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Whether the same measurement process yields the same results. These two concepts are validity and reliability. Reliability and validity are important concepts in research. The everyday use of these terms provides a sense of what they mean For example, your friends are reliable.
Your opinion is valid. In research, their use is a little more complex. How can a researcher be sure that the data gathering instrument being used will measure what it is supposed to measure and will do this in a consistent manner? This is a question that can only be answered by examining the definitions for and methods of establishing the validity and reliability of a research instrument.
These two are very important aspects of research design. If the study were to be done a second time, would it yield the same results? If so, the data are reliable. If more than one person is observing behavior or some event, all observers should agree on what is being recorded in order to claim that the data are reliable.
Reliability also applies to individual measures. When people take a vocabulary test two times, their scores on the two occasions should be very similar. If so, the test can then be described as reliable.
To be reliable, an inventory measuring self-esteem should give the same result if given twice to the same person within a short period of time. The idea behind reliability is that any significant results must be more than a one-off finding and be inherently repeatable.
Other researchers must be able to perform exactly the same experiment, 2 under the same conditions and generate the same results. This will reinforce the findings and ensure that the wider scientific community will accept the hypothesis. Without this replication of statistically significant results, the experiment and research have not fulfilled all of the requirements of testability.
This prerequisite is essential to a hypothesis establishing itself as an accepted scientific truth. As it is already clear that Reliability is the degree to which an assessment tool produces stable and consistent results, there are several types of reliability;1. 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.
The scores from Time 1 and Time 2 can then be correlated in order to evaluate the test for stability over time. Example: A test designed to assess student learning in psychology could be given to a group of students twice, with the second administration perhaps coming a week after the first. The obtained correlation coefficient would indicate the stability of the scores. Parallel forms reliability is a measure of reliability obtained by administering differentversions of an assessment tool both versions must contain items that probe the same construct, skill, knowledge base, etc.
The scores from the two versions can then be correlated in order to evaluate the consistency of results across alternate versions. Example: If you wanted to evaluate the reliability of a critical thinking assessment, you might create a large set of items that all pertain to critical thinking and then randomly split the questions up into two sets, which would represent the parallel forms. Inter-rater reliability is a measure of reliability used to assess the degree to which different judges or raters agree in their assessment decisions.
Inter-rater reliability is useful because human observers will not necessarily interpret answers the same way; raters may disagree as to how well certain responses or material demonstrate knowledge of the construct or skill being assessed.
Example: Inter-rater reliability might be employed when different judges are evaluating the degree to which art portfolios meet certain standards.
Inter-rater reliability is especially useful when judgments can be considered relatively subjective. Thus, the use of this type of reliability would probably be more likely when evaluating artwork as opposed to math problems. Internal consistency reliability is a measure of reliability used to evaluate the degree to which different test items that probe the same construct produce similar results.
Average inter-item correlation is a subtype of internal consistency reliability. It is obtained by taking all of the items on a test that probe the same construct e. This final step yields the average inter-item correlation.
Split-half reliability is another subtype of internal consistency reliability. The process of obtaining split-half reliability is begun by "splitting in half" all items of a test that are intended to probe the same area of knowledge e. The entire test is administered to a group of individuals, the total score for each "set" is computed, and finally the split-half reliability is obtained by determining the correlation between the two totals "set" scores.
Having in mind that Validity refers to how well a test measures what it is purported to measure, there are a number of types of validity; Face ValidityFace Validity ascertains that the measure appears to be assessing the intended construct under study. The stakeholders can easily assess face validity.
Although this is not a very "scientific" type of validity, it may be an essential component in enlisting motivation of stakeholders. If the stakeholders do not believe the measure is an accurate assessment of the ability, they may become disengaged with the task. Example: If a measure of art appreciation is created all of the items should be related to the different components and types of art.
If the questions are regarding historical time periods, with no reference to any artistic movement, stakeholders may not be motivated to give their best effort or invest in this measure because they do not believe it is a true assessment of art appreciation. Construct ValidityConstruct Validity is used to ensure that the measure is actually measure what it is intended to measure i. Using a panel of "experts" familiar with the construct is a way in which this type of validity can be assessed.
The experts can examine the items and decide what that specific item is intended to measure. Students can be involved in this process to obtain their feedback. Example: A women's studies program may design a cumulative assessment of learning throughout the major.
The questions are written with complicated wording and phrasing. This can cause the test inadvertently becoming a test of reading comprehension, rather than a test of women's studies. It is important that the measure is actually assessing the intended construct, rather than an extraneous factor. Criterion-Related ValidityCriterion-Related Validity is used to predict future or current performance -it correlates test results with another criterion of interest.
Example: If a physics program designed a measure to assess cumulative student learning throughout the major. The new measure could be correlated with a standardized measure of ability in this discipline, such as an ETS field test or the GRE subject test. The higher the correlation between the established measure and new measure, the more faith stakeholders can have in the new assessment tool. Formative ValidityFormative Validity when applied to outcomes assessment it is used to assess how well a measure is able to provide information to help improve the program under study.
Example: When designing a rubric for history one could assess student's knowledge across the discipline. If the measure can provide information that students are lacking knowledge in a certain area, for instance the Civil Rights Movement, then that assessment tool is providing meaningful information that can be used to improve the course or program requirements.
Sampling ValiditySampling Validity similar to content validity ensures that the measure covers the broad range of areas within the concept under study. Not everything can be covered, so items need to be sampled from all of the domains. This may need to be completed using a panel of "experts" to ensure that the content area is adequately sampled.
Additionally, a panel can help limit "expert" bias i. Example: When designing an assessment of learning in the theatre department, it would not be sufficient to only cover issues related to acting.
Other areas of theatre such as lighting, sound, functions of stage managers should all be included. The assessment should reflect the content area in its entirety. Ways to improve validity1. Make sure your goals and objectives are clearly defined and put in operation. Expectations should be written down. Match your assessment measure to your goals and objectives.
Additionally, have the test reviewed. Get respondents involved; have the students look over the assessment for troublesome wording, or other difficulties. If possible, compare your measure with other measures, or data that may be available.
Relationship between reliability and validityIf data are valid, they must be reliable. If people receive very different scores on a test every time they take it, the test is not likely to predict anything. However, if a test is reliable, that does not mean that it is valid. For example, we can measure strength of grip very reliably, but 6 that does not make it a valid measure of intelligence or even of mechanical ability. Reliability is a necessary, but not sufficient, condition for validity.
Although they are independent aspects, they are also somewhat related. They allow us to gain firm and accurate results, as well as helping us to generalize our findings to a wider population and, in turn, apply research results to the world to improve aspects Differences between reliability and validityWhen designing an experiment, both reliability and validity are important. With an unreliable experiment, results are untrustworthy.
If replications produce different results, it is impossible to interpret the results; however, even a highly reliable experiment sometimes lacks validity. A poorly-designed experiment may produce results that are not directly relevant to the hypothesis, despite reliable replications.
Instrument is the general term that researchers use for a measurement device survey, test, questionnaire, etc. To help distinguish between instrument and instrumentation, consider that the instrument is the device and instrumentation is the course of action the process of developing, testing, and using the device. Instruments fall into two broad categories, researcher-completed and subject-completed, distinguished by those instruments that researchers administer versus those that are completed by participants. Researchers chose which type of instrument, or instruments, to use based on the research question. Examples are listed below:. Example usability problems include:.
Published on July 3, by Fiona Middleton. Revised on June 26, Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure. Table of contents Understanding reliability vs validity How are reliability and validity assessed? How to ensure validity and reliability in your research Where to write about reliability and validity in a thesis.
As mentioned before this research study was quantitative, with the research design illustrating the procedures followed and how the data was collected and.
Issues of Reliability and Validity. Things are slightly different, however, in Qualitative research. The very nature of qualitative research methods does not lend to statistical or empirical calculations of validity.
Validity is the extent to which a concept , conclusion or measurement is well-founded and likely corresponds accurately to the real world. The validity of a measurement tool for example, a test in education is the degree to which the tool measures what it claims to measure. In psychometrics , validity has a particular application known as test validity : "the degree to which evidence and theory support the interpretations of test scores" "as entailed by proposed uses of tests". It is generally accepted that the concept of scientific validity addresses the nature of reality in terms of statistical measures and as such is an epistemological and philosophical issue as well as a question of measurement. The use of the term in logic is narrower, relating to the relationship between the premises and conclusion of an argument.
The purpose of this paper is to shed light on the threats to quality in mixed methods accounting research, wherein quantitative and qualitative approaches are combined in data collection, analysis and interpretation. The paper is framed according to three perspectives. The authors first synthesize the threats to validity and reliability in quantitative and qualitative parts of mixed methods research using the quality standards of each; they then introduce an integrative framework of mixed methods research quality by Teddlie and Tashakkori.
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PDF | On Jan 1, , Roberta Heale and others published Validity and reliability in quantitative research | Find, read and cite all the research research studies are deficient as far as their method of research is concerned.