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CHAPTER 12

CHAPTER 12. Personality Assessment: An Overview. Personality and Personality Assessment Defined. Traits, Types, and States Personality states : What I do in a particular situation (I am angry because he insults me when I lost my financial aids)

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CHAPTER 12

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  1. CHAPTER 12 Personality Assessment: An Overview

  2. Personality and Personality Assessment Defined • Traits, Types, and States • Personality states : What I do in a particular situation (I am angry because he insults me when I lost my financial aids) • Personality traits: This trait is one of my attributes or characters (bad-tempered, impatience ) • Personality types: this is what you are!!!! (e.g. You are a psychopath; you will rape and kill people no matter what)

  3. Personality state and Situational factors • Milgram study • Zimbardo prison experiment • How could many Germans who were Christians support the Nazi party during WWII? • Why did many Chinese people persecute their parents during the Cultural Revolution in Communist China? • Sonmez, S.; Apostolopoulos, Y; Yu, C. H.; Mattila, A., & Yu, L. C. (2006). Binge drinking and casual sex on spring break. Annals of Tourism Research, 33, 895-917.

  4. John Holland

  5. Myers-Briggs Type Indicator • http://www.humanmetrics.com/cgi-win/jtypes2.asp

  6. Big Five

  7. Some Basic Questions • Who? • The self as the primary referent • Another person as the referent • The cultural background of assessees • Reliability of self-report data (Cook and Campbell 1979) • People tend to report what they believe the researcher expects to see • People report what reflects positively on their own abilities, knowledge, beliefs, or opinions.

  8. Tools of Test Development • Data Reduction Methods • PCA • Factor analysis • cluster analysis • Criterion Groups • The MMPI • The MMPI-2 • The MMPI-A • The MMPI and its Revisions in Perspective

  9. Discriminant analysis (DA) • There is a test procedure that is similar to cluster analysis: Discriminant analysis (DA) • But in DA both the number of groups (clusters) and their content are known. Based on the known information (examples), you assign the new or unknown observations into the existing groups

  10. Cluster analysis • Example-based vs. rule-based • Three types: • Two-step clustering • K-mean clustering • Hierarchical clustering • Three matrices • S = Similarity matrix • D = Dissimilarity matrix • P = Proximity matrix • Warning: If there are too many missing data, no clustering algorithm can yield good results.

  11. K-mean • In a two-dimensional data set (X and Y only), you can “eye-ball” the graph to assign clusters. But it may be subjective. • When there are more than two dimensions, assigning by looking is almost impossible.

  12. K-mean • Select K points as the initial centroids • Assign points to different centroids based upon the P matrix (proximity) • Re-evaluate the centroid of each group • Repeat Step 2 and 3 until the best solution emerges (the centers are stable)

  13. Two Distinct Groups • Compared to Christians in Cluster 1, Christians in Cluster 2 are • Higher on personal focus values, such as self-direction, stimulation, and hedonism, achievement, and power • Lower on social focus values, such as conformity, tradition, benevolence, and universalism. Security is considered self-focused • Cluster 1: Personal-focused Christians • Cluster 2: Social-focused Christians

  14. Cluster Membership and Religious Activities

  15. For example, more social-focused Christians read one more Christian books (larger red area) than did personal-focused Christians.

  16. More social-focused Christians shared their faith with others (larger red area) than did personal-focused Christians.

  17. Cause and effect? • However, we cannot determine the cause-and-effect relationship based on the data alone. • Do Christians read more Christian books and do other things because their personality is more social-focused, or are they social-focused because they do those things?

  18. Hierarchical clustering • grouping/matching people like what e-harmony and Christian-Mingle do. Who is the best match? Who is the second best? The third…etc.

  19. Hierarchical clustering • Top-down or Divisive: start with one group and then partition the data step by step according to the matrices • Bottom-up or Agglomerative: start with one single piece of datum and then merge it with others to form larger groups

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