1 / 18

Intro to Stats

Intro to Stats. Reliability & Validity. Why discuss?. Limits all inferences that can be drawn from later tests If reliable and valid scale, can have confidence in findings If unreliable or invalid scale need to be very cautious. Measurement . Related measures & outcomes. Item 1. CONSTRUCT.

totie
Download Presentation

Intro to Stats

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Intro to Stats Reliability & Validity

  2. Why discuss? • Limits all inferences that can be drawn from later tests • If reliable and valid scale, can have confidence in findings • If unreliable or invalid scale need to be very cautious

  3. Measurement Related measures & outcomes Item 1 CONSTRUCT Item 2 Unrelated measures & outcomes Item 3

  4. Correlation coefficient • Captures how the value of one variable changes when the value of the other changes • Ranges from -1 to +1 • A Pearson correlation is based on continuous variables • Important to remember this is a relationship for a group, not each person/item • Reflects the amount of variability shared by two variables

  5. A correlation matrix

  6. Computations rxy = n ΣXY - ΣX ΣY [n ΣX2 – (ΣX)2][n ΣY2 - (ΣY)2] • rxy = correlation coefficient between x & y • n = size of sample • X = score on X variable • Y = score on Y variable

  7. Interpretations of Size .80 to 1.0 very strong .60 to .80 strong .40 to .60 moderate .20 to .40 weak .00 to .20 weak/none Relationships of .70 or stronger are generally considered acceptable in reliability analyses

  8. Reliability • The extent to which a scale measures construct consistently • Any measurement is an observed score • Reliability = true score/ (true score + error) • Less error = observed score is closer to true score (more reliable) • We never know the “true score”

  9. 1. Test-retest reliability • Extent to which a test is reliable over time • Calculate the correlation between two time points for each person • Items should relate positively *Sometimes you expect the scores to be different

  10. 2. Parallel forms reliability • Extent to which two forms of a test are equivalent • Calculate the correlation between the two forms of the test

  11. 3. Internal consistency reliability • Extent to which items are consistent with one another and represent one dimension • Correlation between individual scores and the total score • Also estimate correlations among the items • Important that all items use the same scale and be in the same direction • Cronbach’s alpha (α)

  12. Cronbach’s alpha α = k s2y – Σs2i k-1 s2y k = number of items S2y =variance associated with observed score Σ s2i =sum of all variances for each item

  13. SPSS output

  14. 4. Interrater reliability • Agreement between two raters ir = # of agreements # of possible agreements

  15. Validity • The extent to which the scale measures what it is intended to measure • Can be reliable without being valid

  16. 1. Content Validity • Items sample the universe of items for a construct • Can ask an expert (or several) whether items seem representative

  17. 2. Criterion Validity • Scale relates to other measures or behaviors in ways that would be expected • Concurrent • At same time • or predictive • Predicts later scores

  18. 3. Construct validity • Scale measures the underlying construct as intended • Relation to the behaviors that the construct represents

More Related