16 - Objective Personality Testing Flashcards
Objective Testing
- Stimulus is presented to a respondent who makes a constrained (close-ended) response
- Cheap, fast, reliable
- Subject is asked to guess what examiner is thinking
Ex: MMPI
Approaches to Developing Scales
- EXTERNAL APPROACH: empirical/data-driven, not on theory, based on external criterion
- Selects items in relation to criterion, regardless of content
- Pros: no knowledge needed, likely generalizable, poor face validity, some practical utility bc it’s related to criterion
- Cons: low content validity, criterion can change, shrinkage may occur (less predictive in second group), time consuming
- DEDUCTIVE APPROACH: theory-driven, dependent on construct (deduce items from the construct)
- Pros: fast, easy, short, content valid, face valid
- Cons: if theory is wrong, scale is wrong; may have poor discriminant validity; face validity may cause fake good/bad
- INDUCTIVE APPROACH: data-driven based on internal criterion; scales are made from pre-existing internal associations between items
- Theoretical knowledge is used to interpret findings
- Groups items into subscales
- FACTOR ANALYSIS
- Pros: simple/homogenous constructs, data reduction for ease of analysis, purer method bc it relies on natural structure, data “speak for themselves:
- Cons: may be difficulty to interpret if not simple, we make a lot of decisions (indeterminacy), FA is not straightforward, and SPSS is not pure
BEST APPROACH (HYBRID): write a large set of items based on theory (deductive approach), then pick items to keep based on criterion-related validity (external approach), and group items into subscales based on internal structure (INDUCTIVE APPROACH).
Factor Analysis
-Aims to identify the optimal latent structure (parsimonious) for a group of variables
- REFLECTIVE LATENT VARIABLES
- What is not common between variables is disregarded as errors
- Best to have at least 4 ordered categories to perform FA
- Does not always work
- When it does work, downfall is in cross-validation with another sample (this never works)
- Confirmatory FA: examines how well a hypothesized model fits in comparison to others
- Exploratory FA: no a-priori hypothesis about structure, just identifies a model that balances accuracy and parsimony
Decisions to make in Factor Analysis
- Method of factor extraction: FA or PCA?
- If FA, what kind? Confirmatory or exploratory?
- How many factors to retain
- Whether and how to rotate factors
- Model selection and interpretation (linear composite)
Principal Component Analysis
- Reduces data matrix to a small number of components that explain as much variance as possible
- FORMATIVE MODEL
- EIGENVECTORS = principal components; can be as many as there are variables in the model
- Does not assume reflective latent construct
- Always works
Retaining Factors after FA, PCA
- Kaiser-Guttman Criterion: factors with eigenvalues greater than 1
- Scree plot: “elbow” minus 1
Rotating Factors
-Goal: make them more interpretable and simple
- ORTHOGONAL: if uncorrelated
- OBLIQUE: if correlated
Flawed Nature of Self-Assessments
- Response bias: faking good/bad, social desirability
- Ambiguity of items
- Lack of insight (and omission errors)
Improvements:
- Use clear items that are behaviorally specific
- Provide frequent, timely feedback
- Self-testing (after a post-study delay)
- Review past performance
- Use peer assessment
- Target motivational basis of over-confidence
- Benchmark performance against others
- Introduce desirable difficulties to instruction (spread/slow learning to improve rates of retaining)
- Add in safety factors and buffer time
Optimizing vs Satisficing
Optimizing: giving their best possible answer (unbiased)
Steps:
- Interpret
- Retrieve
- Judge
- Respond
Satisficing: when pt is not motivated, so just gives a satisfactory response
- Weak: go through all 4 steps with less diligence - Strong: skip steps 2 and 3; superficial
Reasons: task difficulty, low respondent ability or motivation, when “no opinion” responses are an option