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

184 Pages·2016·10.87 MB·English

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
Eidesstattliche Erklärungen
Hiermit versichere ich des Eides statt, dass
• Ich die vorliegende Dissertation mit dem Titel „An Integrative Model to Predict
Scholastic Performance: Fluid Intelligence, Broad Personality Traits, Narrow Traits,
and Their Interplay“ selbständig und ohne unerlaubte Hilfe angefertigt habe.
• Es sich um die Ersteinreichung der vorliegenden Arbeit als Dissertation handelt.
• Ich die Promotionsordnung der Humboldt-Universität zu Berlin zur Kenntnis.

Berlin, den 20.01.2016 Jing Zhang
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Table of Contents
Acknowledgements .................................................................................................................. III
Abstract .................................................................................................................................... III
Zusammenfassung ................................................................................................................. VII
General Introduction .................................................................................................................. 1
Paper 1 ..................................................................................................................................... 30
Paper 2 ..................................................................................................................................... 63
Paper 3 ................................................................................................................................... 111
General Discussion ................................................................................................................ 157
Pulications and Conference Contributions ............................................................................ 173
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Acknowledgements
It was a challenging but exciting adventure when I prepared this dissertation. I could not have
accomplished it without help. Therefore, I would like to express my gratitude to everyone
who had helped me.
I give special thanks to my supervisor, Prof. Dr. Matthias Ziegler. I really appreciate his
prompt response to my emails, his invaluable and constructive feedbacks, and his
professional supervision for every kind of details. I am so lucky to be supervised by you-
Your patience and warm support have been encouraging me constantly and your wisdom and
research attitude have trained and prepared me to be a potential scholar for the future. Thank
you again for giving me such wonderful and unforgettable experiences.
I also would like to thank Prof. Dr. Martin Bäckström and Prof. Dr. Ricarda Steinmayr to
review my dissertation. Their detailed feedbacks on drafts of the dissertation and helpful
suggestions encouraged me to improve my dissertation all the way to its accomplishment.
I especially thank my lovely colleagues in the research group of Psychological Diagnostics. I
appreciate the help from Anja, Doreen, Ulrike, Steffi, Kai, Isabel, and Matthias when I was
preparing the dissertation. I would like to thank John Rauthmann who reviewed my
dissertation and made valuable suggestions. Sabine, a very kind and beautiful woman,
understood the troubles and joys that I had. In addition, my other Chinese friends, whose
companion really encourages me throughout the period of research and writing.
I would like to dedicate the dissertation to my parents. Without their hard work and sacrifice
in the past, I could not be able to enter a university, not to mention coming to Germany for a
doctoral degree. My old brother and two sisters always trust me and cheer me up. I am
grateful for being born and growing up in such a happy family with tons of songs and
laughter.
Last, I want to give my many thanks to China Scholarship Council for supporting me to
pursue my PhD degree in Humboldt-Universität zu Berlin.
There are also too many people who I want to thank but cannot name them here. I bless you
all the best in the future.
Berlin, den 20.01. 2016
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Abstract
This dissertation deals with the prediction of scholastic performance in Chinese culture. All in all,
three studies were conducted. Two studies focus on personality and test the Big Five Narrow
Trait Model (B5NT) which is one of theoretical corner stones of this thesis. The third study
includes cognitive ability and focuses on the interplay between personality and ability predicting
scholastic performance. Thus, the thesis uses the constructs of fluid intelligence (Gf), broad
personality traits (Big Five), narrow personality traits (i.e., self-beliefs and learning approaches),
and their complex interplay (moderation and mediation processes) as predictors of scholastic
performance. Following a general introduction summarizing the theoretical foundations as well as
outlining the derivation of the B5NT, three papers are presented. In the context of Chinese
secondary school students, Paper 1 examined the predictive power of figural reasoning as an
indicator of Gf and personality traits on school grades in three subjects (i.e., Mathematics,
Chinese, and English), and further investigated their potential interactions. Paper 2 integrated the
findings of Paper 1 with the aforementioned B5NT. Within the study, the B5NT is empirically
tested and compared to an alternative model proposed in earlier work, the Double Mediation
model [DM]. Self-beliefs and learning approaches were considered as relevant mediators within
those analyses. In this cross-sectional study, the B5NT model was strongly supported, whereas
the DM model did not find strong empirical support. In order to empirically verify the
underlying processes from a longitudinal perspective, Paper 3 expanded on the B5NT related
findings in a three-wave longitudinal panel design. The findings supported the B5NT model and
further warranted a revision model in which reciprocal effects from performance to big traits are
suggested. Thus, the presented thesis provides a theoretical model explaining the influence of the
Big Five on scholastic performance. Moreover, empirical support for the proposed model from
cross-sectional and longitudinal data was found. Finally, integrating interactions with cognitive
ability rounds off the perspective.
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