Introduction to Data Analysis - pages 280-287
10 important questions on Introduction to Data Analysis - pages 280-287
Cronbach’s alpha
In essence, both of the translation validity types attempt to assess the degree to which you accurately translated your construct into the operationalization.
1.Face validity
A validity that checks ‘on its face’ the operalization seems like a good translation of the construct.
2.Content validity
A check of the operationalization against the relevant content domain for the construct.
How is criterion-related validity different from translation validity?
In translation validity, the question is, how well did you translate the idea of the construct into its manifestation. No other measures comes into play. In criterion-related validity you usually make a prediction about how the operationalization will perform on some other measure, based on your theory of the construct. The difference among the criterion-related validity types are in the criteria they use as the standard for judgment.
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Convergent and Discriminant validity
Threats to construct validity Before we launch into a discussion of the most common threats to construct validity, let's recall what a threat to validity is.
Threats to construct validity: Mono-Method Bias
Threats to construct validity: Interaction of Testing and Treatment
Threats to construct validity: Restricted Generalizability Across Constructs
The "Social" Threats to Construct Validity I've set aside the other major threats to construct validity because they all stem from the social and human nature of the research endeavor.
1.3 Researcher Expectancies
How to improve statistical power?
·Increase the sample size
·Increase the level of significance.
·Increase the effect size
The ratio of the signal to the noise is you research is often called the effect size.
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