m-these jane visser - hogeschool inholland · criterion variables dropout and study success....

The Influence of Psychological Factors and Self-confidence on Study Success and

Dropout

Jane Visser

Student number: 2515838

Supervisor: Dr. F. R. Kappe

Second assessor: L. S. Peperkoorn

Master thesis Work and Organizational Psychology

June, 2017

Vrije Universiteit Amsterdam

THE INFLUENCE OF PSYCHOLOGICAL FACTORS ON STUDY SUCCESS 2

Abstract

There are more and more people who start a higher education in the Netherlands,

however a large group (40%) of students dropout after the first study year. This is due

to several reasons and 15% of the students never return to school. This research

focuses on several predictors of dropout and study success and on the possible effect

of psychological factors. The central research question is ‘To what extent can study

success and dropout be predicted from psychological factors and self-confidence?’ A

quantitative field study is conducted amongst 4650 students from the Inholland

University of Applied Sciences. Participants filled in an online survey, with multiple

questionnaires related to the main research variables: psychological factors and the

criterion variables dropout and study success. Self-confidence is also taken into

account as a moderator and the predictors gender, ethnicity, prior education, intrinsic

motivation and self-discipline are taken into account as control variables. The results

show that there is no direct or indirect effect of psychological factors on study

success. Only the control variables gender, ethnicity and some prior educations were

related to dropout and study success.


Introduction

The number of people who start a higher education in the Netherlands and

other Western countries has increased. This is needed, because today’ society consists

of a less services based economy, but a knowledge based economy that asks for

higher educated people (Brouwer, Jansen, Hofman, & Flache, 2016). This can be a

benefit for the Dutch economy to grow, giving people a bright future, having more

resources to start a family and more resources to live healthy. But there are also some

obstacles. At first, a higher education brings high costs for which many students will

take a loan. The mean study debt was in 2014 15,000 euros (CPB, 2014) and will

increase nowadays even more with the abolition of the basic grant (Markteffect,

2015). Second, 40% of the students quit their study within the first year and become a

dropout, whereof 15% permanently leave higher education (Rusman, 2014). This is

due to several reasons, for example switching to another study or not being capable of

getting the right amount of credits to get through the first school year. Becoming a

dropout brings psychological and financial problems and dropouts are costly to

society (Maynard, Salas-Wright, & Vaughn, 2015).

Therefore, it is becoming increasingly important that students choose the right

study in order to avoid early and later psychological and financial problems. In the

Netherlands Universities and colleges use different tools to see if students fit their

study choice. In particular, the Inholland University of Applied Sciences in the

Netherlands uses the check for study choice (studiekeuzecheck, SKC) to allow

students to check if they made the right study choice and additionally uses some other

tools (Kappe, 2015). For example a personal conversation about the motivation for

the study choice of the student, study experience at a University or college for a day

or doing some homework. So Universities and colleges have different approaches to

check the students’ study choice and are still looking for the advantages and the


disadvantages of certain approaches. Therefore empirical research might help

Universities and colleges to find the right combination of the SKC, such as a

conversation about the motivation of the student together with a personality

questionnaire. This research will focus on finding predictors related to dropout and

study success, so there can be made a better fit between the characteristics of the

students and their study choice in order to reduce the dropout rates.

Furthermore, this research also has a theoretical and practical reason. The

theoretical reason is that this research can add evidence to earlier research that

focused on predictors related to dropout and study success, which in turn contribute to

the evidence of theoretical models of dropout and study success. A practical reason is

that it is necessary to find predictors related to dropout, so Universities and colleges

know what they can do about the dropout rates. It is important to know that

psychological factors are related to dropout, because 49% of the Dutch students say

that they have or have had psychological problems in college and almost 23% of the

Dutch students report psychological problems at this time (Schmidt, & Simons,

2013). In order to explore predictors related to dropout and study success in the

Netherlands this research uses the online survey that is administrated as part of the

check for study choice (SKC) at the Inholland University of Applied Sciences. The

online survey consists of questions about the capacities, motivation, personality, study

situation and study orientation behavior of the students, along with background

information of the students. The SKC is of theoretical relevance, because the SKC is

used to do research about predictors related to dropout and study success, which in

turn contribute to theoretical models of dropout and study success. First, previous

research will be discussed that already focused on predictors related to dropout and

study success.


Early research from Tinto (1975) describes a theoretical model related to

dropout, in which Tinto relates background information, different commitments and

integration into school to dropout. For example, the chance of an individual dropping

out from college is related to the characteristics of the family, especially students

from a lower socioeconomic environment exhibit higher rates of dropout (Tinto,

1975). Tinto also suggests that a student will tend to dropout from college when the

student perceives that an alternative form of investment of time will produce greater

benefits over time than staying in college.

Figure 1. Tinto’s Conceptual Model for Dropout from College.

This model of Tinto is used as a basis for further research and has received some

criticism. Some researchers (Braxton, Milem, & Sullivan, 2000; van den Bogaard,

2011) tested Tinto’s model and only found support for five of the 13 propositions in

the model, for example family background, individual attributes, pre-schooling,

institutional commitment and social integration. Certain aspects of the family

Family Background

Pre-College Schooling

Individual Attributes

Goal Commitment

Institutional Commitment

Commitments Academic System

Grade Performance Intellectual Development

Peer-Group Interactions Faculty Interactions

Social System

Academic Intergration

Social Intergration

Institutional Commitment

Goal Commitment

Commitments

Dropout Decisions


background, individual attributes and pre-schooling lead to institutional commitment

which leads to social integration (Braxton, Milem, & Sullivan, 2000; van den

Bogaard, 2011). Furthermore, different researchers made adaptations to Tinto’s model

but none of the models explain completely why certain students dropout and other

students do not (van den Bogaard, 2011). But these models can still be very useful in

practice, because these models provide multiple predictors of dropout. The most

stable predictor is the ability of the student, besides this other predictors are personal

situation, age, gender, SES, prior education, disabilities, motivation, success intention,

fear of failure, commitment, time spent on studying, goal setting, ability to

concentrate, seek help if needed, attended lecture hours, number of exams

successfully completed, available facilities for the students and the way the education

is organized (van den Bogaard, 2011). Finding support for these hypothesized

predictors of dropout has led to more research. In line with Tinto’s model, research

from Rosenthal (1998) found that socioeconomic status, minority group status,

gender, social support for in school staying, family process, student involvement in

education, autonomy needs versus social conforming, deviance and personality traits,

like conscientiousness are related to dropout. Another research has suggested that

psychiatric disorders, which include anxiety disorder, mood disorder, substance use

and conduct disorder are important determinants of high school students’ educational

attainment and dropout (Carsley, Heath, Gomez-Garibello, & Mills, 2016). This

research suggests that there is a positive relationship between adolescent’s report of

anxiety and dropout, whereby high anxiety is associated with an increased risk of

dropout. However, Carsley et al. (2016) found a small correlation between anxiety

and dropout (r = .36, N = 546). Psychological factors are things like a depression, fear

of failure, substance abuse and anxiety disorders. Furthermore, in the study of


McCaffrey, Pacula, Han and Ellickson (2010) was found a positive association

between marijuana use and dropping out. After controlling for the differences in

observational characteristics and behavior of the students at the start of the study the

association between marijuana use and dropping out in high school is still large

(McCaffrey et al., 2010). Elffers (2012) found that personal circumstances in

students’ lives outside college, such as drug abuse and pregnancy are found as risk

indicators in dropout statistics. In particular, marijuana use is positively related to

school dropout in Dutch higher education and students who use soft drugs also

reported less academic engagement (Elffers, 2012). Only, this research did not show

how strong the relationship is between drug use and dropout, which is one of the

included psychological factors. Furthermore, being a dropout also has consequences

later in life. Prior research has found that dropout is associated with numerous social

and behavioral health problems including poorer mental and physical health, less

positive well-being and greater involvement in criminal activity relative to those who

graduate from high school (Maynard, Salas-Wright, & Vaughn 2015). For example,

dropouts are more likely to report cigarette use and nicotine dependence than college

graduates. Maynard et al. (2015) also found that dropouts are two times more likely to

report attempted suicide and are more likely to experience adverse behavioral health

conditions than those who graduated. Besides this, a strong relationship was found

between criminal behavior and being a dropout (Maynard et al., 2015).

Besides the predictors of dropout, predictors related to study success are also

widely researched. Study success refers to being successful in school, which means

getting the right amount of study points and/or getting high grades. Previous research

has indeed found some predictors of study success. For example, Conscientiousness is

found to be a predictor of performance in higher education for different types of


performance criterion and Neuroticism is also a predictor for higher education

performance, but only when the assessment conditions were less stressful (Kappe, &

van der Flier, 2010). Another predictor would be General Mental Ability (GMA), this

predictor has a significant positive effect on academic performance (Song et al.,

2010). Earlier research also found the ability of a student as a predictor of study

success (van den Bogaard, 2011). Furthermore, intrinsic motivation is found as a

predictor of performance (Cerasoli, Nicklin, & Ford, 2014). Intrinsic motivation

refers to doing something because it is interesting or enjoyable. Intrinsic motivation

has emerged as an important phenomenon for educators, because it results in high

quality learning and creativity (Ryan, & Deci, 2000). Previous research found that the

population-level relation between intrinsic motivation and performance is positive,

indicating that intrinsic motivation is a moderate (ρ = .26, k = 183, N = 212,468)

predictor of performance (Cerasoli, Nicklin, & Ford 2014). Besides this, Komarraju,

Karau and Schmeck (2009) found that intrinsic motivation predicts grade point

average (GPA), but intrinsic motivation explained only 4% of the variance in GPA

and conscientiousness reduced the relationship between intrinsic motivation and

GPA. Another predictor is self-discipline, which is the capacity to do what you want

to do, knowing how to manage your emotions and thoughts and knowing how to plan

your behavior, so you can reach your goals (Duckworth, 2009). Duckworth and

Seligman (2005) found that self-discipline (r = .67, N = 164) predicted the final grade

more robustly than IQ (r = .32, N = 164) did and self-discipline also predicted which

students would improve their grades over the course of the school year, whereas IQ

did not. However, it was not clear if the final grade was based on one or more school

years and Duckworth and Seligman (2005) did not measure the incremental validity

of self-discipline. Furthermore, self-confidence, which is the belief in one’s self and


in his/her powers and abilities (White, 2009; Bandura, 1993) emerged as a predictor

of academic performance (Tavani, & Losh, 2003). Tavani and Losh (2003) reported a

small relationship between student’s self-confidence and academic performance (r =

.17, N = 4,012), whereby higher level’s of student’s self-confidence results in higher

levels of their academic performance (Tavani, & Losh, 2003).

There are many predictors of dropout and study success (Tinto, 1975;

Rosenthal, 1998), but this research will focus on psychological factors and the

students’ self-confidence. Previous studies have already found some implications for

psychological factors and self-confidence being related to dropout (Carsley et al.,

2016; Elffers, 2012; McCaffrey et al., 2010; Tavani, & Losh, 2003). The research

question is ‘To what extent can study success and dropout be predicted from

psychological factors and self-confidence?’ Study success will be expressed in both

European credits (ECs) and grade point average (GPA) and dropout will be expressed

in yes (being a dropout) or no (not being a dropout). This research focuses on these

variables, because earlier research did not find strong relationships and not all of the

previous research had a large sample size. Besides this, little research has been done

in the Netherlands; especially in higher education, while many students study in

higher education.

The first hypothesis is that psychological factors are positively related to (1a)

dropout and negatively related to study success, expressed in (1b) ECs and (1c) GPA.

It is expected that having obstruction from a psychological factor results in an

increase in the change of becoming a dropout and a decrease in study success,

expressed in ECs and GPA. This is expected, because as mentioned earlier previous

research has suggested that psychological disorders, like anxiety disorder and

substance use are important determinants of dropout (Carsley, Heath, Gomez-


Garibello, & Mills, 2016). Furthermore, previous research found that personal

circumstances in students’ live outside school, such as drug abuse are often found as

risk indicators for dropping out (Elffer, 2011; McCaffrey, Pacula, Han, & Ellickson,

2010). This is not yet researched in relationship with study success.

The second hypothesis is that self-confidence moderates the relationship

between psychological factors and (2a) dropout, (2b) ECs and (2c) GPA. It is

expected that the student’s self-confidence weakens the relationship between

psychological factors and dropout and weakens the relationship between

psychological factors and study success (ECs and GPA). The moderating effect of the

students’ self-confidence is expected, because self-confidence is positively related to

academic performance (Tavani, & Losh, 2003). As mentioned earlier higher levels of

self-confidence result in higher levels of their academic performance. However, little

is known about de moderating effect of self-confidence on the relationship between

psychological factors and dropout and study success.

Furthermore, hypothesis one and two will be tested again, controlling for

gender, ethnicity, prior education, intrinsic motivation and self-discipline. Tinto

(1975) and Rosenthal (1998) found that gender, ethnicity and prior education are

related to dropout. Furthermore, intrinsic motivation is related to performance and

predicts GPA (Cerasoli, Nicklin, & Ford, 2014; Komarraju, Karau, & Schmeck,

2009). Duckworth and Seligman (2005) found that self-discipline predicted academic

performance more robustly than IQ did. This is the reason why there will be

controlled for these variables. The third hypothesis is that the relationships of

hypothesis one and two are significant after controlling for gender, ethnicity, prior

education, intrinsic motivation and self-discipline. The conceptual model of the

hypotheses is illustrated in Figure 2. This model is similar to the model of Tinto


(1975), because the model in Figure 2 also shows that characteristics of the students

are related to dropout and study success.

Figure 2. Conceptual model H1, H2, H3

Research method

Participants

The 4650 participants in this quantitative field study are fulltime bachelor

students from the Inholland University of Applied Sciences in the Netherlands. The

participants consisted of 2342 (50,4%) males and 2308 (49,6%) females. The average

age of the participants is 22 (SD = 3.04) years. There are questions about personal

background, personality, capacities, study situation, study orientation behavior,

motivation and specific topics for certain educational studies. The sample exists of

students from different locations of the Inholland University of Applied Sciences who

started their study in September 2015. All students were required to fill in the online

survey after they enrolled in a study of their own choice, so the response rate is almost

100%.

Measures

Dropout

_+

Psychological factors

Self-confidence

Study success

_

Gender Ethnicity Prior education Intrinsic motivation Self-discipline

H2

H3

H1


This study measures demographic information, study situation (psychological

factors), self-confidence, self-discipline, intrinsic motivation, gender, ethnicity, prior

education, dropout and study success, expressed in ECs and GPA. The participants

filled in the online survey on a computer at home. The first questions in the survey are

questions about demographics, like gender, age, ethnicity and current or highest

educational level. These questions are followed by other questionnaires, which were

relevant for this study.

Predictor variables

For measuring psychological factors, this study uses the Study Situation

questionnaire (NOA). The Study Situation questionnaire contains 12 partly closed

items about available study time, physical or psychological factors (fear of failure,

addiction and depression) which may hinder their study, additional learning support,

taking care of others, available help from others with studying, financial situation,

having a good place to study, having a part-time job or job, other extracurricular

activities and average travel time. The important item is ‘There are psychological

factors (fear of failure, addiction and depression) for me to consider in this study’.

The items are answered on a 5-point-likert-scale with 1 = “totally not applicable” to 5

= “well applicable”. This study uses this one item as measure of possible

psychological factors.

Another predictor variable is self-confidence that is measured by using the

Multicultural Personality Test (MPT), which is a personality questionnaire (Stichting

Noa, 2007). The MPT consists of six characteristics related to personality, like

emotional stability, extraversion, conscientiousness, openness, agreeableness and

integrity. In this study an adapted version is used to measure personality

characteristics related to study situation. The adapted version of the MPT consists of


ten subscales, namely rules and orderliness, self-discipline, adaptability, self-esteem

and performance anxiety, extrovert study behavior, integrity study behavior, initiative,

creativity, inquisitiveness and friendly and social behavior. This questionnaire

consists of 92 items and an example item is ‘I often think that a study assignment is

too difficult for me’. The items are answered on a 5-point-likert-scale with 1 =

“totally not applicable” to 5 = “well applicable”. The items are aggregated into the ten

scales and they have an average reliability of .81, this means that there is an average

to high internal consistency.

Control variables

The control variable self-discipline is also measured by using the MPT. The

Motivatie Vragenlijst voor Hoger onderwijs (MV-H) is used to measure the

controlling variable intrinsic motivation (NOA). This questionnaire gives a

representation of the motivation for a particular study. Motivation is divided into four

categories, namely intrinsic motivation, extrinsic motivation, achievement motivation

and confidence and certainty. This questionnaire consists of 55 items and an example

item is ‘I think the content of the study I have chosen is very interesting’. The items

are answered on a 5-point-likert-scale with 1 = “totally not applicable” to 5 = “well

applicable”. The items are aggregated into scales representing the four motivation

categories. Furthermore, the data for the controlling variables gender (male = 0,

female = 1), ethnicity (native = 0, non-native = 1) and prior education are obtained

from the demographic information. A non-native student is someone who is born

abroad or at least one of the parents is born abroad (CBS).

Criterion variables

The criterion variables dropout and study success, expressed in ECs and GPA

were available after the first school year at the student administration and the central


database. The data for dropout (no = 0, yes = 1) is available in the central database

and the participants’ GPA (0-10) and their amount of ECs (0-60) is available at the

student administration.

Procedure

This study uses an online survey in line with the SKC. After signing up for a

study at the Inholland University of Applied Sciences the students receive a link to a

survey containing questions about their background and multiple questionnaires. The

first page of the online survey is an informed consent, which contains information

about the goal of the SKC and that their answers will be used for matching purposes

and not as a selection criterion. This survey also contains a debrief form at the end of

the survey. The debrief form informs the students about the goal of the SKC and that

they can contact the principal investigator of the Inholland University of Applied

Sciences for further questions. As mentioned, after the first study year the grade point

average, number of credit points and dropout were collected using the student

administration and the central database.

Statistical analysis

First, 94 participants are removed from the sample, because some of them did

not fill in the online questionnaire, some of them already had a degree at the Inholland

University, others dropped out after the first year, some of them had dispensation for

ECs and others already had ECs in the third and fourth year of college. As a result, the

sample still consists of 4556 participants. After this, a few variables are transformed,

namely dropout, ethnicity and an interaction variable of psychological factors and

self-confidence. Furthermore, the variable psychological factors is converted, because

a high score on psychological factors means then something positive, for example that

students have no hinder of psychological factors during their study. Subsequently, the


descriptive statistics of the variables gender, ethnicity, prior education, psychological

factors, self-confidence, intrinsic motivation, self-discipline, dropout and study

success, expressed in ECs and GPA are obtained. After that, a correlation matrix is

analyzed in order to see how the variables preliminary correlate with each other. The

first hypothesis is tested with a linear regression analysis for the criterion variable

study success and with a logistic regression analysis for the criterion variable dropout.

In both the analysis the predictor variable is psychological factors. To test the second

hypothesis an interaction variable is made of the variables psychological factors and

self-confidence. To test the part of the second hypothesis with study success as the

criterion variable a regression analysis is used. To test the part of the second

hypothesis with dropout as the criterion variable a logistic regression analysis is used.

To test the third hypothesis with the controlling variables and study success as the

criterion variable a multiple regression analysis is used. For all the analyses a

significance level of p < 0.05 is maintained. The descriptive statistics of the variables

and the hypotheses are measured with the program Statistical Package for the Social

Sciences (SPSS, version 21).

Results

The descriptive statistics of the quantitative variables are in Table 1 and the

descriptive statistics of the categorical variables are in Table 2.

Table 1. Descriptive Statistics of the Quantitative Predictor and Criterion Variables, N = 4556

M SD Range Skewness Kurtosis K-Smir S-Wilk

EC 37.72 22.96 [0-61] -.66 -1.22 .21* .81*

GPA 5.80 1.67 [1-8.60] -1.29 .95 .18* .85*

Self-confidence 37.15 5.71 [13-50] -.41 .31 .07* .99*

Intrinsic motivation 55.61 7.01 [27-74] -.14 -.08 .03* 1.00*

Self-discipline 28.21 4.79 [10-40] -.17 -.00 .06* .99* *Significant at alpha level .05 (2-tailed)


The variable GPA is lightly skewed to the left and the other quantitative variables are

almost perfectly normal distributed. Besides this, the kurtosis of the quantitative

variables does not take extreme values, so it does not pose any problem for the

analyses. Based on the normality test in the two last columns the quantitative

variables are also not perfectly normal distributed. Furthermore, the categorical

variable psychological factors is skewed to the left and is a little bit peaked, this

means that this variable has not a normal distribution. This can cause problems for the

analyses, however the analyses are still carried out because of the theoretical and

practical relevance of this research. Based on the normality test in the last two

columns the categorical variables are not perfectly normal distributed.

Table 2. Descriptive Statistics of the Categorical Predictor and Criterion Variables, N = 4556

Frequency Percentage Cumulative Skewness Kurtosis K-Smir S-Wilk

Dropout

.09 -1.99 .35* .64*

Not a dropout 2382 52.3 52.3

A dropout 2174 47.7 100

Psychological factors

-2.69 7.78 .46* .51*

well applicable 36 .80 .80

pretty applicable 86 1.9 2.7

applicable 194 4.3 6.9

bit applicable 663 14.6 21.5

totally inapplicable 3577 78.5 100

Gender

.02 -2.00 .34* .64*

male 2301 50.5 50.5

female 2255 49.5 100

Prior education

1.02 .82 .32* .75*

abroad 69 1.5 1.5

HAVO 2333 51.2 52.7

HBO 54 1.2 53.9

MBO 1803 39.6 93.5

other 43 .90 94.4

entrance exam 69 1.5 95.9

VWO 183 4.0 100

WO

Ethnicity

.59 -1.65 .41* .61*

native 2811 61.7 61.7

non-native 1566 34.4 100 *Significant at alpha level .05 (2-tailed)


Second, to see how the variables preliminary correlate with each other a correlation

matrix is presented (Table 3). The predictor variable psychological factors does not

correlate with one of the criterion variables dropout, ECs and GPA. However,

psychological factors has a low significant correlation with self-confidence.

Psychological factors has also a significant correlation with gender and self-

discipline, but the correlation coefficients are very small. Furthermore, ECs and GPA

are moderate to strong negatively related to dropout, this means that dropouts have a

lower amount of ECs and have a lower GPA score than non-dropouts. ECs has a

strong significant positive correlation with GPA, this means that a high score on one

variable also gives a high score on the other variable. Furthermore, the predictor

variables self-confidence, intrinsic motivation, self-discipline, gender, prior education

and ethnicity are correlated with the criterion variables dropout, ECs and GPA.

However, these correlations are very small to small, so these correlations will not add

much value. Intrinsic motivation is moderately significant correlated to self-

discipline. The variable self-confidence and the other control variables are very small

to small significant correlated with each other.

Table 3. Pearson Correlation of the Predictor and Criterion Variables, N = 4556

*Significant at alpha level .05 (2-tailed)

Drop-

out EC GPA Psych_f

act Self-

confidence Intr_motiv

ation Self-discipline Gen-der

Prior education

Ethnicity

Dropout - EC -.80* -

GPA -.67* .89* - Psych_fact .01 .01 .02 -

Self-confidence .04* -.06* -.06* .33* - Intr_motivation .03* -.05* -.07* .02 .23* -

Self-discipline .03* -.05* -.06* .14* .39* .57* - Gender -.09* .13* .10* -.07* -.18* .19* .16* -

Prior education .05* -.06* -.06* -.02 .06* .16* .15* .09* - Ethnicity .18* -.23* .27* -.02 .05* .22* .22* .10* .15* -


The first hypothesis is that psychological factors are positively related to (1a)

dropout and negatively related to (1b) ECs and (1c) GPA. This hypothesis was tested

with a linear regression analysis and a log-linear analysis, with the variable

psychological factors as the predictor variable and dropout, ECs and GPA as the

criterion variables. The model with ECs as the criterion variable is not significant,

F(1, 4555) = .52, p = .471, R2 = .00. This means that the variable psychological

factors does not explain any variance in ECs. The model with GPA as the criterion

variable is not significant, F(1, 4374) = 2.49, p = .115, R2 = .00. So psychological

factors did not explain the variance in GPA. The model with dropout as criterion

variables is also not significant, χ2 (4) = 1.40, p = .845, Nagelkerke R2 = .00. This

means that psychological factors are not related to dropout (see Table 4). Hypothesis

one (a, b, c) is rejected.

Table 4. Linear Regression Analysis and Log-linear Analysis Hypothesis 1, N = 4556

Predictor variable Criterion variable b/Beta SE t p 95%CI

Psych_fact EC .34/.01 .48 .72 .471 [-.59, 1.28]

Psych_fact GPA .06/.02 .04 1.58 .115 [-.01, .13]

Psych_fact Dropout - - - .845 - *Significant at alpha level .05 (2-tailed)

The second hypothesis is that self-confidence moderates the relationship

between psychological factors and (2a) dropout and moderated the relationship

between psychological factors and (2b) ECs and (2c) GPA. The predictor variable is

psychological factors and the moderator is an interaction term of psychological

factors and self-confidence. Furthermore, the criterion variables are dropout, ECs and

GPA. To test the hypothesis a multiple linear regression analysis and a multiple log-

linear analysis are used. The model with ECs as the criterion variable is significant,

F(3, 3653) = 5.03, p = .002, R2 = .004. This means that psychological factors and self-


confidence are related to ECs. However, the explained variance in ECs is low and

none of the coefficients are significant. Besides this, the model with GPA as the

criterion variable is also significant, F(3, 3515) = 6.04, p < .001, R2 = .005. This

means that psychological factors and self-confidence are related to GPA, but the

explained variance is again low and the coefficients are not significant. So, the

predictors in the model together are better than a model without predictors, but the

predictors do not predict anything on themselves. The model with dropout as criterion

variable is not significant, χ2 (6) = 7.75, p = .257, Nagelkerke R2 = .003. This means

that psychological factors and self-discipline are not related to dropout. In general,

self-discipline does not moderate the relationship between psychological factors and

study success and the relationship between psychological factors and dropout, so

hypothesis two (a, b, c) is rejected. The results obtained from the analyses are

presented in Table 5.

Table 5. Multiple Linear Regression Analysis and Log-linear Analysis with Moderator, N = 4556

Predictor variable Criterion variable b/Beta/Exp(B) SE t p 95%CI

Psych_fact EC 1.38/.04 2.63 .53 .600 [-3.77, 6.53]

Self-confidence

-.20/-.05 .36 -.56 .573 [-.92, .51]

Interaction

-.01/-.03 .08 -.18 .854 [-.17, .14]

Psych_fact GPA .19/.08 .19 .96 .339 [-.20, .57]

Self-confidence

-.01/-.03 .03 -.31 .759 [-.06, .04]

Interaction

-.003/-.07 .01 -.51 .610 [-.01, .01]

Psych_fact Dropout .841

Self-confidence

.02/1.02 .03

.554

Interaction -.001/.99 .01 .879 *Significant at alpha level .05 (2-tailed)

The third hypothesis is that the relationships of hypothesis one and two are

significant when controlling for gender, ethnicity, prior education, intrinsic

motivation and self-discipline. The analyses of hypothesis one and two have been


carried out again, but then together with the control variables gender, ethnicity, prior

education, intrinsic motivation and self-discipline. This is done, because it was

surprising that there were no relationships found. So, this research checks whether the

control variables, which do predict the criterion variables in earlier research (Tinto,

1975; Rosenthal, 1998; Cerasoli, Nicklin, & Ford, 2014; Komarraju, Karau, &

Schmeck, 2009; Duckworth, & Seligman, 2005) are also significant predictors in this

research. Because of the completeness, this research included these variables in the

model. The results obtained from the analyses are presented in Table 6 and 7. First,

hypothesis one with the control variables. The model with ECs as the criterion

variable is significant, F(12, 3495) = 30.56, p < .001. Psychological factors, intrinsic

motivation, self-discipline, gender, ethnicity and prior education explain 1% (R2 =

.01) of the variance in ECs. But only the variables gender, ethnicity and some prior

educations are significant predictors of ECs. Women have more ECs than men and

native students have more ECs than non-native students. Furthermore, students who

had their prior education abroad, at the hbo or at the mbo have less ECs than students

who had their prior education at the havo. The model with GPA as the criterion

variable is significant, F(12, 3370) = 35.22, p < .001. This means that the model is a

significant predictor of GPA. Psychological factors and the control variables explain

11% (R2 = .11) of the variance in GPA. However, only gender, ethnicity and some

prior educations are significant predictors of GPA. Women have a higher GPA than

men and native students have a higher GPA than non-native students. Besides this,

students who had their prior education abroad or at the mbo have a lower GPA then

students who had their prior education at the havo. Students who had their prior

education at vwo have a higher GPA than students who had their prior education at

the havo. The model with dropout as the criterion variable is significant, χ2 (15) =


184.69, p < .001. This means that this model is a significant predictor of dropout.

Psychological factors and the control variables explain 7% (Nagelkerke R2 = .07) of

the variance in dropout. However, only gender and ethnicity are significant predictors

of dropout. The odd of women dropping out is .59 times less likely than the odd of

men dropping out. The odd of native students dropping out is 1.92 times less likely

than the odd of non-native students dropping out. The results from the second

hypothesis with the control variables are the same as the results of the first hypothesis

for ECs, GPA and dropout in the sense that psychological factors do not have an

effect and that the control variables have the same effects. Based on these results,

hypothesis 3 in not rejected.


Table 6. Multiple Regression Analysis and Multiple Log-linear Analysis with Control Variables, N = 4556

Predictor variable Criterion variable b/Beta/Exp(B) SE t p 95%CI

Psych_fact EC .45/.01 .52 .85 .394 [-.58, 1.47]

Intr_motivation

-.01/-.003 .07 -.13 .898 [-.14, .12]

Self-discipline

.05/.01 .10 .57 .571 [-.13, .24]

Gender

7.36./16 .76 9.67 < .001* [5.86, 8.85]

Ethnicity

-9.77/-.21 .81 -12.02 < .001* [-11.36, -8.18]

PE1

-13.75/-.04 5.16 -2.67 .008* [-23.86, -3.64]

PE3

-15.18/-.07 3.55 -4.27 < .001* [-22.15, -8.21]

PE4

-6.32/-.14 .82 -7.73 < .001* [-7.93, -4.72]

PE5

-3.91/-.02 3.99 -.98 .327 [-11.74, 3.91]

PE6

-.50/-.003 2.95 -.16 .874 [-6.25, 5.31]

PE7

3.78/.03 2.06 1.83 .067 [-.26, 7.81]

PE8

-4.01/-.004 15.30 -.27 .793 [-34.01, 25.99]

Psych_fact GPA .05/.02 .04 1.29 .195 [-.03, .12]

Intr_motivation

-.004/-.02 .01 -.80 .424 [-.01, .01]

Self-discipline

.01/.02 .01 1.00 .318 [-.01, .02]

Gender

.47/.14 .06 8.51 < .001* [.36, .58]

Ethnicity

-.82/-.24 .06 -13.87 < .001* [-.94, -.71]

PE1

-1.09/-.04 .40 -2.71 .007* [-1.88, -.30]

PE3

-.35/-.02 .31 -1.14 .256 [-.95, .25]

PE4

-.47/-.14 .06 -7.88 < .001* [-.59, -.35]

PE5

-.29/-.02 .29 -1.00 .317 [-.86, .28]

PE6

.01/.00 .21 .02 .982 [-.41, .42]

PE7

.43/.05 .15 2.88 .004* [.14, .72]

PE8

1.09/.02 1.09 .99 .320 [-1.06, 3.23]

Psych_fact Dropout .895

Intr_motivation

-.002/1.00 .01

.713

Self-discipline

-.003/1.00 .01

.781

Gender

-.54/.59 .07

< .001*

Ethnicity

.65/1.92 .08

< .001*

PE1

.46/1.58 1.51

.762

PE3

-.22/.80 1.42

.876

PE4

.17/1.19 1.45

.906

PE5

.21/1.24 1.42

.881

PE6

-.31/.73 1.46

.833

PE7

-.49/.61 1.44

.736

PE8 -.52/.59 1.43 .709 *Significant at alpha level .05 (2-tailed) **PE = prior education, 1= abroad, 2= havo (reference), 3 = hbo, 4 = mbo, 5 = other, 6 = entrance examination, 7 = vwo, 8 = wo *** Gender: 0 = male, 1 = female, Ethnicity: 0 = native, 1 = non-native


Table 7.1 Multiple Regression Analysis with Moderator and Control Variables, N = 4556

Predictor variable Criterion variable b/Beta SE t p 95%CI

Psych_fact EC .40/.01 2.65 .15 .879 [-4.79, 5.60]

Self-confidence

-.17/-.04 .36 -.48 .631 [-.89, .54]

Interaction

.01/.02 .08 .12 .903 [-.14, .16]

Intr_motivation

.001/.00 .07 .02 .983 [-.13, .13]

Self-discipline

.11/.02 .10 1.05 .292 [-.09, .30]

Gender

7.01/.15 .79 8.89 < .001* [5.46, 8.55]

Ethnicity

-9.79/-.21 .81 -12.05 < .001* [-11.39, -8.20]

PE1

-13.99/-.04 5.16 -2.71 .007* [-24.10, -3.87]

PE3

-14.83/-.07 3.56 -4.17 < .001* [-21.81, -7.85]

PE4

-6.29/-.14 .82 -7.69 < .001* [-7.90, -4.69]

PE5

-3.84/-.02 3.99 -.96 .336 [-11.66, 3.99]

PE6

-.56/-.003 2.95 -.19 .849 [-6.35, 5.22]

PE7

3.82/.03 2.06 1.86 .063 [-.21, 7.86]

PE8

-4.68/-.005 15.30 -.31 .760 [-34.68, 25.33]

Psych_fact GPA .08/.04 .19 .42 .672 [-.30, .46]

Self-confidence

-.01/-.05 .03 -.49 .623 [-.07, .04]

Interaction

.00/-

.003 .01 -.02 .983 [-.01, .01]

Intr_motivation

-.003/-.01 .01 -.58 .560 [-.01, .01]

Self-discipline

.01/.04 .01 1.67 .094 [-.002, .03]

Gender

.43/.13 .06 7.58 < .001* [.32, .55]

Ethnicity

-.83/-.24 .06 -13.92 < .001* [-.94, -.71]

PE1

-1.11/-.05 .40 -2.75 .006* [-1.90, -.32]

PE3

-.31/-.02 .31 -1.02 .308 [-.91, .29]

PE4

-.47/-.14 .06 -7.84 < .001* [-.58, -.35]

PE5

-.28/-.02 .29 -.98 .329 [-.85, .29]

PE6

-.003/.00 .21 -.01 .990 [-.42, .41]

PE7

.43/.05 .15 2.90 .004* [.14, .72]

PE8 1.02.02 1.09 .93 .352 [-1.13, 3.16] *Significant at alpha level .05 (2-tailed) **PE = prior education, 1= abroad, 2= havo (reference), 3 = hbo, 4 = mbo, 5 = other, 6 = entrance examination, 7 = vwo, 8 = wo *** Gender: 0 = male, 1 = female, Ethnicity: 0 = native, 1 = non-native


Table 7.2 Multiple Log-linear Analysis with Moderator and Control Variables, N = 4556

Predictor variable Criterion variable b/Exp(B) SE χ2 p Exp(B)

Psych_fact Dropout 1.08 .898

Self-confidence

.02/1.02 .04 .21 .647 1.02

Interaction

-.002/1.00 .01 .08 .779 1.00

Intr_motivation

-.003/1.00 .01 .20 .659 1.00

Self-discipline

-.01/1.00 .01 .28 .600 1.00

Gender

-.52/.60 .08 47.32 < .001* .60

Ethnicity

.66/1.93 .08 72.28 < .001* 1.93

PE1

.44/1.55 1.51 .09 .771 1.55

PE3

-.25/.78 1.42 .03 .859 .78

PE4

.12/1.13 1.46 .01 .934 1.13

PE5

.18/1.20 1.42 .02 .899 1.20

PE6

-.34/.71 1.46 .06 .814 .71

PE7

-.51/.60 1.44 .13 .724 .60

PE8 -.57/.57 1.46 .16 .691 .57 *Significant at alpha level .05 (2-tailed) **PE = prior education, 1= abroad, 2= havo, 3 = hbo, 4 = mbo, 5 = other, 6 = entrance examination, 7 = vwo, 8 = wo *** Gender: 0 = male, 1 = female, Ethnicity: 0 = native, 1 = non-native

Hypotheses one and two have been rejected, because this was unexpected

based on the literature several additional analyses were performed. This research

looks further at possible relationships within certain groups. Hypotheses one and two

are tested again within the group of native students and non-native students. Besides

this, hypotheses one and two with study success as the criterion variable are tested

again within the group of dropouts and non-dropouts. Hypothesis one is still not

significant for the three criterion variables within the group of native and non-native

students. This is the same for the second hypothesis with dropout as the criterion

variable. The model of hypothesis two with ECs as the criterion variable is significant

for the native students, F(3, 2186) = 4.10, p = .007 and the variables explain 6% (R2 =

.006) of the variance in ECs. However, there is only a main effect of self-confidence

(p = .023). The model of hypothesis two with ECs as criterion variable is also

significant for the non-native students, F(3, 1308) = 3.39, p = .017 and the variables


explain 8% (R2 = .008) of the variance in ECs. Only the interaction variable is a

significant predictor of ECs (p = .034). Furthermore, the model of hypothesis two

with GPA as the criterion variable is significant for the native students, F(3, 2143) =

3.97, p = .008 and the variables explain 6% (R2 = .006) of the variance in GPA.

However, none of the variables are significant predictors. The model of hypothesis

one with ECs as the criterion variable is still not significant within the group of

dropouts and non-dropouts. On the other hand, the model of hypothesis two with ECs

as the criterion variable is significant for the non-dropout students, F(3, 1873) = 4.43,

p = .004 and the variables explain 7% (R2 = .007) of the variance in ECs. However,

only the variable psychological factors is a significant predictor (p = .026). The model

of hypothesis one with GPA as the criterion variable is significant for the dropout

students, F(1, 1995) = 5.33, p = .021 and psychological factors explain 3% (R2 = .003)

of the variance in GPA. Furthermore, the model of hypothesis two with GPA as the

criterion variable is significant for the non-dropout students, F(3, 1871) = 3.20, p =

.022 and the variables explain 5% (R2 = .005). Psychological factors (p = .029) and

the interaction variable (p = .039) are significant predictors in this model. The model

of hypothesis two with GPA as the criterion variable is also significant for the dropout

students, F(3, 1643) = 4.04, p = .007 and the variables explain 7% (R2 = .007) of the

variance in GPA. This is strange, because in the whole group there is no relationship

found, but for the two groups separately there is found a relationship. However, none

of the variables are significant predictors in this model. This further research has

brought forward some interesting things, but the explained variances of these analyses

are still low and some of the coefficients are not significant. So again the predictors in

the model together are better than a model without predictors, but the predictors do

not predict anything on themselves.


When checking for the assumptions of linear regression there is found

multicolliniairity and no homoscedasticity in the models with the moderator. This is

not a problem, because there are no linear relationships found. The model in Figure 3

shows that the relationships of hypotheses one and two are not found.

Figure 3. Model of hypotheses 1, 2 and 3 representing the results of hypotheses 1 and 2.

Discussion

In this study predictors of dropout and study success, expressed in ECs and GPA were

examined. Especially psychological factors and self-confidence were taken in

account. In conclusion, psychological factors are not related to dropout or study

success, expressed in ECs and GPA. Besides this, self-confidence did not moderate

the relationship between psychological factors and dropout and the relationship

between psychological factors and study success. After controlling for the variables

intrinsic motivation, self-discipline, gender, ethnicity and prior education the models

were significant. However, in the models with study success (ECs and GPA) only

gender, ethnicity and some types of prior education were significant predictors of

Dropout Psychological factors

Self-confidence

Study success

Gender Ethnicity Prior education Intrinsic motivation Self-discipline

H2

H3

H1


study success. Furthermore, in the model with dropout only gender and ethnicity were

significant predictors of dropout. The conclusion is that hypotheses one and two are

rejected. The variable psychological factors is only related to study success in the

model of hypothesis two within the group of non-dropout students. Besides this, there

is only an interaction effect for the second hypothesis with ECs as criterion variable

for the non-native students. And an interaction effect in the model of hypothesis two

with GPA as criterion variable for the non-dropout students.

The expectation that having obstruction from a psychological factor results in

an increase in the change of becoming a dropout and a decrease in study results,

expressed in ECs and GPA was not confirmed by the results. This is not in line with

previous research from Carsley, Heath, Gomez-Garibello and Mills (2016) that

suggested that psychiatric disorders, anxiety disorder, mood disorder, substance use

and conduct disorder are important predictors of educational attainment and dropout.

This might be because Carsley et al. (2016) used different tools to measure

psychological factors. Besides this, the results did not show that self-confidence

moderated the relationship between psychological factors and dropout and did not

moderate the relationship between psychological factors and study success. This is

not in line with the expectation that the student’s self-confidence weakens the

relationship between psychological factors and dropout and weakens the relationship

between psychological factors and study success. This might be, because little was

known about the moderating effect of self-confidence. This is also not in line with

earlier research that found that there is a small relationship between student’s self-

confidence and academic performance, whereby higher level’s of student’s self-

confidence results in higher levels of their academic performance (Tavani & Losh,

2003). The reason that Tavani and Losh (2003) did find a relationship might be


because they conducted their research among high school students instead of college

students. Furthermore, the expectation that the hypothesized relationships are

significant when there is controlled for the variables gender, prior education,

ethnicity, intrinsic motivation and self-discipline was confirmed. However, only

gender, ethnicity and some prior education are related to dropout and study success.

This finding is in line with research from Tinto (1975) and Rosenthal (1998) who

found that gender, ethnicity and prior education are related to dropout. Intrinsic

motivation and self-discipline were not related to dropout and study success. This

does not confirm earlier research from Cerasoli, Nicklin and Ford (2014) who

suggested that the population-level relation between intrinsic motivation and

performance is positive. That Cerasoli et al. (2014) did find a relationship might be

because they did not conduct their research among college students. This is also not in

line with earlier research that indicated that self-discipline predicted final grade more

robustly than IQ did and self-discipline also predicted which students would improve

their grades over the course of the school year, whereas IQ did not (Duckworth &

Seligman, 2005). The reason that Duckworth and Seligman (2005) did find a

relationship might be that they also did not conduct their research among college

students. Furthermore, this research also looked further and tested hypothesis one and

two again within certain groups. The results showed that hypothesis two with ECs as

the criterion variable is significant within the group of native and non-native students.

There was an interaction effect, but only within the group of non-native students.

Furthermore, psychological factors are related to study success within the group of

non-dropout students for the second hypothesis. For GPA there was also an

interaction effect within the group of non-dropout students for hypothesis two. This is

somewhat in line with earlier research, because earlier research did not focus in


relationships within certain groups (Carsley, Heath, Gomez-Garibello, & Mills, 2016;

Tavani, & Losh, 2003).

The theoretical reason was that this research could add evidence to earlier

research about predictors related to dropout and study success. And the practical

reason was that it is necessary to find predictors related to dropout, so Universities

and colleges know what they can do about the dropout rates. However this is not

applicable, because the results did not show that was expected. Not finding a

relationship between psychological factors and dropout and study success does not

have to be a bad thing. This could mean that students still could finish their studies,

while having obstruction from psychological factors. It is possible that the early

detection of psychological factors during the SKC has led to a better guidance.

Universities and colleges still do not know what they can do about the dropout rates

and need to look further other than psychological factors.

This research has a few limitations, what might have led to not finding a direct

nor indirect effect for psychological factors on dropout and study success. First, the

question about psychological factors consisted of only 1 item, namely ‘There are

psychological factors (fear of failure, addiction and depression) for me to consider in

this study’. So, it is not clear which factors may or may not be included. This can lead

to students who do not indicate that they have obstruction from psychological factors

while they actually have. It would have been better to have a questionnaire with a

clear description of psychological factors. Besides this, students can answer that they

do not have psychological factors when they actually have, because of social

desirability and think that it may influence their selection procedure for the study.

Second, the variable psychological factors is not normal distributed, this may have


caused that there is not found a relationship between psychological and dropout and

study success. Furthermore, students from a more masculine culture were maybe to

proud to say that they have obstruction from psychological factors what could lead to

less variance in psychological factors. However, the additional analyses did show that

there was an interaction effect within the group of non-native students for hypothesis

two with ECs as the criterion variable. Another limitation is that dropouts are not only

students who failed but are also students who go to a University after gaining their

propedeuse in college. Moreover, this research has also a strong point and that is that

this research had a large sample size what was not always the case in previous

research. So the Dutch population was better represented than when there is a small

sample size. It is possible that previous research also has not found the hypothesized

relationships and that these studies are therefore not publicized. Furthermore, this

research noted that 21% of the students had obstruction from psychological factors at

this moment. This is in line with earlier research that reported that almost 23% of the

Dutch students have psychological problems at this time (Schmidt & Simons, 2013).

However, the majority of the 21% students indicate that they have little obstruction

from psychological factors. In future research the limitations can be resolved by using

a proper questionnaire with a clear description of psychological factors and that it is

clear which factors are included. Social desirable answers are also being taken into

account by being clearer about the SKC not being a selection tool. Future research can

also focus on why some significant relationships were found within certain group, for

example the native versus non-native students and the dropout versus the non-dropout

students.

In conclusion, in this study no relationship between psychological factors and

dropout and study success, expressed in ECs and GPA was found. Besides this, self-


confidence did not moderate the relationship between psychological factors and

dropout and did not moderate the relationship between psychological factors and

study success. However, some of the control variables did affect these relationships.

For example, gender, ethnicity and some prior educations are related to dropout and

study success. This results in that there cannot be said to what extent study success

and dropout be predicted from psychological factors and self-confidence. By

performing this research again in a follow-up study, taking into account the

suggestions for future research there might be found a relationship between

psychological factors and dropout and study success. And find a moderating effect of

self-confidence. A follow-up study can also help Universities and colleges to find

predictors related to dropout, so that they can do something about the dropout rates.


References

Bandura, A. (1993). Perceived Self-Efficacy in Cognitive Development and

Functioning. Educational Psychologist.

https://doi.org/10.1207/s15326985ep2802_3

Braxton, J. M., Milem, J. F., & Sullivan, A. S. (2000). The influence of active

learning on the college student departure process: Toward a revision of Tinto’s

theory. The Journal of Higher Education, 71(5), 569–590.

https://doi.org/10.2307/2649260

Brouwer, J., Jansen, E., Hofman, A., & Flache, A. (2016). Early tracking or finally

leaving? Determinants of early study success in first-year university students.

Research in Post-Compulsory Education, doi:

10.1080/13596748.2016.1226584

Carsley, D., Heath, N. L., Gomez-Garibello, C., & Mills, D. J. (2016). The

importance of mindfulness in explaining the relationship between adolescents’

anxiety and dropout intentions. School Mental Health.

https://doi.org/10.1007/s12310-016-9196-x

CBS (Centraal Bureau voor Statistiek). Definitie allochtoon. Retrieved from

https://www.cbs.nl

Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic Motivation and

Extrinsic Incentives Jointly Predict Performance: A 40-Year Meta-Analysis.

Psychological Bulletin, 140(4), 980–1008. https://doi.org/10.1037/a0035661

CPB (2014). Gemiddelde aflossing en inkomenseffecten sociaal leenstelsel. CPB

Notitie, 22 mei 2014. Retrieved from http://www.cpb.nl

Duckworth, A. L. (2009). Self-Discipline Is Empowering. Phi Delta Kappan, 90(7),

536. Retrieved from

http://search.ebscohost.com/login.aspx?direct=true&db=eue&AN=504255035&s

ite=ehost-live

Duckworth, A. L., & Seligman, M. E. P. (2005). Self-discipline outdoes IQ in

predicting academic performance of adolescents. Psychological Science, 16(12),

939–944. https://doi.org/10.1111/j.1467-9280.2005.01641.x


Elffers, L. (2012). One foot out the school door? Interpreting the risk for dropout

upon the transition to post-secondary vocational education. British Journal of

Sociology of Education, 33(1), 41–61.

https://doi.org/10.1080/01425692.2012.632866

Kappe, R. (2015). Studiekeuzechecks in het hbo. HO Management, 2015(7, oktober),

18-21.

Kappe, R., & van der Flier, H. (2010). Using multiple and specific criteria to assess

the predictive validity of the Big Five personality factors on academic

performance. Journal of Research in Personality, 44(1), 142–145.

https://doi.org/10.1016/j.jrp.2009.11.002

Komarraju, M., Karau, S. J., & Schmeck, R. R. (2009). Role of the Big Five

personality traits in predicting college students’ academic motivation and

achievement. Learning and Individual Differences, 19(1), 47–52.

https://doi.org/10.1016/j.lindif.2008.07.001

Markteffect (2015). BLOG; Afschaffing basisbeurs. Markteffect, 21 september

2015. Retrieved from http://markteffect.nl

Maynard, B. R., Salas-Wright, C. P., & Vaughn, M. G. (2015). High School Dropouts

in Emerging Adulthood: Substance Use, Mental Health Problems, and Crime.

Community Mental Health Journal, 51(3), 289–299.

https://doi.org/10.1007/s10597-014-9760-5

McCaffrey, D. F., Pacula, R. L., Han, B., & Ellickson, P. (2010). Marijuana use and

high school dropout: the influence of unobservables. Health Economics, 19(11),

1281–1299.

Rosenthal, B. S. (1998). Non-school correlates of dropout: An integrative review of

the literature. Children and Youth Services Review, 20(5), 413–433.

https://doi.org/10.1016/S0190-7409(98)00015-2

Rusman, F. (2014, 30 oktober). ’40 procent van de studenten stopt binnen een

jaar’. NRC. Retrieved from http://www.nrc.nl

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and Extrinsic Motivations: Classic

Definitions and New Directions. Contemporary Educational Psychology, 25, 54–

67. https://doi.org/10.1006/ceps.1999.1020


Schmidt, E., & Simons, M. (2013). Psychische klachten onder studenten. Utrecht:

LSVB, 13. Retrieved from http://lsvb.nl

Song, L. J., Huang, G. -h., Peng, K. Z., Law, K. S., Wong, C.-S., & Chen, Z. (2010).

The differential effects of general mental ability and emotional intelligence on

academic performance and social interactions. Intelligence, 38(1).

https://doi.org/10.1016/j.intell.2009.09.003

Statistical Package for the Social Sciences (SPSS, versie 21).

Stichting NOA. (2007). Voorlopige handleiding Multiculturele Persoonlijkheidstest

Big Six (MPTBS). Amsterdam: NOA/VU

Tavani, C. M., & Losh, S. C. (2003). Motivation, Self-Confidence, and Expectations

as Predictors of the Academic Performances Among our High School Students.

Child Study Journal.

Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent

Research. Review of Educational Research, 45(1), 89–125.

https://doi.org/10.3102/00346543045001089

van den Bogaard, M. (2011). Explaining student success in engineering education in

Delft University of Technology; a synthesis of literature. In WEE 2011.

Retrieved from http://www.sefi.be/wp-content/papers2011/T7/13.pdf

White, K. (2009). Self-Confidence: A Concept Analysis. Nursing Forum, 44(2), 103–

114. https://doi.org/10.1111/j.1744-6198.2009.00133.x

m-these jane visser - hogeschool inholland · criterion variables dropout and study success....

Documents