Reliability and validity are two important aspects that every researcher has to consider while conducting research. It justifies whether the conditions, factors, and the assessment itself is accurate or not. If you are wondering, "What does reliable and validity mean in research?" you will get your desired answer from this blog.
If you explore the Internet carefully, you will come across questions like "What is reliability definition in research?" You will get an answer to it in this section.
Reliability in research refers to how consistently a method measures something. But what makes the research reliable? Well, it is reliable if the same result can be consistently achieved by using the same methods under the same circumstances.
For instance, when you estimate the temperature of a sample under identical conditions, you get the same results. Hence, we conclude that the data is reliable.
Now, we will take a look at validity in research. It denotes how accurately a method measures what it is intended to measure. When research shows high validity, it means we get results that correspond to real properties, characteristics, and variations in the physical or social world.
For instance, if a thermometer displays different temperatures each time under controlled conditions, it means that it is malfunctioning. Hence, the temperature is invalid.
So what is the difference between validity and reliability in research methodology? Well, reliability means how consistent the measurement is. On the other hand, validity suggests the accuracy of a measurement.
The four different types of reliability in research is as follows:
It measures the degree of agreement between different people assessing or observing the same aspect. This finds application when researchers collect data assigning scores, rating measures the degree of agreement between different people assessing the same thing. An example of inter-rater reliability in research would be checking for the progress of wound healing in patients.
Reliable research is aimed at minimizing subjectivity so that different researchers could replicate the same results. Once the measurement is achieved, one calculates the correlation between different sets of results. The test has high interrater reliability if all the researchers give similar ratings.
One might ask you, "Define test-retest reliability in research." It measures the consistency of results when you repeat the same test on the same sample at a different point in time. Various factors can influence results at different points in time: for example, different moods, or external conditions.
Thus, this reliability is used to assess how well a method resists the factors over time. For example, the test-retest reliability of the IQ questionnaire is low. For proper designing of tests, one has to formulate questions, statements and tasks in a way that would be influenced by external factors.
It measures the correlation between two equivalent versions of a test. One can use it when they have two distinct assessment tools designed to measure the same thing. An example would include formulating a set of questions to measure financial risk aversion in a group of respondents. Producing a larger set of questions to evaluate the same thing is the most common way of measuring parallel forms reliability.
Following this, the questions would be divided into two random sets. When the same group of respondents answers both sets, you calculate the correlation between the results. A high correlation between the two denotes high parallel forms reliability.
The last type of reliability is internal consistency reliability. It assesses the correlation between multiple items in a test that are intended to measure the same construct. For instance, if you want to measure customer satisfaction, you could create a questionnaire with statements that the consumers might agree or disagree with.
Measurement of internal consistency reliability can be carried out without repeating the test or involving other researchers. Hence, it is a good way of assessing reliability when you only have one data set.
There are two common methods of estimating it:
You calculate the correlation between the results of all possible pairs of items and then calculate the average. It is done for a set of measures designed to assess the same construct.
In this type of research, you randomly split a set of measures into two sets. You can calculate the correlation between the two sets of responses, after testing the entire set on the respondents.
In the next section, we will take a look at the different types of validity in research.
Similar to reliability in research, there are four main types of validity. When you have in-depth knowledge of reliability and validity, you can conduct research successfully.
This type of validity evaluates whether a measurement tool represents the thing we are interested in measuring. It plays a central role in establishing the overall validity of a method.
For example, we can measure 'depression' based on existing theory and psychological research. We also take into consideration the symptoms and indicators, like low energy levels and self-confidence.
Content validity is used to assess whether a test is representative of all aspects of the construct. The content of a test, survey, or measurement method must cover all relevant parts of the subject, in order to produce valid results.
For instance, a teacher conducts a calculus test in her class. If the test does not involve all the chapters, the test might not be an accurate indication of the students' comprehension of the subject.
This type of validity considers how suitable the content of a test seems to be on the surface. It is quite similar to content validity, but face validity is a more informal and subjective assessment. Unfortunately, it is often considered the weakest form of validity, as it is a subjective measure.
For example, it can be used to measure the consistency of people's dietary habits. Upon reviewing the survey items, you ask questions about each meal for three to four days of the week. The survey is a good representation of what you wish to test, so you consider it face validity.
The final type of validity is criterion validity. It evaluates how closely the results of your test correspond to the results of a different test. For estimation, you calculate the correlation between the results of your measurement and the results of the criterion measurement. A high correlation is a good indication that your test is measuring what it intends to measure.
For instance, a professor decides to test the English writing prowess of the students. He finds an existing test to assess how well the test measures the students' ability. He compares the results for the same group of students to the previous tests. If the outcome is similar, then the new test has high criterion validity.
In the next section, we will take a look at some of the validity and reliability in research methods examples.
We will take a look at various examples in this section.
Example 1 (Criterion Validity): A physics program designed to assess cumulative student learning throughout the major. The new measure could be correlated with the GRE subject or ETS field test. The higher the correlation between the established measure and new measure, the more faith researchers will have in the new assessment tool.
Example 2 (Construct Validity): Designing a rubric for history, one can test student's knowledge. If the measure lets us know that students lack knowledge in a certain area, then that assessment tool is providing meaningful information. It can be used to improve the course program requirements.
Example 3 (Content Validity): While assessing learning in the theatre department, it would not be sufficient to only cover issues related to acting. One should consider lighting, sound, functions of stage managers. The assessment should reflect the content area in its entirety.
Example 4 (Test-retest Reliability): An assessment of psychology could be given to a group of students twice, with a gap of one week. The obtained correlation coefficient would indicate the stability of the scores.
Example 5 (Parallel forms Reliability): Evaluating the reliability of a critical thinking assessment, you have to create a large set of items pertaining to critical thinking. Then, you should randomly split the questions up into two sets, which would represent the parallel forms.
Example 6 (Inter-rater Reliability): When different judges are evaluating the degree to which art portfolios meet certain standards, inter-rater reliability can be used. This is because judgments are subjective.
Hopefully, you have a better grasp of reliability and validity.
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