Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance
Abstract
:1. Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance
1.1. Fluid Intelligence and Scholastic Performance
1.2. Personality and Scholastic Performance
1.3. Intelligence and the Domains of the FFM
1.4. Interaction Hypotheses
1.5. Aims of the Study
2. Method
2.1. Sample and Procedure
2.2. Measures
2.2.1. Scholastic Performance
2.2.2. Figural Reasoning
2.2.3. Domains of the FFM
2.3. Statistical Analyses
3. Results
3.1. Missing Data Analysis
3.2. Correlational Analyses
3.3. Latent Moderated Structural Equation Modeling
Variable | Bivariate Correlations | Descriptive Statistics | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | M | SD | ||||
1. Gender | --- | 1.52 | 0.50 | |||||||||||
2. Age | 0.01 | --- | 15.35 | 1.51 | ||||||||||
3. Figural reasoning | < 0.01 | 0.14 ** | (0.97) | 46.85 | 12.15 | |||||||||
4. Neuroticism | 0.14 ** | 0.17 ** | < 0.01 | (0.83) | 35.28 | 7.76 | ||||||||
5. Extraversion | 0.03 | −0.12 ** | 0.01 | −0.37 *** | (0.81) | 42.49 | 6.60 | |||||||
6. Openness | 0.02 | 0.10 * | 0.09 * | −0.03 | 0.14 ** | (0.67) | 41.78 | 5.70 | ||||||
7. Agreeableness | 0.01 | 0.10 * | −0.01 | 0.38 *** | −0.16 ** | −0.07 | (0.63) | 29.03 | 5.16 | |||||
8. Conscientiousness | 0.07 * | −0.08 * | −0.03 | −0.41 *** | 0.16 ** | 0.20 *** | −0.30 *** | (0.82) | 38.34 | 6.30 | ||||
9. Math grades | −0.06 | 0.07 | 0.31 *** | −0.12 ** | 0.05 | 0.21 *** | −0.04 | 0.13 ** | --- | 96.47 | 33.10 | |||
10. Chinese grades | 0.23 ** | −0.13 ** | 0.11 ** | −0.07 | 0.13 ** | 0.18 *** | −0.09 * | 0.14 ** | 0.54 *** | --- | 97.52 | 19.19 | ||
11. English grades | 0.13 ** | 0.14 ** | 0.31 *** | −0.03 | 0.06 | 0.28 *** | −0.01 | 0.11 * | 0.67 *** | 0.61 *** | --- | 98.07 | 32.56 |
School Subject | Model | χ2 (df) | RMSEA [90% CI] | CFI | SRMR | BIC | Chi-square Difference Test (TRd) |
---|---|---|---|---|---|---|---|
Grade Composite | Step 1 | 817.32 (208) | 0.059 [0.055,0 .063] | 0.920 | 0.057 | 40731.53 | |
Step 2 | --- | --- | --- | --- | 40706.37 | ∆ χ2 (df) = 9.69 (1), p < 0.001 | |
Chinese | Step 1 | 614.72 (168) | 0.056 [0.052, 0.061] | 0.933 | 0.054 | 27303.51 | |
Step 2 | --- | --- | --- | --- | 27289.58 | ∆ χ2 (df) = 6.27 (1), p < 0.05 | |
Math | Step 1 | 632.28 (168) | 0.057 [0.053, 0.062] | 0.931 | 0.055 | 28100.34 | |
Step 2 | --- | --- | --- | --- | 28085.67 | ∆ χ2 (df) = 11.87 (1), p < 0.001 | |
English | Step 1 | 638.98 (168) | 0.058 [0.053, 0.063] | 0.930 | 0.055 | 28055.25 | |
Step 2 | --- | --- | --- | --- | 28041.59 | ∆ χ2 (df) = 11.03 (1), p < 0.001 |
Enter Variables | Chinese Grade | Math Grade | English Grade | Grade Composite | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
ß | R2 | ΔR2 | ß | R2 | ΔR2 | ß | R2 | ΔR2 | ß | R2 | ΔR2 | |
Step 1 | 0.15 *** | 0.05 * | 0.17 *** | 0.08 ** | 0.21 *** | 0.10 *** | 0.25 *** | 0.12 *** | ||||
Gender | 0.23 *** | −0.05 | 0.13 *** | 0.13 ** | ||||||||
Age | −0.14 ** | 0.04 | 0.10 ** | 0.06 | ||||||||
Figural reasoning | 0.12 ** | 0.29 *** | 0.26 *** | 0.29 *** | ||||||||
Neuroticism | −0.03 | −0.12 * | −0.08 | −0.09 | ||||||||
Extraversion | 0.04 | −0.02 | −0.02 | −0.01 | ||||||||
Openness | 0.19 *** | 0.23 *** | 0.31 *** | 0.33 *** | ||||||||
Agreeableness | −0.02 | 0.11 | 0.09 | 0.09 | ||||||||
Conscientiousness | 0.05 | 0.12 * | 0.07 | 0.09 * | ||||||||
Step 2 | 0.22 *** | 0.07 ** | 0.24 *** | 0.07 ** | 0.25 *** | 0.04 * | 0.36 *** | 0.11 *** | ||||
Gender | 0.22 *** | −0.05 | 0.13 *** | 0.09 ** | ||||||||
Age | −0.15 *** | 0.01 | 0.09 * | 0.02 | ||||||||
Figural reasoning | 0.12 ** | 0.31 *** | 0.28 *** | 0.25 *** | ||||||||
Neuroticism | −0.04 | −0.13 * | −0.08 | −0.08 # | ||||||||
Extraversion | 0.03 | −0.03 | −0.03 | −0.02 | ||||||||
Openness | 0.25 *** | 0.28 *** | 0.35 *** | 0.28 *** | ||||||||
Agreeableness | 0.01 | 0.14 * | 0.11 # | 0.09 # | ||||||||
Conscientiousness | 0.06 | 0.13 * | 0.08 # | 0.08 # | ||||||||
Figural reasoning *Openness | −0.27 *** | −0.28 *** | −0.26 *** | −0.27 *** |
4. Discussion
4.1. Fluid Intelligence
4.2. Domains of the FFM
4.3. Moderation
4.4. Limitations of the Current Study
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Appendix
Enter Variables | Chinese Grade | Math Grade | English Grade | ||||||
---|---|---|---|---|---|---|---|---|---|
ß | R2 | ΔR2 | ß | R2 | ΔR2 | ß | R2 | ΔR2 | |
Step 1 | 0.15 *** | 0.16 *** | 0.18 *** | ||||||
Gender | 0.20 *** | −0.05 | 0.14 *** | ||||||
Age | −0.21 *** | −0.02 | 0.06 # | ||||||
Figural reasoning | 0.14 ** | 0.31 *** | 0.28 *** | ||||||
Neuroticism | −0.01 | −0.06 | −0.02 | ||||||
Extraversion | 0.05 | 0.01 | −0.01 | ||||||
Openness | 0.15 *** | 0.16 *** | 0.23 *** | ||||||
Agreeableness | −0.05 | 0.03 | 0.03 | ||||||
Conscientiousness | 0.07 # | 0.11 ** | 0.08 * | ||||||
Step 2 | 0.17 *** | 0.02 * | 0.18 *** | 0.02 * | 0.20 *** | 0.02 * | |||
Gender | 0.20 *** | −0.05 | 0.14 *** | ||||||
Age | −0.22 *** | −0.02 | 0.05 | ||||||
Figural reasoning | 0.12 ** | 0.29 *** | 0.27 *** | ||||||
Neuroticism | −0.01 | −0.06 | −0.03 | ||||||
Extraversion | 0.05 | 0.01 | −0.01 | ||||||
Openness | 0.16 *** | 0.17 *** | 0.23 *** | ||||||
Agreeableness | −0.05 | 0.03 | 0.03 | ||||||
Conscientiousness | 0.08 # | 0.12 ** | 0.09 * | ||||||
Figural reasoning * Openness | −.016 *** | −0.17 *** | −0.16 *** |
References
- Ceci, S.J.; Williams, W.M. Schooling, intelligence, and income. Am. Psychol. 1997, 52, 1051–1058. [Google Scholar] [CrossRef]
- Deary, I.J.; Strand, S.; Smith, P.; Fernandes, C. Intelligence and educational achievement. Intelligence 2007, 35, 13–21. [Google Scholar] [CrossRef]
- Gottfredson, L.S. g: Highly general and highly practical. In The General Factor of Intelligence: How General is it; Erlbaum: Mahwah, NJ, USA, 2002; pp. 331–380. [Google Scholar]
- Kuncel, N.R.; Hezlett, S.A.; Ones, D.S. Academic performance, career potential, creativity, and job performance: Can one construct predict them all? J. Personal. Soc. Psychol. 2004, 86, 148–161. [Google Scholar] [CrossRef] [PubMed]
- Bratko, D.; Chamorro-Premuzic, T.; Saks, Z. Personality and school performance: Incremental validity of self-and peer-ratings over intelligence. Personal. Individ. Differ. 2006, 41, 131–142. [Google Scholar] [CrossRef]
- Greene, B.A.; Miller, R.B.; Crowson, H.M.; Duke, B.L.; Akey, K.L. Predicting high school students’ cognitive engagement and achievement: Contributions of classroom perceptions and motivation. Contemp. Educ. Psychol. 2004, 29, 462–482. [Google Scholar] [CrossRef]
- Laidra, K.; Pullmann, H.; Allik, J. Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school. Personal. Individ. Differ. 2007, 42, 441–451. [Google Scholar] [CrossRef]
- Lu, L.; Weber, H.S.; Spinath, F.M.; Shi, J. Predicting school achievement from cognitive and non-cognitive variables in a Chinese sample of elementary school children. Intelligence 2011, 39, 130–140. [Google Scholar] [CrossRef]
- Spinath, B.; Harald Freudenthaler, H.; Neubauer, A.C. Domain-specific school achievement in boys and girls as predicted by intelligence, personality and motivation. Personal. Individ. Differ. 2010, 48, 481–486. [Google Scholar] [CrossRef]
- Spinath, B.; Spinath, F.M.; Harlaar, N.; Plomin, R. Predicting school achievement from general cognitive ability, self-perceived ability, and intrinsic value. Intelligence 2006, 34, 363–374. [Google Scholar] [CrossRef]
- Steinmayr, R.; Bipp, T.; Spinath, B. Goal orientations predict academic performance beyond intelligence and personality. Learn. Individ. Differ. 2011, 21, 196–200. [Google Scholar] [CrossRef]
- Poropat, A.E. A meta-analysis of the five-factor model of personality and academic performance. Psychol. Bull. 2009, 135, 322–328. [Google Scholar] [CrossRef] [PubMed]
- Richardson, M.; Abraham, C.; Bond, R. Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychol. Bull. 2012, 138, 363–387. [Google Scholar] [CrossRef] [PubMed]
- Ackerman, P.L.; Heggestad, E.D. Intelligence, personality, and interests: Evidence for overlapping traits. Psychol. Bull. 1997, 121, 219–245. [Google Scholar] [CrossRef] [PubMed]
- Dweck, C.; Molden, D.C. Self-Theories. In Handbook of Competence and Motivation; The Guilford Press: New York, NY, USA, 2005; pp. 122–140. [Google Scholar]
- Tong, N.; Zhao, R.; Yang, X. An investigation into the current ideology of middle school students. Chin. Educ. 1985, 17, 6–21. [Google Scholar]
- Tweed, R.G.; Lehman, D.R. Learning considered within a cultural context: Confucian and Socratic approaches. Am. Psychol. 2002, 57, 89–99. [Google Scholar] [CrossRef] [PubMed]
- Furnham, A.; Monsen, J. Personality traits and intelligence predict academic school grades. Learn. Individ. Differ. 2009, 19, 28–33. [Google Scholar] [CrossRef]
- Ziegler, M.; Cengia, A.; Mussel, P.; Gerstorf, D. Openness as a Buffer Against Cognitive Decline: The Openness-Fluid-Crystallized-Intelligence (OFCI) Model Applied to Late Adulthood. Psychol. Aging 2015, 30, 573–588. [Google Scholar] [CrossRef] [PubMed]
- Ziegler, M.; Danay, E.; Heene, M.; Asendorpf, J.; Bühner, M. Openness, fluid intelligence, and crystallized intelligence: Toward an integrative model. J. Res. Personal. 2012, 46, 173–183. [Google Scholar] [CrossRef]
- Cattell, R.B. Theory of fluid and crystallized intelligence: A critical experiment. J. Educ. Psychol. 1963, 54, 1–22. [Google Scholar] [CrossRef]
- Cattell, R.B. Intelligence: Its Structure, Growth and Action; North-Holland: Oxford, UK, 1987. [Google Scholar]
- McGrew, K.S. CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence 2009, 37, 1–10. [Google Scholar] [CrossRef]
- Primi, R.; Ferrão, M.E.; Almeida, L.S. Fluid intelligence as a predictor of learning: A longitudinal multilevel approach applied to math. Learn. Individ. Differ. 2010, 20, 446–451. [Google Scholar] [CrossRef]
- Ackerman, P.L.; Beier, M.E.; Boyle, M.D. Individual differences in working memory within a nomological network of cognitive and perceptual speed abilities. J. Exp. Psychol. Gen. 2002, 131, 567–589. [Google Scholar] [CrossRef] [PubMed]
- Blair, C. How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. Behav. Brain Sci. 2006, 29, 109–160. [Google Scholar] [CrossRef] [PubMed]
- Colom, R.; Garcıa-López, O. Sex differences in fluid intelligence among high school graduates. Personal. Individ. Differ. 2002, 32, 445–451. [Google Scholar] [CrossRef]
- Furnham, A.; Forde, L.; Cotter, T. Personality and intelligence. Personal. Individ. Differ. 1998, 24, 187–192. [Google Scholar] [CrossRef]
- McGrew, K.S. CHC COG-ACH Correlates Meta-Analysis; Institute for Applied Psychometrics: Minneapolis, MN, USA, 2008. [Google Scholar]
- Taub, G.E.; Floyd, R.G.; Keith, T.Z.; McGrew, T.Z. Effects of general and broad cognitive abilities on mathematics achievement. Sch. Psychol. Quart. 2008, 23, 187–198. [Google Scholar] [CrossRef]
- Chamorro-Premuzic, T.; Furnham, A. Intellectual competence and the intelligent personality: A third way in differential psychology. Rev. Gen. Psychol. 2006, 10, 251–267. [Google Scholar] [CrossRef]
- Chamorro-Premuzic, T.; Furnham, A. Personality, intelligence and approaches to learning as predictors of academic performance. Personal. Individ. Differ. 2008, 44, 1596–1603. [Google Scholar] [CrossRef]
- Di Fabio, A.; Busoni, L. Fluid intelligence, personality traits and scholastic success: Empirical evidence in a sample of Italian high school students. Personal. Individ. Differ. 2007, 43, 2095–2104. [Google Scholar] [CrossRef]
- Furnham, A.; Chamorro-Premuzic, T. Personality and intelligence as predictors of statistics examination grades. Personal. Individ. Differ. 2004, 37, 943–955. [Google Scholar] [CrossRef]
- Heaven, P.C.; Ciarrochi, J. When IQ is not everything: Intelligence, personality and academic performance at school. Personal. Individ. Differ. 2012, 53, 518–522. [Google Scholar] [CrossRef]
- Noftle, E.E.; Robins, R.W. Personality predictors of academic outcomes: Big five correlates of GPA and SAT scores. J. Personal. Soc. Psychol. 2007, 93, 116–130. [Google Scholar] [CrossRef] [PubMed]
- De Raad, B.; Schouwenburg, H.C. Personality in learning and education: A review. Eur. J. Personal. 1996, 10, 303–336. [Google Scholar] [CrossRef]
- O’Connor, M.C.; Paunonen, S.V. Big Five personality predictors of post-secondary academic performance. Personal. Individ. Differ. 2007, 43, 971–990. [Google Scholar] [CrossRef]
- Digman, J.M. Personality structure: Emergence of the five-factor model. Annu. Rev. Psychol. 1990, 41, 417–440. [Google Scholar] [CrossRef]
- Barrick, M.R.; Mount, M.K. FFM personality dimensions and job performance: A meta-analysis. Pers. Psychol. 1991, 44, 1–26. [Google Scholar] [CrossRef]
- Steinmayr, R.; Spinath, B. Sex differences in school achievement: What are the roles of personality and achievement motivation? Eur. J. Personal. 2008, 22, 185–209. [Google Scholar] [CrossRef]
- Major, D.A.; Turner, J.E.; Fletcher, T.D. Linking proactive personality and FFM to motivation to learn and development activity. J. Appl. Psychol. 2006, 91, 927–935. [Google Scholar] [CrossRef] [PubMed]
- De Feyter, T.; Caers, R.; Vigna, C.; Berings, D. Unraveling the impact of FFM personality traits on academic performance: The moderating and mediating effects of self-efficacy and academic motivation. Learn. Individ. Differ. 2012, 22, 439–448. [Google Scholar] [CrossRef]
- Komarraju, M.; Karau, S.J.; Schmeck, R.R. Role of FFM personality traits in predicting college students’ academic motivation and achievement. Learn. Individ. Differ. 2009, 19, 47–52. [Google Scholar] [CrossRef]
- Martin, J.H.; Montgomery, R.L.; Saphian, D. Personality, achievement test scores, and high school percentile as predictors of academic performance across four years of coursework. J. Res. Personal. 2006, 40, 424–431. [Google Scholar] [CrossRef]
- Nguyen, N.T.; Allen, L.C.; Fraccastoro, K. Personality predicts academic performance: Exploring the moderating role of gender. J. High. Educ. Policy Manag. 2005, 27, 105–117. [Google Scholar] [CrossRef]
- Hogan, R.; Hogan, J. Hogan Personality Inventory Manual, 2nd ed.; Hogan Assessment Systems: Tulsa, OK, USA, 1995. [Google Scholar]
- Ziegler, M.; Bensch, D.; Maaß, U.; Schult, V.; Vogel, M.; Bühner, M. Big Five facets as predictor of job training performance: The role of specific job demands. Learn. Individ. Differ. 2014, 29, 1–7. [Google Scholar] [CrossRef]
- Ziegler, M.; Danay, E.; Schölmerich, F.; Bühner, M. Predicting academic success with the Big 5 rated from different points of view: Self-rated, other rated and faked. Eur. J. Personal. 2010, 24, 341–355. [Google Scholar] [CrossRef]
- Chamorro-Premuzic, T.; Von Stumm, S.; Furnham, A. (Eds.) The Wiley-Blackwell Handbook of Individual Differences; John Wiley & Sons: Hoboken, NJ, USA, 2015; Volume 3.
- Heider, F. The Psychology of Interpersonal Relations; Wiley: New York, NY, USA, 1958. [Google Scholar]
- Blumberg, M.; Pringle, C.D. The missing opportunity in organizational research: Some implications for a theory of work performance. Acad. Manag. Rev. 1982, 7, 560–569. [Google Scholar]
- Traag, T.; van der Valk, J.; van der Velden, R.; de Vries, R.; Wolbers, M.H.J. Why does education matter? Explaining the effect of the highest level of education attained by school leavers on their labor market position. Pedagog. Stud. 2005, 82, 453–469. [Google Scholar]
- Zeidner, M. Personality trait correlates of intelligence. In International Handbook of Personality and Intelligence; Saklofske, D.H., Zeidner, M., Eds.; Plenum Press: New York, NY, USA, 1995. [Google Scholar]
- Campbell, J.P. Psychometric theory. In Handbook of Industrial and Organizational Psychology; Dunnette, M.D., Ed.; Rand McNally: Chicago, IL, USA, 1976. [Google Scholar]
- Maier, N.R.F. Psychology in Industry; Houghton-Mifflin: Boston, MA, USA, 1958. [Google Scholar]
- Mount, M.K.; Barrick, M.R.; Strauss, J.P. The joint relationship of conscientiousness and ability with performance: Test of the interaction hypothesis. J. Manag. 1999, 25, 707–721. [Google Scholar] [CrossRef]
- Sackett, P.R.; Gruys, M.L.; Ellingson, J.E. Ability-personality interactions when predicting job performance. J. Appl. Psychol. 1998, 83, 545–556. [Google Scholar] [CrossRef]
- Denissen, J.J.A.; Penke, L. Motivational individual reaction norms underlying the Five-Factor model of personality: First steps towards a theory-based conceptual framework. J. Res. Personal. 2008, 42, 1285–1302. [Google Scholar] [CrossRef]
- Ziegler, M.; Knogler, M.; Bühner, M. Conscientiousness, Achievement Striving, and Intelligence as Performance Predictors in a Sample of German Psychology Students: Always a Linear Relationship? Learn. Individ. Differ. 2009, 19, 288–292. [Google Scholar] [CrossRef]
- Raven, J. Manual for Raven’s Progressive Matrices and Mill Hill Vocabulary Scales; Oxford Psychologists Press: Oxford, UK, 1981. [Google Scholar]
- Jensen, A.R. The g Factor: The Science of Mental Ability; Praeger: New York, NY, USA, 1998. [Google Scholar]
- Revelle, W.; Zinbarg, R.E. Coefficients alpha, beta, omega, and the glb: Comments on Sijtsma. Psychometrika 2009, 74, 145–154. [Google Scholar] [CrossRef]
- Zinbarg, R.E.; Revelle, W.; Yovel, I.; Li, W. Cronbach’s α, Revelle’s β, and McDonald’s ω H: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika 2005, 70, 123–133. [Google Scholar] [CrossRef]
- Yao, R.S.; Liang, L.Y. Analysis of the application of simplified NEO-FFI to undergraduates. Chin. J. Clin. Psychol. 2010, 18, 457–459. [Google Scholar]
- Yangang, N.; Boxing, L.; Junqian, L. A Study on the Relationship of Adolescents’ Big Five Personality and Coping Style With Traits of Mental Crises. Psychol. Sci. 2010, 3, 055. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2012. [Google Scholar]
- Klein, A.; Moosbrugger, H. Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika 2000, 65, 457–474. [Google Scholar] [CrossRef]
- Satorra, A.; Bentler, P.M. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 2001, 66, 507–514. [Google Scholar] [CrossRef]
- Beauducel, A.; Wittmann, W.W. Simulation study on fit indexes in CFA based on data with slightly distorted simple structure. Struct. Equ. Model. 2005, 12, 41–75. [Google Scholar] [CrossRef]
- Hu, L.T.; Bentler, P.M. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol. Methods 1998, 3, 424–453. [Google Scholar] [CrossRef]
- Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
- Marsh, H.W.; Hau, K.T.; Grayson, D. Goodness of fit in structural equation models. In Contemporary Psychometrics: A Festschrift for Roderick P. McDonald; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 2005. [Google Scholar]
- Maydeu-Olivares, A.; McArdle, J.J. (Eds.) Contemporary Psychometrics; Psychology Press: East Sussex, UK, 2005.
- Heene, M.; Hilbert, S.; Draxler, C.; Ziegler, M.; Bühner, M. Masking misfit in confirmatory factor analysis by increasing unique variances: A cautionary note on the usefulness of cutoff values of fit indices. Psychol. Methods 2011, 16, 319–336. [Google Scholar] [CrossRef] [PubMed]
- Kline, R.B. Principles and Practice of Structural Equation Modelingc, 3rd ed.; The Guilford Press: New York, NY, USA, 2011. [Google Scholar]
- Little, R.J.A.; Rubin, D.B. Statistical Analysis with Missing Values; Wiley: New York, NY, USA, 2002. [Google Scholar]
- Muthén, L.K.; Muthén, B.O. Mplus User’s Guide, 7th ed.; Muthen & Muthen: Los Angeles, CA, USA, 1998–2012. [Google Scholar]
- Preacher, K.J.; Curran, P.J.; Bauer, D.J. Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. J. Educ. Behav. Stat. 2006, 31, 437–448. [Google Scholar] [CrossRef]
- Preacher, K.J.; Curran, P.J.; Bauer, D.J. Simple Intercepts, Simple Slopes, and Regions of Significance in MLR 2-Way Interactions. Available online: http://quantpsy.org/interact/mlr2.htm (accessed on 2 July 2015).
- Little, T.D.; Rhemtulla, M.; Gibson, K.; Schoemann, A.M. Why the items versus parcels controversy needn’t be one. Psychol. Methods 2013, 18, 285–300. [Google Scholar] [CrossRef] [PubMed]
- Brown, T.A. Confirmatory Factor Analysis for Applied Research; The Guilford Press: New York, NY, USA, 2006. [Google Scholar]
- Lynn, R.; Allik, J.; Irwing, P. Sex differences on three factors identified in Raven’s Standard Progressive Matrices. Intelligence 2004, 32, 411–424. [Google Scholar] [CrossRef]
- Schafer, J.L.; Graham, J.W. Missing data: Our view of the state of the art. Psychol. Methods 2002, 7, 147–177. [Google Scholar] [CrossRef] [PubMed]
- Schafer, J.L.; Graham, J.W. Missing data: Our view of the state of the art. Psychol. Methods 2002, 7, 147–177. [Google Scholar] [CrossRef] [PubMed]
- Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics; Allyn and Bacon: Needham Heights, MA, USA, 2001. [Google Scholar]
- Beauducel, A.; Brocke, B.; Liepmann, D. Perspectives on fluid and crystallized intelligence: Facets for verbal, numerical, and figural intelligence. Personal. Individ. Differ. 2001, 30, 977–994. [Google Scholar] [CrossRef]
- Wittmann, W.W.; Süß, H.M. Investigating the Paths between Working Memory, Intelligence, Knowledge, and Complex Problem-Solving Performances via Brunswik Symmetry; American Psychological Association: Washington, DC, USA, 1999. [Google Scholar]
- Ziegler, M.; Dietl, E.; Danay, E.; Vogel, M.; Bühner, M. Predicting Training Success with General Mental Ability, Specific Ability Tests, and (Un) Structured Interviews: A meta analysis with unique samples. Int. J. Sel. Assess. 2011, 19, 170–182. [Google Scholar] [CrossRef]
- Poropat, A.E. Other-rated personality and academic performance: Evidence and implications. Learn. Individ. Differ. 2014, 34, 24–32. [Google Scholar] [CrossRef]
- Brogden, H.E.; Taylor, E.K. The theory and classification of criterion bias. Educ. Psychol. Meas. 1950, 10, 159–183. [Google Scholar] [CrossRef]
- Ziegler, M.; Brunner, M. Test Standards and Psychometric Modeling. In Psychosocial Skills and School Systems in the Twenty-First Century: Theory, Research, and Applications; Lipnevich, A.A., Preckel, F., Roberts, R., Eds.; Springer: Göttingen, Germany, in press.
- 1In order to decide whether an item parcel loaded appropriately on its respective factor, we used a cut-off of 0.40 for standardized factor loadings [86]. In our study, most of the standardized factor loadings were close to or larger than 0.40, except for some indicators of Openness and Agreeableness that were slightly lower than 0.40.
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Zhang, J.; Ziegler, M. Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance. J. Intell. 2015, 3, 91-110. https://doi.org/10.3390/jintelligence3030091
Zhang J, Ziegler M. Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance. Journal of Intelligence. 2015; 3(3):91-110. https://doi.org/10.3390/jintelligence3030091
Chicago/Turabian StyleZhang, Jing, and Matthias Ziegler. 2015. "Interaction Effects between Openness and Fluid Intelligence Predicting Scholastic Performance" Journal of Intelligence 3, no. 3: 91-110. https://doi.org/10.3390/jintelligence3030091