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The Pros and Cons of using SPSS as a Research Tool to explore Individual D ifferences






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The Pros and Cons of using SPSS as a Research Tool to explore Individual D ifferences. SPSS User Group Meeting York October 2011 Sophie von Stumm, University of Edinburgh. Aims of Individual Differences. A research area of psychology that aims to
The Pros and Cons of using SPSS as a Research Tool to explore Individual D ifferences

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Slide 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

Slide 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

Slide 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

Slide 4

Overview

  • Collect data

  • Enter and screen data (SPSS)

  • Identify latent traits

  • Develop comprehensive models of individual differences (AMOS)

Slide 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

Slide 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)

Slide 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

http://www.idealware.org/articles/fgt_online_surveys.php

Slide 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

Slide 9

Intelligence Tests

“jovial “ has a similar meaning as:

refreshing

scare

thickset

wise

jolly

Think of as many words as you can starting with a) pro and b) sub

A – B – C – K

Slide 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

Slide 11

Latent Trait Model

η

λ1

λ2

λ3

λ4

Y1

Y2

Y3

Y4

ε1

ε2

ε3

ε4

Slide 12

Investment

  • 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

Slide 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)

Slide 14

Analysis

Gf (fluid

intelligence)

?

Gc (crystallized

intelligence)

?

?

Need for Cognition

1. What is the relationship between gf and gc?2. What is the relationship between Need for Cognition and gc?

3. What is the relationship between gfand Need for Cognition?

Slide 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

Slide 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?

Slide 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

Slide 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…

Slide 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?

Slide 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 (!?)

Slide 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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