COMM 88 Lecture Notes - Lecture 7: Face Validity, Predictive Validity, Convergent Validity

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17 May 2018
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4/24/18
Measurement -- operationalizing variables (both IVs and DVs)
Assessing reliability
Inter-item reliability
You want all items to be indicators of the SAME variable
If so, you get a high internal consistency (high Cronbach's alpha)
A good “unidimensional” variable/concept
Example variable: candidate credibility
What if credibility involves more than just trustworthiness? Would need
multidimensional” scale
Trustworthiness ---- untrustworthy
Honest ---- dishonest
Sincere ---- insincere
Or
Knowledgeable ---- not knowledgeable
Experienced ---- inexperienced
Competent ---- incompetent
Cant put all 6 together because it will throw off credibility scale because they are really
two separate parts of credibility (someone can be knowledgeable but not honest)
Different aspects of credibility — the sub-scales are each unidimensional but
together are multidimensional
Make sure to evaluate reliability separately for each sub-scale
Assessing reliability
For measures using coders (e.g. behavioral observations):
Inter-coder reliability
Compare multiple coders
Intra-coder reliability
Compare multiple observations of the same coder
Validity of measurement
Does your measure really capture the concept you intend to be measuring?
Good fit of measure with concept
Subjective types of validity:
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Document Summary

Measurement -- operationalizing variables (both ivs and dvs) You want all items to be indicators of the same variable. If so, you get a high internal consistency (high cronbach"s alpha) Cant put all 6 together because it will throw off credibility scale because they are really two separate parts of credibility (someone can be knowledgeable but not honest) Different aspects of credibility the sub-scales are each unidimensional but together are multidimensional. Make sure to evaluate reliability separately for each sub-scale. For measures using coders (e. g. behavioral observations): Compare multiple observations of the same coder. The measure looks/sounds good on the face of it . The measure captures the full range of meaning/dimensions of the concept. The measure is shown to predict (not cause) scores on an appropriate future measure. Ex: sat scores (your potential to achieve) college gpa (your achievement) The measure is shown to get same result as another measure of the same thing.

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