• FAQ
  • About us
    • Our editors
    • Apply as editor
    • Team
    • Jobs
    • Contact
  • My account
    • Orders
    • Upload
    • Account details
    • Logout
  • My account
    • Overview
    • Availability
    • Information package
    • Account details
    • Logout
  • Admin
  • Login
  • Search
Scribbr - Scriptie laten nakijken
  • Proofreading & Editing
      • Thesis
      • PhD dissertation
      • Essay
      • Paper
      • Personal statement
      • APA editing
      • Spanish, French, or German
      • About our services
      • Our services
      • Editing example
      • Rates
      • How it works
      • Our editors
      • Happiness guarantee
  • Plagiarism Checker
  • Citation Tools
      • APA Citation Generator
      • MLA Citation Generator
      • Citation Checker  New
      • Citation Editing
      • Citation style guides
      • Citing sources
      • APA Style
      • MLA Style
      • Chicago Style
  • Knowledge Base
    • All articles
    • Language rules
    • Academic writing
    • Research process
    • Research methods
    • Dissertation structure
    • Research paper
    • Essay
    • Plagiarism
Scribbr logo
Logo Scribbr - Icon only
  • Proofreading & Editing
  • Plagiarism Checker
  • Citation Tools
  • Knowledge Base
  • FAQ
  • About us
  • My account
  • My account
  • Admin
  • Login
Back
    • Thesis
    • PhD dissertation
    • Essay
    • Paper
    • Personal statement
    • APA editing
    • Spanish, French, or German
    • About our services
    • Our services
    • Editing example
    • Rates
    • How it works
    • Our editors
    • Happiness guarantee
Back
    • APA Citation Generator
    • MLA Citation Generator
    • Citation Checker  New
    • Citation Editing
    • Citation style guides
    • Citing sources
    • APA Style
    • MLA Style
    • Chicago Style
Back
  • All articles
  • Language rules
  • Academic writing
  • Research process
  • Research methods
  • Dissertation structure
  • Research paper
  • Essay
  • Plagiarism
Back
  • Our editors
  • Apply as editor
  • Team
  • Jobs
  • Contact
Back
  • Orders
  • Upload
  • Account details
  • Logout
Back
  • Overview
  • Availability
  • Information package
  • Account details
  • Logout

Frequently asked questions

See all
  1. Home
  2. Frequently asked questions
  3. How do you avoid sampling bias?

How do you avoid sampling bias?

Using careful research design and sampling procedures can help you avoid sampling bias. Oversampling can be used to correct undercoverage bias.


Frequently asked questions: Methodology

What are the benefits of collecting data?

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
  • You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods)

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

What is the difference between a control group and an experimental group?

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Are Likert scales ordinal or interval scales?

Individual Likert-type questions are generally considered ordinal data, because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

What is a Likert scale?

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey, you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

What’s the difference between concepts, variables, and indicators?

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization.

How do you analyze qualitative data?

There are various approaches to qualitative data analysis, but they all share five steps in common:

  1. Prepare and organize your data.
  2. Review and explore your data.
  3. Develop a data coding system.
  4. Assign codes to the data.
  5. Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis, thematic analysis, and discourse analysis.

What are the main qualitative research approaches?

There are five common approaches to qualitative research:

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organization to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.
What is hypothesis testing?

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses, by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

What is operationalization?

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data, it’s important to consider how you will operationalize the variables that you want to measure.

Do experiments always need a control group?

Yes. In an experiment, you need to include a control group that is identical to the treatment group in every way except that it does not receive the experimental treatment.

Without a control group, you can’t know whether it was the treatment or some other variable that caused the outcome of the experiment. By including a control group, you can eliminate the possible impact of all other variables.

What is data collection?

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

How do I prevent confounding variables from interfering with my research?

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction, you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching, you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable.

In statistical control, you include potential confounders as variables in your regression.

In randomization, you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

What is the difference between confounding variables, independent variables and dependent variables?

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the suppose cause, while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Why do confounding variables matter for my research?

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables, or even find a causal relationship where none exists.

Can I include more than one independent or dependent variable in a study?

Yes, but including more than one of either type requires multiple research questions.

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable.

To ensure the internal validity of an experiment, you should only change one independent variable at a time.

Can a variable be both independent and dependent?

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

What is an example of an independent and a dependent variable?

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment.

  • The type of soda – diet or regular – is the independent variable.
  • The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.
When should I use simple random sampling?

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity. However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

How do I perform systematic sampling?

There are three key steps in systematic sampling:

  1. Define and list your population, ensuring that it is not ordered in a cyclical or periodic order.
  2. Decide on your sample size and calculate your interval, k, by dividing your population by your target sample size.
  3. Choose every kth member of the population as your sample.
What is systematic sampling?

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling.

Can I stratify by multiple characteristics at once?

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

When should I use stratified sampling?

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

What is stratified sampling?

In stratified sampling, researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment, etc).

Once divided, each subgroup is randomly sampled using another probability sampling method.

What are some advantages and disadvantages of cluster sampling?

Cluster sampling is more time- and cost-efficient than other probability sampling methods, particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling, because it is difficult to ensure that your clusters properly represent the population as a whole.

What are the types of cluster sampling?

There are three types of cluster sampling: single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

  • In single-stage sampling, you collect data from every unit within the selected clusters.
  • In double-stage sampling, you select a random sample of units from within the clusters.
  • In multi-stage sampling, you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample size.
What is cluster sampling?

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

Why are independent and dependent variables important?

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

What is an example of simple random sampling?

The American Community Survey is an example of simple random sampling. In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

What is simple random sampling?

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population. Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

When should I use a quasi-experimental design?

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment.

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.

What is a quasi-experiment?

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Why is blinding important?

Blinding is important to reduce bias and ensure a study’s internal validity.

If participants know whether they are in a control or treatment group, they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

What is the difference between single-blind, double-blind and triple-blind studies?
  • In a single-blind study, only the participants are blinded.
  • In a double-blind study, both participants and experimenters are blinded.
  • In a triple-blind study, the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.
What is blinding?

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment.

What is the difference between discrete and continuous variables?

Discrete and continuous variables are two types of quantitative variables:

  • Discrete variables represent counts (e.g. the number of objects in a collection).
  • Continuous variables represent measurable amounts (e.g. water volume or weight).
What is an example of a longitudinal study?

The 1970 British Cohort Study, which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study.

What are the pros and cons of a longitudinal study?

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

What is the difference between a longitudinal study and a cross-sectional study?

Longitudinal studies and cross-sectional studies are two different types of research design. In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal study Cross-sectional study
Repeated observations Observations at a single point in time
Observes the same group multiple times Observes different groups (a “cross-section”) in the population
Follows changes in participants over time Provides snapshot of society at a given point
What are threats to internal validity?

There are eight threats to internal validity: history, maturation, instrumentation, testing, selection bias, regression to the mean, social interaction and attrition.

What is internal validity?

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

What is mixed-methods research?

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

How do I decide which research methods to use?

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 analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is 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.
What is a confounding variable?

A confounding variable, also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design, it’s important to identify potential confounding variables and plan how you will reduce their impact.

How long is a longitudinal study?

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

What is the difference between quantitative and categorical variables?

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results.

What are independent and dependent variables?

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause, while a dependent variable is the effect.

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

  • The independent variable is the amount of nutrients added to the crop field.
  • The dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design.

What is experimental design?

Experimental design means planning a set of procedures to investigate a relationship between variables. To design a controlled experiment, you need:

  • A testable hypothesis
  • At least one independent variable that can be precisely manipulated
  • At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

  • How you will manipulate the variable(s)
  • How you will control for any potential confounding variables
  • How many subjects or samples will be included in the study
  • How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

 

What is the difference between internal and external validity?

Internal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables.

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design.

What’s the difference between reliability and validity?

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

What is sampling?

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.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

What’s the difference between quantitative and qualitative methods?

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 analyzing data, while qualitative methods allow you to explore ideas and experiences in depth.

What’s the difference between method and methodology?

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 analyze data (for example, 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.

Why do a cross-sectional study?

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

What are the disadvantages of a cross-sectional study?

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study.

What is external validity?

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

What are the two types of external validity?

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

What are threats to external validity?

There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment and situation effect.

Why are samples used in research?

Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient and manageable.

When are populations used in research?

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

What’s the difference between a statistic and a parameter?

A statistic refers to measures about the sample, while a parameter refers to measures about the population.

What is sampling error?

A sampling error is the difference between a population parameter and a sample statistic.

What is sampling bias?

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

Why is sampling bias important?

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

What are some types of sampling bias?

Some common types of sampling bias include self-selection, non-response, undercoverage, survivorship, pre-screening or advertising, and healthy user bias.

What is probability sampling?

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

What is non-probability sampling?

In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.

Questions?

Ask our team

Want to contact us directly? No problem. We are always here for you.

  • Email info@scribbr.com
  • Start live chat
  • Call +1 (510) 822-8066
Support team - Alice Rougeaux Support team - Julia Gieseck Support team - Sjoerd Keij

Frequently asked questions

See all
How does Scribbr help students graduate?

Our team helps students graduate by offering:

  • Free citation generators
  • Scribbr Plagiarism Checker
  • Innovative Citation Checker software
  • Professional proofreading & editing services
  • Over 300 helpful articles about academic writing, citing sources, plagiarism, and more
What type of documents does Scribbr proofread?

Scribbr specializes in editing study-related documents. We proofread:

  • Essays
  • Papers
  • Theses
  • PhD dissertations
  • Research proposals
  • Personal statements
  • Admission essays
  • Motivation letters
  • Reports
  • Reflection papers
  • Journal articles
  • Capstone projects
What technology does the Scribbr Plagiarism Checker use?

The Scribbr Plagiarism Checker is powered by elements of Turnitin’s Similarity Checker, namely the plagiarism detection software and the Internet Archive and Premium Scholarly Publications content databases.

What citation styles does the Scribbr Citation Generator support?

The Scribbr Citation Generator currently supports the following citation styles, and we’re working hard on supporting more styles in the future.

  • APA (6th edition and 7th edition)
  • MLA (8th edition)

Scribbr uses industry-standard citation styles from the Citation Styles Language project.

Scribbr

Scribbr

  • Our editors
  • Jobs
  • FAQ
  • Partners
  • Sitemap

Our services

  • Plagiarism Checker
  • Proofreading & Editing
  • Citation Checker
  • APA Citation Generator
  • MLA Citation Generator
  • Knowledge Base

Contact

  • info@scribbr.com
  • +1 (510) 822-8066
Trustpilot logo4.9
  • Nederlands
  • English
  • Deutsch
  • Français
  • Italiano
  • Español
  • Svenska
  • Dansk
  • Suomi
  • Norwegian Bokmål
  • Terms of use
  • Privacy Policy
  • Happiness Guarantee
Search...

    0 results