Validity and Reliability

1. Validity and Reliability

2. Validity • Is the translation from concept to operationalization accurately representing the underlying concept. • Does your variables measure what you think in abstract concepts. • This is more familiarly called Construct Validity. • empirical study with high construct validity would ensure the studied parameters are relevant to the research questions. • Without a valid design, valid scientific conclusions cannot be drawn

3. Types of construct validity • Translation validity (Trochims term) • Face validity • Content validity • Criterion-related validity • Predictive validity • Concurrent validity • Convergent validity • Discriminant validity

4. Translation validity • Is the operationalization a good reflection of the construct? • This approach is definitional in nature • assumes you have a good detailed definition of the construct • and you can check the operationalization against it. • Example software success. • Does your definition representative of SW success construct? • E.g. Application software is a software used to assist end users.

5. Face Validity • “On its face" does it seems like a good translation of the construct. • If the respondent knows what information we are looking for, • they can use that “context” to help interpret the questions and provide more useful, accurate answers

6. Content Validity • Check the operationalization against the relevant content domain for the construct. • For example, • a depression measure should cover the checklist of depression symptoms • World history – its content must include major histories from all continents or countries • Interface Usability – should include all valid usability measures: • learnability, efficiency, memorability (low cognitive overload), error recovery and the like

7. Criteria-Related Validity • Check the performance of operationalization against some criterion. • it compares the test with other measures or outcomes (the criteria) already held to be valid. • For example, employee selection tests are often validated against measures of job performance (the criterion), and • IQ tests are often validated against measures of academic performance (the criterion).

8. Predictive Validity • Assess the operationalization'sability to predict something it should theoretically be able to predict. • A high correlation would provide evidence for predictive validity • Examples • Measures of job applicant is supposed to measure the new applicant performance at work. • If the applicant performs well at his job when measured after one year, our applicant measurement is a good predictive measure. • Measures of Interface Usability can predict later SW utilization. • High correlation is an indication of measures of predictive validity

9. Concurrent Validity • Assess the operationalization'sability to distinguish between groups that it should theoretically be able to distinguish between. • It is similar to predictive validity but the measures are taken at the same time. • If measure of subordinate rating and supervisor rating positively correlate on job performance, it has high concurrent validity • Compares the results of two measures

10. Convergent Validity • Examine the degree to which the operationalization is similar to (converges on) other operationalizations that it theoretically should be similar to. • This compares two or more attributes of the same construct • To show the convergent validity of a test of arithmetic skills, • one might correlate the scores on a Math test with scores on other tests (e.g problem solving ability) that support to measure basic math ability, • The measure learnability should have high correction with efficiency, memorability, errors and satisfaction • All measures measure the same construct • There is also instrument measure convergence – If measure of Interview and questionnaire produce the same result to say the instruments are convergent

11. Discriminant Validity • Examine the degree to which the operationalization is not similar to (diverges from) other operationalizations that it theoretically should be not similar to. • A test of a concept is not highly correlated with other tests designed to measure theoretically different concepts. • Measure the overlap between two scales using a formula

12. Discriminate … • where  rxyis correlation between x and y,   • rxxis the reliability of x, and  • ryyis the reliability of y:  • a result less than .85 tells us existence of discriminant validity • > .85, the two constructs overlap greatly and they are likely measuring the same thing.

13. Discriminate … • Measuring the concept of Narcissism and Self-esteem • Narcissism is a term with a wide range of meanings, usually is used to describe some kind of problem in a person or group's relationships with self and others. • Self-esteem is a term in psychology to reflect a person's overall evaluation or appraisal of her or his own worth. • Self-esteem encompasses beliefs (for example, "I am competent", "I am worthy") and emotions such as triumph, despair, pride and shame • The Researchers show that their new scale measures Narcissism and not simply Self-esteem.

14. Discriminate … • First, we can calculate the Average Inter-Item Correlations within and between the two scales: • Narcissism — Narcissism: 0.47 • Narcissism — Self-esteem: 0.30 • Self-esteem — Self-esteem: 0.52 • We then use the correction for attenuation formula:

15. Discriminate … • Since 0.607 is less than 0.85, we can conclude that discriminant validity exists between the scale measuring narcissism and the scale measuring self-esteem. • e.g., a new measure of depression should … • have negative correlations with measures of “happiness” • have “minimal” correlations with tests of “physical health”,

16. Internal and External Validity

17. Internal Validity • Inferences are said to possess internal validity if a causal relation between two variables is properly demonstrated. •  A causal inference may be based on a relation when three criteria are satisfied: • the "cause" precedes the "effect" in time (temporal precedence), • the "cause" and the "effect" are related (covariation), and • there are no plausible alternative explanations for the observed covariation

18. Example - Internal • The researcher hypothesized that computer training will increase software usability • Training (IV) and usability (DV) • Positive correlation between the two indicates high internal validity. • This can be done with Spearman Rank Correlation or Pearson Correlation • Can be easily done with SPSS software

19. Internal … • In many cases, however, the magnitude of effects found in the dependent variable may not just depend on • variations in the independent variable, • the power of the instruments and statistical procedures used to measure and detect the effects, and • the choice of statistical methods • Other variables or circumstances uncontrolled for (or uncontrollable) may lead to additional or alternative explanations • (a) for the effects found and/or • (b) for the magnitude of the effects found.

20. Internal … • highly controlled true experimental designs, i.e • random selection, • random assignment to either the control or experimental groups, • reliable instruments, • reliable manipulation processes, and • safeguards against confounding factors may be the "gold standard" of scientific research. • the very strategies employed to control these factors may also limit the generalizability or External Validity of the findings.

21. External validity • external validity refers to the applicability of study or experimental results to realms beyond those under immediate observation. • Refers to generalizability of the research finding to other similar cases • Does the software solution for one case is also applicable to other similar cases in other organization or country. • Does the solution has wider application and audience or acceptance. We need that solution! • Researchers prize studies with external validity, since the results can be widely applied to other scenarios.

22. External … • External validity for a given study has several aspects: • whether the study generalizes to other subjects in the domain • whether there exist enough evidence and arguments to support the claimed generalizability • whether the study outcomes validate predicted theories

23. Reliability • Means "repeatability" or "consistency". • A measure is considered reliable if it would give us the same result over and over again (assuming that what we are measuring isn't changing!). • Measuring the same distance at different times should give the same result if the instrument (e.g. meter) is reliable. • There are four general classes of reliability estimates, each of which estimates reliability in a different way.

24. Types of Reliability Estimation • Inter-rater or inter-observer reliability • Is used to assess consistency of different raters • Test-retest reliability • Is used to assess the consistency of a measure from one time to another • Parallel-forms reliability • Is used to assess the consistency of the results of two tests constructed in the same way from the same content domain • Internal consistency reliability • Is used to assess the consistency of results across items within a test

25. Inter-Rater or Inter-Observer Reliability • Used to assess the degree to which different raters/observers give consistent estimates of the same phenomenon. • Establish reliability on pilot data or a subsample of data and retest often throughout. • For categorical data a X2 (Chaisqaure) can be used and for continuous data an R (regression) can be calculated.

26. Test-Retest Reliability • Used to assess the consistency of a measure from one time to another. • This approach assumes that there is no substantial change in the construct being measured between the two occasions. • The amount of time allowed between measures is critical. • The shorter the time gap, the higher the correlation; the longer the time gap, the lower the correlation

27. Parallel-Forms Reliability • Used to assess the consistency of the results of two tests constructed in the same way from the same content domain. • Create a large set of questions that address the same construct and then • randomly divide the questions into two sets and administer both instruments to the same sample of people.  • The correlation between the two parallel forms is the estimate of reliability.  • One major problem with this approach is that you have to be able to generate lots of items that reflect the same construct. 

28. Split Half Reliability • Collect your data with the instrument to measure your construct. • Split the data into halve and do correlation between the two data sets • Positive correlation indicates high reliability

29. Reliability and Validity

30. Research Ethics

31. Ethics – a definition “Research should avoid causing harm, distress, anxiety, pain or any other negative feeling to participants. Participants should be fully informed about all relevant aspects of the research, before they agree to take part” [1]

32. THIS IS A HYPOTHETICAL QUESTION - DO NOT ANSWER THIS ARE YOU HOMOSEXUAL? Research Methodology

33. Research questions – ethical or not? • Research may ask a taboo or personal question • What if you were asked if you are homosexual • How would you feel if you were asked this? • Would you feel awkward? • Would you lie? • Would you answer truthfully? • Why are we asking this question anyway? • Could we phrase the question better?

34. Pause for thought Is it morally correct to carry out research by any means whatsoever providing that the end result increases the sum of human knowledge or provides some tangible benefit to mankind? Does the end justify the means? DISCUSS

35. Ethics before Research begins • Inform all participants fully • What about children • Mentally deficient people • Those with poor language skills • Obtain consent • Define the ‘gatekeeper’ • Craft your research methods carefully • No distortion of the data

36. Ethics during Research DISCUSS • Field notes – what are they? • Do we need these? • Can we use these in our research? • Consent issues • Content issues • Moral issues • You have heard about a crime – do you report it? Research Methodoogy

37. Confidentiality of respondent data • How do we keep track of respondents? • Should we keep track of respondents? • How do we de-personalise gathered data? • If data are depersonalised, is it morally correct to reuse this data for a new research project? DISCUSS

38. Ethics after Research • Disposal of data – paper or digital? • Freedom of Information Act • Reuse of data – is this ethical? • Are there occasions where reuse of gathered data for another purpose is ok? • Requesting permission from respondents • Difficulties of contacting original respondents

39. Engineering and Ethics • Confidentiality of data • Ownership of research results • Consider research results • Is a cure for a disease as the direct result of research ‘good’? • Is the creation of a powerful bomb as the direct result of research ‘good’? • e.g. the atom bomb DISCUSS

40. Research Ethics Committees • Monitor ethical issues in research programmes Before during and after research • Makes decisions and enforces these • Gives researchers organisational support • Reassurance to researchers about moral issues related to a particular research project

41. Plagiarism • What is plagiarism? • How do we avoid plagiarism? • What are the dangers that plagiarism causes? • State some examples of plagiarism. DISCUSS

42. Summary - Ethics • Ethics are moral issues relating to the prior design, gathering and usage of data for research purposes • Think before, during and after • Consult gatekeepers and respondents • Never act alone – consult your supervisor if in doubt