types of research in statistics

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative
Quantitative		.

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary
Secondary

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive
Experimental

Prevent plagiarism, run a free check.

Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Research methods for analysing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyse data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyse the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyse data collected from interviews, focus groups or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyse large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Community Blog

Keep up-to-date on postgraduate related issues with our quick reads written by students, postdocs, professors and industry leaders.

Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

Need to write a list of abbreviations for a thesis or dissertation? Read our post to find out where they go, what to include and how to format them.

There are various types of research that are classified by objective, depth of study, analysed data and the time required to study the phenomenon etc.

Choosing a good PhD supervisor will be paramount to your success as a PhD student, but what qualities should you be looking for? Read our post to find out.

Join thousands of other students and stay up to date with the latest PhD programmes, funding opportunities and advice.

Browse PhDs Now

Find out how you can use Scrivener for PhD Thesis & Dissertation writing to streamline your workflow and make academic writing fun again!

An abstract and introduction are the first two sections of your paper or thesis. This guide explains the differences between them and how to write them.

Lewis is a third-year PhD student at CVSSP at the University of Surrey. His research involves using multi-camera broadcast footage of sports, and using this data to create new viewpoints in virtual and augmented reality.

Sam is a new PhD student at Teesside University. Her research is focussed on better understanding how writing poetry can help cancer survivors to work through mental and emotional issues.

Join Thousands of Students

Effective Use of Statistics in Research – Methods and Tools for Data Analysis

Remember that impending feeling you get when you are asked to analyze your data! Now that you have all the required raw data, you need to statistically prove your hypothesis. Representing your numerical data as part of statistics in research will also help in breaking the stereotype of being a biology student who can’t do math.

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings. In this article, we will discuss how using statistical methods for biology could help draw meaningful conclusion to analyze biological studies.

Table of Contents

Role of Statistics in Biological Research

Statistics is a branch of science that deals with collection, organization and analysis of data from the sample to the whole population. Moreover, it aids in designing a study more meticulously and also give a logical reasoning in concluding the hypothesis. Furthermore, biology study focuses on study of living organisms and their complex living pathways, which are very dynamic and cannot be explained with logical reasoning. However, statistics is more complex a field of study that defines and explains study patterns based on the sample sizes used. To be precise, statistics provides a trend in the conducted study.

Biological researchers often disregard the use of statistics in their research planning, and mainly use statistical tools at the end of their experiment. Therefore, giving rise to a complicated set of results which are not easily analyzed from statistical tools in research. Statistics in research can help a researcher approach the study in a stepwise manner, wherein the statistical analysis in research follows –

1. Establishing a Sample Size

Usually, a biological experiment starts with choosing samples and selecting the right number of repetitive experiments. Statistics in research deals with basics in statistics that provides statistical randomness and law of using large samples. Statistics teaches how choosing a sample size from a random large pool of sample helps extrapolate statistical findings and reduce experimental bias and errors.

2. Testing of Hypothesis

When conducting a statistical study with large sample pool, biological researchers must make sure that a conclusion is statistically significant. To achieve this, a researcher must create a hypothesis before examining the distribution of data. Furthermore, statistics in research helps interpret the data clustered near the mean of distributed data or spread across the distribution. These trends help analyze the sample and signify the hypothesis.

3. Data Interpretation Through Analysis

When dealing with large data, statistics in research assist in data analysis. This helps researchers to draw an effective conclusion from their experiment and observations. Concluding the study manually or from visual observation may give erroneous results; therefore, thorough statistical analysis will take into consideration all the other statistical measures and variance in the sample to provide a detailed interpretation of the data. Therefore, researchers produce a detailed and important data to support the conclusion.

Types of Statistical Research Methods That Aid in Data Analysis

Statistical analysis is the process of analyzing samples of data into patterns or trends that help researchers anticipate situations and make appropriate research conclusions. Based on the type of data, statistical analyses are of the following type:

1. Descriptive Analysis

The descriptive statistical analysis allows organizing and summarizing the large data into graphs and tables . Descriptive analysis involves various processes such as tabulation, measure of central tendency, measure of dispersion or variance, skewness measurements etc.

2. Inferential Analysis

The inferential statistical analysis allows to extrapolate the data acquired from a small sample size to the complete population. This analysis helps draw conclusions and make decisions about the whole population on the basis of sample data. It is a highly recommended statistical method for research projects that work with smaller sample size and meaning to extrapolate conclusion for large population.

3. Predictive Analysis

Predictive analysis is used to make a prediction of future events. This analysis is approached by marketing companies, insurance organizations, online service providers, data-driven marketing, and financial corporations.

4. Prescriptive Analysis

Prescriptive analysis examines data to find out what can be done next. It is widely used in business analysis for finding out the best possible outcome for a situation. It is nearly related to descriptive and predictive analysis. However, prescriptive analysis deals with giving appropriate suggestions among the available preferences.

5. Exploratory Data Analysis

EDA is generally the first step of the data analysis process that is conducted before performing any other statistical analysis technique. It completely focuses on analyzing patterns in the data to recognize potential relationships. EDA is used to discover unknown associations within data, inspect missing data from collected data and obtain maximum insights.

6. Causal Analysis

Causal analysis assists in understanding and determining the reasons behind “why” things happen in a certain way, as they appear. This analysis helps identify root cause of failures or simply find the basic reason why something could happen. For example, causal analysis is used to understand what will happen to the provided variable if another variable changes.

7. Mechanistic Analysis

This is a least common type of statistical analysis. The mechanistic analysis is used in the process of big data analytics and biological science. It uses the concept of understanding individual changes in variables that cause changes in other variables correspondingly while excluding external influences.

Important Statistical Tools In Research

Researchers in the biological field find statistical analysis in research as the scariest aspect of completing research. However, statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible.

1. Statistical Package for Social Science (SPSS)

It is a widely used software package for human behavior research. SPSS can compile descriptive statistics, as well as graphical depictions of result. Moreover, it includes the option to create scripts that automate analysis or carry out more advanced statistical processing.

2. R Foundation for Statistical Computing

This software package is used among human behavior research and other fields. R is a powerful tool and has a steep learning curve. However, it requires a certain level of coding. Furthermore, it comes with an active community that is engaged in building and enhancing the software and the associated plugins.

3. MATLAB (The Mathworks)

It is an analytical platform and a programming language. Researchers and engineers use this software and create their own code and help answer their research question. While MatLab can be a difficult tool to use for novices, it offers flexibility in terms of what the researcher needs.

4. Microsoft Excel

Not the best solution for statistical analysis in research, but MS Excel offers wide variety of tools for data visualization and simple statistics. It is easy to generate summary and customizable graphs and figures. MS Excel is the most accessible option for those wanting to start with statistics.

5. Statistical Analysis Software (SAS)

It is a statistical platform used in business, healthcare, and human behavior research alike. It can carry out advanced analyzes and produce publication-worthy figures, tables and charts .

6. GraphPad Prism

It is a premium software that is primarily used among biology researchers. But, it offers a range of variety to be used in various other fields. Similar to SPSS, GraphPad gives scripting option to automate analyses to carry out complex statistical calculations.

This software offers basic as well as advanced statistical tools for data analysis. However, similar to GraphPad and SPSS, minitab needs command over coding and can offer automated analyses.

Use of Statistical Tools In Research and Data Analysis

Statistical tools manage the large data. Many biological studies use large data to analyze the trends and patterns in studies. Therefore, using statistical tools becomes essential, as they manage the large data sets, making data processing more convenient.

Following these steps will help biological researchers to showcase the statistics in research in detail, and develop accurate hypothesis and use correct tools for it.

There are a range of statistical tools in research which can help researchers manage their research data and improve the outcome of their research by better interpretation of data. You could use statistics in research by understanding the research question, knowledge of statistics and your personal experience in coding.

Have you faced challenges while using statistics in research? How did you manage it? Did you use any of the statistical tools to help you with your research data? Do write to us or comment below!

Frequently Asked Questions

Statistics in research can help a researcher approach the study in a stepwise manner: 1. Establishing a sample size 2. Testing of hypothesis 3. Data interpretation through analysis

Statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible. They can manage large data sets, making data processing more convenient. A great number of tools are available to carry out statistical analysis of data like SPSS, SAS (Statistical Analysis Software), and Minitab.

nice article to read

Holistic but delineating. A very good read.

(5/5) an informative article to read. thanks

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

Empowering Researchers, Enabling Progress: How Enago Academy contributes to the SDGs

Promoting Research
Thought Leadership
Trending Now

How Enago Academy Contributes to Sustainable Development Goals (SDGs) Through Empowering Researchers

The United Nations Sustainable Development Goals (SDGs) are a universal call to action to end…

Reporting Research

Research Interviews: An effective and insightful way of data collection

Research interviews play a pivotal role in collecting data for various academic, scientific, and professional…

Planning Your Data Collection: Designing methods for effective research

Planning your research is very important to obtain desirable results. In research, the relevance of…

Language & Grammar

Best Plagiarism Checker Tool for Researchers — Top 4 to choose from!

While common writing issues like language enhancement, punctuation errors, grammatical errors, etc. can be dealt…

Industry News
Publishing News

2022 in a Nutshell — Reminiscing the year when opportunities were seized and feats were achieved!

It’s beginning to look a lot like success! Some of the greatest opportunities to research…

2022 in a Nutshell — Reminiscing the year when opportunities were seized and feats…

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

2000+ blog articles
50+ Webinars
10+ Expert podcasts
50+ Infographics
10+ Checklists
Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

Skip to secondary menu
Skip to main content
Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Sampling Methods: Different Types in Research

By Jim Frost 3 Comments

What Are Sampling Methods?

Sampling methods are the processes by which you draw a sample from a population . When performing research, you’re typically interested in the results for an entire population. Unfortunately, they are almost always too large to study fully. Consequently, researchers use samples to draw conclusions about a population—the process of making statistical inferences.

Sampling methods will draw a sample from a population.

A population is the complete set of individuals that you’re studying. A sample is the subset of the population that you actually measure, test, or evaluate and base your results. Sampling methods are how you obtain your sample.

Before beginning your study, carefully define the population because your results apply to the target population. You can define your population as narrowly as necessary to meet the needs of your study—for example, adult Swedish women who are otherwise healthy but have osteoporosis. Then choose your sampling method.

Learn more about populations and samples , inferential vs. descriptive statistics and populations and parameters .

In research and inferential statistics , sampling methods are a vital issue. How you draw your sample affects how much you can trust the results! If your sample doesn’t reflect the population, your results might not be valid. It’s a crucial part of experimental design .

In this post, learn more about sampling methods, which ones produce representative samples, and the pros and cons of each procedure.

Probability vs Non-Probability Sampling Methods

Sampling methods have the following two broad categories:

Probability sampling : Entails random selection and typically, but not always, requires a list of the entire population.
Non-probability sampling : Does not use random selection but some other process, such as convenience. Usually does not sample from the whole population.

Probability sampling is typically more difficult and costly to implement, but, in exchange, these processes tend to increase validity by producing representative samples. In short, you can make valid conclusions about the population. A statistical inference is when you use a sample to learn about a population. Learn more about Making Statistical Inferences .

On the other hand, non-probability sampling methods are often easier and less expensive, but the trade-off is that the validity of your conclusions is questionable. You might not be able to trust the results. Sampling bias is more likely to occur.

Learn more about Validity in Research and Psychology: Types & Examples and Internal and External Validity .

Probability Sampling Methods

Given the benefits of using representative samples, you’ll typically want to use a probability sampling method whenever possible. Let’s go over the standard methods. They each have pros and cons. Click the links to learn more about each sampling method and see examples. Learn more about representative samples .

To use a probability method, you’ll first need to develop a sampling frame, which lists all members of your target population. Then you can use one of the following methods.

Learn more about Sampling Frames: Definition, Examples & Uses .

Simple Random Sampling (SRS)

In simple random sampling (SRS), researchers take a complete list of the population and randomly select participants from it. All population members have an equal likelihood of being selected. Out of all sampling methods, statisticians consider this one to be the gold standard for producing representative samples. It’s entirely random, leaving little room for accidentally biasing the results.

However, this sampling method has some drawbacks.

First and foremost, this method can be pretty unwieldy and require abundant resources. For one thing, it requires a list of all population members, which can be a tremendous hurdle by itself. Attempting to perform SRS with an incomplete population list causes undercoverage bias and a nonrepresentative sample.

Furthermore, while random selection is beneficial, it also ensures that the subjects are maximally dispersed, making them harder to contact.

SRS can exclude smaller but crucial subpopulations purely by chance. Additionally, this approach produces less precise estimates for subgroups and the differences between subgroups than some other probability sampling methods.

Learn more about Simple Random Sampling and Undercoverage Bias: Definition & Examples

Systematic Sampling

Systematic sampling is similar to SRS but attempts to ease some of the difficulties for researchers. There are several versions of this method.

One form uses a complete list of the population. The researchers randomly select the first subject and then move down the list choosing every X th subject rather than using a randomized technique.

The other form does not use a complete list of the population. This sampling method is suitable for populations that are tough to document, such as the homeless, because a comprehensive list won’t exist. The essential requirement for this sampling method is knowing how to locate them. While it’s not perfect, it’s a feasible option when you can’t obtain the full list.

Suppose you want to survey theater patrons but lack a complete list. Instead, you can use systematic sampling and recruit every 20th person who exits the theater. This approach works because they leave randomly.

This sampling method has some disadvantages. The form that uses a complete list of the population can closely mirror the results of simple random sampling. However, the non-randomness increases the potential for manipulation, even if accidentally. Additionally, patterns in the list can unintentionally create a non-representative sample.

The form that doesn’t use a list has more potential problems. Namely, it increases the potential for missing subgroups and acquiring a non-representative sample. This sampling method increases the knowledge you must have about the population and their habits. Without that knowledge, you won’t be able to find subjects that reflect the whole population.

Learn more about Systematic Sampling .

Stratified Sampling

In stratified sampling, researchers divide a population into similar subpopulations (strata). Then they randomly sample from the strata.

This sampling method can guarantee the presence of small but vital subpopulations in the sample. Relative to SRS, this method can increase the precision of subgroup estimates and the differences between subgroups. In short, it helps researchers gain a better understanding of the subgroups. Dividing the whole population into smaller, more similar subsets can also reduce costs and simplify data collection.

The drawbacks are that this sampling method requires additional upfront knowledge and planning. The researchers must know enough about the subgroups to devise an effective strata scheme. Then they must have sufficient information about all population members to assign them to the correct strata.

Learn more about Stratified Sampling .

Cluster Sampling

Like stratified sampling, the cluster sampling method divides the whole population into smaller groups. However, unlike strata, each cluster mirrors the full diversity present in the population. Then the researchers randomly sample from some of these clusters.

The primary benefit of this sampling method is that it reduces the costs of studying large, geographically dispersed populations. Using this method, researchers don’t need to sample the entire geographic region but only certain areas because they know individual clusters are similar to the population. Additionally, they don’t need to develop a list of potential subjects for clusters from which they’re not sampling. These considerations can significantly reduce planning, administrative, and travel costs.

When researchers can’t create a list of the entire population, cluster sampling can be an excellent choice.

On the downside, cluster sampling increases the design complexity. Researchers must understand how well each cluster approximates the whole population. If the clusters don’t fully represent the population, results can be biased. In real-world studies, clusters tend to be naturally occurring groups that don’t mirror the population, which reduces the ability to draw valid conclusions.

Learn more about Cluster Sampling .

Non-Probability Sampling Methods

Non-probability sampling methods don’t use random selection, and they typically don’t use a complete population list. While these methods are simpler and less expensive, your results are more likely to be biased, reducing your ability to make sound conclusions.

Researchers often use non-probability sampling methods for exploratory research, pilot studies, and qualitative research . These sampling methods provide quick and rough assessments, help work kinks out of measurement instruments and procedures, and help refine the design for a more rigorous study in the future.

Below are several standard non-probability sampling methods:

Convenience sampling : The main criteria for recruiting subjects are those who are easy to contact and willing to participate. There are no inclusion requirements. Online polls are a type of convenience sampling. Learn more about Convenience Sampling .
Quota Sampling : Non-random selection of subjects from population subgroups that the researchers define. Learn more about Quota Sampling .
Purposive sampling : Investigators use subject-area knowledge to handpick a sample they think will help their study. Learn more about Purposive Sampling .
Snowball sampling : Researchers use subjects to find and recruit other subjects. This method is helpful when a population is hard to contact. When recruits help you find more recruits, and those help find even more, and so on, the total number snowballs. Learn more about Snowball Sampling .

As you can see, there are many sampling methods. Each one has benefits and disadvantages. When designing a study, evaluate the nature of your target population, your research goals, and the available time and resources to choose your sampling method. After deciding between the sampling methods, calculate your sample size using a power analysis .

Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards (nih.gov)

Reader Interactions

July 24, 2024 at 8:56 am

Hello Mr. Frost,

I would like to know whether people with mild Parkinson’s Disease symptoms are less likely to have kidney stones. Do PwP (People with Parkinson’s) have significantly less incidences of kidney stones than in the general population (~ 10%). So far, I have asked 12 people I know who has been diagnosed with Parkinson’s and 0% had kidney stones. I would like to increase my sampling size by randomly sampling members of a forum for PwP I belong to. Should I get a list of all forum subscribers and randomly select around 40 forum members to pose the question, “If you have been officially diagnosed with Parkinson’s, have also had a kidney stone?”. What would you suggest? I had posed the question in the forum before, but only PwP folks that had a Kidney stone responded.

Thanks, Mike

May 17, 2022 at 12:38 am

I think stratified sampling will work __ mke two groups as stratas _ then use SRS to obtain a complete sample .

May 15, 2022 at 7:37 pm

hi.what sampling technique will i use if my respondents are 1st yr college students awardees vs non awardees of different courses?

Comments and Questions Cancel reply

Prompt Library
DS/AI Trends
Stats Tools
Interview Questions
Generative AI
Machine Learning
Deep Learning

Different Types of Statistical Tests: Concepts

Last updated: 18th Nov, 2023

Statistical tests are an important part of data analysis. They help us understand the data and make inferences about the population. They are used to examine relationships between variables based on hypothesis testing. They are a way of analyzing data to see if there is a significant difference between the two groups or a group and population. In statistics , there are two main types of tests: parametric and non-parametric . Both types of tests are used to make inferences about a population based on a sample. The difference between the two types of tests lies in the assumptions that they make about the data. Parametric tests make certain assumptions about the data, while non-parametric tests do not make any assumptions about the data. In this blog post, we will discuss the different types of statistical tests and related concepts with the help of examples. As a data scientist , you must get a good understanding of different types of statistical tests.

Statistical tests can also be classified based on their application in quantitative or qualitative research . This classification hinges primarily on the nature of the data being analyzed: quantitative research deals with numerical data, while qualitative research often involves non-numerical data. Statistical tests used in qualitative research, particularly when dealing with categorical data, are essential for uncovering relationships and associations between different qualitative or categorical variables.

Table of Contents

Parametric Statistical Tests & Types: Concepts, Examples

Parametric statistical tests are a group of statistical tests that make certain assumptions about the data. These tests are used to make inferences about a population based on a sample. The main assumption that these tests make is that the data is normally distributed. This means that the data follows a specific pattern where the values are evenly spread out around the mean. There are several different parametric statistical tests, including t-tests, ANOVA, and Pearson’s correlation. The following is the high-level detail of these parametric tests:

Independent t-tests : An independent t-test is a statistical test used to determine whether the means of two groups are statistically different from each other. This test is often used when the data in each group are supplied by different people or when the groups are randomly assigned. The independent t-test is a parametric test, meaning that it requires that the data be normally distributed. The benefits of using an independent t-test include that it is relatively easy to use and has high statistical power. Let’s understand individual t-tests with an example. For example, a researcher might be interested in comparing the average reading scores of two groups of students – one group that is taking a course in English literature and one group that is taking a course in math. In this case, the researcher would use an independent t-test to compare the average reading scores of the two groups. The independent t-test allows for the comparison of two groups of unequal sizes. However, the independent t-test is limited to the comparison of two groups and cannot be used to compare more than two groups.
Paired t-tests : The paired t-test is a statistical test that is used to compare the means of two groups. The groups are usually matched or paired together in some way. For example, you might have a group of people who receive a new treatment and a group of people who receive a placebo treatment. The two groups are then compared to see if there is a difference in the mean scores. The paired t-test is also used to compare the pre-treatment and post-treatment scores of a single group of people.
ANOVA tests : ANOVA tests are a type of statistical test that is used to compare the means of more than two groups. There are several different types of ANOVA tests, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA. Each type of ANOVA test is used to compare different combinations of groups. The benefits of using an ANOVA test include that it is relatively easy to use and has high statistical power. Let’s understand with an example of where a one-way ANOVA test can be used. One real-world example of the one-way ANOVA in action is a study that can be conducted to compare the GRE scores of students from different income levels and find whether there are significant differences between the means of the three groups. One possible outcome of the tests can be that the students from families with higher incomes tended to score higher on the GRE than students from families with lower incomes. This study can be used to assess and examine inequalities in society.
MANOVA tests : MANOVA is a statistical test that is used to determine whether or not there are significant differences between two or more group means. It is similar to ANOVA, but it can be used with more than one dependent variable. MANOVA is a powerful statistical tool that can be used to examine the relationships between multiple dependent variables and a single independent variable. It can also be used to examine the relationships between multiple dependent variables and multiple independent variables. MANOVA is an important statistical test that should be used when investigating the relationships between multiple variables.
F-test : The F-test is a statistical test that is used to determine whether or not there is a significant difference between the variance of two or more groups.
Z-test : The Z-test is a statistical test that is used to determine the statistical significance of a difference between two groups. It is most commonly used when the groups are small. The Z-test is based on the standard normal distribution, which is a statistical model that assumes that all observations are drawn from a population that has a normal distribution. This test is used to determine whether the difference between the means of the two groups is statistically significant.
Correlation test (Pearson’s) : Correlation tests are statistical tests that assess the strength of the relationship between two variables. The most common type of correlation test is Pearson’s Correlation Coefficient, which measures the linear relationship between two variables. Correlation tests are used in a variety of fields, including psychology, sociology, and economics. Correlation tests can be used to study the cause-and-effect relationship between two variables or to predict future behavior based on past behavior. For example, a correlation test could be used to determine if there is a relationship between IQ and income. Correlation tests are also used to predict future events. For example, a correlation test could be used to predict the likelihood of a person getting divorced based on their age and education level.

Non-Parametric Statistical Tests & Types: Concepts, Examples

Non-parametric tests do not make any assumptions about the data. They can be used with data that is not normally distributed and with data that does not have equal variances. Non-parametric statistical tests are used when the assumptions of parametric statistical tests are not met, or when the data are not normally distributed. Some examples of non-parametric statistical tests include the Wilcoxon rank-sum test, the Kruskal-Wallis test. etc. Statisticians have developed many different non-parametric statistical tests, each with its own advantages and disadvantages. When choosing a non-parametric statistical test, it is important to consider the specific research question and the type of data that are available. The following is a brief introduction to different types of non-parametric tests:

Wilcoxon rank-sum test : The Wilcoxon rank-sum test is a statistical test used to compare the difference between two groups of data. It is often used when the data is not normally distributed. The test works by ranking the data from both groups, and then summing the ranks for each group. The difference between the two sums is then compared to a table of values to determine whether or not there is a significant difference between the two groups. The Wilcoxon rank-sum test is a powerful statistical tool that can be used to compare data sets of all sizes. Wilcoxon rank-sum test is also known as the Mann-Whitney U test .
Kruskal-Wallis H test : The Kruskal-Wallis H test is a statistical test that can be used to compare the means of two or more groups. It is similar to the ANOVA, but it is more robust and can be used when the assumptions of the ANOVA are not met. The Kruskal-Wallis test is also known as a non-parametric ANOVA, or analysis of variance. The Kruskal-Wallis test is used when the assumptions of the parametric ANOVA test are not met. The Kruskal-Wallis test can be used with either continuous or categorical data. To run the Kruskal-Wallis test, the data must be in the form of ranks. The Kruskal-Wallis test is based on the ranks of the data, not the actual values. When using categorical data, the Kruskal-Wallis test is often used to determine if there are significant differences between the means of the groups. When using quantitative data, the Kruskal-Wallis test can be used to determine if there are significant differences between the distributions of the groups.
Chi-square test of independence : Chi-square test of independence is a statistical test used to determine whether two variables are independent. It is a non-parametric test, meaning that it does not make assumptions about the distributions of the variables. The chi-square test is used to calculate a statistic called the chi-square statistic. This statistic is then compared to a critical value to determine whether the two variables are independent. If the chi-square statistic is greater than the critical value, then the two variables are considered to be dependent. Chi-square test of independence can be used to test for independence in a variety of situations, including comparing proportions, testing for association, and testing for goodness of fit.
The Friedman Test : The Friedman test is a non-parametric statistical test used to compare more than two groups of data. The test is used when the data are not normally distributed and when the groups are related to each other, such as in a repeated measures design. The test is based on the ranks of the data, rather than the actual values.
The Cochran’s Q Test : The Cochran’s Q test is a non-parametric statistical test used to compare more than two groups of data. The test is used when the data are not normally distributed and when the groups are independent of each other.
The Jonckheere-Terpstra Test : The Jonckheere-Terpstra test is a rank-based non-parametric statistical test used to compare more than two groups of data. The test is used when the data are not normally distributed and when the groups are ordered, such as in an experiment with treatments that are administered in increasing order of intensity.

Statistical Tests in Quantitative Research: Examples

Quantitative research involves the collection and analysis of numerical data. Most statistical tests, especially parametric tests, are used in quantitative research due to the numerical nature of the data. The ones listed below and discussed in the previous sections can be used for quantitative research:

One-Sample, Independent Two-Sample, Paired
One-Way, Two-Way, Repeated Measures
Simple, Multiple
Pearson’s Correlation Coefficient , Spearman’s Rank Correlation Coefficient
Mann-Whitney U Test
Wilcoxon Signed-Rank Test
Kruskal-Wallis Test

Statistical Tests in Qualitative Research: Examples

The following methods and tests are integral in qualitative research for analyzing categorical data. They help in understanding the relationships and associations between different categories, which is essential in fields like medicine, social science, biology and psychology, where categorical variables are frequently encountered. The choice of test depends on the nature of the data, the size of the sample, and the specific research questions being addressed.

This is a primary test used to determine if there is a significant association between two categorical variables.
It’s applicable when data is presented in a contingency table format, where frequencies or counts of occurrences in each category are compared.
The χ2 test evaluates whether the distribution of sample categorical data matches an expected distribution.
When dealing with small sample sizes, modifications to the χ2 test are necessary.
Fisher’s Exact Test is often used as an alternative in these scenarios, especially when the sample size is too small for the χ2 test to be reliable.
This test is relevant when at least one of the variables is ordinal (i.e., the categories have a natural order, like age groups).
It assesses if there’s a trend or consistent pattern across categories of an ordinal variable.
Involves calculating odds ratios and risk ratios.
These measures are crucial in understanding the likelihood or risk of a certain event occurring in one group compared to another.
This method involves calculating the confidence intervals to understand the range within which the true proportion or difference in proportions lies, with a certain level of confidence.
McNemar’s test is particularly useful in matched pair studies, where participants are paired in a way that controls for an extraneous variable.
It’s used for dichotomous (binary) outcomes in paired samples to determine if there are differences in the paired proportions.
This is a correction applied to the χ2 test to adjust for continuity when dealing with small sample sizes.
It’s typically used when the total sample size is small and the data is distributed in a 2×2 contingency table.

In conclusion, there are two main types of statistical tests: parametric and non-parametric. Parametric tests make certain assumptions about the data, while non-parametric tests do not make any assumptions about the data. Both types of tests are used to make inferences about a population based on a sample. The choice of which type of test to use depends on the type of data that is available.

Ajitesh Kumar

5 responses.

Your explanations are very useful and easy to understand by anybody. Thanks for posting it. All the Best

Useful for Doctor profession

Search for:

ChatGPT Prompts (250+)

Generate Design Ideas for App
Expand Feature Set of App
Create a User Journey Map for App
Generate Visual Design Ideas for App
Generate a List of Competitors for App
Credit Card Fraud Detection & Machine Learning
Neural Network Types & Real-life Examples
Invoke Python ML Models from Other Applications – Examples
Principal Component Analysis (PCA) & Feature Extraction – Examples
Content-based Recommender System: Python Example

Data Science / AI Trends

• Sentiment Analysis Real World Examples
• Prepend any arxiv.org link with talk2 to load the paper into a responsive chat application
• Custom LLM and AI Agents (RAG) On Structured + Unstructured Data - AI Brain For Your Organization
• Guides, papers, lecture, notebooks and resources for prompt engineering
• Common tricks to make LLMs efficient and stable

Free Online Tools

Create Scatter Plots Online for your Excel Data
Histogram / Frequency Distribution Creation Tool
Online Pie Chart Maker Tool
Z-test vs T-test Decision Tool
Independent samples t-test calculator

Obesity and Severe Obesity Prevalence in Adults: United States, August 2021–August 2023 (Published: 9/24/2024)

NCHS Data Brief No. 508, September 2024

PDF Version (454 KB)

Samuel D. Emmerich, D.V.M., Cheryl D. Fryar, M.S.P.H., Bryan Stierman, M.D., M.P.H., and Cynthia L. Ogden, Ph.D., M.R.P.

Key findings

What was the prevalence of obesity in adults during August 2021–August 2023?

Were there differences in the prevalence of obesity in adults by education level during august 2021-august 2023, what was the prevalence of severe obesity in adults during august 2021–august 2023, what are the recent trends in obesity and severe obesity in adults, data source and methods, about the authors, suggested citation.

Data from the National Health and Nutrition Examination Survey

During August 2021–August 2023, the prevalence of obesity in adults was 40.3%, with no significant differences between men and women. Obesity prevalence was higher in adults ages 40–59 than in ages 20–39 and 60 and older.
The prevalence of obesity was lower in adults with a bachelor’s degree or more than in adults with less education.
The prevalence of severe obesity in adults was 9.4% and was higher in women than men for each age group.
From 2013–2014 through August 2021–August 2023, the age-adjusted prevalence of obesity did not change significantly, while severe obesity prevalence increased from 7.7% to 9.7%.

Obesity is a chronic condition that increases the risk of hypertension, type 2 diabetes, coronary heart disease, stroke, and certain cancers ( 1 ). Obesity and severe obesity prevalence increased from 1999–2000 through 2017–2018 ( 2 ). This report provides prevalence estimates of adult obesity and severe obesity during August 2021–August 2023 by age and sex, as well as obesity prevalence by education level. Trends in the prevalence of adult obesity and severe obesity over the previous 10 years are also shown.

Keywords : body mass index (BMI) , trends , National Health and Nutrition Examination Survey (NHANES)

The prevalence of obesity among adults was 40.3% during August 2021–August 2023 ( Figure 1 , Table 1 ). The prevalence was 39.2% in men and 41.3% in women. No significant differences between men and women were seen overall or in any age group.

The prevalence of obesity in adults ages 40–59 was 46.4%, which was higher than the prevalence in adults ages 20–39 (35.5%) and 60 and older (38.9%). This pattern was seen in both men and women.

Figure 1. Prevalence of obesity in adults age 20 and older, by sex and age: United States, August 2021–August 2023



					Age adjusted
Sex and age group	Sample size	Percent	Standard error	Percent	Standard error
Total
20 and older	5,929	40.3	1.8	40.3	1.9
20–39	1,498	35.5	3.0	…	…
40–59	1,709	46.4	1.8	…	…
60 and older	2,722	38.9	1.5	…	…
Men
20 and older	2,680	39.2	1.9	39.3	1.9
20–39	672	34.3	2.8	…	…
40–59	762	45.4	1.9	…	…
60 and older	1,246	38.0	2.3	…	…

20 and older	3,249	41.3	2.2	41.4	2.3
20–39	826	36.8	3.6	…	…
40–59	947	47.4	2.1	…	…
60 and older	1,476	39.6	1.9	…	…

… Category not applicable. NOTE: Estimates for adults age 20 and older were age adjusted by the direct method to the U.S. Census 2000 population using age groups 20–39, 40–59, and 60 and older. SOURCE: National Center for Health Statistics, National Health and Nutrition Examination Survey, August 2021–August 2023.

The prevalence of obesity was lower in adults with a bachelor’s degree or more (31.6%) than in adults with less education ( Figure 2 , Table 2 ). The difference in obesity prevalence between adults with a high school diploma or less (44.6%) and those with some college (45.0%) was not significant. No significant differences between men and women were seen in obesity prevalence by education level.

Figure 2. Prevalence of obesity in adults age 20 and older, by sex and education level: United States, August 2021–August 2023


Sex and education level	Sample size	Percent	Standard error
Total
High school diploma or less	2,005	44.6	2.1
Some college	1,813	45.0	1.7
Bachelor’s degree or more	2,108	31.6	2.5
Men
High school diploma or less	990	43.3	2.3
Some college	748	43.0	2.1
Bachelor’s degree or more	941	31.1	3.0
Women
High school diploma or less	1,015	46.0	2.8
Some college	1,065	46.9	2.4
Bachelor’s degree or more	1,167	31.9	3.0

NOTE: The category of high school diploma or less includes GED. SOURCE: National Center for Health Statistics, National Health and Nutrition Examination Survey, August 2021–August 2023.

The prevalence of severe obesity in adults was 9.4% during August 2021–August 2023, and was higher in adults ages 20–39 (9.5%) and 40–59 (12.0%) than in adults age 60 and older (6.6%) ( Figure 3 , Table 3 ).

The prevalence of severe obesity in men (6.7%) was lower than in women (12.1%) overall and for each age group. Among men, the prevalence was highest in those ages 40–59. Among women, the prevalence was higher in those ages 20–39 and 40–59 than in those age 60 and older.

Figure 3. Prevalence of severe obesity in adults age 20 and older, by sex and age: United States, August 2021–August 2023



				Age adjusted
Sex and age group	Sample size	Percent	Standard error	Percent	Standard error
Total
20 and older	5,929	9.4	0.7	9.7	0.7
20–39	1,498	9.5	1.0	…	…
40–59	1,709	12.0	1.0	…	…
60 and older	2,722	6.6	0.6	…	…
Men
20 and older	2,680	6.7	0.7	6.8	0.7
20–39	672	6.1	0.8	…	…
40–59	762	9.2	1.3	…	…
60 and older	1,246	4.3	0.6	…	…

20 and older	3,249	12.1	0.9	12.6	1.0
20–39	826	13.0	1.5	…	…
40–59	947	14.7	1.4	…	…
60 and older	1,476	8.4	0.8	…	…

From 2013–2014 through August 2021–August 2023, the age-adjusted prevalence of obesity in adults did not change significantly, while the age-adjusted prevalence of severe obesity increased from 7.7% to 9.7% ( Figure 4 , Table 4) . Changes in the prevalence of obesity and severe obesity between the two most recent survey cycles, 2017–March 2020 and August 2021–August 2023, were not significant.

Figure 4. Trends in age-adjusted obesity and severe obesity prevalence in adults age 20 and older: United States, 2013–2014 through August 2021–August 2023



	Obesity (age adjusted)			Severe obesity (age adjusted)
Survey cycle	Sample size	Percent	Standard error	Percent	Standard error
2013–2014	5,455	37.7	0.9	7.7	0.7
2015–2016	5,337	39.6	1.6	7.7	0.6
2017–2020	8,295	41.9	1.2	9.2	0.6
2021–2023	5,929	40.3	1.9	9.7	0.7

NOTE: Estimates for adults age 20 and older were age adjusted by the direct method to the U.S. Census 2000 population using age groups 20–39, 40–59, and 60 and older. SOURCE: National Center for Health Statistics, National Health and Nutrition Examination Survey, 2013–2014 through August 2021–August 2023.

During August 2021–August 2023, the prevalence of obesity among adults in the United States was 40.3%. Obesity prevalence was highest in adults ages 40–59 compared with other age groups and was lowest in adults with a bachelor’s degree or more compared with those with less education. The prevalence of severe obesity was 9.4%. Severe obesity prevalence was higher in women than men for each age group.

In this report, obesity is defined by body mass index (BMI), which has limitations. Body fat may vary by sex, age, and race and Hispanic origin at a given BMI level ( 3 , 4 ). BMI does not measure body fat directly, nor does it provide information on body fat distribution ( 5 ). The distribution of excess body fat, especially visceral fat, contributes to the risk of cardiovascular and metabolic disease ( 6 ). Despite these limitations, BMI is a simple and inexpensive screening tool for conditions that may increase the risk of certain chronic diseases.

In the United States, the prevalence of obesity in adults remains above the Healthy People 2030 goal of 36.0% ( 7 ), but from 2013–2014 through August 2021–August 2023, the age-adjusted prevalence of obesity in adults did not change significantly. Monitoring obesity prevalence is important for understanding trends over time.

Body mass index (BMI) : Calculated as weight in kilograms divided by height in meters squared, rounded to one decimal place. Obesity : Defined as a BMI of greater than or equal to 30. Severe obesity : Defined as a BMI of greater than or equal to 40.

Data from the August 2021–August 2023 National Health and Nutrition Examination Survey (NHANES) were used to estimate obesity and severe obesity prevalence and to test for differences between subgroups. Data from four NHANES cycles (2013–2014, 2015–2016, 2017–March 2020, and August 2021–August 2023) were used to assess 10-year trends.

NHANES is a cross-sectional survey designed to monitor the health and nutritional status of the U.S. civilian noninstitutionalized population and is conducted by the National Center for Health Statistics ( 8–10 ). It consists of home interviews followed by standardized health examinations conducted in mobile examination centers. The NHANES sample is selected through a complex, multistage probability design.

From 1999 through March 2020, NHANES was conducted continuously. Following a pause in data collection in March 2020 due to the COVID-19 pandemic, field operations resumed in August 2021 with modifications to the survey content, procedures, and methodologies ( 11 ). New screening and safety measures were implemented at the mobile examination centers, but no changes from past protocols were made to the anthropometry (body measures) component of the survey. For NHANES August 2021–August 2023, oversampling by race, Hispanic origin, and income was eliminated to reduce the number of households that needed to be screened

This analysis included all examined NHANES participants age 20 and older. Pregnant women and adults with missing height or weight measurements were excluded from the analysis. Measured height and weight were used to calculate BMI. Examination sample weights, which account for the differential probabilities of selection and nonresponse, were incorporated into the analysis. The analysis accounted for the survey’s complex, multistage probability design. For August 2021–August 2023, differences between estimates overall, among subgroups, and compared with 2017–March 2020 were evaluated using t tests at the 0.05 level. Polynomial regression was used to test the significance of linear and nonlinear 10-year trends, accounting for the unequal spacing and lengths of survey cycles.

Data management and statistical analyses were conducted using SAS System for Windows version 9.4 (SAS Institute, Inc., Cary, N.C.), SUDAAN version 11.0.4 (RTI International, Research Triangle Park, N.C.), and R version 4.4.0, including the R survey package version 4.4–2 ( 12 , 13 ).

Samuel D. Emmerich is an Epidemic Intelligence Service Officer assigned to the National Center for Health Statistics, Division of Health and Nutrition Examination Surveys. Cheryl D. Fryar, Bryan Stierman, and Cynthia L. Ogden are with the National Center for Health Statistics, Division of Health and Nutrition Examination Surveys.

National Heart, Lung, and Blood Institute. Managing overweight and obesity in adults: Systematic evidence review from the Obesity Expert Panel . 2013.
Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity and severe obesity among adults: United States, 2017–2018. NCHS Data Brief, no 360 . Hyattsville, MD: National Center for Health Statistics. 2020.
Caleyachetty R, Barber TM, Mohammed NI, Cappuccio FP, Hardy R, Mathur R, et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: A population-based cohort study . Lancet Diabetes Endocrinol 9(7):419–26. 2021.
Flegal KM, Ogden CL, Yanovski JA, Freedman DS, Shepherd JA, Graubard BI, Borrud LG. High adiposity and high body mass index-for-age in U.S. children and adolescents overall and by race-ethnic group . Am J Clin Nutr 91(4):1020–6. 2010.
Neeland IJ, Poirier P, Després JP. The cardiovascular and metabolic heterogeneity of obesity: Clinical challenges and implications for management . Circulation 137(13):1391–406. 2018.
Neeland IJ, Ross R, Després JP, Matsuzawa Y, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: A position statement . Lancet Diabetes Endocrinol 7(9):715–25. 2019.
U.S. Department of Health and Human Services. Office of Disease Prevention and Health Promotion. Healthy People 2030 objectives and data: Overweight and obesity .
Johnson CL, Dohrmann SM, Burt VL, Mohadjer LK. National Health and Nutrition Examination Survey: Sample design, 2011–2014 . National Center for Health Statistics. Vital Health Stat 2(162). 2014.
Chen TC, Clark J, Riddles MK, Mohadjer LK, Fakhouri THI. National Health and Nutrition Examination Survey, 2015−2018: Sample design and estimation procedures . National Center for Health Statistics. Vital Health Stat 2(184). 2020.
Akinbami LJ, Chen TC, Davy O, Ogden CL, Fink S, Clark J, et al. National Health and Nutrition Examination Survey, 2017–March 2020 prepandemic file: Sample design, estimation, and analytic guidelines . National Center for Health Statistics. Vital Health Stat 2(190). 2022.
Terry AL, Chiappa MM, McAllister J, Woodwell DA, Graber JE. Plan and operations of the National Health and Nutrition Examination Survey, August 2021–August 2023 . National Center for Health Statistics. Vital Health Stat 1(66). 2024.
RTI International. SUDAAN (Release 11.0.3) [computer software]. 2020.
R Foundation. R version 4.4.0 [computer program]. 2024.

Emmerich SD, Fryar CD, Stierman B, Ogden CL. Obesity and severe obesity prevalence in adults: United States, August 2021–August 2023. NCHS Data Brief, no 508. Hyattsville, MD: National Center for Health Statistics. 2024. DOI: https://dx.doi.org/10.15620/cdc/159281 .

Copyright information

All material appearing in this report is in the public domain and may be reproduced or copied without permission; citation as to source, however, is appreciated.

National Center for Health Statistics

Brian C. Moyer, Ph.D., Director Amy M. Branum, Ph.D., Associate Director for Science

Division of Health and Nutrition Examination Surveys

Alan E. Simon, M.D ., Director Lara J. Akinbami, M.D., Associate Director for Science

Get E-mail Updates
Data Visualization Gallery
NHIS Early Release Program
MMWR QuickStats
Government Printing Office Bookstore

Français
Español

National Research and Gender Statistics Consultant

Advertised on behalf of.

Juba, SOUTH SUDAN

Type of Contract :

Individual Contract

Starting Date :

Application deadline :.

11-Oct-24 (Midnight New York, USA)

Post Level :

National Consultant

Duration of Initial Contract :

Time left :.

11d 20h 18m

Languages Required :

Expected duration of assignment :.

UNDP is committed to achieving workforce diversity in terms of gender, nationality and culture. Individuals from minority groups, indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the strictest confidence. UNDP does not tolerate sexual exploitation and abuse, any kind of harassment, including sexual harassment, and discrimination. All selected candidates will, therefore, undergo rigorous reference and background checks.

UN Women, grounded in the vision of equality enshrined in the Charter of the United Nations, works on the elimination of discrimination and violence against women and girls, the empowerment of women, and the achievement of equality between women and men as partners and beneficiaries of development, human rights, humanitarian action and peace and security. UN Women provides support to the Government of South Sudan in meeting its gender equality goals and in building effective partnerships with civil society and other relevant actors. Placing women's rights at the centre of all its efforts, UN Women leads and coordinates United Nations system efforts in South Sudan to ensure commitment to gender equality.

In September 2015, governments united behind the ambitious 2030 Agenda for Sustainable Development, which features 17 new Sustainable Development Goals (SDGs) and 169 targets that aim to end poverty, combat inequalities, and promote prosperity by 2030 while protecting the environment. The 2030 Agenda sets out a historic and unprecedented level of ambition to “Achieve gender equality and empower all women and girls by 2030” (SDG 5), and it includes 37 gender-related targets in 10 other SDGs. It commits to addressing core issues of gender equality, such as eliminating all forms of violence against women and girls, eradicating discriminatory laws and constraints on sexual and reproductive health and reproductive rights, recognizing and valuing unpaid care and domestic work and increasing women’s participation in decision-making.

In this context, UN Women is seeking the services of a highly qualified Gender Statistician to support the entity’s research and data work. The successful candidate will work under direct supervision by the Deputy Country Representative and collaborate closely with the Regional Gender Statistics Specialist based in the East and Southern Africa Regional office in Nairobi and the gender machinery and National Statistics Office in South Sudan. The position will be based in the South Sudan National Bureau of Statistics(SSNBS).

Duties and Responsibilities

The incumbent will provide strategic and technical advice to the National Bureau of Statistics, ensuring strategic cross-cutting mainstreaming on GEWE while contributing to the achievement of the UN Women’s South Sudan Strategic Note Output 1.1.3: The National Statistical System is strengthened to increase the production, analysis, availability and use of high-quality gender statistics and sex-disaggregated data, to inform decision-making and enable reporting at all levels of government and others closely related to or benefitting from gender statistics and research work as a cross-cutting function.

The following are the detailed duties and responsibilities of the National Gender and Statistics Officer:

Design and formulate gender statistics related programme/ project proposals and initiatives with the goal of addressing gender statistical challenges experienced at the country level
Draft inputs to country strategy documents, briefs, policy dialogue and other documents related to Gender Statistics.
Provide timely progress and financial reports using results-based management.
Build and manage relationships with national partners to support the implementation and expansion of the Gender Statistics programme.
Facilitate coordination mechanisms by maintaining a relationship with the project by providing technical assistance.
Identify and provide technical assistance for capacity building of SSNBS staff and other partners; provide technical/ programming support and training to partners.
Provide technical support in the implementation of programme activities; develop technical knowledge products.
Manage the monitoring of programme/ project implementation using results-based management tools.
Review quarterly reports and donor reports, focusing on results, output, and outcomes, with a focus on enriching the reports with relevant gender data.
Provides technical assistance and contribute to donor and UN Women reports.
Develop and manage partnerships, including with the National Bureau of Statics in South Sudan, donors, international development agencies, civil society, philanthropic organizations, and the private sector.
Strengthen UN Women’s partnerships with key regional and national stakeholders, including, UN agencies, civil society organizations and other data collecting organizations in the country to present a wide range of relevant data on gender equality and women’s rights to contribute to the regional and thematic monitoring of SDGs, other publications and other associated communications materials.
Participate and/or represent UN Women in regional inter-agency statistical forums.
Develop and implement partnerships and resource mobilization strategies.
Provide inputs in relevant documentation on donors and potential opportunities for resource mobilization.
Analyse and research information on donors, prepare substantive briefs on possible areas of cooperation, identification of opportunities for cost-sharing.
Provide technical inputs to technical inter-agency forums and discussions related to SDGs indicators and monitoring in the region.
Participate and/or represent UN Women in national inter-agency statistical forums.
Develop and review background documents, briefs and presentations related to Gender Statistics.
Represent UN Women in meetings and policy dialogues on issues related to Gender Statistics as necessary.
Develop gender statistics advocacy strategies and oversee their implementation.
Identify best practices and lessons learned to guide project improvement and strategy planning.
Develop knowledge management strategies, products, and methodologies on Gender Statistics.

7. Support the UN Women South Sudan office with any other gender data and statistics work as required.

Competencies

Core Values:

Respect for Diversity
Professionalism

Core Competencies:

Awareness and Sensitivity Regarding Gender Issues
Accountability
Creative Problem Solving
Effective Communication
Inclusive Collaboration
Stakeholder Engagement
Leading by Example

Functional Competencies

Strong programme formulation, implementation, monitoring, and evaluation skills
Strong knowledge of Results Based Management
Ability to synthesize program performance data and produce analytical reports to inform management and strategic decision-making.
Strong knowledge of gender statistics and familiarity with the SDGs
Strong communication skills in spoken and written English
Strong writing skills and ability to produce a variety of knowledge products for different audiences and purposes.
Ability to produce impactful communications materials and knowledge products.
Strong commitment to knowledge-sharing within a multicultural environment

Ability to design and deliver training and other capacity-building strategies in gender statistics Basic project management skill

Required Skills and Experience

Education and certification:

Master’s degree or equivalent in Statistics, Demography, Development Economics, International Development or any other relevant field. A first-level university degree with a combination of two additional years of experience can be accepted in lieu of an advanced degree.

Experience:

5 years of progressively responsible experience with research, data collection, processing, and analysis.
Experience in programme design, coordination, management and implementation of a gender statistics programme is an asset;
Technical experience in Gender Statistics is an added advantage;
Experience coordinating and liaising with government agencies and/or donors is an asset;
Experience in working in a computer environment using multiple office software packages. Experience in working with Quantum/PRIMAVERA is an advantage.
Knowledge of gender issues and women´s empowerment
Work experience within UN Women and or the UN system is an asset.
Proven experience with socioeconomic policy analysis and mastery of quantitative and qualitative analysis and methods;
Experience in drafting quality and timely research reports, policy briefs, and position papers.

Language Requirements:

Fluency in English is required;
Working knowledge of the other UN official working language is an asset;
Working knowledge of Arabic is an added asset.

Application:

All applications must include (as an attachment) the completed UN Women Personal History form (P-11), which can be downloaded from https://www.unwomen.org/sites/default/files/Headquarters/Attachments/Sections/About%20Us/Employment/UN-Women-P11-Personal-History-Form.doc . Kindly note that the system will only allow one attachment. Applications without the completed UN Women P-11 form will be treated as incomplete and will not be considered for further assessment.

In July 2010, the United Nations General Assembly created UN Women, the United Nations Entity for Gender Equality and the Empowerment of Women. The creation of UN Women came about as part of the UN reform agenda, bringing together resources and mandates for greater impact. It merges and builds on the important work of four previously distinct parts of the UN system (DAW, OSAGI, INSTRAW and UNIFEM), which focused exclusively on gender equality and women's empowerment.

Diversity and inclusion:

At UN Women, we are committed to creating a diverse and inclusive environment of mutual respect. UN Women recruits, employs, trains, compensates, and promotes regardless of race, religion, colour, sex, gender identity, sexual orientation, age, ability, national origin, or any other basis covered by appropriate law. All employment is decided based on qualifications, competence, integrity, and organizational need.

If you need any reasonable accommodation to support your participation in the recruitment and selection process, please include this information in your application.

UN Women has a zero-tolerance policy on conduct that is incompatible with the aims and objectives of the United Nations and UN Women, including sexual exploitation and abuse, sexual harassment, abuse of authority and discrimination. All selected candidates will be expected to adhere to UN Women’s policies and procedures and the standards of conduct expected of UN Women personnel and will, therefore, undergo rigorous reference and background checks. (Background checks will include the verification of academic credential(s) and employment history. Selected candidates may be required to provide additional information to conduct a background check.

Technology & Telecommunications ›
Telecommunications

Industry-specific and extensively researched technical data (partially from exclusive partnerships). A paid subscription is required for full access.

Number of mobile data subscriptions in Canada 2015-2024, by type

There were more than 37 million mobile data subscriptions in Canada in 2023. The majority of these were 4G connections, with 5G connections accounting for a 25 percent share.

Number of mobile broadband subscriptions in Canada from 2015 to 2024, by mobile network generation (in millions)

Characteristic	2G	3G	4G	5G
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-
-	-	-	-	-

Immediate access to 1m+ statistics
Incl. source references
Download as PNG, PDF, XLS, PPT

Additional Information

Show sources information Show publisher information Use Ask Statista Research Service

Global Monitor

2015 to 2024

Statistics on " Mobile internet usage in Canada "

Canada: digital population January 2024
Canada distribution of online traffic 2015-2024, by device
Most common internet accesses by type in Canada 2024
Mobile internet users in Canada 2020-2029
Mobile internet usage reach in Canada 2020-2029
Canada: monthly time spent on select social apps 2023
Number of mobile subscriptions in Canada Q1 2014-Q2 2023, by type
Wireless data traffic in Canada Q1 2014-Q2 2023
Forecast 5G share of mobile connections 2030, by selected country
Mobile internet usage in Canada 2024
Most popular mobile browsers in Canada Q2 2023, by market share
Most used devices for digital videos in Canada 2023
Digital audio usage by device in Canada 2023
Most used messenger by brand in Canada 2024
WhatsApp users in Canada 2020-2029
Video gaming penetration in Canada 2023, by device
Canada smartphone gamers 2024, by gender
Canada: mobile app spend 2020-2023
Canada: mobile app downloads 2020-2023
Hours spent on mobile apps 2019-2023, by country
Leading iPhone apps in Canada 2024, by downloads
Most used smartphone apps by type in Canada 2023
Most downloaded marketplace apps in Canada 2023
Fitness and health app usage in Canada 2023
Active mobile social media penetration in the Americas 2024
Canada mobile social media visit share 2024
Canada: Facebook Messenger users 2024, by age group
Canada: Facebook Messenger users 2024, by gender
Canada: Instagram users 2024, by gender
Canada: distribution of TikTok audiences 2024, by gender

Other statistics that may interest you Mobile internet usage in Canada

Mobile market overview

Basic Statistic Canada: digital population January 2024
Basic Statistic Canada distribution of online traffic 2015-2024, by device
Basic Statistic Canada leading online access devices 2017-2024
Premium Statistic Most common internet accesses by type in Canada 2024
Basic Statistic Mobile internet users in Canada 2020-2029
Basic Statistic Mobile internet usage reach in Canada 2020-2029
Premium Statistic Canada: monthly time spent on select social apps 2023

Connectivity

Premium Statistic Number of mobile data subscriptions in Canada 2015-2024, by type
Premium Statistic Number of mobile subscriptions in Canada Q1 2014-Q2 2023, by type
Premium Statistic Wireless data traffic in Canada Q1 2014-Q2 2023
Premium Statistic Forecast 5G share of mobile connections 2030, by selected country

Mobile usage and engagement

Basic Statistic Mobile internet usage in Canada 2024
Premium Statistic Most popular mobile browsers in Canada Q2 2023, by market share
Premium Statistic Most used devices for digital videos in Canada 2023
Premium Statistic Digital audio usage by device in Canada 2023
Premium Statistic Most used messenger by brand in Canada 2024
Premium Statistic WhatsApp users in Canada 2020-2029
Premium Statistic Video gaming penetration in Canada 2023, by device
Premium Statistic Canada smartphone gamers 2024, by gender

Mobile apps

Premium Statistic Canada: mobile app spend 2020-2023
Premium Statistic Canada: mobile app downloads 2020-2023
Premium Statistic Hours spent on mobile apps 2019-2023, by country
Premium Statistic Leading Android apps in Canada 2024, by downloads
Premium Statistic Leading iPhone apps in Canada 2024, by downloads
Premium Statistic Most used smartphone apps by type in Canada 2023
Premium Statistic Most downloaded marketplace apps in Canada 2023
Basic Statistic Fitness and health app usage in Canada 2023

Spotlight: mobile social

Premium Statistic Active mobile social media penetration in the Americas 2024
Basic Statistic Canada mobile social media visit share 2024
Premium Statistic Canada: Facebook Messenger users 2024, by age group
Premium Statistic Canada: Facebook Messenger users 2024, by gender
Premium Statistic Canada: Instagram users 2024, by age group
Premium Statistic Canada: Instagram users 2024, by gender
Premium Statistic Canada: distribution of TikTok audiences 2022, by age group
Premium Statistic Canada: distribution of TikTok audiences 2024, by gender

Further Content: You might find this interesting as well

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

Knowledge Base

Descriptive Statistics | Definitions, Types, Examples

Published on July 9, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Descriptive statistics summarize and organize characteristics of a data set. A data set is a collection of responses or observations from a sample or entire population.

In quantitative research , after collecting data, the first step of statistical analysis is to describe characteristics of the responses, such as the average of one variable (e.g., age), or the relation between two variables (e.g., age and creativity).

The next step is inferential statistics , which help you decide whether your data confirms or refutes your hypothesis and whether it is generalizable to a larger population.

Types of descriptive statistics, frequency distribution, measures of central tendency, measures of variability, univariate descriptive statistics, bivariate descriptive statistics, other interesting articles, frequently asked questions about descriptive statistics.

There are 3 main types of descriptive statistics:

The distribution concerns the frequency of each value.
The central tendency concerns the averages of the values.
The variability or dispersion concerns how spread out the values are.

You can apply these to assess only one variable at a time, in univariate analysis, or to compare two or more, in bivariate and multivariate analysis.

Go to a library
Watch a movie at a theater
Visit a national park

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

Academic style
Vague sentences
Style consistency

See an example

A data set is made up of a distribution of values, or scores. In tables or graphs, you can summarize the frequency of every possible value of a variable in numbers or percentages. This is called a frequency distribution .

Simple frequency distribution table
Grouped frequency distribution table

Gender	Number
Male	182
Female	235
Other	27

From this table, you can see that more women than men or people with another gender identity took part in the study. In a grouped frequency distribution, you can group numerical response values and add up the number of responses for each group. You can also convert each of these numbers to percentages.

Library visits in the past year	Percent
0–4	6%
5–8	20%
9–12	42%
13–16	24%
17+	8%

Measures of central tendency estimate the center, or average, of a data set. The mean, median and mode are 3 ways of finding the average.

Here we will demonstrate how to calculate the mean, median, and mode using the first 6 responses of our survey.

The mean , or M , is the most commonly used method for finding the average.

To find the mean, simply add up all response values and divide the sum by the total number of responses. The total number of responses or observations is called N .

Mean number of library visits
Data set	15, 3, 12, 0, 24, 3
Sum of all values	15 + 3 + 12 + 0 + 24 + 3 = 57
Total number of responses	= 6
Mean	Divide the sum of values by to find : 57/6 =

The median is the value that’s exactly in the middle of a data set.

To find the median, order each response value from the smallest to the biggest. Then , the median is the number in the middle. If there are two numbers in the middle, find their mean.

Median number of library visits
Ordered data set	0, 3, 3, 12, 15, 24
Middle numbers	3, 12
Median	Find the mean of the two middle numbers: (3 + 12)/2 =

The mode is the simply the most popular or most frequent response value. A data set can have no mode, one mode, or more than one mode.

To find the mode, order your data set from lowest to highest and find the response that occurs most frequently.

Mode number of library visits
Ordered data set	0, 3, 3, 12, 15, 24
Mode	Find the most frequently occurring response:

Measures of variability give you a sense of how spread out the response values are. The range, standard deviation and variance each reflect different aspects of spread.

The range gives you an idea of how far apart the most extreme response scores are. To find the range , simply subtract the lowest value from the highest value.

Standard deviation

The standard deviation ( s or SD ) is the average amount of variability in your dataset. It tells you, on average, how far each score lies from the mean. The larger the standard deviation, the more variable the data set is.

There are six steps for finding the standard deviation:

List each score and find their mean.
Subtract the mean from each score to get the deviation from the mean.
Square each of these deviations.
Add up all of the squared deviations.
Divide the sum of the squared deviations by N – 1.
Find the square root of the number you found.

Raw data	Deviation from mean	Squared deviation
15	15 – 9.5 = 5.5	30.25
3	3 – 9.5 = -6.5	42.25
12	12 – 9.5 = 2.5	6.25
0	0 – 9.5 = -9.5	90.25
24	24 – 9.5 = 14.5	210.25
3	3 – 9.5 = -6.5	42.25
= 9.5	Sum = 0	Sum of squares = 421.5

Step 5: 421.5/5 = 84.3

Step 6: √84.3 = 9.18

The variance is the average of squared deviations from the mean. Variance reflects the degree of spread in the data set. The more spread the data, the larger the variance is in relation to the mean.

To find the variance, simply square the standard deviation. The symbol for variance is s 2 .

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Univariate descriptive statistics focus on only one variable at a time. It’s important to examine data from each variable separately using multiple measures of distribution, central tendency and spread. Programs like SPSS and Excel can be used to easily calculate these.

	Visits to the library
	6
Mean	9.5
Median	7.5
Mode	3
Standard deviation	9.18
Variance	84.3
Range	24

If you were to only consider the mean as a measure of central tendency, your impression of the “middle” of the data set can be skewed by outliers, unlike the median or mode.

Likewise, while the range is sensitive to outliers , you should also consider the standard deviation and variance to get easily comparable measures of spread.

If you’ve collected data on more than one variable, you can use bivariate or multivariate descriptive statistics to explore whether there are relationships between them.

In bivariate analysis, you simultaneously study the frequency and variability of two variables to see if they vary together. You can also compare the central tendency of the two variables before performing further statistical tests .

Multivariate analysis is the same as bivariate analysis but with more than two variables.

Contingency table

In a contingency table, each cell represents the intersection of two variables. Usually, an independent variable (e.g., gender) appears along the vertical axis and a dependent one appears along the horizontal axis (e.g., activities). You read “across” the table to see how the independent and dependent variables relate to each other.

	Number of visits to the library in the past year
Group	0–4	5–8	9–12	13–16	17+
Children	32	68	37	23	22
Adults	36	48	43	83	25

Interpreting a contingency table is easier when the raw data is converted to percentages. Percentages make each row comparable to the other by making it seem as if each group had only 100 observations or participants. When creating a percentage-based contingency table, you add the N for each independent variable on the end.

	Visits to the library in the past year (Percentages)
Group	0–4	5–8	9–12	13–16	17+
Children	18%	37%	20%	13%	12%	182
Adults	15%	20%	18%	35%	11%	235

From this table, it is more clear that similar proportions of children and adults go to the library over 17 times a year. Additionally, children most commonly went to the library between 5 and 8 times, while for adults, this number was between 13 and 16.

Scatter plots

A scatter plot is a chart that shows you the relationship between two or three variables . It’s a visual representation of the strength of a relationship.

In a scatter plot, you plot one variable along the x-axis and another one along the y-axis. Each data point is represented by a point in the chart.

From your scatter plot, you see that as the number of movies seen at movie theaters increases, the number of visits to the library decreases. Based on your visual assessment of a possible linear relationship, you perform further tests of correlation and regression.

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.

Statistical power
Pearson correlation
Degrees of freedom
Statistical significance

Methodology

Cluster sampling
Stratified sampling
Focus group
Systematic review
Ethnography
Double-Barreled Question

Research bias

Implicit bias
Publication bias
Cognitive bias
Placebo effect
Pygmalion effect
Hindsight bias
Overconfidence bias

Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.

The 3 main types of descriptive statistics concern the frequency distribution, central tendency, and variability of a dataset.

Distribution refers to the frequencies of different responses.
Measures of central tendency give you the average for each response.
Measures of variability show you the spread or dispersion of your dataset.
Univariate statistics summarize only one variable at a time.
Bivariate statistics compare two variables .
Multivariate statistics compare more than two variables .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 21). Descriptive Statistics | Definitions, Types, Examples. Scribbr. Retrieved September 30, 2024, from https://www.scribbr.com/statistics/descriptive-statistics/

Is this article helpful?

Pritha Bhandari

Other students also liked, central tendency | understanding the mean, median & mode, variability | calculating range, iqr, variance, standard deviation, inferential statistics | an easy introduction & examples, what is your plagiarism score.

COMMENTS

Introduction to Research Statistical Analysis: An Overview of the
Introduction. Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology.
Research Methods
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
The Beginner's Guide to Statistical Analysis
This article is a practical introduction to statistical analysis for students and researchers. We'll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables. Example: Causal research question.
Statistical Analysis in Research: Meaning, Methods and Types
A Simplified Definition. Statistical analysis uses quantitative data to investigate patterns, relationships, and patterns to understand real-life and simulated phenomena. The approach is a key analytical tool in various fields, including academia, business, government, and science in general. This statistical analysis in research definition ...
Types of Research Designs Compared
Other interesting articles. 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. Statistics. Normal distribution. Skewness. Kurtosis. Degrees of freedom. Variance. Null hypothesis.
1.3: Types of Statistical Studies (1 of 4 ...
But in these types of questions, we used words like associated, correlated, linked to, and connected. These words do not imply a cause-and-effect relationship between the variables. We can investigate these types of questions without conducting an experiment - an observational study will do. We study observational studies in "Collecting ...
Research Methods
Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. ... Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
1.6: Types of Statistical Studies (4 of 4 ...
To answer a question about cause-and-effect we conduct an experiment. There are two types of statistical studies: Observational studies: An observational study observes individuals and measures variables of interest. We conduct observational studies to investigate questions about a population or about an association between two variables.
Types of Research
This type of research is subdivided into two types: Technological applied research: looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes. Scientific applied research: has predictive purposes. Through this type of research design, we can ...
Role of Statistics in Research
Role of Statistics in Biological Research. Statistics is a branch of science that deals with collection, organization and analysis of data from the sample to the whole population. Moreover, it aids in designing a study more meticulously and also give a logical reasoning in concluding the hypothesis.
Basic statistical tools in research and data analysis
Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research ...
An Introduction to Statistics
INTRODUCTION. In the first article of this series, we look at types of data and the methods used to describe or summarize data. Data is defined as 'factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation'. 1 As statistics begins with data collection, understanding data is important ...
The Importance of Statistics in Research (With Examples)
In the field of research, statistics is important for the following reasons: Reason 1: Statistics allows researchers to design studies such that the findings from the studies can be extrapolated to a larger population. Reason 2: Statistics allows researchers to perform hypothesis tests to determine if some claim about a new drug, new procedure ...
19 Types of Research (With Definitions and Examples)
Cohort study: research traces a subpopulation over time. Panel study: research traces the same sample over time. Example: A researcher examines if and how employee satisfaction changes in the same employees after one year, three years and five years with the same company. 16. Mixed research.
PDF Introduction to Statistics
Statistics is a branch of mathematics used to summarize, analyze, and interpret a group of numbers or observations. We begin by introducing two general types of statistics: •• Descriptive statistics: statistics that summarize observations. •• Inferential statistics: statistics used to interpret the meaning of descriptive statistics.
Choosing the Right Statistical Test
ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults). Predictor variable. Outcome variable. Research question example. Paired t-test. Categorical. 1 predictor. Quantitative. groups come from the same population.
Sampling Methods: Different Types in Research
A sample is the subset of the population that you actually measure, test, or evaluate and base your results. Sampling methods are how you obtain your sample. Before beginning your study, carefully define the population because your results apply to the target population. You can define your population as narrowly as necessary to meet the needs ...
2.5: Scientific method and where statistics fits
There is a broader concern about the reliability of research, and the debate about how to improve reliability comes from a call to understand how to do statistics better and, more importantly to understand how statistics are to be used in making claims from statistical results (Ioannidis 2007; but see Goodman and Greenland 2007). A key element ...
7 Types of Statistical Analysis Techniques (And Process Steps)
3. Data presentation. Data presentation is an extension of data cleaning, as it involves arranging the data for easy analysis. Here, you can use descriptive statistics tools to summarize the data. Data presentation can also help you determine the best way to present the data based on its arrangement. 4.
Different Types of Statistical Tests: Concepts
In statistics, there are two main types of tests: parametric and non-parametric. Both types of tests are used to make inferences about a population based on a sample. The difference between the two types of tests lies in the assumptions that they make about the data. Parametric tests make certain assumptions about the data, while non-parametric ...
Key Statistical Publications You Should Know
Statistical research has produced several landmark papers and books that are crucial for understanding and applying statistical methods. Here's a simplified guide to ten influential statistical publications: 1. The Design of Experiments, by Ronald A. Fisher (1935) Fisher's book is fundamental for learning about experimental design. He ...
An Introduction to Statistics: Choosing the Correct Statistical Test
A bstract. The choice of statistical test used for analysis of data from a research study is crucial in interpreting the results of the study. This article gives an overview of the various factors that determine the selection of a statistical test and lists some statistical testsused in common practice. How to cite this article: Ranganathan P.
Methods of compiling questionnaires and questionnaires in legal statistics
The correct compilation of questionnaires and questionnaires is one of the most important parts of the work to obtain a direct opinion of the audience and analysis in statistics.
Products
Obesity is a chronic condition that increases the risk of hypertension, type 2 diabetes, coronary heart disease, stroke, and certain cancers ().Obesity and severe obesity prevalence increased from 1999-2000 through 2017-2018 ().This report provides prevalence estimates of adult obesity and severe obesity during August 2021-August 2023 by age and sex, as well as obesity prevalence by ...
Types of Variables in Research & Statistics
Examples. Discrete variables (aka integer variables) Counts of individual items or values. Number of students in a class. Number of different tree species in a forest. Continuous variables (aka ratio variables) Measurements of continuous or non-finite values. Distance.
National Research and Gender Statistics Consultant
The incumbent will provide strategic and technical advice to the National Bureau of Statistics, ensuring strategic cross-cutting mainstreaming on GEWE while contributing to the achievement of the UN Women's South Sudan Strategic Note Output 1.1.3: The National Statistical System is strengthened to increase the production, analysis ...
Canada: Mobile data subscriptions by type 2024
As your partner for data-driven success, we combine expertise in research, strategy, and marketing communications to deliver comprehensive solutions. ... Most common internet accesses by type in ...
Types of Variables and Commonly Used Statistical Designs
Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study.[1] Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis.[1] Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of ...
Descriptive Statistics
Types of descriptive statistics. There are 3 main types of descriptive statistics: The distribution concerns the frequency of each value. The central tendency concerns the averages of the values. The variability or dispersion concerns how spread out the values are. You can apply these to assess only one variable at a time, in univariate ...
FBI Releases 2023 Crime in the Nation Statistics
The FBI released detailed data on over 14 million criminal offenses for 2023 reported to the Uniform Crime Reporting (UCR) Program by participating law enforcement agencies.

Have a language expert improve your writing

Research Methods | Definition, Types, Examples

Table of contents

Qualitative vs quantitative data

Primary vs secondary data

Descriptive vs experimental data

Prevent plagiarism, run a free check.

Qualitative analysis methods

Quantitative analysis methods

Is this article helpful?

Community Blog

Types of Research – Explained with Examples

Types of Research

Classification of Types of Research

According to its Purpose

Applied Research

According to your Depth of Scope

Descriptive Research

Explanatory Research

Correlational Research

According to the Type of Data Used

Quantitative Research

According to the Degree of Manipulation of Variables

Non-Experimental Research

Quasi-Experimental Research

According to the Type of Inference

Inductive Research

Hypothetical-Deductive Investigation

According to the Time in Which it is Carried Out

Cross-Sectional Study (also referred to as Synchronous Research)

According to The Sources of Information

Secondary research

According to How the Data is Obtained

From Laboratory

Mixed-Method: Documentary, Field and/or Laboratory

Browse PhDs Now

Join Thousands of Students

Effective Use of Statistics in Research – Methods and Tools for Data Analysis

Role of Statistics in Biological Research

1. Establishing a Sample Size

2. Testing of Hypothesis

3. Data Interpretation Through Analysis

Types of Statistical Research Methods That Aid in Data Analysis

1. Descriptive Analysis

2. Inferential Analysis

3. Predictive Analysis

4. Prescriptive Analysis

5. Exploratory Data Analysis

6. Causal Analysis

7. Mechanistic Analysis

Important Statistical Tools In Research

1. Statistical Package for Social Science (SPSS)

2. R Foundation for Statistical Computing

3. MATLAB (The Mathworks)

4. Microsoft Excel

5. Statistical Analysis Software (SAS)

6. GraphPad Prism

Use of Statistical Tools In Research and Data Analysis

Frequently Asked Questions

Enago Academy's Most Popular Articles

How Enago Academy Contributes to Sustainable Development Goals (SDGs) Through Empowering Researchers

2022 in a Nutshell — Reminiscing the year when opportunities were seized and feats were achieved!

Sampling Methods: Different Types in Research

What Are Sampling Methods?

Probability vs Non-Probability Sampling Methods

Probability Sampling Methods

Simple Random Sampling (SRS)

Systematic Sampling

Stratified Sampling

Cluster Sampling

Non-Probability Sampling Methods

Share this:

Reader Interactions

Comments and Questions Cancel reply

Different Types of Statistical Tests: Concepts

Parametric Statistical Tests & Types: Concepts, Examples

Non-Parametric Statistical Tests & Types: Concepts, Examples

Statistical Tests in Quantitative Research: Examples

Statistical Tests in Qualitative Research: Examples

Recent Posts