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JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

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Page 1: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

JS MrunaliniLecturer

RAKMHSU

Data Collection Considerations:Validity, Reliability, Generalizability, and Ethics

Page 2: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Reliability and Validity

•Before using a measuring instrument it is important to be assured that it has acceptable levels of reliability and validity.

Page 3: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity

•How we know that the data we collect (test scores, for example) accurately gauge what we are trying to measure

•The degree to which scientific observations actually measure or record what they purport to measure

Page 4: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Types of Validity

•Criterion•Content•Construct

Page 5: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Criterion Validity

•Using another measuring instrument as a criterion to check the validity of the new one.

Page 6: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Content Validity

•The content of the instrument needs to be relevant to the concept of what is being measured.

•For instance, an instrument designed to rate depression that asked questions not relevant to depression would have problems with content validity.

Page 7: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Construct Validity

•Describes the extent to which an instrument measures a theoretical construct.

•This is the most difficult validity to establish.

•Basically this approach ties the instrument in with a theoretical perspective.

Page 8: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity•Linked to numerically based research •Convinces the researcher and the

researchee that the results of the research were right, accurate, and withstand scrutiny from other researchers

•Qualitative research –▫Trustworthiness (Kinchloe)▫Understanding (Wolcot)

Page 9: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity•Guba•Trustworthiness is established through

credibility, transferability, dependability, and confirmability

•Credibility▫Researcher’s ability to take into account all

of the complexities that present themselves in a study and to deal with patterns that re not easily explained

Page 10: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity

•Transferability▫Qualitative researchers’ beliefs that

everything they study is context bound and that the goal of their work is not to develop “truth” statements that can be generalized to larger groups.

▫Depends on whether the consumer of the research can identify with the setting

Page 11: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity

•Dependability▫The stability of the data

Triangulation of data tow or more methods Audit trail-examine the interpretive accounts

that are grounded in the language of the people studied and in their own words

Page 12: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity•Theoretical validity

▫The ability of the research report to explain the phenomenon that has been studied and described

•Generalizability▫Within the community that has been

studied (internal)▫To settings that were not studied by the

researcher (external)

Page 13: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity

•Evaluative validity▫Whether the researcher was objective

enough to report the data in an unbiased way

Page 14: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity In Action Research

•Action researchers need a system for judging the quality of their inquiries that is specifically tailored to their classroom-based research projects

•Democratic validity requires that multiple perspectives of all participants have been accurately represented

Page 15: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity In Action Research

•Outcome validity required that the action emerging from the study leads to the successful resolution of the problem▫Your study is valid if you learn something

that can be applied to subsequent research•Process validity requires that a study has

been conducted in a dependable and competent manner

Page 16: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Validity In Action Research

•Catalytic validity requires that the participants are moved to take action o the basis of their heightened understanding of the subject of the study

•Dialogic validity involved having a critical conversation with others about your research findings and practices

Page 17: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Establishing Validity

•Talk little, listen a lot•Record observations accurately•Begin writing early•Let readers see for themselves•Report fully•Be candid•Seek feedback•Write accurately

Page 18: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Reliability•The consistency with which our data

measures what we are attempting to measure over time

•Getting the same results over time•As you think about the results of your

inquiry, consider whether you think that your data would be consistently collected if the same technique were used over time

Page 19: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Reliability•The extent to which a measure reveals actual

differences in what is being measured. It is possible for a measuring instrument to show variance that has nothing to do with what it is actually supposed to be measuring.

•You could construct an IQ test that produces different scores but the difference in the scores is actually caused by the way the test is constructed rather than differences in the IQ of persons taking the test.

Page 20: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Sources of Error

•Sources of error in an instrument can be caused by:▫definitions are unclear/vague▫retrospective information, i.e., information

that is gathered by recall or recollection▫variations in collection conditions▫structure of the instrument

Page 21: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Testing Reliability

•There are four basic methods for testing the reliability of an instrument.▫Test-retest▫Alternate form▫split half▫observer reliability

Page 22: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Test-Retest

•Repeated administration of the instrument to the same people on separate occasions.

•This is done to test the reliability of the instrument - not during the actual research project that the instrument is going to be used in.

Page 23: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Alternate Form

•Variations of the form, alternates, are used on the same individuals and then compared.

Page 24: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Split Half

•Items in the instrument are divided into comparable segments in such a way that the different segments should have comparable scores.

•This type of test for reliability is used to screen the internal reliability, or consistency, of the instrument.

Page 25: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Observer Reliability

•Compares the administration of an instrument performed by different administrators who are trained to use the same protocol.

Page 26: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Correlation Coefficient

Page 27: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Correlation Coefficient

•A statistical procedure that measures the extent that the comparisons are similar or not.

•Used for testing the reliability of a survey, instrument, or test.

•Correlation coefficients range from 0.0 to 1.0.

Page 28: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Interpreting the Correlation Coefficient•The Correlation Coefficient can produce a

result resulting from 0.0 to 1.0.•1.0 is a perfect correlation and rarely, if

ever, achieved. Usually a coefficient of .80 or better suggest that the instrument is reasonably reliable.

•0.0 is the other end of the scale indicating that the instrument is not at all reliable.

Page 29: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Generalizability

•Refers to the applicability of findings to settings and context different from the one in which they were obtained.

•Can it be explained to a wider group of people

•Goal of action research is to understand what is happening in your school or your classroom and to determine what might improve things in that context

Page 30: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Personal Bias

•If we conduct our research in a systematic, disciplined manner, we will go a long way toward minimizing personal bias in our findings

•Challenge is to remain open and objective, to look, and to reflect on what we see

Page 31: JS Mrunalini Lecturer RAKMHSU Data Collection Considerations: Validity, Reliability, Generalizability, and Ethics

Ethics•How each of us treats the individuals with

whom we interact•There is little distance between teacher

researchers and their subjects—their students

•Qualitatively oriented action research is open ended

•Informed consent