Summary of Results In your write up, you will need to give details of the following: 1. Summary of item analysis Need to include a summary table of the item data and give a clear account (with justification) of which items were removed at this stage 2. Summary of factor analysis Need to give a clear narrative overview of the process of exploratory factor analysis and justify reason for exclusion of items at this stage. You do not need to present every factor analysis you have run for this process just present the final factor structure. In other words, just present your set of retained items and give the factor loadings on your final factor(s). Interpret your factor(s) by looking at item content 3. Present your final measure and describe the scoring system Present your final scale and explain how it is scored. Calculate a new variable for the total score of your new scale. Give some descriptive stats to show how your measure total scores are distributed within your sample. Remember to provide a fully formatted paper copy of your scale as an appendix. 4. Reliability Report on the int
ernal consistency of your final scale, using Cronbachs alpha &/or item-total correlations 5. Validity You will to report different types of validity for your final scale. For some of these you will need to make sure you have calculated some total score variables one for your new scale and total scores for all other scales you have administered. Use the transform, compute command in SPSS to do this. Content validity What is your scale measuring and how comprehensively does it do this? How does your scale content compare with the original conceptual framework? Criterion validity (concurrent) Correlation with a gold-standard measure Construct Validity Convergent validity Correlations (strong/moderate/weak) with measures of related constructs. How do these correlations compare to your expectations? Discriminant validity Correlations with measures of unrelated constructs. How do these correlations compare to your expectations? Within your construct validity testing you can also use demographic data to look at age and sex differences in your construct across your sample.