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.
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.
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Content validity and face validity are both types of measurement 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.
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.
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.
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.
The 12 main threats to internal validity are:
There are several ways to counter these threats to internal validity, for example, through randomization, the addition of control groups, and blinding.
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.
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.
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, 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:
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.
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.
These are three major mistakes to avoid when writing a research proposal:
A research proposal has three main parts: the introduction, the literature review, and the methods section.
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.
Ordinal is the second level of measurement. It has two main properties:
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.
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.
Content validity and predictive validity are two types of validity in research:
Content validity and criterion validity are two types of validity in research:
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.
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.
The interview type with the highest predictive validity differs based on the goal of the interview.
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.
Criterion validity measures how well a test corresponds to another measure, or criterion. The two types of criterion validity are concurrent and predictive 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.
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.
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:
Construct validity assesses how well a test reflects the phenomenon it’s supposed to measure. Construct validity cannot be directly measured; instead, you must gather evidence in favor of it.
This evidence comes in the form of other types of validity, including face validity, content validity, criterion validity, convergent validity, and divergent validity. The stronger the evidence across these measures, the more confident you can be that you are measuring what you intended to.
Construct validity evaluates how well a test reflects the concept it’s designed to measure.
Criterion validity captures how well a test correlates with another “gold standard” measure or outcome of the same construct.
Although both construct validity and criterion validity reflect the validity of a measure, they are not the same. Construct validity is generally considered the overarching concern of measurement validity; criterion validity can therefore be considered a form of evidence for construct validity.
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:
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).
The research process comprises five steps.
Once you’ve written your proposal, you may need your advisor’s approval of your plan before you can dive into the research process.
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.
Examples of action research papers are:
Topics for action research in education are:
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.
Simple random sampling is one of the most commonly used probability sampling methods.
The most important pros of simple random sampling are:
The most important cons of simple random sampling are:
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.
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.
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.
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.
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.
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:
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.
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.
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.
In experimental design, the two main groups are:
In other words, the control group is used as a baseline to compare with the treatment group, which receives the experimental treatment or intervention.
Randomization is a crucial component of experimental design, and it’s important for several reasons:
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.
The four principles of experimental design are:
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:
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.
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).
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).
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.
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.
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).
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).
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.
The variable age can be measured at the ordinal or ratio level.
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.