What is the difference between concurrent and predictive validity?

Concurrent validity and predictive validity are both types of criterion validity. Both assess how well one test corresponds to another, theoretically related, test or outcome. However, the key difference is when each test is conducted:

Concurrent validity compares one measure to a second, well-established measure that acts as a gold-standard. Both measures should be obtained at the same time, or concurrently.
Predictive validity instead captures how well a measure corresponds to a measure taken later in time (i.e., how well one measure predicts a future measure).

What is the difference between concurrent and predictive validity?: Research

What are the parts of a research paper?: Most research papers contain at least an introduction and sections for methodology, results, discussion, and references. Many also include an abstract and a literature review. Some other common elements are a title page, a table of contents, tables and figures, and appendices.
What are properties of ordinal data?: Ordinal is the second level of measurement. It has two main properties:

Ordinal data can be grouped into categories

Ordinal data can be ranked in a logical order (e.g., low, medium, high)
What is the difference between content validity and face validity?: Content validity and face validity are both types of measurement validity.

Content validity refers to the degree to which the items or questions on a measure accurately reflect all elements of the construct or concept that’s being measured. It assesses whether the items are accurate, relevant, and comprehensive in measuring the construct.

Face validity refers to the degree to which a measure seems to be measuring what it claims to measure. It assesses whether the measure appears to be relevant.
What is the difference between construct validity and face validity?: Face validity refers to the extent to which a research instrument appears to measure what it’s supposed to measure. For example, a questionnaire created to measure customer loyalty has high face validity if the questions are strongly and clearly related to customer loyalty.

Construct validity refers to the extent to which a tool or instrument actually measures a construct, rather than just its surface-level appearance.
What is the best way for a researcher to judge the face validity of items on a measure?: The best way for a researcher to judge the face validity of items on a measure is by asking both other experts and test participants to evaluate the instrument.

The combination of experts with background knowledge and research experience, along with test participants who form the target audience of the instrument, provides a good idea of the instrument’s face validity.
In which ways are content and face validity similar?: Content validity and face validity are both types of measurement validity. Both aim to ensure that the instrument is measuring what it’s supposed to measure.

However, content validity focuses on how well the instrument covers the entire construct, whereas face validity focuses on the overall superficial appearance of the instrument.
What is the difference between construct validity and internal validity?: Construct validity refers to the extent to which a study measures the underlying concept or construct that it is supposed to measure.

Internal validity refers to the extent to which observed changes in the dependent variable are caused by the manipulation of the independent variable rather than other factors, such as extraneous variables or research biases.

Construct validity vs. internal validity example
You’re studying the effect of exercise on happiness levels.

Construct validity would ask whether your measures of exercise and happiness levels accurately reflect the underlying concepts of physical activity and emotional state.

Internal validity would ask whether your study’s results are due to the exercise itself, or if some other factor (e.g., changes in diet or stress levels) might be causing changes in happiness levels.
What are the 12 threats to internal validity?: The 12 main threats to internal validity are:

History: Changes in the environment or events that occur outside of the study can affect the outcome.

Maturation: Changes in the participants over time (e.g., age, skill level) can affect the outcome.

Testing: The act of testing or measurement itself can affect the outcome (testing effect, practice effect, or carryover effect).

Instrumentation: Changes in the measuring instrument or tool used to collect data can affect the outcome.

Statistical regression to the mean: The tendency of extreme scores to regress towards the mean, which can lead to a loss of statistical significance.

Selection: The selection of participants for the study can affect the outcome (selection bias), especially in the case of non-probability sampling.

Experimental mortality or attrition bias: The loss of participants or dropouts during the study can affect the outcome.

Multiple-treatment interference: The interaction between different treatments or conditions can affect the outcome.

Social desirability bias: The participants’ awareness of being in a study and their desire to be well-liked by researchers can affect the outcome.

Social interaction: The participants’ awareness of being treated differently than people in other groups can affect the outcome.

Residual confounding: The presence of unmeasured or uncontrolled extraneous or confounding variables that affect the outcome and are not accounted for in the analysis.

Order effect: The order of the independent variable levels affects the dependent variable.

There are several ways to counter these threats to internal validity, for example, through randomization, the addition of control groups, and blinding.
Is systematic sampling random?: Systematic sampling is a random sampling method. Another name for random sampling is probability sampling.

In systematic sampling, the researcher chooses a random starting point in a list of the population (e.g., by using a random number generator) before selecting subjects for the sample at a regular sampling interval (n). The random starting point and regular interval ensure the random nature of this sampling method.
When is it inappropriate to use systematic random sampling?: It is inappropriate to use systematic random sampling when your population has a periodic or cyclic order. This could result in only including individuals with a specific characteristic (e.g., age) in your sample.

Systematic sampling example: Unrepresentative sample
Your list of employees alternates between men, women, and nonbinary people. You select every third individual, which means you’re only selecting nonbinary people. This wouldn’t be a representative sample because the sample doesn’t contain any people who identify as men or women, whereas they make up most of the population.
Why might a researcher choose purposive sampling over systematic sampling?: Purposive sampling is often chosen over systematic sampling in situations where the researcher wants to select subjects that have specific traits that are needed in their sample.

Systematic sampling is a probability sampling method where the researcher systematically selects every nth member of the population with a random starting point. The researcher is unable to influence the characteristics of the people that end up in the sample.

Purposive sampling is a non-probability sampling method where the researcher selects specific subjects with traits that can provide the best information to achieve the research aims.
Is systematic sampling biased?: Systematic sampling is a probability sampling method, which typically ensures a lower risk of bias than nonprobability sampling methods.

However, systematic sampling can be vulnerable to sampling bias, especially if the starting point isn’t truly random. The choice of sampling interval can also introduce bias:

If the interval is too small, the sample can lack representativeness of the population.

If the interval is too large, the sample might not capture all the variation that exists in the population.
What is the formula for systematic sampling?: You can use a formula to calculate the sampling interval in systematic sampling, which is a probability sampling method where the researcher systematically selects subjects for their sample at a regular interval.

You can calculate the sampling interval (n) by dividing the total population by the desired sample size.

Formula: n = population size/sample size

Example: I = 2,000/200 = 10

In some cases, people might use a different letter to indicate the sampling interval (e.g., k). This is irrelevant to the use of the formula.
What are some major mistakes to avoid when writing a research proposal?: These are three major mistakes to avoid when writing a research proposal:

Failing to connect your potential research to previous studies, from the research question to the contribution your research will make.

Failing to maintain a clear and cohesive focus on the research topic throughout your research questions, aims, objectives, and methods.

Failing to determine realistic research steps and explain them clearly enough.
What are the 3 chapters of a research proposal?: A research proposal has three main parts: the introduction, the literature review, and the methods section.
Is age ordinal data?: The variable age can be measured at the ordinal or ratio level.

If you ask participants to provide you with their exact age (e.g., 28), the data is ratio level.

If you ask participants to select the bracket that contains their age (e.g., 26–35), the data is ordinal.

Ordinal data and ratio data are similar because they can both be ranked in a logical order. However, for ratio data, the differences between adjacent scores are equal and there’s a true, meaningful zero.
How do I cite sources for a research paper?: As you research, write down citation information for any sources you plan to use. Record quotes and ideas carefully, along with the page numbers where you found them. You can write them on note cards, on paper, or in a digital document.

When writing your first draft, include enough citation information in the text to ensure accurate referencing. After finishing the draft, you can go through your paper and add the full citations, following the style guide.

QuillBot’s Citation Generator can help you automatically generate in-text citations and a reference list for your paper.

Finally, use QuillBot’s Plagiarism Checker to double-check your work and avoid plagiarism.
Why is it important for a study of eyewitness memory to have a high level of ecological validity?: When a study has high ecological validity, the findings are more likely to generalize to real-world situations, making them more applicable and useful for practical purposes, such as improving witness testimony and investigative procedures.

High ecological validity minimizes the influence of factors that can affect results, such as laboratory settings or overly structured procedures, which can lead to biases or unrepresentative data.

Ecological validity is a subtype of external validity.
What is the difference between ecological validity and external validity?: Ecological validity is a subtype of external validity that is specifically concerned with the extent to which the study environment, tasks, and conditions reflect the real-world settings in which the behavior naturally occurs.

External validity also consists of population validity, which refers to the extent to which the results of a study can be generalized to the larger population from which the sample was drawn.
What is the difference between content validity and predictive validity?: Content validity and predictive validity are two types of validity in research:

Content validity ensures that an instrument accurately measures all elements of the construct it intends to measure.

A test designed to measure anxiety has high content validity if its questions cover all relevant aspects of the construct “anxiety.”

Predictive validity demonstrates that a measure can forecast future behavior, performance, or outcomes. It is a subtype of criterion validity.

A test designed to predict student retention has high predictive validity if it accurately predicts which students still participate in the study program 2 years later.
What is the difference between content and criterion validity?: Content validity and criterion validity are two types of validity in research:

Content validity ensures that an instrument measures all elements of the construct it intends to measure.

A survey to investigate depression has high content validity if its questions cover all relevant aspects of the construct “depression.”

Criterion validity ensures that an instrument corresponds with other “gold standard” measures of the same construct.

A shortened version of an established anxiety assessment instrument has high criterion validity if the outcomes of the new version are similar to those of the original version.
Does random assignment increase external validity?: Random assignment can increase external validity, but it has a bigger impact on internal validity.

Random assignment helps to reduce confounding variables and ensures that the treatment and control groups are comparable in all aspects except for the independent variable.

This increases the confidence that any observed differences between the groups can be attributed to the treatment rather than other factors, which means an increase in internal validity.

It can also improve external validity because random assignment of participants prevents researchers from inadvertently selecting participants who may be more or less likely to respond to the treatment.

However, the external validity may still be limited by sampling bias if the participants are not representative of the target population, which is why choosing the appropriate sampling method is also important to ensure external validity.

A probability sampling method, such as simple random sampling, stratified sampling, cluster sampling, or systematic sampling, is always the best choice.
What kind of sample is best for external validity?: To ensure high external validity, it’s important to draw a sample that’s representative of the population you want to generalize to. It’s always best to choose a probability sampling (also known as random sampling) method for this.

The most popular sampling methods are stratified sampling, systematic sampling, simple random sampling, and cluster sampling.

A probability sampling method also increases other types of validity, such as internal validity, and it reduces bias.
Which type of interview has been shown to have the highest predictive validity?: The interview type with the highest predictive validity differs based on the goal of the interview.

Generally speaking, a structured interview has the highest predictive validity.

Unstructured interviews have the lowest predictive validity, especially in recruitment or job performance settings.

Semi-structured interviews have adequate predictive validity but not as high as structured interviews.

Situational questions, work sample requests, and interview questions about past behavior are the best question types in the case of job interviews.

When designing job interview questions, make sure to minimize bias and to also account for other types of validity, such as construct validity and content validity.

You can use QuillBot’s Grammar Checker to make sure your interview questions are error-free.
What is the difference between construct validity and predictive validity?: Construct validity assesses how well a test measures the concept it was meant to measure, whereas predictive validity evaluates to what degree a test can predict a future outcome or behavior.
What are the two types of criterion validity?: Criterion validity measures how well a test corresponds to another measure, or criterion. The two types of criterion validity are concurrent and predictive validity.

Concurrent validity compares two measures obtained at the same time.

Predictive validity indicates how well a test correlates with a measurement taken later on.
What is the difference between convergent and concurrent validity?: Convergent and concurrent validity both indicate how well a test score and another variable compare to one another.

However, convergent validity indicates how well one measure corresponds to other measures of the same or similar constructs. These measures do not have to be obtained at the same time.

Concurrent validity instead assesses how well a measure aligns with a benchmark or “gold-standard,” which can be a ground truth or another validated measure. Both measurements should be taken at the same time.
What is a construct?: A construct is a phenomenon that cannot be directly measured, such as intelligence, anxiety, or happiness. Researchers must instead approximate constructs using related, measurable variables.

The process of defining how a construct will be measured is called operationalization. Constructs are common in psychology and other social sciences.

To evaluate how well a construct measures what it’s supposed to, researchers determine construct validity. Face validity, content validity, criterion validity, convergent validity, and divergent validity all provide evidence of construct validity.
What are the disadvantages of cluster sampling?: Cluster sampling usually harms internal validity, especially if you use multiple clustering stages. The results are also more likely to be biased and invalid, especially if the clusters don’t accurately represent the population. Lastly, cluster sampling is often much more complex than other sampling methods.
How many steps make up the research process?: The research process comprises five steps.

Choosing a topic

Identifying a problem

Formulating research questions

Creating a research design

Writing a research proposal

Once you’ve written your proposal, you may need your advisor’s approval of your plan before you can dive into the research process.
What is a research design?: The research design is the backbone of your research project. It includes research objectives, the types of sources you will consult (i.e., primary vs secondary), data collection methods, and data analysis techniques.

A thorough and well-executed research design can facilitate your research and act as a guide throughout both the research process and the thesis or dissertation writing process.
What are examples of action research papers?: Examples of action research papers are:

“Participatory Action Research for Conservation and Development: Experiences from the Amazon” by Perz et al. (2022)

“Exploring Social Innovation through Co-creation in Rural India Using Action Research” by Cornet and Barpanda (2021)
What is a list of topics for action research in education?: Topics for action research in education are:

Developing a standards-based grading system to improve students’ understanding of assessment criteria

Designing a technology-enhanced curriculum to improve student learning outcomes and engagements

Developing a co-teaching model to improve student outcomes for students with special needs
Why is systematic random sampling sometimes used in place of simple random sampling?: Systematic sampling is sometimes used in place of simple random sampling because it’s easier to implement.

With systematic sampling, you only draw one random number and then select subjects at regular intervals. This is especially helpful when the population is large.
What are the pros and cons of simple random sampling?: Simple random sampling is one of the most commonly used probability sampling methods.

The most important pros of simple random sampling are:

Ease of implementation. This method is relatively easy to implement. You don’t have to think about strata (like with stratified sampling) or clusters (like with cluster sampling).

Representative sample. Simple random sampling provides a representative sample of the population, with each unit having an equal chance of being selected.

Lack of bias. Because of the random nature of this technique, the risk of research biases is minimized. Researchers can’t influence the selection process.

The most important cons of simple random sampling are:

Limited flexibility. This sampling method is a fixed-probability sampling method, which means it can’t be adapted to changing circumstances during the sampling process.

Requirement of a large sample size. This technique typically requires large sample sizes to achieve acceptable levels of precision and accuracy, which can be expensive and time-consuming.

Difficulty of obtaining a list of entire population. It can be very difficult to obtain an exhaustive list of the entire population. This means some individuals who should be on the list have no chance of ending up in the sample.
Is simple random sampling probability or nonprobability sampling?: Simple random sampling is a common probability sampling technique.

In probability sampling, each individual in the population has the same chance of being selected for the sample. With simple random sampling, individuals are chosen from a list at random, which makes it a probability sampling method.

Other examples of probability sampling are stratified sampling, systematic sampling, and cluster sampling. Examples of nonprobability sampling are convenience sampling, quota sampling, self-selection sampling, snowball sampling, and purposive sampling.
What’s the difference between stratified and systematic sampling?: Stratified sampling and systematic sampling are both probabilistic sampling methods used to obtain representative samples from a population, but they differ significantly in their approach and execution.

Stratified sampling involves dividing the population into distinct subgroups (strata) based on specific characteristics (e.g., age, gender, income level) and then randomly sampling from each stratum. It ensures representation of all subgroups within the population.

Systematic sampling involves selecting elements from an ordered population at regular intervals, starting from a randomly chosen point. For example, you have a list of students from a school and you choose students at an interval of 5. This is a useful method when the population is homogeneous or when there is no clear stratification. It’s much easier to design and less complex than stratified sampling.
What is disproportionate stratified sampling?: Disproportionate sampling in stratified sampling is a technique where the sample sizes for each stratum are not proportional to their sizes in the overall population.

Instead, the sample size for each stratum is determined based on specific research needs, such as ensuring sufficient representation of small subgroups to draw statistical conclusions.

For example, the population you’re interested in consists of approximately 60% women, 30% men, and 10% people with a different gender identity. With disproportionate sampling, your sample would have 33% women, 33% men, and 33% people with a different gender identity. The sample’s distribution does not match the population’s.
What is proportionate stratified sampling?: Proportionate sampling in stratified sampling is a technique where the sample size from each stratum is proportional to the size of that stratum in the overall population.

This ensures that each stratum is represented in the sample in the same proportion as it is in the population, representing the population’s overall structure and diversity in the sample.

For example, the population you’re investigating consists of approximately 60% women, 30% men, and 10% people with a different gender identity. With proportionate sampling, your sample would have a similar distribution instead of equal parts.
Is stratified sampling random?: Yes, stratified sampling is a random sampling method (also known as a probability sampling method). Within each stratum, a random sample is drawn, which ensures that each member of a stratum has an equal chance of being selected.
What are the different types of cluster sampling?: In all three types of cluster sampling, you start by dividing the population into clusters before drawing a random sample of clusters for your research. The next steps depend on the type of cluster sampling:

Single-stage cluster sampling: you collect data from every unit in the clusters in your sample.

Double-stage cluster sampling: you draw a random sample of units from within the clusters and then you collect data from that sample.

Multi-stage cluster sampling: you repeat the process of drawing random samples from within the clusters until you’ve reached a small enough sample to collect data from.
What are the advantages of cluster sampling?: Cluster sampling is generally more inexpensive and efficient than other sampling methods. It is also one of the probability sampling methods (or random sampling methods), which contributes to high external validity.
What is the first step in the research process?: Before you can conduct a research project, you must first decide what topic you want to focus on. In the first step of the research process, identify a topic that interests you. The topic can be broad at this stage and will be narrowed down later.

Do some background reading on the topic to identify potential avenues for further research, such as gaps and points of debate, and to lay a more solid foundation of knowledge. You will narrow the topic to a specific focal point in step 2 of the research process.
What are advantages of using a within-participant design in experimental research?: A within-participant design, also known as a repeated-measures design, is a type of experimental design where the same participants are assigned to multiple groups or conditions. Some advantages of this design are:

Increased statistical power: By using the same participants across multiple conditions, you can reduce the number of participants needed to detect a significant effect, which can lead to increased statistical power.

Reduced between-participants variability: Since each participant is tested multiple times, the variability between participants is reduced, which can result in more accurate and reliable estimates of the effect.

Better control over extraneous variables: By using the same participants across multiple conditions, you can better control for extraneous variables that might affect the outcome, as these variables are likely to be constant across conditions.

Increased precision: Within-participant designs can provide more precise estimates of the effect size, as the same participants are used across all conditions.

Reduced sample size: Depending on the research question and design, a within-participant design can require fewer participants than a between-participants design, which can reduce costs and increase efficiency.

It’s important to note that within-participant designs also have some limitations, such as increased risk of order effects (where the order of conditions affects the outcome) and carryover effects (where the effects of one condition persist into another condition).
What are the two groups in an experimental design?: In experimental design, the two main groups are:

Treatment group: This group is exposed to the manipulated independent variable, and the researcher measures the effect of the treatment on the dependent variable.

Control group: This group is not exposed to the manipulated independent variable (the variable being changed or tested). The control group serves as a reference point to compare the results of the experimental group to.

In other words, the control group is used as a baseline to compare with the treatment group, which receives the experimental treatment or intervention.

Two groups in experimental design example
You want to test a new medication to treat headaches. You randomly assign your participants to one of two groups:

The treatment group, who receives the new medication

The control group, who receives a placebo
Why is randomization important in an experimental design?: Randomization is a crucial component of experimental design, and it’s important for several reasons:

Prevents bias: Randomization ensures that each participant has an equal chance of being assigned to any condition, minimizing the potential for bias in the assignment process.

Controls for confounding variables: Randomization helps to distribute confounding variables evenly across conditions, reducing the risk of spurious correlations between the independent variable and the outcome.

Increases internal validity: By randomly assigning participants to conditions, you can increase the confidence that any observed differences between conditions are due to the independent variable and not some other factor.
Are data at the nominal level of measurement quantitative or qualitative?: Data at the nominal level of measurement is qualitative.

Nominal data is used to identify or classify individuals, objects, or phenomena into distinct categories or groups, but it does not have any inherent numerical value or order.

You can use numerical labels to replace textual labels (e.g., 1 = male, 2 = female, 3 = nonbinary), but these numerical labels are random and are not meaningful. You could rank the labels in any order (e.g., 1 = female, 2 = nonbinary, 3 = male). This means you can’t use these numerical labels for calculations.
What are the 4 principles of experimental design?: The four principles of experimental design are:

Randomization: This principle involves randomly assigning participants to experimental conditions, ensuring that each participant has an equal chance of being assigned to any condition. Randomization helps to eliminate bias and ensures that the sample is representative of the population.

Manipulation: This principle involves deliberately manipulating the independent variable to create different conditions or levels. Manipulation allows researchers to test the effect of the independent variable on the dependent variable.

Control: This principle involves controlling for extraneous or confounding variables that could influence the outcome of the experiment. Control is achieved by holding constant all variables except for the independent variable(s) of interest.

Replication: This principle involves having built-in replications in your experimental design so that outcomes can be compared. A sufficient number of participants should take part in the experiment to make sure that randomization allows for groups with a similar distribution. This increases the chance of detecting true differences.
What is an experimental design diagram?: An experimental design diagram is a visual representation of the research design, showing the relationships among the variables, conditions, and participants. It helps researchers to:

Clarify the research question and hypotheses

Identify the independent, dependent, and control variables

Determine the experimental conditions and treatment levels

Plan the sampling and data collection procedures

Visualize the flow of participants through the study
What is a pre-experimental design?: A pre-experimental design is a simple research process that happens before the actual experimental design takes place. The goal is to obtain preliminary results to gauge whether the financial and time investment of a true experiment will be worth it.

Pre-experimental design example
A researcher wants to investigate the effect of a new type of meditation on stress levels in college students. They decide to conduct a small pre-experiment with 10 college students who are already interested in meditation.

The students are asked to participate in a 30-minute meditation session once a week for 4 weeks. The students’ stress levels are measured before and after the meditation sessions with a standardized questionnaire.

The researcher compares the outcomes and notices significant differences in stress scores. They decide to move forward with a more costly and time-consuming experiment where they take into account all criteria for an experimental design (e.g., random assignment of participants, control group, controlling for extraneous variables).
Can you use nominal data in an ANOVA test?: You can’t use an ANOVA test if the nominal data is your dependent variable. The dependent variable needs to be continuous (interval or ratio data).

The independent variable for an ANOVA should be categorical (either nominal or ordinal data).
Does nominal data involve the use of variables that have been rank ordered?: No, nominal data can only be assigned to categories that have no inherent order to them.

Categorical data with categories that can be ordered in a meaningful way is called ordinal data.
What type of information does data at the nominal level describe?: Data at the nominal level of measurement typically describes categorical or qualitative descriptive information, such as gender, religion, or ethnicity.

Contrary to ordinal data, nominal data doesn’t have an inherent order to it, so you can’t rank the categories in a meaningful order.
What is the difference between nominal and ordinal data?: Nominal data and ordinal data are similar because they can both be grouped into categories. However, ordinal data can be ranked in a logical order (e.g., low, medium high), whereas nominal data can’t (e.g., male, female, nonbinary).
Is ordinal data qualitative or quantitative?: Ordinal data is usually considered qualitative in nature. The data can be numerical, but the differences between categories are not equal or meaningful. This means you can’t use them to calculate measures of central tendency (e.g., mean) or variability (e.g., standard deviation).
What is the difference between ordinal and interval data?: Ordinal data and interval data are similar because they can both be ranked in a logical order. However, for interval data, the differences between adjacent scores are equal.