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Review of statistics related to measurement

Review of statistics related to measurement. Chong Ho Yu, Ph.D. Likert scale. Strongly agree ---- Strongly disagree No equal spacing, not precise measurement It could under-estimate or over-estimate A single item is ordinal

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Review of statistics related to measurement

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  1. Review of statistics related to measurement Chong Ho Yu, Ph.D.

  2. Likertscale • Strongly agree ---- Strongly disagree • No equal spacing, not precise measurement • It could under-estimate or over-estimate • A single item is ordinal • But if you combine many items together to form an continuous scale, the errors cancel out each other.

  3. Use single 7-point Likertscales • In SPSS the output shows the number first. • The software app will output some numbers for you and the numbers may look good. • Correlation coefficient: .563 (Max. = 1, .563 is moderately high) • Significance: Yes (We will discuss correlation and significance later).

  4. Use a single 7-point Likert scale to predict another one • JMP shows the graph first • But when you look at the scatterplot, you will not see a clear associational pattern • Why? The scale is too narrow (1-7).

  5. Use composite (sum) scores • The correlational pattern is clear. • Why? The composite score forms a wider distribution (7 X 8 = 56; 7 X 7 = 49).

  6. Use composite (sum) scores • It works if and only if there are no missing data. If there are “holes” in the spreadsheet, you can use the average scores instead. The result will be the same. • When averaging the scores, the formula will not take the missing cell into the equation. For example, suppose there are five items. If the scores are 4, 5, missing, 3, 1, the sum of 4, 5, 3, 1 will be divided by 4, not 5.

  7. Example: DUREL • Duke University Religion Index (DUREL): A brief measure of religiosity • Five items and three dimensions: • Organizational religious activity: Attending church • Non-organizational religious activity: Prayer, meditation • Intrinsic religiosity: Subjective

  8. What are these items? Ordinal? Continuous?

  9. Chi-square • The Chi-square test is an important statistics because many assessment procedures are based upon Chi-square e.g. confirmatory factor analysis, item fitness…etc. • But the Chi-square result is affected by the sample size and degrees of freedom. • Usually the psychometric report is adjusted by the degrees of freedom e.g. Chi-square/degree of freedom.

  10. Assignment • Download the file “resilence.jmp” from http://www.creative-wisdom.com/teaching/480/chapter6 • Run a regression analysis using Fit Y by X from Analyze. You can choose ANY single item as the response variable and ANY single item as the factor (independent variable). Can you see any pattern? • Factor 1 sum is the sum of nine items whereas Factor 2 sum is the sum of 5 items. Now use Factor 1 sum as Y and factor 2 sum as X. Can you see a clear pattern? • Do the same thing using Factor 1 average as Y and factor 2 average as X. Can you see a clear pattern? • Copy and paste all graphs into a Word document, and then upload it to Sakai.

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