cross-cultural scale effects for a short locus of control

Wageningen University - Department of Social Sciences

MSc Thesis Chair Group Research Methodology

Cross-cultural differences in

the effect of number of response categories on Locus

of Control

August 2015

MSc Programme: Master Management, Economics

And Consumer Studies

Name of student: Lisette Feijen

Specialisation:

Health and Society

Name of Supervisor:

Ruud Zaalberg

Thesis code: YRM-80333

Master Thesis - Lisette Feijen

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Copyright © 2015 All rights reserved. No part of this publication may be reproduced or distributed in

any form or by any means, without the prior consent of the author(s). This report is produced by a

student of Wageningen University, as part of her MSc. programme. It is not an official publication of

Wageningen University or Wageningen UR and the content herein does not represent any formal

position or representation by Wageningen University.


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Preface

A young modern composer said: “you can go to school forever, but you’re never really going to

learn until you just do it” (Michael Giacchino). This quote inspired me to finish my Master’s degree

with this thesis and prepare myself for the work life, by just doing it.

The starting point of the subject was Locus of Control, a concept which is related to my background

of Health and Society. This concept intrigues me because it gives insights on personal attributes of

people and can help to give personal advice. Knowing whether people are independent by

themselves or rely mostly on others will help to give people advice that suits their personality.

In discussing Locus of Control an interesting feature of the phenomena of the scale came up. Not

the scale length (number of items), because this already has been researched, but the number of

response categories. When using a Likert-type scale you can vary the number of response

categories used in order to investigate how many response categories would be most or more

suited. Comparing the number of response categories asks for quantitative analyses, an area of

analyses that is of interest to me. Quantitative methods are more straightforward and are more

clear for interpretation compared to qualitative methods. Also a large number of responses can be

generated.

The subject of Locus of Control and scale aspects give me the opportunity to work on a more

quantitative basis within the field of Health and Society. It also gives me the opportunity to further

develop quantitative data analysis skills. This thesis research enables me to get more knowledge of

and experience in research methodology, which is a generic field and can be applied within different

research domains. This adds to my personal development and future career.

During my thesis I learned a lot from my supervisors and their feedback. I also further developed

my data analysis skills, in which I feel really comfortable. This gave me a direction for my future

career. This thesis and its process made me realize what aspects of the academic world I am most

interested in, namely quantitative data analysis and consultancy. I hope to work with these aspects

during my career.

I would like to conclude with: “life is full of turns which you take at your own speed, as long as you

keep moving and continue on a straight road”.


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Acknowledgements

Here I would like to thank some people. First, I would like to thank my supervisors for their guidance.

Ruud Zaalberg, I would like to thank you for your teachings about multivariate quantitative analysis,

our fruitful meetings and the discussions we had. Hilde Tobi, I would like to thank you for your

critical questions and challenges you gave me. These were very good for my development. Second,

I also would like to thank my friends who supported me during my final presentation. Third, I want

to thank my parents for their support during my academic career. Lastly, I want to thank Erik for his

support during the process of my thesis.


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Summary

The ‘second generation’ of measuring the Locus of Control (LOC) construct uses Likert-type scales.

Many LOC scales have been developed for specific domains and research thus far has focused on

the number of items measuring LOC. There is not sufficient research with regard to the number of

response categories in measuring LOC. Researchers of the LOC construct use 5 to 7 response

categories as a standard. This thesis investigates the influence of a larger number of response

categories, 9-points, on the data structure of LOC. This means that the relative mean and frequency

of extreme scores for Internal Locus of Control (ILOC) and External Locus of Control (ELOC) are

investigated. The association between Internal Locus of Control and External Locus of Control is

also researched. Locus of control can be predicted and moderated by culture, therefore it is

interesting to investigate LOC across cultures. Such a cross-cultural analysis provides insight into

cross-cultural differences in LOC and gives researchers and other professionals tools for

implementation of the LOC construct in practice and input for further research.

A heterogeneous sample is used consisting of students at Wageningen University.

Questionnaires are distributed by teachers, N=500. There are two versions of the questionnaire.

Due to classification of Culture into two dominant cultures (Western vs. Non-Western) there are

nine missing values, leaving N=491. A Principal Axis Factoring is performed to determine the factor

structure (2 correlated factors). Based on this a MANOVA has been conducted with a 2 x 2 x 2

design. Chi-square tests are performed to analyse the frequency of extreme scores (0=no extreme

score, 1=extreme score) and Cochran’s chi-square tests are performed to test for cross-cultural

differences regarding the effect of the number of response categories on the frequency of extreme

scores. Correlation coefficients are used to test the association between Internal Locus of Control

and External Locus of Control.

Results show a significant effect of the number of response categories on the relative mean

and frequency of extreme scores for both ILOC and ELOC. There is no significant effect of culture

nor of version and no significant interaction effects on the relative mean of ILOC and ELOC. There

is a significant effect of culture on the frequency of extreme scores on Internal Locus of Control,

not on External Locus of Control. There are cross-cultural differences regarding the effect of the

number of response categories on the frequency of extreme scores for ILOC and ELOC. Internal

Locus of Control has more extreme scores from the Non-Western culture while External Locus of

Control has more extreme scores from the Western culture. There is no significant (negative)


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correlation between ILOC and ELOC in the whole sample. There is a significant negative correlation

between ILOC and ELOC in the Western culture, but not in the Non-Western culture. There are no

cross-cultural differences on the association between ILOC and ELOC.


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Content Preface ____________________________________________________________________________________________ 5

Acknowledgements ______________________________________________________________________________ 6

Summary __________________________________________________________________________________________ 7

1. Introduction _________________________________________________________________________________ 15

1.1 Locus of Control ________________________________________________________________________________ 15

1.2 Short scales _____________________________________________________________________________________ 16

1.3 Response Categories ___________________________________________________________________________ 17

1.3.1 Cultural differences in extreme response style _________________________________________ 18

1.4 Culture and Locus of Control ___________________________________________________________________ 19

1.5 Aim of research _________________________________________________________________________________ 20

1.6 Research questions and hypotheses ___________________________________________________________ 20

1.6.1 Theoretical model ______________________________________________________________________ 23

1.7 Relevance of research __________________________________________________________________________ 24

2. Methods _____________________________________________________________________________________ 25

2.1 Development of 4-item LOC Scale _____________________________________________________________ 25

2.2 Sample __________________________________________________________________________________________ 26

2.3 Questionnaire ___________________________________________________________________________________ 27

2.4 Classification ____________________________________________________________________________________ 27

3. Data Analysis ________________________________________________________________________________ 29

3.1 Technical Design ________________________________________________________________________________ 29

3.2 Data analyses procedures ______________________________________________________________________ 30

3.2.1 Prior to data analyses___________________________________________________________________ 30

Correction for version ________________________________________________________________________ 30

Rescaling _____________________________________________________________________________________ 30

Factor analysis _______________________________________________________________________________ 31

Calculating scale scores for ILOC and ELOC __________________________________________________ 31

Extreme scores _______________________________________________________________________________ 31


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3.2.2 Analysis assumptions ___________________________________________________________________ 31

MANOVA - Normality ________________________________________________________________________ 31

MANOVA - Homogeneity ____________________________________________________________________ 32

Contingency tables - Chi-square test of homogeneity _______________________________________ 32

3.2.3 Statistics ________________________________________________________________________________ 32

Multivariate analysis of variance _____________________________________________________________ 32

Pearson Chi-square test ______________________________________________________________________ 32

Cochran’s chi-square test ____________________________________________________________________ 33

Correlations __________________________________________________________________________________ 33

4. Results _________________________________________________________________________________________ 34

4.1 Sample description _____________________________________________________________________________ 34

4.2 The effect on the relative mean of ILOC and ELOC ____________________________________________ 35

4.2.1. Item characteristics ____________________________________________________________________ 35

4.2.2 Principal Axis Factoring _________________________________________________________________ 35

4.2.3 Calculation of scale scores ______________________________________________________________ 36

4.2.4 Multivariate Analysis of Variance _______________________________________________________ 36

4.3 The effect on the frequency of extreme scores for ILOC and ELOC ___________________________ 39

4.3.1 The number of response categories ____________________________________________________ 39

4.3.2 Culture _________________________________________________________________________________ 42

4.3.3 Interaction effect _______________________________________________________________________ 45

4.4 Association between Internal and External Locus of Control _________________________________ 47

4.4.1 Correlation coefficient __________________________________________________________________ 47

4.4.2 Cross-cultural differences on the association between Internal and External Locus of

Control _______________________________________________________________________________________ 47

5. Discussion _____________________________________________________________________________________ 48

5.1 Results __________________________________________________________________________________________ 48

5.1.1 Number of response categories ________________________________________________________ 48

5.1.2 Culture _________________________________________________________________________________ 49

5.1.3 Moderating effect of Culture ___________________________________________________________ 49

5.1.4. Association ILOC and ELOC ____________________________________________________________ 50

5.2 Strengths ________________________________________________________________________________________ 51


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5.3 Limitations ______________________________________________________________________________________ 51

5.4 Recommendations ______________________________________________________________________________ 52

6. References_____________________________________________________________________________________ 53

7. Appendices ____________________________________________________________________________________ 56

Appendix I – Questionnaire ________________________________________________________________________ 57

Appendix II – Syntax for SPSS ______________________________________________________________________ 60

Syntax 1 - Correction for version _____________________________________________________________ 60

Syntax 2 – Calculation of ILOC and ELOC ____________________________________________________ 60

Syntax 3 – Correction for scale width (via recoding) _________________________________________ 60

Syntax 4 – Calculation scale scores ILOC and ELOC __________________________________________ 61

Syntax 5 – Extreme scores ____________________________________________________________________ 61

Syntax 6 – recode in no extreme and extreme score _________________________________________ 61

Appendix III – Output from SPSS __________________________________________________________________ 62

Principal Axis Factoring ______________________________________________________________________ 62

Correlations __________________________________________________________________________________ 65


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List of Tables

Table 2.1 Classification to cultural groups ______________________________________________________ 28

Table 2.2 Classification to dominant Cultures __________________________________________________ 28

Table 3.1 Group sizes for analysis ______________________________________________________________ 29

Table 4.1 Sample description per scale width __________________________________________________ 34

Table 4.2 Characteristics of items _______________________________________________________________ 35

Table 4.3 Characteristics of dimensions ________________________________________________________ 36

Table 4.4 Frequency of extreme score for each scale end for scale width ______________________ 39

Table 4.5 Contingency table Internal Locus of Control*Scale width ____________________________ 40

Table 4.6 Contingency table External Locus of Control*Scale width ____________________________ 41

Table 4.7 Frequency of extreme score for each scale end for culture ___________________________ 42

Table 4.8 Contingency table Internal Locus of Control*Culture _________________________________ 43

Table 4.9 Contingency table External Locus of Control*Culture ________________________________ 44


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List of Figures

Figure 1.1 Theoretical model with independent and dependent variables _____________________ 23

Figure 2.1 Locus of Control – Construct with two correlated dimensions ______________________ 26

Figure 3.1 Rescaling of 9-point scale ___________________________________________________________ 30

Figure 4.1 Mean of Internal Locus of Control for scale width ___________________________________ 36

Figure 4.2 Mean of External Locus of Control for scale width __________________________________ 36

Figure 4.3 Mean of Internal Locus of Control for culture _______________________________________ 37

Figure 4.4 Mean of External Locus of Control for culture _______________________________________ 37

Figure 4.5 Mean of Internal Locus of Control for version _______________________________________ 38

Figure 4.6 Mean of External Locus of Control for version_______________________________________ 38

Figure 4.7 Frequency of extreme scores for ILOC in short and wide scale ______________________ 40

Figure 4.8 Frequency of extreme scores for ELOC in short and wide scale _____________________ 41

Figure 4.9 Frequency of extreme scores for ILOC per culture ___________________________________ 43

Figure 4.10 Frequency of extreme scores for ELOC per culture _________________________________ 44

Figure 4.11 Frequency of extreme scores for ILOC per scale width in Western culture _________ 45

Figure 4.12 Frequency of extreme scores for ILOC per scale width in Non-Western culture____ 45

Figure 4.13 Frequency of extreme scores for ELOC per scale width in Western culture ________ 46

Figure 4.14 Frequency of extreme scores for ELOC per scale width in Non-Western culture ___ 46


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1. Introduction

Locus of Control (LOC) has been applied in different fields of research; psychology, behavioural

studies, health, and marketing. The past decades there has been a deep interest in the concept of

Locus of Control. It has been researched, adapted, improved and applied for specific contexts. The

concept is so broad that domain specific Locus of Control scales have been developed, such as the

Multidimensional Health Locus of Control Scale by Wallston, Wallston and DeVellis (1978), the

Multidimensional Traffic Locus of Control Scale (T-LOC) by Özkan and Lajunen (2005), Sales Locus

of Control Scale (SLCS) by Chung and Ding (2002), Weight Locus of Control Scale (WLOC) by Saltzer

(1982) and the Parental Locus of Control Scale (PLOC) by Campis, Lyman, and Prentice-Dunn (1986).

Although the scale itself is of particular interest, not only for the content but also for the length of

the scale, the response categories have received only minor alterations. Originally, it was a

dichotomous response scale (either yes or no) until 1972, after which Levenson used a 6-point

Likert-type scale, an ordinal scale, to measure LOC known as the ‘second-generation’. Since then,

researchers have used the Likert-type scale to measure LOC and use the Likert-type scale in the

development of their own scales. Researchers of the LOC construct use mostly 5 to 7 points

response categories out of convenience or tradition (Peter, 1979; Cox, 1980; Preston & Colman,

2000; Lozano, García-Cueto, & Muñiz, 2008). However, there is not sufficient research with respect

to the number of response categories in the LOC scale. This is important because the number of

response categories in a scale may have an influence on the reliability and the validity of the scale

(Lee & Paek, 2014). This gives reason to research the LOC construct further and the development

of LOC scales with regard to scale length and the number of response categories. This thesis

investigates what the influence of a larger number of response categories, 9-points, is on the data

structure of LOC. Data structure entails the relative mean, the frequency of extreme response, and

the covariance between LOC constructs.

1.1 Locus of Control

The construct of Locus of Control was first described by Rotter (1966). Locus of Control emerged

from his social learning theory. Locus of control is considered to be an aspect in the social learning

theory. In order to define Locus of Control, Rotter (1966) uses the following three concepts:

expectancy, behaviour potential, and reinforcement value. Locus of Control in the social learning

theory represents generalized expectancies and helps in the refinement of predicting


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reinforcements to change behaviour. Change in behaviour or behaviour potential (BP) is a function

of the expectancies (E) and the reinforcement value (RV) in the formula: 𝐵𝑃 = 𝑓(𝐸 ∗ 𝑅𝑉). The

construct LOC is seen as generalized expectancies for control of reinforcement. Rotter (1966) set a

unidimensional scale with two ends. These ends are defined as respectively external and internal

LOC. External and internal Locus of Control have received many descriptions in literature (Rotter,

1966; Lefcourt, 1972; Smith, Dugan, & Trompenaars, 1997; Kovaleva, 2012), but all concentrate on

the same underlying thought. Rotter (1966) describes external LOC (ELOC) as beliefs that his/her

life is in the hands of his environment, fate, chance or others. Internal LOC (ILOC) is described as

beliefs that one is in charge of his/her own life.

The LOC construct was initially meant as a bipolar continuum (Rotter, 1966). However, many

researchers showed that LOC can be seen as a multidimensional construct (Lefcourt, 1972, 1976,

1992; Levenson, 1973; Wallston, Wallston, & DeVellis, 1978; Özkan & Lajunen, 2005). Although there

is no consensus about the unidimensionality or the multidimensionality in LOC, there is a distinction

in LOC between internal LOC and external LOC as two separate dimensions. Kovaleva (2012)

examined the factor structure of two LOC scales; German Social Economic Panel (GSOEP) and KMKB

by comparing two correlated factors with two independent factors with the help of Confirmatory

Factor Analysis (GSOEP) and Principal Component Analysis (KMKB). There is a good fit for the 2

correlated factors model in both scales while the 2 independent factors yield a poor fit. These factor

analyses also indicate a negative association between Internal LOC and External LOC (Kovaleva,

2012). For the purpose of this thesis LOC is assumed to have two correlated dimensions: internal

LOC and external LOC. Where internal LOC is defined as “belief that events in life are contingent on

one’s behaviour and these can be controlled by this”. And where external LOC is defined as

“personal belief that life events is contingent on external factors (others and bad luck) and they are

controlled by these factors”.

1.2 Short scales

There is a growing interest in shorter scales, which is also the case for LOC. Less items would be

easier to use, to interpret, and easier to integrate into existing questionnaires, because it takes a

small amount of space in a questionnaire and only a few items need to be analysed and interpreted.

Why use 29 items to measure LOC when you can also use 4 items? Short scales are seen as more

efficient and less time-consuming in practice. The test power is not decided by the number of items,

but relies more on the number of respondents (Scott et al., 2009; Kovaleva, 2012). The gain in power

from increasing the number of items occurs mostly when going from a two-item scale to a three


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item-scale (Scott et al., 2009). Scott el al. (2009) state that as a general rule of thumb to ensure

adequate statistical power (of the ordinal logistic regression) of >80% each group should exist of a

minimum of 200 respondents. For a two-item scale a minimum of 300 respondents was suggested.

However, scale length has an influence on specific psychometric properties, such as reliability

(Komorita & Graham, 1965; Lee & Paek, 2014). Lee and Paek (2014) state that shorter scales that

use less items (5 items vs. 20 items) seem to show a larger decline of the statistical properties

(reliability and validity), when the number of response categories is low. The number of response

categories is a moderator on the relationship between scale width and reliability. To prevent decline

in reliability when shortening the scale length, the number of response categories should be

broadened. There seems to be a combined effect between the number of items used (scale length)

and the number of response categories for these items. This combined effect is larger with respect

to correlation and reliability than for each effect separately, scale length or the number of response

categories. Therefore this combined effect as an unique contribution to changes in the

psychometric properties (Lee & Paek, 2014).

1.3 Response Categories

In general, there is no consensus regarding the optimal number of response categories, and much

is based on convenience or tradition (Cox, 1980; Fox & Jones, 1998; Lee & Paek, 2014). Most often,

between 4 and 6 response categories are chosen. However, item discrimination (success on an item

is also success on the test) is better in scales with a larger number of response categories than in

scales with a smaller number of response categories (Preston & Colman, 2000; Lee & Paek, 2014).

A greater number of response categories is associated with better psychometric properties such as

reliability and validity, when there are less items (Lee & Paek, 2014). Since reliability and validity are

associated with item variance, the item variance tend to be larger when there are more response

categories. In general the reliability increases as the number of response categories increases.

However, reliability decreases when item correlations decrease (Lozano et al., 2008). An optimal

number of response categories is often referred as an optimal range, although research (Givon &

Shapira, 1984; Lozano et al., 2008) has shown there was no difference between a 4-point scale and

a 7-point scale or between a 5-point scale and a 9-point scale with respect to test-retest reliability,

scale variance, Cronbach’s alpha and inter-item correlations (Lee & Paek, 2014). These non-

differences are based on scales with a large number of items, respectively 30 items (Lozano et al.,

2008) and 27 items (Givon & Shapira, 1984). This does not imply that there will be no differences


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regarding test-retest reliability, scale variance, Cronbach’s alpha and inter-item correlations

between a 5-point scale and a 9-point scale for a short scale with a small number of items.

Research devoted to the effects of variations in rating scale formats, such as differences in

the number of response categories, in the field of psychology mostly use rating scales as a

measurement instrument (Preston & Colman, 2000). Measures with 9-point scale scores seem to

correlate better than 5-point scale scores and tend to suggest that measurement validity increases

with increasing number of response categories (Hancock & Klockars, 1991; Preston & Colman,

2000). Lozano et al. (2008) point out that the validity of the questionnaire is dependent on the

number of response categories. This is of importance because the number of response categories

potentially affects the measurement validity. Measurement validity is necessary in order to establish

that the instrument is measuring what it intends to measure and has the ability to distinguish

between groups (conform a given theory). A larger number of response categories should still have

measurement validity, otherwise the LOC construct is not measured as intended and cannot be

compared to other similar LOC measurement tools. It is important in this research that the LOC

construct has measurement validity when there is a higher number of response categories.

Response categories are either labelled at every category or only labelled at the end (no

verbal anchoring in-between). For example, ‘1=doesn’t apply at all, 2=applies a bit, 3=applies

somewhat, 4=applies mostly and 5=applies completely’ is labelling at every category and in no

verbal anchoring in-between it looks like ‘1=doesn’t apply at all and 5=applies completely’.

However, there is evidence that omission of a mid-point mainly effects the frequencies of the

neighbouring scale responses (Spagna, 1984 as cited in Dawes, 2002) and that mid-points do not

affect the mean of the response categories (Si & Cullen, 1998 as cited in Dawes, 2002).

1.3.1 Cultural differences in extreme response style

Extreme response style refers to a greater tendency of respondents to select the extremes of a

response scale (Johnson, 2005). Extreme response style is related to response categories. As Dawes

(2002) states “in general there is support for the view that more scale categories reduce the

incidence of extreme response, and may produce more dispersion in data”. However, Clarke III

(2001) found that increasing the number of scale categories beyond five categories had little effect

on extreme response. Johnson (2005) studied four relations between extreme response style and

the Hofstede’s cultural dimensions. One of the hypotheses he studied is: extreme response styles

may be more common among people from individualist countries and a moderate response style


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is more suited in collectivist countries. Johnson’s reason for this is that many studies have reported

evidence that acquiescence is more common in collectivist countries, since collectivistic people tend

to conform and agree with others. However, people from collectivist countries tend to have a

acquiescent response style (Smith, 2004; Johnson, 2005; Harzing, 2006). They act as ‘yea-sayers’ and

therefore do not score at the low end ‘doesn’t apply at all’ of either Internal Locus of Control or

External Locus of Control. Johnson (2005) also states that extreme response style may be more

common among people from individualist countries, because they are less concerned with the

consequences of expressing strong opinions (Harzing, 2006). Harzing (2006) mentions that

individualistic countries are expected a higher willingness to disagree, indicating an extreme

response style at the low end of the scale. The study of Johnson (2005) refutes the relation that

extreme response style is more common in individualist countries and results that individualism

was not associated with extreme response style. There are cross-cultural differences in extreme

response style and these are usually contrasted with a moderate response style. A moderate

response style is seen as the opposite of an extreme response style and used in cross-cultural

research for comparison (Hamamura, Heine, & Paulhus, 2008). Smith (2004) states that response

style differences might come from psychological constructs, because cultural trends in response

style might be due to response habit only. Having an extreme response style (mostly individualistic

countries) or having a moderate response style (mostly collectivistic countries) could be related to

having either more ILOC or ELOC.

1.4 Culture and Locus of Control

Individualism and collectivism exists in all cultures and countries, but one tends to predominate.

Individualism predominates in Europe, USA, Canada, Australia and New Zealand. Individualist

countries are mainly Anglo-European countries, but also other western countries are considered to

be individualistic (Frese, Kring, Soose, & Zempel, 1996; Spector et al., 2002). People in individualistic

cultures, and thus countries, value independence and achievement through their own actions.

Countries with a predominant collectivistic culture are mainly found in Africa, Latin America and

Asia (Gudykunst, 1998). People in collectivist countries value autonomy less and perceive less

autonomy than people in individualist countries (Spector et al., 2001). This means that people in

collectivist countries feel less moral responsibility and accountability for one’s actions and find this

less important. Thus, collectivist countries are less inclined to have high Internal Locus of Control.

Besides this, collectivistic Asians are more external in their general Locus of Control than

individualistic Americans and people from other western countries (Hui, 1982; Spector et al., 2001),


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since they feel that others have control over their actions which is related to External Locus of

Control. Eastern European countries, former Soviet Republic, are also collectivistic (Spector et al.,

2001) as well as other Asian countries and Latin America (Spector et al., 2002). So, Internal Locus of

Control is mostly present in individualist countries and less present in collectivist countries.

1.5 Aim of research

Locus of Control is a construct that is present in each culture, but can also be sensitive to values of

autonomy and cultural norms and values. Therefore, it is interesting to investigate LOC across

cultures. A heterogeneous sample in terms of culture is necessary to investigate these cross-cultural

differences or similarities. Next to cross-cultural aspects of LOC, there is a growing global interest

in short scales and the practical use of these. Shorter scales tend to, according to Lee and Paek

(2014), need larger number of response categories in order to stay reliable. So, how to decide on

the number of response categories to use? There is no clear formula or standard. Most research

about Locus of Control use five or six response categories. The scale length remains the same in

this research, the focus is on the number of response categories. In this research a comparison is

made between five response categories and nine response categories. The aim is to investigate the

effect of the number of response categories on the data structure of the LOC construct and to

perform a cross-cultural analysis in order to gain insight into culture as a moderator.

1.6 Research questions and hypotheses

There are four research questions. These research questions are supported by 14 hypotheses,

alternative hypotheses are given (for one-sided hypotheses the null hypothesis is given as well).

The dependent variables are Internal Locus of Control (ILOC) and External Locus of Control (ELOC).

This thesis investigates the relative mean and extreme response (data structure) as hypothesized.

Besides this, between group comparisons are made based on culture. Research question 1 and 2

are main effects of the independent variables on the dependent variables. Culture is perceived as a

moderator in research question 3. Also the correlation between the two dependent variables ILOC

and ELOC is investigated, as well as the cross-cultural differences in this association, research

question 4. This is further explained in chapter 2, Methods.


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Research question 1

What is the effect of the number of response categories on the data structure of the LOC construct?

Hypothesis 1: The relative mean of the short scale for ILOC differs from the relative mean of the

wide scale for ILOC.

Hypothesis 2: The relative mean of the short scale for ELOC differs from the relative mean of the

wide scale for ELOC.

Hypothesis 3: The frequency of extreme scores of ILOC on the short scale differs from the

frequency of extreme scores of ILOC on the wide scale.

Hypothesis 4: The frequency of extreme scores of ELOC on the short scale differs from the

frequency of extreme scores of ELOC on the wide scale.

Research question 2

What is the effect of culture on the data structure of the LOC construct?

Hypothesis 5: There are cross-cultural differences in the relative mean of ILOC.

Hypothesis 6: There are cross-cultural differences in the frequency of extreme scores

of ILOC.

Hypothesis 7: There are cross-cultural differences in the relative mean of ELOC.

Hypothesis 8: There are cross-cultural differences in the frequency of extreme scores

of ELOC.


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Research question 3

Are there cross-cultural differences in the effect of the number of response categories on the data

structure of the LOC construct?

Hypothesis 9: There are cross-cultural differences regarding the effect of the number of

response categories on the relative mean of ILOC.


response categories on the frequency of extreme scores of ILOC.


response categories on the relative mean of ELOC.


response categories on the frequency of extreme scores of ELOC.

Research question 4

Are there cross-cultural differences in the association between ILOC and ELOC?

Hypothesis 13: There is a negative association between ILOC and ELOC.

H0: there is no association between ILOC and ELOC

Hypothesis 14: There are cross-cultural differences regarding the association between

ILOC and ELOC.


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1.6.1 Theoretical model

In Figure 1.1 a schematic view of the theoretical model with the independent and dependent variables is shown, including the effects studied.

Figure 1.1 Theoretical model with independent variables ‘number of response categories’ and ‘culture’ and dependent variables ‘Internal Locus of Control’ and ‘External Locus of

Control’

Main effect: Black arrow | RQ 1+ 2

Association/correlation: Green arrow | RQ 4

Interaction Effect: Blue arrow | RQ 3

Effect: Purple arrow | RQ 4


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1.7 Relevance of research

This research further elaborates on scale effects within a cross-cultural context. The number of

response categories has been researched by many, however not in relation to Locus of Control.

Also the use of a short scale with just four items in combination with a variety in number of response

categories has not yet been subject of investigation. Besides that, a short four-item scale with an

effect of the number of response categories may influence other researchers to look further in

developing or adjusting scales to be shorter.

From a societal point of view, this research gives insight in differences and similarities between

cultures. Any insights on cross-cultural issues give researchers and implementers knowledge that

could be used in a practical matter in the everyday field, but also knowledge to further investigate

cultural issues.

In relation to health, Locus of Control is a well-established concept at a macro level. A review by

Wallston, Wallston, and DeVellis (1978) looked into Locus of Control in different health behaviours.

These health behaviours include smoking, birth control and weight loss as well as sick-role

behaviour. Locus of Control is important in predicting health behaviour and sick roles. Specific

health behaviours are: seeking information, taking medication, making and keeping appointments

with the physician, maintaining a diet and giving up smoking. Both Internals and Externals have a

belief to deal with these behaviours, the health professionals can adapt current affairs to make

healthy behaviour easier for all. In other words, tailoring health programs to the individuals’ Locus

of Control. Suggested in Wallston et al. (1978) is that internally orientated health programs can

provide more choice of treatment, more involvement of the patient in making choices and

emphasize on individual responsibility. Whereas externally oriented health programs rely more on

social support systems and the importance of compliance with the health professional (Wallston et

al., 1978). A short scale for LOC could be more easily implemented in current activities by health

professionals. Also cross-cultural differences would help guide the health professional to give

tailored advice.

Master Thesis - Lisette Feijen 25

2. Methods

In this section the development of the 4-item LOC scale, a fundamental part in this research, is

described. After this, the sample, questionnaire, group classification and data analyses procedures

of this research are described.

2.1 Development of 4-item LOC Scale

This research used the 4-item LOC scale as developed by GESIS (Kovaleva, 2012). This scale was

constructed after comparing four existing LOC scales (ROT-IE, IPC, GSOEP, KMKB). ROT-IE is the 29

item unidimensional scale by Rotter (1966) , IPC is the 24 item multidimensional scale developed

by Levenson (1972), GSOEP (German Social Economic Panel) is a German scale that used 8 items to

measure LOC with two (internal and external) dimensions (Nolte, Weischer, Wilkesmann, Maetzel,

& Tegethoff, 1997) and KMKB is also a German scale which measures LOC, but used only 6 items.

The KMKB used two separate subscales, one for ILOC and one for ELOC, each consisted of three

items (Jakoby & Jacob, 1999). These four scales (ROT-IE, IPC, GSOEP and KMKB) were the basis of

the 4-item LOC scale.

The process of developing this 4-item scale, executed by Kovaleva (2012) consisted of four steps.

The first step was comparing the LOC scales selected and identifying the crucial aspects of LOC.

The four crucial aspects of the concept LOC were 1) the expectancy of personal belief to have

control over life events (ILOC), 2) the expectancy of a contingency between one’s efforts and the

results achieved (ILOC), 3) the expectancy of power of other people (ELOC) and 4) the expectancy

of chance and fate (ELOC). The second step was creating an item pool consisting of 20 items, 10 for

ILOC and 10 for ELOC. This was done to be able to start the reduction process. The reduction

process took place based on criteria as: avoiding words denoting quality or quantity, fluency, and

short sentences were preferred. The 20-item pool was reduced to 10 items, 5 for ILOC and 5 for

ELOC. Next to testing the content of items via interviews (in the third step) with regard to possible

response bias in relation to level of education, there remained a selection of items based on

psychometric properties (reliability and inter-item correlations). Via Exploratory Factor Analysis

(EFA) the item pool was reduced to four items only (the fourth step), taking into account the four

crucial aspects of LOC and the factor loadings (Kovaleva, 2012).


Originally this short LOC scale and the items were in German, however GESIS made an English

translation of the scale (Kovaleva, 2012). The four items were formulated as follows: 1) “I’m my own

boss”, 2) “If I work hard, I will succeed”, 3) “Whether at work or in my private life: what I do is mainly

determined by others” and 4) “Fate often gets in the way of my plans”. The Research Methodology

chair group of the Wageningen University adapted fate into bad luck as perceptions of fate differed

between cultures. This English scale is the base of the LOC construct in this thesis. The 4-item scale

for LOC is shown in Figure 2.1.

Figure 2.1 Locus of Control – Construct with two correlated dimensions and two items per dimension

2.2 Sample

There were two independent samples each receiving a different number of response categories.

One sample (in the year 2013) received a 5-point Likert scale, the short scale, and the other sample

(in the year 2014) received a 9-point Likert scale, the wide scale. For the short scale and the wide

scale the sample sizes were respectively: n=278 and n=222. Respondents were students in either a

Master in Environmental Studies or a Master in Social Studies. The students signed in on either of

two similar courses, YRM-20306 and YRM-20806, at the University of Wageningen. The courses

were similar with respect to learning objectives, prerequisite demands and format (lectures,

workshops and group work).

Internal

Locus of

Control

(ILOC)

External

Locus of

Control

(ELOC)

BOSS SUCCEED OTHERS BAD LUCK


2.3 Questionnaire

The questionnaires had either a short scale or a wide scale. The short scale functioned as reference

year. There were also two versions for each scale. The manipulation of ordering in the two versions

originated from a preceding research and was thus an independent variable in this study.

The questionnaire (appendix I) was distributed during a course at the University of Wageningen.

The teacher was asked to hand-out the versions of the questionnaire randomly among the students.

The number of questionnaires and equal proportion of versions was taken into account beforehand.

Participation was on voluntary basis and anonymous. The questionnaire was in English, a non-native

language for most of the respondents (n=489; 97,8%). Respondents were asked to fill in

characteristics, such as: age, gender, country of origin and first language. Other questions in the

questionnaire entailed satisfaction with working in groups at the university, number of time spent

on working for the course, English proficiency skills (read, write, hear, speak) and the 4-item LOC

construct.

2.4 Classification

The classification to create cultural groups for comparison was based on Tobi and Kampen (2013),

which is a precursor of this research and handles similar data. The classification was adapted

according to the actual data acquired. Adaptions made included combining regions such as

Germanic Africa and other Africa into Africa, because there were low n-values (German Africa: 21;

Other Africa: 10) and including more countries (added countries) based on raw data (see Table 2.1).

Table 2.1 shows eight cultural groups from all over the world. These cultural groups (and its

countries) were identified as individualistic or collectivistic. The individualistic cultural groups were

defined as western societies, whereas the collectivistic cultural groups were non-western societies.

This means that there are two dominant cultures, Western versus Non-Western which consist of

several cultural groups, Table 2.2 gives an overview of this. The reason to compress the cultural

groups to two dominant cultures was to make cultural groups comparable with one another, since

group size of cultural groups differed very much. Thus creating similar group sizes, which was

desirable for the multivariate analysis.


Table 2.1 Classification to cultural groups

Cultural group Countries Added countriesb

Latin America Surinam, Colombia, Honduras, Ecuador,

Mexico, Bolivia, Peru, Chile, Costa Ricac,

Brazil

Cuba, Panama

Africa Eritrea, Ghana, Kenya, Tanzania, South

Africac, Zimbabwe, Cameroonc, Nigeria,

Zambia, Uganda, Ethiopia, Rwanda,

Mozambiquec, Senegalc

Morocco

Dutch The Netherlands

Germanic Europe UK, Sweden, Norwayc, Belgium, Germany Austria, Switzerland

Latin Europe Romania, Moldavia Republicc, France,

Spain, Italy, Portugal

Other Europe Hungaryc, Finland, Lithuania, Albaniac,

Armeniac, Greece, Macedoniac, Turkeyc,

Czech Republic, Slovakia, Ukrainec,

Polandc, Serbiac, Croatia, Bulgaria

Russia, Uzbekistan, Yugoslavia,

Cyprus,

Tadzjikistana, Kazakstana ,

Kyrgyzstana

China China

Other Asia Malaysiac, Sri Lanka, Indonesia, Nepal,

Bhutanc, India, Mongoliac, Bangladesh,

Vietnamc, Cambodiac, Laosc, Thailand,

Philippines

Iran, , Afghanistan, Brunei,

Taiwan, Korea, Iraq, Pakistan,

Japan

a based on main language and only recently independent from Soviet Republic. Officially part of central-Asia. b Not included: Canada c No respondents in this study

Table 2.2 Classification to dominant Cultures

Dominant Culture

Western Non-Western

Cu

ltu

ral

gro

up

Dutch

Germanic Europe

Latin Europe

Latin America

Africa

Other Europe

China

Other Asia


3. Data Analysis

In this chapter the technical design of this research is elaborated on, as well as the data analyses

procedures.

3.1 Technical Design

A quasi-experimental design with two dependent variables and three independent variables, a 2 x

2 x 2 (Scale width [short scale, wide scale] x Culture [Western, Non-Western] x Version [Order 1 vs.

Order 2]) design (see Table 3.1). The dependent variables are Internal Locus of Control (ILOC) and

External Locus of Control (ELOC).

Table 3.1 Group sizes for analysis

Number of response categories

Short scale Wide scale Total

Cu

ltu

re

Western Order 1 78 61 n=139

Order 2 46 65 n=111

Non-

Western

Order 1 76 46 n=122

Order 2 67 45 n=112

Total n=267 n=217 N=484

Note: group sizes in conditions are based on the combined use of ILOC and ELOC in a MANOVA

The interest in this study lay on the relative mean and the frequency of extreme scores for these

two dependent variables. There were three independent variables ‘scale width’, ‘culture’ and

‘version’. The independent variable ‘scale width’ refers to the number of response categories used,

and is of the most interesting in this specific research. The independent variable ‘culture’ (originated

from country of origin) is first classified to eight cultural groups and derived from this classification

two dominant cultures emerged: Western versus Non-Western. The independent variable ‘version’

was a manipulation (ordering) in the questionnaire that was already present and therefore was

taken into account.


3.2 Data analyses procedures

IBM SPSS Statistics 22.0 © Copyright IBM Corporation and other(s) 1989-2013 was used for data

entry, data management and data analysis.

3.2.1 Prior to data analyses

There were five steps prior to the data analyses: 1) correction for question order, 2) rescaling, 3)

factor analysis, 4) calculating scale scores for ILOC and ELOC and 5) extreme scores. The steps were

crucial for analysing the data properly.

Correction for question order

The data entry needed to be corrected for version since question order differed (appendix II; syntax

1). From the corrected item scores, scale scores for ILOC and ELOC are computed (appendix II;

syntax 2).

Rescaling

A rescaling took place to correct for scale width in order to make the data from both scale widths

comparable. The 5-point scale was used as reference and the 9-point scale was rescaled to a 5-

point scale. So, the width of both scales became the same. The number of response categories in

the rescaled scale were the same as in the original and there was an assumption of equal intervals

between the response categories. To support this rescaling the following formula was applied:

new rating = SUM(old plus preceding ratings) #involved response categories⁄ .

All item scores were recoded (see appendix II; syntax 3). This resulted in a recode of the 9-point

scale illustrated in Figure 3.1.

Rescaling was also important with regard to the Principal Axis Factoring (PAF), since a PAF on the

full dataset gives the required factor structure (despite any manipulation of scale width). PAF on

raw data would have resulted in an unjustified factor structure because the scale widths are not

comparable in the raw data and a one-factor structure would emerge.

Figure 3.1 Rescaling of 9-point scale, scale ends correspond with a 5-point scale, number of categories equal

to nine and equal distances between categories


Factor analysis

To determine the factor structure of Locus of Control in this sample a Principal Axis Factoring was

performed. The input for this factor analyses were the rescaled item scores. The number of factors

extracted was based on eigenvalues > 1 to give the PAF the freedom to extract the actual number

of factors. An oblique rotation was added to allow for correlating factors, since this is expected from

research (Kovaleva, 2012). A pattern matrix provided the factor structure (see appendix III). This

procedure was performed for the full dataset and for Western and Non-Western culture separately

(appendix III).

Calculating scale scores for ILOC and ELOC

In order to compute scale scores for ILOC and ELOC the factor structure was important. Based on

the factor structure ELOC was computed by adding the item scores and calculating the mean. This

process was also done for ILOC (see appendix II; syntax 4). For respondents that have one or more

missing item scores a dimension score cannot be computed, since each dimension needs two item

scores in order for it to be calculated.

Extreme scores

Frequency of extreme scores is operationalized as having an extreme score (1 or 5) on either or

both items. Respondents had no extreme score or an extreme score on either or both of the

dependent variables (DVs). All extreme scores were recoded into a 1 (extreme score) for each item,

the non-extreme scores (1.5 – 4.5) were recoded into a 0 (no extreme score). These scores, both

extreme and non-extreme, on the items are summed for each DV leading to values ranging from 0

to 2 (appendix II; syntax 5). A zero meant that there was no extreme score at all, a one or two

indicated that at least one of the two items for that DV had an extreme score. An extreme score

was identified when at least one of the two items for that DV had an extreme score. Therefore the

DV scores were re-coded in only a 0 (no extreme score) or 1 (at least on extreme score) (appendix

II; syntax 6).

3.2.2 Analysis assumptions

MANOVA - Normality

The normality check via visual inspection of univariate normality (Q-Q plots, histograms and

characteristics of the dependent variables) and bivariate normality (bivariate scatterplots) revealed

a sufficient normal distribution. The Q-Q plot indicated that the observations in each cell followed

the normal distribution, with a slight skewness. The histograms confirmed these slightly skewed

distributions. The mode, mean and median of both dimensions were not in the centre of the range


(3.0), but the actual range did not have the full range (5.0) either. So, the distribution was a bit

shifted from the centre, but this is not a problem for normality. The bivariate scatterplots showed

an ellipse form when looking at 60% around the centre (largest cluster of observations). Many

observations had the same values, this fact had to be taken into account in the interpretation of

the scatterplots.

MANOVA - Homogeneity

The tests for equality of variances showed Box’s M (p=.000) and Levene’s test for ILOC (F7,475=3.45,

p=.001) and for ELOC (F7,475=3.18, p=.003). The null hypotheses of equality of variances was

rejected, there was a difference between the variances of the groups in the sample. However, the

group sizes of each cell in the 2 x 2 x 2 (Scale width [short scale vs. wide scale] x Culture [Western

vs. Non-Western] x Version [Order 1 vs. Order 2]) analysis of variance (ANOVA) were similar enough

by a rule used in practice: smallest group*1.5 is maximum size largest cell. Therefore, the test for

equality of variances can be considered as sufficient. So, the assumption of homogeneity was

sufficient.

Contingency tables - Chi-square test of homogeneity

The analysis assumption was that the expected count must be at least 1 and no more than 20% of

the expected counts can be less than 5. In the designs 0 cells (0.0%) had an expected count less

than 5.

3.2.3 Statistics

In the data analysis five procedures were used to test the hypotheses posed.

Multivariate analysis of variance

A multivariate analysis of variance (MANOVA) of the 2 x 2 x 2 design was performed to test the

main effects of the number of response categories (hypotheses 1 and 2), the main effect of culture

(hypotheses 5 and 7) and the effect of culture on the effect of the number of response categories

(hypotheses 9 and 11). Other interaction effects were also tested, because they were part of the

technical design.

Pearson Chi-square test

Based on the theoretical model there were two contingency tables tested for each DV. The

contingency tables for scale width had a 2 x 2 (Extreme scores [no extreme score vs. extreme score]

x Scale width [short scale vs. wide scale]) design (hypotheses 3 and 4). The contingency tables for

culture has a 2 x 2 (Extreme scores [no extreme score vs. extreme score] x Culture [Western vs. Non-


Western]) design (hypotheses 6 and 8). On all contingency tables a χ2 test of homogeneity was

performed. A Pearson Chi-square was calculated to test if the column distributions (frequencies

extreme scores and no extreme scores) are the same. Also the odds ratio was calculated indicating

the odds that there is an extreme score in the short scale compared to the odds of an extreme score

is the wide scale. This odds ratio is a measure for effect size or strength.

Cochran’s chi-square test

A test of conditional independence (Ott & Longnecker, 2010) was performed by calculating

Cochran’s chi-square to test cross-cultural differences regarding the effect of the number of

response categories on the frequency of extreme scores (hypotheses 10 and 12). This test statistic

tests a set of q 2x2 contingency tables. In this study q represented culture, there were 2 groups, so

q=2.

Correlations

A Pearson correlation coefficient was calculated to test a negative association between ILOC and

ELOC (hypothesis 13). To test cross-cultural differences regarding the association between ILOC

and ELOC (hypothesis 14) a test of the difference between two independent correlation coefficients

was done by following the procedure of Preacher (2002). Each correlation coefficient was converted

to a z-score by Fisher’s r-to-z transformation and these z-scores together with the sample sizes are

compared using formula 2.8.5 provided by Cohen & Cohen (1983) (cited by Preacher, 2002).


34

4. Results

In the first section, the results of this study regarding the relative mean of ILOC and ELOC are

presented. First, basic characteristic of the items are described. Second, a Principal Axis Factoring

(PAF) on the items gives the factor structure for further analyses. Based on this, Internal Locus of

Control and External Locus of Control scale scores are calculated. Lastly, a multivariate analysis of

variance (MANOVA) on the 2 x 2 x 2 (Scale width [short scale vs. wide scale] x Culture [Western vs.

Non-Western] x Version [Order 1 vs. Order 2]) is performed to test hypotheses 1, 2, 5, 7, 9 and 11.

The second section presents the results regarding the frequency of extreme response for ILOC and

ELOC, testing hypotheses 3, 4, 6, 8, 10, 12. Lastly the association between ILOC and ELOC,

hypotheses 13 and 14, is presented.

4.1 Sample description

The sample sizes for the short and wide scale were respectively: n=278 and n=222. In total there

were 500 respondents, people from the Western culture and Non-Western culture were included.

This meant that three respondents were excluded, because of their country of origin (Canada and

two error codes1). There were six missing values for culture. The total number of respondents

N=491. A description of the sample per scale width is shown in Table 4.1. The age distribution of

the respondents of the short scale ranged from 20 to 43 years. Most respondents are 23 years old

(n=68; 24,5%), or 22 years old (n=38; 13,7%). The age distribution ranged from 19 to 36 years for

the wide scale. Most respondents were 22 years old (n=57; 25,7%), or 23 years old (n=45; 20,3%).

Table 4.1 Sample description per scale width (N=491)

N %

Short scale Wide scale Total Short scale Wide scale

Sample size 270 221 491 55.0 45.0

Course

YRM-20806

YRM-20306

123

147

116

105

239

252

45.6

54.4

52.5

47.5

Version

Order 1

Order 2

155

115

110

111

265

226

57.4

42.6

49.8

50.2

Gender

Male

Female

124

146

99

122

223

268

45.9

54.1

44.8

55.2

1 Error code is a numerical code that is not identified in the codebook and the original data (questionnaires) could not be consulted

since these are missing from the archive.


4.2 The effect on the relative mean of ILOC and ELOC

4.2.1. Item characteristics

The four rescaled items had n=486 (Boss), n=488 (Succeed, Others, Bad Luck). There were missing

values for all items; Boss had five missing values; Succeed, Others, Bad Luck had three missing

values each. Characteristics of these items were the mean, median, mode, range and standard

deviation (see Table 4.2). The means, medians and modes are close to each other and the range is

the same for each item. The most deviant item is Succeed, because there is some diversity between

the mean, median and mode.

Table 4.2 Characteristics of items

Mean Median Mode Std.

Deviation Range

Boss 3.703 4.0 4.0 .9678 4.0

Succeed 4.300 4.5 5.0 .7282 4.0

Others 2.096 2.0 2.0 .8169 4.0

Bad Luck 2.144 2.0 2.0 .8909 4.0

4.2.2 Principal Axis Factoring

In the Principal Axis Factoring (PAF) the rescaled items were used as input, total N=491. In SPSS a

PAF was run and the number of factors was based on eigenvalues > 1. This resulted in a total

variance explained of 33.5% and two factors were extracted. The pattern matrix (appendix III)

showed factor 1 ‘External Locus of Control’ had two items; Others (λ=.442) and Bad Luck (λ=.728),

and factor 2 ‘Internal Locus of Control had also two items; Boss (λ=.647) and Succeed (λ=.422). The

factor correlation matrix (appendix III) indicated that there was indeed a negative correlation, r=-

.172, between the two factors; ILOC and ELOC. In the Western culture two factors were extracted

(based on eigenvalues > 1) with a 30.7% Total variance explained. The pattern matrix (appendix III)

of the Western culture showed factor 1 ‘Internal Locus of Control’ had two items; Boss (λ=.584) and

Succeed (λ=.568) and factor 2 ‘External Locus of Control’ had also two items; Others (λ=.610) and

Bad Luck (λ=.421). The factor correlation matrix (appendix III) of the Western culture indicated that

there was a negative correlation, r=-.281, between the two factors. In the Non-Western culture

there are also two factors extracted based on eigenvalues >1. This resulted in a total variance

explained of 40.6%. Factor 1 ‘External Locus of Control’ had two items; Others (λ=.432) and Bad

Luck (λ=.883), and factor 2 ‘Internal Locus of Control had also two items; Boss (λ=.672) and Succeed

(λ=.414), shown by the pattern matrix (appendix III). The factor correlation matrix (appendix III) of


the Non-Western culture indicated that there was a negative correlation, r=-.105, between the two

factors.

4.2.3 Calculation of scale scores

The calculation of scale scores for ILOC and ELOC resulted in a n=485 (for ILOC) and a n=487 (for

ELOC). These scale scores for ILOC and ELOC were used for further analyses: Multivariate Analysis

of Variance and correlation coefficients. Some characteristics of the scale scores are shown in Table

4.3. The mean, median and mode are close to each other for each DV. There is a shorter range for

ELOC than for ILOC.

Table 4.3 Characteristics of dimensions

Mean Median Mode Std.

Deviation Range

Internal Locus of

Control 3.998 4.0 4.0 .6807 4.0

External Locus of

Control 2.118 2.0 2.0 .6971 3.5

4.2.4 Multivariate Analysis of Variance

The assumptions of normality and homogeneity were checked before analysis, see chapter 3.

The effect of the number of response categories

In the MANOVA there were two dependent variables: ILOC and ELOC and three independent

variables: scale width, culture and version. The multivariate test showed a main effect of the number

of response categories, indicated by scale width F1,475=15.7, p=.000, ηp2=.062 (medium effect size).

Figure 4.1 Mean of Internal Locus of Control for scale width Figure 4.2 Mean of External Locus of Control for scale width


The univariate tests showed this main effect for the DV ELOC (F1,475=25.2, p=.000) and for the DV

ILOC (F1,475=4.1, p=.043). These effects had small effect sizes ILOC: ηp2= .009 and ELOC: ηp

2=.050

(almost medium effect size). Hypotheses 1 and 2 were accepted. The mean (SD) of ILOC for the

wide scale was higher M=4.1 (.587) than the short scale M=3.9 (.743), see Figure 4.1. In Figure 4.2

the mean (SD) of ELOC for the wide scale is higher M=2.3 (.682) than the short scale M=1.9 (.665).

The manipulation had the same effect on the mean score differences for ILOC and ELOC. In other

words, the mean of the wide scale was higher than the mean of the short scale despite ILOC and

ELOC.

The effect of culture

There is no significant effect of culture on the relative mean of Locus of Control (p=.395). This effect

is also not present at univariate level, ELOC (p=.461) and ILOC (p=.232). Our hypotheses 5 and 7

were rejected. Figure 4.3 and Figure 4.4 show the mean of resp. ILOC and ELOC for culture and

shows that the means are similar.

Figure 4.4 Mean of External Locus of Control for Culture Figure 4.3 Mean of Internal Locus of Control for Culture


The effect of version

There is no significant effect of version on the relative mean of Locus of Control (p=.247). This effect

is also not present at univariate level for ELOC (p=.327) and for ILOC (p=.208). The means for ILOC

and ELOC are shown in Figure 4.5 and Figure 4.6. The mean are similar, there is an indication that

order 2 increases the mean for both ILOC and ELOC.

The interaction effects

There were four interaction effects tested between the IV’s; scale width, culture and version. There

were three two-way interactions and one three-way interaction. No significant interaction effect

was present, not on multivariate nor on univariate level. The interaction scale width*culture

(p=.758), the interaction scale width*version (p=.477), the interaction culture*version (p=.471) and

the interaction scale width*culture*version (p=.643). Hypotheses 9 and 11 were rejected. Western

and Non-Western cultures are equally sensitive to the manipulation of scale width and version.

Version had no interaction effect with scale width and two versions of the questionnaire have no

significant effect on the effect of the number of response categories. Scale width did not interact

in combination with the other two IV’s.

Figure 4.6 Mean of External Locus of Control for Version Figure 4.5 Mean of Internal Locus of Control for Version


4.3 The effect on the frequency of extreme scores for ILOC and ELOC

There was missing data on extreme scores for both ILOC and ELOC, both had two missing values

which resulted in a N=489.

4.3.1 The number of response categories

An overview of the extreme scores on both ends for each scale width is given in Table 4.4. This

shows that extreme score on ILOC are mainly at the high end (‘applies completely’) while the

extreme scores on ELOC are mainly at the low end (‘doesn’t apply at all’). There is also a difference

between the short and the wide scale.

Table 4.4 Frequency of extreme scores for each scale end (1 and 5) for scale width

Internal Locus of Control

The percentage of extreme scores in the short scale is 68.4% and in the wide scale is 31.6%, n=206

(see table 4.5 and figure 4.7). To test if there was a difference in frequency in extreme scores for

scale width (hypothesis 3) a Pearson chi-square test was performed. The test statistic χ2=25.9

(α=.05), df=1, p=.000. The column distributions were not the same. Hypothesis 3 was accepted.

There was a difference between the scale widths. The odds of an extreme score in the wide scale

was 0.381 times the odds of an extreme score in the short scale with a 95% CI [.261 , .554]. It was

2.6 times, 95% CI [1.8 , 3.8], more likely that a person scores an extreme value in the short scale

than in the wide scale.

Width

Short Wide

1 5 1 5

Boss 14 46 2 23

Succeed 2 119 . 54

Others 84 1 15 .

Bad Luck 82 2 18 3


Table 4.5 Contingency table Internal Locus of Control*Scale width

Scale width

Total

Short

scale

Wide

scale

Internal Locus of

Control

no extreme value Count 128 155 283

% within Internal Locus

of Control 45,2% 54,8% 100,0%

extreme value Count 141 65 206


of Control 68,4% 31,6% 100,0%

Total Count 269 220 489


of Control 55,0% 45,0% 100,0%

Figure 4.7 Frequency of extreme scores for ILOC in

short and wide scale

n=141

n=65


External Locus of Control

The percentages of extreme scores are resp. 81% (short scale) and 19% (wide scale), n=158 (see

Table 4.6 and Figure 4.8). To test if there is a difference in frequency in extreme scores for scale

width (hypothesis 4) a Pearson chi-square test was performed. The test statistic χ2=64.7 (α=.05),

df=1, p=.000. The column distributions are not the same. Hypothesis 4 is accepted. There is a

difference between the scale widths. The odds of an extreme score in the wide scale was 0.172 times

the odds of an extreme score in the short scale with a 95% CI [.109 , .270]. It was 5.8 times, 95% CI

[3.7 , 9.2], more likely that a person scores an extreme value in the short scale than in the wide scale.

Table 4.6 Contingency table External Locus of Control*Scale width

Scale width

Total Short scale Wide

scale

External Locus of

Control


% within External

Locus of Control 42,3% 57,7% 100,0%


% within External



% within External


Figure 4.8 Frequency of extreme scores for ELOC in

short and wide scale

n=30

n=128


4.3.2 Culture

An overview of the extreme scores on both ends for each dominant culture is given in table 4.7.

This shows that extreme score on ILOC are mainly at the high end (‘applies completely’) while the

extreme scores on ELOC are mainly at the low end (‘doesn’t apply at all’). There is not much

difference between Western and Non-Western, except for the item Succeed (and thus DV ILOC).

Table 4.7 Frequency of extreme scores for each scale end (1 and 5) for culture


To test if there was a cross-cultural difference in the frequency of extreme scores (hypothesis 6) a

Pearson chi-square was calculated to test if the column distributions (frequencies of extreme scores

and no extreme scores) are the same. The percentage of extreme scores in the Western Society is

38.8% and in the Non-Western Society is 61.2%, n=206 (see Table 4.8 and Figure 4.9). The test

statistic χ2=22.9 (α=.05), df=1, p=.000. The column distributions were not the same. Hypothesis 6

was accepted. There was a difference between the Western and Non-Western culture. The odds of

an extreme score in the Non-Western culture was 2.4 times the odds of an extreme score in the

Western with a 95% CI [1.689 , 3.526]. It was 2.4 times more likely that a person scores an extreme

value in the Non-Western culture than in the Western culture.

Culture

Western Non-Western

1 5 1 5

Boss 4 34 12 35

Succeed 1 65 1 108

Others 43 . 56 1

Bad Luck 48 1 52 4


Table 4.8 Contingency table Internal Locus of Control*Culture

Culture

Total Western Non-

Western

Internal Locus of

Control


% within Internal

Locus of Control 60,8% 39,2%

100,0

%


% within Internal


100,0

%


% within Internal


100,0

%

Figure 4.9 Frequency of extreme scores for ILOC

per Culture

n=80

n=126



To test if there was a cross-cultural difference in the frequency of extreme scores (hypothesis 8) a

Pearson chi-square was calculated to test if the column distributions (frequencies of extreme scores

and no extreme scores) are the same. The percentage of extreme scores in the Western culture is

46.2% and in the Non-Western culture is 53.8%, n=158 (see Table 4.9 and Figure 4.10). The test

statistic χ2=2.7 (α=.05), df=1, p=.103. The column distributions are almost the same. Hypothesis 8

is rejected. There was no difference between the Western and Non-Western culture. The odds of

an extreme score in the Non-Western culture was 1.4 times the odds of an extreme score in the

Western culture with a 95% CI [.938 , 2.006]. It was 1.4 times more likely that a person scores an

extreme value in the Non-Western culture than in the Western culture.

Table 4.9 Contingency table External Locus of Control*Culture

Culture

Total Western Non-

Western

External Locus of

Control


% within External


100,0

%


% within External


100,0

%


% within External


100,0

%

Figure 4.10 Frequency of extreme scores for ELOC

per Culture

n=73

n=85


4.3.3 Interaction effect


To test cross-cultural differences regarding the effect of the number of response categories on the

frequency of extreme scores in ILOC (hypothesis 10) a Cochran chi-square was calculated. This test

statistic of conditional independence Cochran’s χ2=22.1, df=1, p=.000 (N=489). Hypothesis 10 is

accepted. This indicated that there is an association between culture and scale width into the

frequency of extreme scores on ILOC. The manipulation seems to work more extreme among Non-

Western cultures, as shown in Figure 4.11 and Figure 4.12. The portion (30%; n=38) of extreme

scores in the wide scale in the Non-Western culture (Figure 4.12) is smaller than this respective

portion (34%; n=27) in the Western culture (Figure 4.11).

n=27

n=53

Figure 4.11 Frequency of extreme scores for ILOC per scale

width in Western Culture

n=38

n=88

Figure 4.12 Frequency of extreme scores for ILOC per scale

width in Non-Western Culture



To test cross-cultural differences regarding the effect of the number of response categories on the

frequency of extreme scores in ELOC (hypothesis 12) a Cochran chi-square was calculated. This test

of conditional independence Cochran’s χ2 was 62.9, df=1, p=.000 (N=489). Hypothesis 12 was

accepted. This indicated that there was an association between culture and scale width into the

frequency of extreme scores on ELOC. The manipulation seems to work more extreme among

Western cultures, as shown in Figure 4.13 and Figure 4.14. The portion (15%; n=11) of extreme

scores in the wide scale in the Western culture (Figure 4.13) is smaller than this respective portion

(22%; n=19) in the Non-Western culture (Figure 4.14). The portions of extreme scores in the wide

scale were even smaller in External Locus of Control than in Internal Locus of Control. The

manipulation had even more effect on the frequency of extreme scores in ELOC.

Figure 4.13 Frequency of extreme scores for ELOC per scale

width in Western Culture

n=62

n=11

Figure 4.14 Frequency of extreme scores for ELOC per scale

width in Non-Western Culture

n=66

n=19


4.4 Association between Internal and External Locus of Control

4.4.1 Correlation coefficient

To test if there was a negative association between ILOC and ELOC (hypothesis 13) a Pearson

correlation coefficient was calculated. The Pearson correlation coefficient was based on N=483 of

the rescaled data. There is no significant negative correlation between ILOC and ELOC, the

correlation coefficient r=-.067, p=.072. Hypothesis 13 was rejected. Internal and External Locus of

Control are two independent uncorrelated dimensions.

4.4.2 Cross-cultural differences on the association between Internal and External Locus of Control

In order to test the difference between two independent correlation coefficients (hypothesis 14)

both correlation coefficients and their respectable group size were necessary. In this study there

were two independent samples, namely Western (n=249) and Non-Western (n=234) culture. The

correlation coefficient between ILOC and ELOC in the Western culture was r=-.142, p=.025 (n=249).

There was a negative association between ILOC and ELOC. The correlation coefficient between ILOC

and ELOC in the Non-Western culture r=-.008, p=.901 (n=234). There was no association between

ILOC and ELOC. Fisher’s z-score was computed z=-1.473 (p=.141), hypothesis 14 was accepted.

There was no cross-cultural difference in the association between ILOC and ELOC.


5. Discussion

In this discussion the results of this study are discussed as well as strengths and limitations. Besides,

recommendations for future research are given.

5.1 Results

5.1.1 Number of response categories

The results show a significant effect of the number of response categories on the mean and

frequency of extreme scores. When inspecting the mean and the scores on ILOC and ELOC, it can

be observed that for ILOC the mean scores are around response category 4 and for ELOC this is

around response category 2. From a practical point of view this manipulation will not have major

implications, because the integer does not differ due to the manipulation (despite the statistical

significant difference). However, when integers change due to the manipulation of number of

response categories the practical relevance increases. This research suggests that for LOC and

especially ELOC this could be true, since there is a significant difference. The partial effect size of

ELOC indicated an almost medium effect size with an increase in mean score of 0.4. Using this LOC

scale in practice results in a higher mean score, this is applicable to all respondents and therefore

should be taken into account when comparing this scale with others.

The frequency of extreme scores decreases when the number of response categories increases, as

is consisted with Dawes (2002). This is what you expect, as the variance (number of options) is

increased and the respondents can score high or low at the scale end without scoring on the actual

extreme. However, Clarke III (2001) found that increasing the number of scale categories beyond

five categories had little effect on extreme response. This study shows that there is an effect when

the number of response categories is increased from five to nine. The OR shows that this effect is

quite large for ELOC, 5.8 times more likely to score extreme in a short scale than a wide scale, and

seems to influence External Locus of Control more than Internal Locus of Control. In this study the

operationalization of extreme scores is defined as scoring extreme on either or both items (after

rescaling). Adapting this definition, into for instance scoring extreme on both items, could result

into different results. Also comparing three groups of extreme scores (none, one and both) could

give more insight into differences between extreme scores.


5.1.2 Culture

The results show no significant effect of culture on the mean scores of ILOC and ELOC. Perhaps

compressing the heterogeneous sample into two groups was not sufficient for any significant

differences to arise. Another classification of culture could result in different outcomes. However,

culture has a significant effect on the frequency of extreme scores, so the classification made is

sufficient to be detected. The frequency of extreme scores for ILOC are more extreme in the Non-

Western Culture. In the Non-Western Culture people tend to agree (score 5) on either of both items

or ILOC (‘I’m my own Boss’ and ‘If I work hard, I will succeed’), this could indicate that there might

be a sort of acquiescence for ILOC in the Non-Western Culture. This is also claimed by Smith (2004)

who refers to the in-group harmony in collectivist countries which might lead to acquiescence.

Whereas Non-Westerners are known to be more externally oriented (score higher on ELOC)

(Hamamura et al., 2008) and overall are perceived to have a more moderate response style

(Hamamura et al., 2008; Harzing, 2006). From this perspective you would expect moderate mean

scores on ILOC within Non-Western Culture and not significantly more extreme scores (as found).

This reasoning is also supported by the idea that Westerners are considered to be less concerned

with the consequences of expressing strong opinions and thus extreme response style (Harzing,

2006; Johnson, 2005). However, Johnson (2005) found similar results as in this study with a

heterogeneous sample of adults from 19 countries. This would support the results from this study

in another heterogeneous sample.

5.1.3 Moderating effect of Culture

Culture has no significant moderating effect on the effect of the number of response categories on

the mean. Both cultures are equally sensitive to the manipulation of number of response categories.

This would mean that the 4-item LOC scale can be applied cross-culturally to measure LOC.

However, this is only the case for the classification of culture in this study. Another classification

could result in different outcomes when investigating moderating effects of culture on the number

of response categories.

The inspection of both dependent variables (separately) with regard to the frequency of

extreme scores it is apparant that Internal Locus of Control is affected (with a significant p-value)

by both #RC, culture, and their interplay. External Locus of Control is significantly affected by the

number of response categories and the moderating effect of Culture. This moderating effect

differed in each Culture. From this we can derive that the number of response categories has a

stronger influence or a larger effect on the frequency of extreme scores than a single effect of


culture. The interesting feature of this study is the moderating effect of culture on the frequency of

extreme scores.

There is a significant moderating effect of culture on the effect of the number of response

categories on the frequency of extreme scores in ILOC and ELOC. This moderating effect shows that

the manipulation of scale width is more extreme in ILOC in the Non-Western culture and more

extreme in ELOC in the Western culture. When the number of response categories increase the

frequency of extreme scores decreases. This phenomenon is more extreme for ILOC in the Non-

Western culture and for ELOC even more extreme in the Western culture. Using more response

categories would have less possible acquiescence on ILOC for the Non-Western culture, since Non-

Westerners score significantly less extreme on ILOC then Westerners when more response

categories are used. Therefore using more response categories would result in lower acquiescence

in ILOC.

5.1.4. Association ILOC and ELOC

Based on Kovaleva (2012) it was expected that there is a negative association between ILOC and

ELOC. However, this study reveals that this hypothesis could not be accepted. The null hypothesis

that there is no association was not rejected and therefore accepted. This would entail that Internal

Locus of Control and External Locus of Control are two separate orthogonal dimensions, as

presented by other authors (Lefcourt, 1972; Levenson, 1972; Wallston et al., 1978). On the other

hand, this study did not perform a Confirmatory Factor Analysis to determine the factor structure,

instead an Exploratory Factor Analysis (PAF) is used. However, when investigating this association

between ILOC and ELOC for each dominant culture, which is part of the test of the difference

between two independent correlation coefficients (Preacher, 2002), there is a difference. In the

Western culture there is a significant negative association between ILOC and ELOC while in the

Non-Western culture there is not. This has implications on the factor structure, since there is a two

correlated factors structure in the Western Culture and a two uncorrelated factor structure in the

Non-Western culture. Using the 4-item LOC scale would therefore have other interpretations in

practice dependent on dominant culture.


5.2 Strengths

The sample consists of two heterogeneous independent samples. One sample received the original

4-items LOC scale (5 response categories) as part of a larger questionnaire, the other sample

received the same questionnaire with the manipulation (9 response categories). The number of

respondents and the recruitment of respondents is based on course registration. The researcher

has no influence on this. With regard to the questionnaire the course registration has a role as well.

Based on the enrolment for either of the two courses the exact number of questionnaires can be

printed and prepared, also the distribution of version (order 1 and order 2) is equal for each course.

This results in a balanced distribution of versions of the questionnaire. Besides this, the

questionnaire is handed out by the teacher of the course and not by the researcher. There is more

familiarity with the teacher than an outside researcher, so there will be less a ‘guinea-pig’ effect.

Respondents have less the idea that they are a test subject when a familiar teacher asks them to fill

in a questionnaire compared to an non-familiar outsider. Therefore it is expected that respondents

are more authentic in answering the questions although it is already anonymous. Lastly, the

classification process in this study is based on existing research like Tobi and Kampen (2013) whom

handled similar data from the same questionnaire in a previous research.

5.3 Limitations

The sample consists solely of students of Wageningen University and this has an influence on the

external validity. These students are not representative for their country of origin and therefore

culture. A limited age group is included in the sample and students are highly educated people.

Other levels of education are not represented in the sample. Besides this, one can deliberate if

highly educated people express Internal LOC and/or External LOC differently than not high-

educated people. Perhaps high-educated are in general more Internal and/or more inclined to have

an extreme response style. This feature is not included in this study. The process of distribution of

the questionnaires is dependent on the teacher. Therefore instructions need to be clear and trust

that the teacher distributes the questionnaires as intended. This also related to the distribution of

version. In 2014 when the wide scale was distributed the distribution of version is similar, but in

2013 (short scale) there is a large difference in versions. There is a difference of 40 questionnaires

between order 1 and order 2. This could have an impact on the 2 x 2 x 2 design. The group sizes in

the cells of the design could differ too much, which can affect the homogeneity of variances (on

the analysis assumptions). Lastly, the labelling of the response categories. In this study the labelling

of the response categories differed, the short scale has labelling at every scale point while the wide

scale has labelling at the ends. This is a confounder that could not be measured nor corrected for

in this study. Therefore we cannot say with absolute certainty that the effect of the number of


response categories is due to the number of response categories. However, there is some evidence

on the use and omission of mid-points. Mid-points have no effect on the mean of the response

levels (Si & Cullen, 1998 cited by Dawes, 2002) and omission of a mid-point mainly effects the

frequencies of the neighbouring response categories (Spagna, 1984 cited by Dawes, 2002). The

confounding effect of labelling could be less influential than anticipated.

5.4 Recommendations

Cross-cultural analysis in the frequencies of extreme scores can be expanded by including more

levels of extreme response and compare extreme response with moderate response. Also, a

distinction between extreme scores at the different ends can be made to gain insight on

acquiescence and disacquiescence. This would result in differences in extreme scores between

Cultures at each scale end, such as more extreme scores at low end for ELOC in Western Culture.

Future research can also focus on excluding labelling as a possible confounder for the effect of the

number of response categories, comparing a 5-point scale with a 9-point scale.


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7. Appendices

Appendix I – Questionnaire

Appendix II – Syntax for SPSS

Syntax 1 - Correction for version

Syntax 2 – Calculation of ILOC and ELOC

Syntax 3 – Correction for scale width (via recoding)

Syntax 4 – Calculation scale scores ILOC and ELOC

Syntax 5 – Extreme scores

Syntax 6 – recode in no extreme and extreme score

Appendix III – Output Results

Principal Axis Factoring

Correlations (Inter-item, Item-whole and Dimensions)


Appendix I – Questionnaire

At Wageningen University many different students take one of our courses in Research

Methodology. To get a better insight in the students who take this particular course, we would

like to invite you to answer the questions below. Please, be so kind as to fill in this questionnaire. It will only take about 5 minutes.

1. What is the code of your MSc-programme? Please, answer the question by circling the

number in front of the correct answer.

1. MAB 6. MBT 11. MFQ 16. MID 21. MME 26. MPS

2. MAF 7. MCS 12. MFS 17. MIL 22. MMS 27. MSS

3. MAS 8. MEA 13. MFT 18. MLE 23. MNH 28. MUE

4. MBF 9. MES 14. MGI 19. MLP 24. MOA 29. Other

5. MBI 10. MFN 15. MHW 20. MMA 25. MPB

2. In a typical week, how many hours do you spend on this course (YRM20806) including

classes? Please circle the answer that is most appropriate for you.

(1) < 5 hrs

(2) 6 - 10 hrs

(3) 11 - 15 hrs

(4) 16 - 20 hrs

(5) > 20 hrs

3. Will you consider taking any of the following research methodology modules in ACT

(Academic Consultancy Training)? Please, circle the correct answer.

Interview techniques (1) yes (2) no (3) don’t know

Observation techniques (1) yes (2) no (3) don’t know

Questionnaire construction (1) yes (2) no (3) don’t know

4. How satisfied are you with working in groups in general? Please, circle the number

that best reflects your answer.

Not at all Extremely

satisfied satisfied

1 2 3 4 5 6 7 8 9

5. How satisfied are you with working in groups in this course? Please, circle the number that best reflects your answer.


satisfied satisfied 1 2 3 4 5 6 7 8 9

6. How would you rate your ability? Please, circle the correct number.

Excellent Good Fair Poor

a. to speak English? (4) (3) (2) (1)

b. to understand spoken English? (4) (3) (2) (1)

c. to read English? (4) (3) (2) (1)

d. to write English? (4) (3) (2) (1)


7. Did you take the Test Of English as a Foreign Language (TOEFL)?

(1) No

(2) Yes, my score was ……..

8. What is your first language? ……………………………………..

9. What is your second language? ……………………………………..

10. What is your third language? ..……………………………………

11. Do you consider yourself bi-lingual or multi-lingual? Please circle the correct answer.

(1) No, monolingual

(2) Yes, bilingual

(3) Yes, with three or more languages

12. Which language did/do you speak with your parents/caretakers?

…………………………………………………….

13. Which was/were the language(s) of instruction at your high school?

………………………………………….………..…………………………………………………….

The following statements may apply more or less to you. To what extent do you think each statement applies to you personally.

Doesn't

apply at all

Applies

completely

14. I'm my own boss. (1) (2) (3) (4) (5) (6) (7) (8) (9)

15. If I work hard, I will succeed. (1) (2) (3) (4) (5) (6) (7) (8) (9)

16. Whether at work or in my private

life: what I do is mainly determined by others.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

17. Bad luck often gets in the way of

my plans.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

18. What is your country of origin? ……………………………………….

19. How long have you been living in the Netherlands? Please, circle the correct answer.

(1) Less than 6 months

(2) Between 6 and 12 months

(3) Between 1 and 2 years

(4) More than 2 years

20. What is your age? ……….. years

21. What is your gender? Please, circle the correct answer.

(1) male

(2) female

Thank you for your cooperation!


The questionnaire on the previous page had order 1. Order 2 had different ordering in these

questions below. Except for question 2, the scales in the answers options differed, and question 10

where the order of response categories differed.

2. In a typical week, how many hours do you spend on this course (YRM20806) including

classes? Please circle the answer that is most appropriate for you.

(1) < 20 hrs

(2) 21 - 25 hrs

(3) 26 - 30 hrs

(4) 31 - 35 hrs

(5) > 35 hrs

8. How satisfied are you with working in groups in this course? Please, circle the number



satisfied satisfied 1 2 3 4 5 6 7 8 9

9. How satisfied are you with working in groups in general? Please, circle the number



satisfied satisfied

1 2 3 4 5 6 7 8 9

10. How would you rate your ability? Please, circle the correct number.

Excellent Good Fair Poor a. to speak English? (1) (2) (3) (4)

b. to understand spoken English? (1) (2) (3) (4)

c. to read English? (1) (2) (3) (4)

d. to write English? (1) (2) (3) (4)

The following statements may apply more or less to you. To what extent do you think each

statement applies to you personally.

Doesn't

apply at all

Applies

completely

14. I'm my own boss. (1) (2) (3) (4) (5) (6) (7) (8) (9)

15. Bad luck often gets in the way of

my plans.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

16. Whether at work or in my private

life: what I do is mainly

determined by others.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

17. If I work hard, I will succeed. (1) (2) (3) (4) (5) (6) (7) (8) (9)


Appendix II – Syntax for SPSS

Syntax 1 - Correction for version

If (version=1) General = Q4.

If (version=1) Group = Q5.

If (version=2) Group = Q4.

If (version=2) General = Q5.

FREQUENCIES VARIABLES=General, Group.

If (version=1) BOSS = Q14.

If (version=1) SUCCEED = Q15.

If (version=1) OTHERS = Q16.

If (version=1) FATE = Q17.

If (version=2) BOSS = Q14.

If (version=2) FATE = Q15.

If (version=2) OTHERS = Q16.

If (version=2) SUCCEED = Q17.

FREQUENCIES VARIABLES=BOSS, Succeed, Others, fate.

Syntax 2 – Calculation of ILOC and ELOC

IF (BOSS ge 1 AND SUCCEED ge 1) Int_LOC=mean(BOSS, SUCCEED).

IF ( Others ge 1 and fate ge 1) Ext_LOC=mean(Others, fate).

EXAMINE VARIABLES=Int_LOC, Ext_LOC

/PLOT NONE

/STATISTICS DESCRIPTIVES

/CINTERVAL 95

/MISSING PAIRWISE

/NOTOTAL.

Syntax 3 – Correction for scale width (via recoding)

DO IF (Year = 4).

RECODE BOSS SUCCEED OTHERS FATE (1=1) (2=2) (3=3) (4=4) (5=5) INTO Boss_rescaled

Succeed_rescaled

Others_rescaled BadLuck_rescaled.

END IF.

VARIABLE LABELS Boss_rescaled 'Boss_rescaled' /Succeed_rescaled 'Succeed_rescaled'

/Others_rescaled 'Others_rescaled' /BadLuck_rescaled 'BadLuck_rescaled'.

EXECUTE.

DO IF (Year = 5).

RECODE BOSS SUCCEED OTHERS FATE (1=1) (2=1.5) (3=2) (4=2.5) (5=3) (6=3.5) (7=4) (8=4.5) (9=5)

INTO

Boss_rescaled Succeed_rescaled Others_rescaled BadLuck_rescaled.

END IF.

VARIABLE LABELS Boss_rescaled 'Boss_rescaled' /Succeed_rescaled 'Succeed_rescaled'

/Others_rescaled 'Others_rescaled' /BadLuck_rescaled 'BadLuck_rescaled'.

EXECUTE.


Syntax 4 – Calculation scale scores ILOC and ELOC

IF (Boss_rescaled ge 1 and Succeed_rescaled ge 1) Internal_LOC=mean(Boss_rescaled,

Succeed_rescaled).

IF (Others_rescaled ge 1 and BadLuck_rescaled ge 1) External_LOC=mean(Others_rescaled,

BadLuck_rescaled).IF (Boss_rescaled ge 1 and Succeed_rescaled ge 1).

COMPUTE Internal_LOC=MEAN(Boss_rescaled, Succeed_rescaled).

IF (Boss_rescaled ge 1 and Succeed_rescaled ge 1).

VARIABLE LABELS Internal_LOC 'COMPUTE Internal_LOC=MEAN(Boss_rescaled, Succeed_rescaled)'.

EXECUTE.

COMPUTE External_LOC=MEAN(Others_rescaled, BadLuck_rescaled).

IF (Others_rescaled ge 1 and BadLuck_rescaled ge 1).

VARIABLE LABELS External_LOC 'COMPUTE EXternal_LOC=MEAN(Others_rescaled, BadLuck_rescaled)'.

EXECUTE.

Syntax 5 – Extreme scores

RECODE Boss_rescaled Succeed_rescaled Others_rescaled BadLuck_rescaled (1=1) (5=1)

(SYSMIS=SYSMIS)

(1.5 thru 4.5=0) INTO Boss_Extreme Succeed_Extreme Others_Extreme BadLuck_Extreme.

VARIABLE LABELS Boss_Extreme 'Boss_Extreme' /Succeed_Extreme 'Succeed_Extreme' /Others_Extreme

'Others_Extreme' /BadLuck_Extreme 'BadLuck_Extreme'.

EXECUTE.

IF (Society >= 1) Internal_Extreme=SUM(Boss_Extreme,Succeed_Extreme).

VARIABLE LABELS Internal_Extreme 'IF (Society >= 1) '+

'Internal_Extreme=SUM(Boss_Extreme,Succeed_Extreme) '.

EXECUTE.

IF (Society >= 1) External_Extreme=SUM(Others_Extreme,BadLuck_Extreme).

VARIABLE LABELS External_Extreme 'IF (Society >= 1) '+

'External_Extreme=SUM(Others_Extreme,BadLuck_Extreme) '.

EXECUTE.

Syntax 6 – recode in no extreme and extreme score

DO IF (Society >= 1).

RECODE Internal_Extreme External_Extreme (0=0) (SYSMIS=SYSMIS) (1=1) (2=1) INTO

ILOC_Extreme_yes_no

ELOC_Extreme_yes_no.

END IF.

VARIABLE LABELS ILOC_Extreme_yes_no 'ILOC_Extreme_yes_no' /ELOC_Extreme_yes_no

'ELOC_Extreme_yes_no'.

EXECUTE.


Appendix III – Output from SPSS

Principal Axis Factoring

Total Sample

Pattern Matrixa

Factor

1 2

Boss_rescaled ,096 ,647

Succeed_rescaled -,091 ,422

Others_rescaled ,442 -,059

BadLuck_rescaled ,728 ,074

Extraction Method: Principal Axis Factoring.

Rotation Method: Oblimin with Kaiser

Normalization.a

a. Rotation converged in 4 iterations.

Total Variance Explained

Factor

Initial Eigenvalues Extraction Sums of Squared Loadings

Rotation Sums of

Squared

Loadingsa

Total % of Variance Cumulative % Total % of Variance Cumulative % Total

1 1,402 35,038 35,038 ,759 18,966 18,966 ,743

2 1,207 30,166 65,204 ,573 14,317 33,283 ,611

3 ,767 19,180 84,384

4 ,625 15,616 100,000


a. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

Factor Correlation Matrix

Factor 1 2

1 1,000 -,172

2 -,172 1,000

Extraction Method: Principal Axis

Factoring.

Rotation Method: Oblimin with

Kaiser Normalization.


Western Culture

Total Variance Explaineda

Factor


Rotation Sums of

Squared

Loadingsb


1 1,482 37,050 37,050 ,809 20,225 20,225 ,716

2 1,117 27,933 64,983 ,421 10,525 30,749 ,619

3 ,756 18,900 83,883

4 ,645 16,117 100,000


a. Only cases for which Society = Western are used in the analysis phase.

b. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

Pattern Matrixa,b

Factor

1 2

Boss_rescaled ,584 -,077

Succeed_rescaled ,568 ,054

Others_rescaled -,001 ,610

BadLuck_rescaled -,002 ,421



Normalization.


b. Only cases for which Society = Western are

used in the analysis phase.

Factor Correlation Matrixa

Factor 1 2

1 1,000 -,281

2 -,281 1,000


Factoring.



a. Only cases for which Society =

Western are used in the analysis

phase.


Non-Western Culture

Total Variance Explaineda

Factor


Rotation Sums of

Squared

Loadingsb


1 1,441 36,034 36,034 1,002 25,041 25,041 ,999

2 1,254 31,357 67,391 ,622 15,555 40,595 ,622

3 ,770 19,245 86,636

4 ,535 13,364 100,000


a. Only cases for which Society = Non-Western are used in the analysis phase.

b. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.

Pattern Matrixa,b

Factor

1 2

Boss_rescaled ,157 ,672

Succeed_rescaled -,151 ,414

Others_rescaled ,432 -,045

BadLuck_rescaled ,883 ,087



Normalization.


b. Only cases for which Society = Non-Western

are used in the analysis phase.

Factor Correlation Matrixa

Factor 1 2

1 1,000 -,105

2 -,105 1,000


Factoring.



a. Only cases for which Society =

Non-Western are used in the

analysis phase.


Correlations

Inter-Items correlations and Item-whole correlations

Correlations

Boss_rescal

ed

Succeed_re

scaled

Others_resc

aled

BadLuck_re

scaled

Internal

Locus of

Control

External

Locus of

Control

Boss_rescaled Pearson Correlation 1 ,275** -,056 ,047 ,857** -,007

Sig. (1-tailed) ,000 ,110 ,150 ,000 ,443

N 486 485 485 485 485 484

Succeed_rescaled Pearson Correlation ,275** 1 -,060 -,105* ,730** -,105*

Sig. (1-tailed) ,000 ,094 ,010 ,000 ,010

N 485 488 486 486 485 485

Others_rescaled Pearson Correlation -,056 -,060 1 ,336** -,077* ,798**

Sig. (1-tailed) ,110 ,094 ,000 ,046 ,000

N 485 486 488 487 484 487

BadLuck_rescaled Pearson Correlation ,047 -,105* ,336** 1 -,027 ,836**

Sig. (1-tailed) ,150 ,010 ,000 ,279 ,000

N 485 486 487 488 484 487

Internal Locus of

Control

Pearson Correlation ,857** ,730** -,077* -,027 1 -,067

Sig. (1-tailed) ,000 ,000 ,046 ,279 ,072

N 485 485 484 484 485 483

External Locus of

Control

Pearson Correlation -,007 -,105* ,798** ,836** -,067 1

Sig. (1-tailed) ,443 ,010 ,000 ,000 ,072

N 484 485 487 487 483 487

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Correlation between ILOC and ELOC

Correlations

Internal Locus of

Control

External Locus

of Control

Internal Locus of Control Pearson Correlation 1 -,067

Sig. (1-tailed) ,072

N 485 483

External Locus of Control Pearson Correlation -,067 1

Sig. (1-tailed) ,072

N 483 487

cross-cultural scale effects for a short locus of control

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