

• Received 13 February 2024
• Accepted 28 May 2024

DOI: https://doi.org/10.1103/PhysRevLett.132.263801

© 2024 American Physical Society

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Authors & Affiliations

• 1 Key Laboratory for Physical Electronics and Devices of the Ministry of Education & Shaanxi Key Lab of Information Photonic Technique, School of Electronic Science and Engineering, Faculty of Electronics and Information, Xi’an Jiaotong University , Xi’an 710049, China
• 2 Institut Pascal , PHOTON-N2, Université Clermont Auvergne, CNRS, Clermont INP, F-63000 Clermont-Ferrand, France
• 3 International Research Center for Renewable Energy and State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University , Xi’an 710049, China
• 4 Department of Physics, University of Arkansas , Fayetteville, Arkansas 72701, USA
• 5 National Laboratory of Solid State Microstructures and School of Physics, Nanjing University , Nanjing 210093, China
• 6 Institut Universitaire de France (IUF) , 75231 Paris, France
• * Corresponding author: [email protected]
• † Corresponding author: [email protected]
• ‡ Corresponding author: [email protected]

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(a) Experimental scheme. (b) The experimentally generated dodecagonal quasicrystal lattice formed by two hexagonal patterns rotated by 30°. (c) Reciprocal-space image of the experimental quasicrystal lattice exhibiting a twelvefold symmetry in 3 orders of diffraction.

Wave packet expansion and the localization transition with the increase of the second lattice strength. (a)–(c) Spatial images of the wave packet after its evolution in the Hermitian lattice (lattice intensity ratio I 2 / I 1 = 0 , 0.4 , 1 , respectively), magenta line marks the wave packet size. (d) Wave packet width w normalized by the lattice site width w s . Red arrows mark the correspondence with panels (a)–(c). (e) The dispersion of a single honeycomb lattice through Γ K M K ′ Γ ′ points. (f) The dispersion of a quasicrystal showing multiple gaps. (g) The comparison of the DOS for a periodic honeycomb lattice and a quasicrystal. The gaps appear as zeroes of the DOS.

Localization-delocalization transition in a 2D non-Hermitian quasicrystal. (a)–(c) Spatial images of the wave packet after its evolution in the non-Hermitian lattice (lattice intensity ratio I 2 / I 1 = 0.1 , 0.4 , 1 , respectively). Magenta line marks the wave packet size. (d) Wave packet width w normalized by the reference width w 0 (corresponding to I 2 / I 1 = 0 ). Points with error bars (instrumental uncertainty)—experiment, dash-dotted line—theory. (e) Real (black) and imaginary (red) parts of the eigenenergies of the weak complex potential model. (f) Fourier transform of the angular pattern of the panel (c) ( I 2 / I 1 = 1 ) exhibiting a maximum corresponding to dodecagonal symmetry C 12 . (g) Intensity of the C 12 maximum of the Fourier transform as a function of I 2 / I 1 : the symmetry of the wave packet inherits that of the lattice.

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