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TitleFluid Intelligence, Broad Personality Traits, Narrow Traits, and Their I
LanguageEnglish
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Total Pages184
Table of Contents
                            Table of Contents
Acknowledgements
Abstract
Zusammenfassung
General Introduction
	Moderating Processes
	Basic Definitions
	Mediating Processes
	Aims and Research Questions of the Current Dissertation
	References
Paper I Interaction Effects between Openness and Fluid Intelligence PredictingScholastic Performance
Paper II How do the Big Five influence Scholastic Performance? A Big Five-NarrowTraits Model or A Double Mediation Model
Paper III A Three-Wave Longitudinal Study: A Process Model from Personality toScholastic Performance
General Discussion
	Summary of Main Findings
	Limitations of the Current Dissertation and Directions for Future Research
	Contributions and Implications of the Current Dissertation
	Conclusions
Pulications and Conference Contributions
                        
Document Text Contents
Page 1

An Integrative Model to Predict Scholastic Performance:

Fluid Intelligence, Broad Personality Traits, Narrow Traits, and Their Interplay

Dissertation

zur Erlangung des akademischen Grades

Dr. rer. Nat. im Fach Psychologie

eingereicht an der

Mathematisch-Naturwissenschaftlichen Fakultät II

der Humboldt-Universität zu Berlin

von

Dipl. Psych. Jing Zhang



Präsident/Präsidentin der Humboldt-Universität zu Berlin

Prof. Dr.-Ing. Dr. Sabine Kunst

Dekan der Mathematisch-Naturwissenschaftlichen Fakultät II:

Gutachter/Gutachterin

1. Prof. Dr. Matthias Ziegler

2. Prof. Dr. Martin Bäckström

3. Prof. Dr. Ricarda Steinmayr

Tag der Verteidigung: 01.07. 2016

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Table 4. Standardized estimate and specific indirect influences of the Big Five and scholastic performance.
Models


Math grades Chinese grades English grades
Direct effects
(DM/B5NT)

Indirect effects
(DM/B5NT)

Direct effects
(DM/B5NT)

Indirect effects
(DM/B5NT)

Direct effects
(DM/B5NT)

Indirect effects
(DM/B5NT)

Openness -.026/-.026 .017/.017 .180*/.180*
→ Subject-specific self-efficacy -.028/-.028 -.031/-.031 .001/.001
→ Subject-specific self-concept .095**/.095** .044*/.044* .094**/.094**
→ Deep-learning approaches .080**/.105** .085#/.088# .011/.012
→ Surface-learning approaches .102**/.101** .121**/.122** .084**/.087**
→ Self-efficacy → deep approaches .025*/— .005/— .001/—
Conscientiousness -.096/-.096 -.165*/-.165* -.191*/-.191*
→ Subject-specific self-efficacy -.022/-.022 -.033/-.033 .001/.001
→ Subject-specific self-concept .125**/.125** .062*/.062* .179**/.179**
→ Deep-learning approaches .109*/.129** .105#/.108# .014/.015
→ Surface-learning approaches .020/.020 .024/.023 .013/.016
→ Self-efficacy → deep approaches .020*/— .005/— .002/—
Extraversion .020/.020 .136*/.136* .058/.058
→ Subject-specific self-efficacy -.003/-.003 -.024/-.024 .001/.001
→ Subject-specific self-concept .005/.005 .037/.037 .052/.052
→ Deep-learning approaches .043/.046 .037/.040 .005/.005
→ Surface-learning approaches -.043*/-.043* -.049*/-.049* -.037*/-.036*
→ Self-efficacy → deep approaches .003/— .004/— .001/—
Neuroticism .048/.048 .077/.077 .003/.003
→ Subject-specific self-efficacy .015/.015 -.009/-.009 .001/.001
→ Subject-specific self-concept -.106*/-.106* .011/.011 .012/.012
→ Deep-learning approaches .013/.001 -.001/.001 .001/.001
→ Surface-learning approaches -.073**/.073** -.088*/-.087* -.062*/-.062*
→ Self-efficacy → deep approaches -.013/— .001/— .001/—
Agreeableness .116/.116 -.011/-.011 .101/.101
→ Subject-specific self-efficacy .005/.015 -.004/-.004 .000/.000
→ Subject-specific self-concept -.019/-.019 .032/.032 .109#/.109#
→ Deep-learning approaches .083/.083 .014/.014 .002/.002
→ Surface-learning approaches -.011/-.011 -.015/-.017 -.017/-.016
→ Self-efficacy → deep approaches .010/— .001/— .001/—

Note: * p < .05, ** p < .01, # p < .10. For better comparability, we display three decimal places for specific indirect effects obtained with the DM model (on the left side of the
forward slash) and the B5NT model (on the right side of the forward slash).

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Table 5. Model fits for Multiple Group SEMs Across Age.

School subject Model χ2 (df) RMSEA [90% CI] CFI SRMR BIC
Chinese N → multiple mediators→ grades 966.12 (524) 0.059 [0.055,0 .063] 0.931 0.058 32649.21
E → multiple mediators→ grades 983.22(524) 0.058 [0.052,0 .063] 0.923 0.059 31383.66
O → multiple mediators→ grades 1038.74 (524) 0.061 [0.056, 0.066] 0.918 0.067 31917.71
A → multiple mediators→ grades 973.49 (524) 0.057 [0.051, 0.063] 0.923 0.062 32219.92
C → multiple mediators→ grades 1052.71(524) 0.062 [0.056, 0.067] 0.922 0.059 31523.54
Math N → multiple mediators→ grades 898.75(524) 0.052 [0.046, 0.058] 0.939 0.055 34818.99
E → multiple mediators→ grades 941.46(524) 0.055 [0.049, 0.061] 0.930 0.059 34358.29
O → multiple mediators→ grades 924.28(524) 0.054 [0.048, 0.059] 0.932 0.062 34108.57
A → multiple mediators→ grades 932.62(524) 0.054 [0.049, 0.060] 0.927 0.067 34408.05
C → multiple mediators→ grades 989.88(524) 0.058 [0.052, 0.064] 0.928 0.059 33713.04
English N → multiple mediators→ grades 981.62(524) 0.058 [0.052, 0.063] 0.937 0.060 33783.54
E → multiple mediators→ grades 1022.17(524) 0.060 [0.055, 0.065] 0.930 0.060 33306.00
O → multiple mediators→ grades 1006.57(524) 0.059 [0.054, 0.065] 0.932 0.063 33054.31
A → multiple mediators→ grades 1003.73(524) 0.062 [0.056, 0.067] 0.929 0.062 33348.33
C → multiple mediators→ grades 1053.06(524) 0.062 [0.056, 0.067] 0.930 0.059 32643.69
Note. N = 792: Grade 7 = 105, Grade 8 = 275, Grade 10 = 412. Of note, we also attempted to run the multiple age SEMs with all the Big Five
domains and all the potential mediators for each school subject, but the models do not converge. So we tested a series of multiple age SEMs
separately for each of the Big Five domains and for each subject.

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