External reliability etc. Face validity is when the tool appears to be measuring what it is supposed to measure with the content of test matching instructional objectives. Test-retest reliability is a measure of reliability obtained by administering the same test twice over a period of time to a group of individuals. Construct validity seeks the implications between a theoretical concept and a specific measuring device. It includes constructs like concepts, ideas, theories, etc. Parallel forms reliability is a measure of reliability obtained by administering different versions of an assessment tool both versions must contain items that probe the same construct, skill, knowledge base, etc.
So a measure of mood that produced a low test-retest correlation over a period of a month would not be a cause for concern. On the Rosenberg Self-Esteem Scale, people who agree that they are a person of worth should tend to agree that that they have a number of good qualities.
This is as true for behavioural and physiological measures as for self-report measures. For example, people might make a series of bets in a simulated game of roulette as a measure of their level of risk seeking. Like test-retest reliability, internal consistency can only be assessed by collecting and analyzing data. One approach is to look at a split-half correlation. This involves splitting the items into two sets, such as the first and second halves of the items or the even- and odd-numbered items.
Then a score is computed for each set of items, and the relationship between the two sets of scores is examined. For example, Figure 5. For example, there are ways to split a set of 10 items into two sets of five. Many behavioural measures involve significant judgment on the part of an observer or a rater. Inter-rater reliability is the extent to which different observers are consistent in their judgments. Validity is the extent to which the scores from a measure represent the variable they are intended to.
But how do researchers make this judgment? We have already considered one factor that they take into account—reliability. When a measure has good test-retest reliability and internal consistency, researchers should be more confident that the scores represent what they are supposed to. There has to be more to it, however, because a measure can be extremely reliable but have no validity whatsoever. Although this measure would have extremely good test-retest reliability, it would have absolutely no validity.
Here we consider three basic kinds: face validity, content validity, and criterion validity. Most people would expect a self-esteem questionnaire to include items about whether they see themselves as a person of worth and whether they think they have good qualities. So a questionnaire that included these kinds of items would have good face validity. The finger-length method of measuring self-esteem, on the other hand, seems to have nothing to do with self-esteem and therefore has poor face validity.
Although face validity can be assessed quantitatively—for example, by having a large sample of people rate a measure in terms of whether it appears to measure what it is intended to—it is usually assessed informally. Face validity is at best a very weak kind of evidence that a measurement method is measuring what it is supposed to.
It is also the case that many established measures in psychology work quite well despite lacking face validity. The Minnesota Multiphasic Personality Inventory-2 MMPI-2 measures many personality characteristics and disorders by having people decide whether each of over different statements applies to them—where many of the statements do not have any obvious relationship to the construct that they measure.
This ensures that your discussion of the data and the conclusions you draw are also valid. Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory.
Methods of estimating reliability and validity are usually split up into different types. The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods. To assess the validity of a cause-and-effect relationship, you also need to consider internal validity the design of the experiment and external validity the generalizability of the results.
Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words and awkward phrasing. See editing example. The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.
Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data. Ensure that your method and measurement technique are high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge. For example, to collect data on a personality trait, you could use a standardized questionnaire that is considered reliable and valid.
If you develop your own questionnaire, it should be based on established theory or findings of previous studies, and the questions should be carefully and precisely worded. To produce valid generalizable results, clearly define the population you are researching e. Ensure that you have enough participants and that they are representative of the population. Reliability should be considered throughout the data collection process.
Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved. For example, if you are conducting interviews or observations, clearly define how specific behaviours or responses will be counted, and make sure questions are phrased the same way each time. When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.
For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions. Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy. Have a language expert improve your writing. Correlation describes an association between variables: when one variable changes, so does the other.
A correlation is a statistical indicator of the relationship between variables. Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.
Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly. Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents.
A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects. Questionnaires can be self-administered or researcher-administered. Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.
Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions. A research design is a strategy for answering your research question. It defines your overall approach and determines how you will collect and analyze data. The priorities of a research design can vary depending on the field, but you usually have to specify:.
A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources. This allows you to draw valid , trustworthy conclusions. Quantitative research designs can be divided into two main categories:. Qualitative research designs tend to be more flexible.
Common types of qualitative design include case study , ethnography , and grounded theory designs. Correlation coefficients always range between -1 and 1. The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.
The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes. In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.
This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings e. Triangulation means using multiple methods to collect and analyze data on the same subject. By combining different types or sources of data, you can strengthen the validity of your findings. These are four of the most common mixed methods designs :.
Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame. But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples.
In multistage sampling , you can use probability or non-probability sampling methods. For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.
Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.
Scientists and researchers must always adhere to a certain code of conduct when collecting data from others. These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity. Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.
Both are important ethical considerations. You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos. You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.
Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.
Want to contact us directly? No problem. We are always here for you. Scribbr specializes in editing study-related documents. We proofread:. You can find all the citation styles and locales used in the Scribbr Citation Generator in our publicly accessible repository on Github. Frequently asked questions See all. 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. What is sampling? What is the difference between internal and external validity? What is experimental design?
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 are independent and dependent variables? 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. What is the difference between quantitative and categorical variables? What is the difference between discrete and continuous variables?
Discrete and continuous variables are two types of quantitative variables : Discrete variables represent counts e. Continuous variables represent measurable amounts e. What is a confounding variable? How do I decide which research methods to use?
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 mixed methods research? What is internal validity? What are threats to internal validity? What is the difference between a longitudinal study and a cross-sectional study?
What are the pros and cons of a longitudinal study? What is an example of a longitudinal study? How long is a longitudinal study? Why do a cross-sectional study? What are the disadvantages of a cross-sectional study? What is external validity? What are the two types of external validity? What are threats to external validity? Why are samples used in research? When are populations used in research?
What is sampling error? What is sampling bias? Why is sampling bias important? What are some types of sampling bias? How do you avoid sampling bias? What is probability sampling? What is non-probability sampling? Why are independent and dependent variables important? What is an example of an independent and a dependent variable? The type of soda — diet or regular — is the independent variable.
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