The Pros and Cons of using SPSS as a Research Tool to explore Individual Differences
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Aims of Individual Differences. A research area of psychology that aims to Identify dimensions of individual differencesObserve dimensions and describe of individual differencesExplore causes of individual differencesExplore the long-term consequences of individual differences. Study of Indi
The Pros and Cons of using SPSS as a Research Tool to explor...

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1. The Pros and Cons of using SPSS as a Research Tool to explore Individual Differences SPSS User Group Meeting York October 2011 Sophie von Stumm, University of Edinburgh

2. Aims of Individual Differences A research area of psychology that aims to Identify dimensions of individual differences Observe dimensions and describe of individual differences Explore causes of individual differences Explore the long-term consequences of individual differences How and why people differ in their lifespan development/ affect, behaviour and cognitionHow and why people differ in their lifespan development/ affect, behaviour and cognition

3. Study of Individual Differences Focus on latent (unobservable) psychological constructs that underlie psychometric (observed) test scores, such as its core pillars are intelligence and personality traits

4. Overview Collect data Enter and screen data (SPSS) Identify latent traits Develop comprehensive models of individual differences (AMOS)

5. Data Experimental design: mostly survey/ questionnaires Two types of psychometric tests: maximum versus typical performance (ability versus personality) Testing settings: groups and individuals; supervision; timing

6. Test in Lab Paper-and-pencil mode Individual desks, supervised, timed Ideal for ability testing (battery of intelligence tests) Recruitment online (gumtree), flyers (job centers), press (psychology today) Data entry manually from test booklets (coding; labelling; editing data)

7. Test Online Adapt questionnaires to format of online survey tool Surveymonkey (www.surevymonkey.com) or Unipark (www.unipark.com) Important: data format; N; survey design There are many online survey tools today, depending on how much one pays one gets better features Even if data delivered in SPSS, data coding often off, missing values ? careful checkThere are many online survey tools today, depending on how much one pays one gets better features Even if data delivered in SPSS, data coding often off, missing values ? careful check

8. The Current Data Research project (Central Research Fund of the University of London) to test 200 adults from the London area on Intelligence Personality (investment) traits Bunch of demographics and other stuff

9. Intelligence Tests Ravens ? gf Nonsense, lettersets, gf Verb fluency, vocab, reasoning - gcRavens ? gf Nonsense, lettersets, gf Verb fluency, vocab, reasoning - gc

10. Fluid and Crystallized Intelligence Raven?s, letter sets, and nonsense syllogisms are tests of fluid intelligence, i.e. capacity for knowledge; pure reasoning power Verbal fluency, vocabulary, and verbal reasoning are tests of crystallized intelligence, i.e. knowledge possessed; information learned through experience

11. Latent Trait Model

12. Crystallized intelligence develops through the investment of fluid intelligence Investment is determined by personality traits ? Personality traits determine where, when and how people invest their ability Investment

13. Personality The Big Five: Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness ? Universal language of personality (very universal and very lumpy) More specific - Investment traits: ?the tendency to seek out, engage in, enjoy, and continuously pursue opportunities for effortful cognitive activity? Need for Cognition (Cacioppo & Petty, 1982), 18 item Likert scale (1 to 5 rating scales)

14. Analysis

15. Analysis Screen data for frequencies and errors; check labels and missing data codes Latent traits of crystallized and fluid intelligence (each derived from 3 indicator variables/ observed test scores) Build composite scores from z-scores Composite score for Need for Cognition; internal consistency Correlations and regression models

16. What have we learned? There are two factors of intelligence (gf and gc ? a little shaky) Need for Cognition correlates positively with both gf and gc but more so with gc Gf and Need for Cognition account for significant amounts of variance in gc; i.e. ability and investment affect knowledge possessed But what?s the relationship between gf and Need for Cognition in the regression model?

17. Limitations of SPSS Does not support Structural Equation Modelling (by and large, an extension method of regression models based on covariance matrix) Does not allows for simultaneous estimation of regression parameters and associations between independent (predictor) variables Does not provides model fit indices to evaluate how well data is represented Does not allows including latent traits without building composite scores or extracting factor regression scores

18. AMOS - Extension of SPSS Amos has an extremely user-friendly interface (no syntax writing needed if preferred) Coupled with SPSS, allowing for easy editing and switching between methods of analysis/ programs Let?s fit a regression model with latent traits?

19. Results Gf accounts directly for 79% of the variance in gc Gf accounts indirectly for an additional 3% of the variance in gc, as mediated by Need for Cognition Need for Cognition accounts for 0.2% of the variance (ns) in gc Evidence against the investment theory? Unlcear factor solution; poor model fit; cross-sectional designUnlcear factor solution; poor model fit; cross-sectional design

20. Conclusions SPSS is practical for (1) entering data manually and (2) editing data from various sources (e.g. online survey) SPSS allows for thorough exploration of data (frequencies; means; distributions; correlations) Extension programs (AMOS) enable comprehensive models of mechanisms of associations Investment is important for crystallized intelligence (!?)

21. Thank you Acknowledgements: Eva Zoubek ; the Central Research Fund of the University of London, and the ESRC von Stumm, S., Hell, B., & Chamorro-Premuzic, T. (2011). The hungry mind: Intellectual curiosity as third pillar of academic performance. Perspectives on Psychological Science, in press. von Stumm, S., Chamorro-Premuzic, T., Ackerman, P. L. (2011). Re-visiting intelligence-personality associations: Vindicating intellectual investment. In T. Chamorro-Premuzic, S. von Stumm, & A. Furnham (eds.), Handbook of Individual Differences. Chichester, UK: Wiley-Blackwell.


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