the influence of ethical work climate (ewc) and

THE INFLUENCE OF ETHICAL WORK CLIMATE

(EWC) AND DEMOGRAPHIC VARIABLES ON

AUDITORS’ ETHICAL EVALUATION

SKRIPSI

By

Devi Selena

008201200053

presented to the

Faculty of Business President University

in partial fulfillment of the requirements for

Bachelor Degree in Economics Major in Accounting

President University

CikarangBaru – Bekasi

Indonesia

2016

iv

ABSTRACTS

Ethics are considered to be important in audit practice. The intensive

works lead ethical dilemma, especially when there is no exact rules exist.

Researchers have responded by attempting to investigate and analyze the ethical

behavior. While each ethical reasoning process is important, the ability of

auditors to evaluate ethical problems (second phase of Rest’s ethical reasoning

process) that may not be obvious should be studied and understood. This study

will indentify the influence of ethical work climate (EWC) and specified

demographic variables (age, gender, and length of experience) on ethical

evaluation. The climate of accounting public firm is examined based on Victor

and Cullen’s ethical work climate type.

Findings in this study are based on response to scenarios that is related to

cost and time pressures in the fieldwork such as underreporting of time (URT)

and quality threatening behavior (QTB). We targeted the respondents who work

as professional public auditors in Java Island, Indonesia. From 300 questionnaires

distributed, researcher got feedback from 283 auditors. The data is analyzed by

using structural equation model method. Structural equation method is used

because ethical evaluation and ethical work climate cannot be measured directly.

The finding revealed that ethical work climate has significant influence on

auditors’ ethical evaluation. Ethical work climate which exist in public

accounting firm in Java area is composed by six dimensions such as efficiency,

friendship, team interest, social responsibility/public interest, company rules and

procedures, and laws and professional codes climates. Specified demographic

variables are found to give insignificant influence on auditors’ ethical evaluation.

Implications of findings and areas for future research are discussed in the last

chapter.

KEY WORDS: auditors’ ethical evaluation, ethical work climate, age, gender,

length of experience, underreporting of time (URT), quality threatening behavior

(QTB).

v

ACKNOWLEDGMENT

This research is hardly to be done without the big supports from many

parties. Author would like to express my gratitude to:

1. Ida Sang Hyang Widhi Wasa for His favor and the wisdom that are given

during the thesis process.

2. Dr. Sumarno Zain, SE, Ak, M.B.A as advisor of my thesis for giving me

advice, guidance, and recommendation to collate and arrange the thesis

properly.

3. Drs. Gatot Imam Nugroho, Ak, MBA, CA and Drs. Asep Supriatna, MBA

as examiners during defense and comprehensive test for giving me

excellent experience.

4. Putu Purna Wirawan and Ni Nyoman Dwi Adnyani as my parents for their

loves and pray for my success. Nadia Indah Devianty and Made Sandika

D. as my sister and brother in law for giving inspiration during writing

thesis.

5. Ketut Dwi Adnyawati as my twin mother, Putu Venessa, and Made Wina

Sadina as my beloved cousins for helping me to correct my grammar.

6. Ongky Aristian for giving me spirit and love to finish my thesis.

7. Angelina Suryani and Geraldo Risa Maranatha for helping me in

distributing my questionnaires to public accounting firms.

8. Wratsari Windrawati W., Tasya Firsty Annissa, and Deviani Riasari

Nalurita as my roommates for encouraging me when I am feeling down.

9. Daisy Wijaya and Prasetio Nur as my best senior for giving guidance in

each obstacle that I have found in university life.

10. Febru Aulia Ramadhani and Nursa Sherli Yoanita as my beloved team in

competition for attending and support me during defense.

11. Natasya, Ariana, Adam Maulana Akbar, Merinda, Melisa Anggreni, and

Vania Marleen for attending my defense.

12. All members in Accounting Club for encourage me and becoming my

family.

vi

13. KAP Hendrawinata Eddy Siddharta & Tanzil, KAP Mulyamin Sensi

Suryanto & Lianny, KAP Joachim Poltak Lian Michell dan Rekan, KAP

Meidina Ratna, KAP Jansen & Ramdan, KAP Drs. Bernardi & Rekan,

KAP Jojo Sunarjo & Rekan, KAP Drs. Selamat, Ak., BAP, KAP Drs.

Bambang Mudjiono & Widiarto, KAP Maurice Ganda Nainggolan, KAP

Rama Wendra, KAP Aria Kanaka & Rekan, KAP Basyiruddin & Wildan,

KAP Abdul Aziz Fiby Ariza, KAP Warnoyo, S.E., M.Si., KAP Yuwono

H., KAP Drs. Bambang Sudaryono & Rekan, KAP Effendy & Rekan,

KAP Heliantono & Rekan (Cabang Bekasi), KAP Drs. Mohammad

Yoesoef dan Rekan, KAP Moh. Mahsun, Ak, M.Si, CPA, KAP Toton

Sucipto and all external auditors as respondents for giving feedback of my

questionnaires.

14. All accounting students batch 2012.

15. All related people that cannot be mentioned one by one.

Researcher needs critics and suggestions to get better knowledge about what parts

to be improved in this research. Researcher hopes that this thesis will be useful

for the reader. Thank you.

Cikarang, 1st February 2016

Devi Selena

vii

TABLE OF CONTENTS

THE ADVISER RECOMMENDATION LETTER .............................................. i

DECLARATION OF ORIGINALITY ................................................................ ii

PANELS OF EXAMINERS APPROVAL SHEET ............................................ iii

APPROVAL SHEET ......................................................................................... iii

ABSTRACTS .................................................................................................... iv

ACKNOWLEDGMENT ...................................................................................... v

LIST OF TABLES ............................................................................................... x

LIST OF FIGURES ........................................................................................... xi

LIST OF ACRONYMS .................................................................................... xii

CHAPTER I ........................................................................................................ 1

INTRODUCTION ............................................................................................... 1

1.1. Research Background ............................................................................ 1

1.2. Problem Identification ........................................................................... 2

1.3. Statement of the Problem ....................................................................... 3

1.4. Research Objective ................................................................................ 4

1.5. Significance of the Study ....................................................................... 4

1.6. Scope and Limitation of the Study Assumption...................................... 6

1.7. Definition of Terms ............................................................................... 6

CHAPTER II ....................................................................................................... 7

LITERATURE REVIEW .................................................................................... 7

2.1. Rest’s Ethical Reasoning Process ........................................................... 7

2.2. Kohlberg Theory of Cognitive Development ......................................... 8

2.3. Ethical Work Climate Theory .............................................................. 10

2.4. Ethical Behaviors Examined ................................................................ 12

viii

2.5. Relationship between Ethical Evaluation and Ethical Work Climate

(EWC) ........................................................................................................... 13

2.6. Relationship between Ethical Evaluation and Demographics Variables 15

2.7. Theoretical Framework ........................................................................ 18

2.8. Assumption and Hypothesis ................................................................. 21

Chapter III ......................................................................................................... 22

Data Processing Method .................................................................................... 22

3.1. Research Method ................................................................................. 22

3.2. Operational Variable Identification ...................................................... 23

3.3. Data Collection Method ....................................................................... 25

3.4. Sampling Design ................................................................................. 25

3.5. Data Analysis ...................................................................................... 26

Stage 1: Defining individual constructs (Pretesting questionnaire) .............. 28

Stage 2: Developing and specifying the measurement model ...................... 28

Stage 3: Designing a study to produce empirical results .............................. 29

Stage 4: Assessing measurement model validity ......................................... 29

Stage 5: Specifying the structural model ..................................................... 33

Stage 6: Assessing the structural model validity .......................................... 33

3.6. Refining measures model ..................................................................... 34

3.6.1. Model I ......................................................................................... 34

3.6.2. Model II ....................................................................................... 40

3.6.3. Model III ...................................................................................... 46

3.6.4. Model IV ...................................................................................... 52

3.7. Hypothesis Testing .............................................................................. 58

3.8. Limitations .......................................................................................... 58

CHAPTER IV.................................................................................................... 59

ix

ANALYSIS OF DATA AND INTERPRETATION OF RESULTS ................... 59

4.1. Structural Model .................................................................................. 59

4.2. Testing of Structural Model Validity.................................................... 59

4.3. Hypothesis Testing .............................................................................. 60

4.4. Data Interpretation ............................................................................... 62

4.4.1. The influence ethical work climate on ethical evaluation .............. 62

4.4.2. The influence specified demographic variables (age, gender, and

length of experience) on ethical evaluation ................................................. 63

CHAPTER V ..................................................................................................... 66

CONCLUSIONS AND RECOMMENDATIONS .............................................. 66

5.1. Conclusion .......................................................................................... 66

5.2. Recommendations ............................................................................... 66

REFERENCES

APPENDIX 1 - Definition of Terms

APPENDIX 2A - Model I

APPENDIX 2B - Model II

APPENDIX 2C - Model III

APPENDIX 2D - Model IV

APPENDIX 3 - Model that has deleted demographic variables

APPENDIX 4 - Ethical evaluation: Means

APPENDIX 5 - Questionnaire

APPENDIX 6 - List of public accounting firms

x

LIST OF TABLES

Table 3.1 Operational variable identification ...................................................... 24

Table 3.2 Demographics details of the sample ................................................... 26

Table 3.3 Goodness-of-fit indices based on situational criterion (Hair, Black,

Babin, Anderson, & Tatham, 2006) ................................................................... 30

Table 3.4 Pre-testing of reliability ethical valuation variables model I ................ 34

Table 3.5 Pre-testing of reliability ethical work climate variables model I .......... 35

Table 3.6 Pre-testing of validity model I ............................................................ 35

Table 3.7 GOF measurement model I ................................................................. 38

Table 3.8 Reliability test of model I ................................................................... 38

Table 3.9 Validity test of model I ....................................................................... 39

Table 3.10 Pre-testing of reliability ethical evaluation variables model II ........... 40

Table 3.11 Pre-testing of reliability ethical work climate variables model II ....... 40

Table 3.12 Pretesting of validity model II .......................................................... 41

Table 3.13 Goodness-of-fit measurement model II ............................................. 43

Table 3.14 Reliability test of measurement model II .......................................... 44

Table 3.15 Validity test of model II ................................................................... 45

Table 3.16 Pre-testing of reliability ethical evaluation variables model III ......... 46

Table 3.17 Pre-testing of reliability ethical work climate variables model III ..... 46

Table 3.18 Pre-testing of validity model III ........................................................ 47

Table 3.19 GOF measurement model III ............................................................ 49

Table 3.20 Reliability test model III ................................................................... 50

Table 3.21 Validity test of measurement model III ............................................. 51

Table 3.22 Pre-testing of ethical evaluation variables model IV ......................... 52

Table 3.23 Pre-testing of reliability EWC variables model IV ............................ 52

Table 3.24 Pre-testing validity model IV ............................................................ 53

Table 3.25 Goodness-of-fit measurement model IV ........................................... 56

Table 3.26 Reliability test of measurement model IV ......................................... 56

Table 3.27 Validity test of measurement model IV............................................. 57

Table 4.1 t-value result ...................................................................................... 60

xi

LIST OF FIGURES

Figure 2.1 Rest's ethical reasoning process (Rest & Narvaez, Moral development

in the professions, 1994) ...................................................................................... 7

Figure 2.2 Teorectical ethical climate type (Victor & Cullen, A theory and

measure of ethical climate in organization, 1987) ............................................... 11

Figure 3.1 Six stages process of SEM (Hair, Black, Babin, Anderson, & Tatham,

2006) ................................................................................................................. 27

Figure 3.2 x-measurement model I ..................................................................... 36

Figure 3.3 y-measurement model I ..................................................................... 36

Figure 3.4 x-measurement model II................................................................... 41

Figure 3.5 y-measurement model II ................................................................... 42

Figure 3.6 x-measurement model III .................................................................. 47

Figure 3.7 y-measurement model III .................................................................. 48

Figure 3.8 x-measurement model IV .................................................................. 54

Figure 3.9 y-measurement model IV .................................................................. 54

Figure 4.1 Structural model ................................................................................ 59

xii

LIST OF ACRONYMS

EWC : Ethical Work Climate

PSO : Premature Sign Off

QTB : Quality Threatening Behavior

URT : Underreporting of Time

SEM : Structural Equation Model

GAAP : Generally Accepted Accounting Principles

IFRS : International Financial Reporting Standards

GAAS : Generally Accepted Auditing Standard

1

CHAPTER I

INTRODUCTION

1.1. Research Background

The well-known accounting case, Enron, has raised question about the

auditors’ creditability as consequences of their external auditors’ role play in the

manipulation of financial statements. The implication of Enron’s external auditor,

Arthur Andersen, to engage with unethical action was suspended permanently,

although they have given assurance services for a long time. The Enron case also

initiated crisis facing the profession. The trust of auditors was wrecked. Auditors

suppose to have greater responsibility to the public rather than their client because

they have main responsibility to assure the financial statement that prepared by

management of company is reported fairly which will be used by external users.

The society expects auditors to have high standard of ethical behavior. Due to the

auditors’ ethical crisis, society doubt whether they could rely on to the

information given in audited financial statements. US Congress responded the

ethical crisis by issuing Sarbanes-Oxley Act in 2002 which legislates ethical

behavior for both public company and accounting public firms. Canary &

Jennings (2008) study found that corporation, particularly in the post-SOX time

frame, is at least attempting to make ethics a central concern of everyday

practices.

Ethics are considered to be important in every aspect, especially while

performing audit quality. But, auditors still engage in unethical behavior such as a

quality threatening behavior (QTB) and underreporting of time (URT) cause of

extremely time and cost pressure on audit. Being labor intensive, controlling cost

and time on an audit experience are high time and cost pressure on audit and put a

lot of stress on auditors. In the Janis and Mann’s decision model, stress is a key of

conflict model in ethical dilemmas (Janis & Mann, 1977). The auditors should

have technical and ethical experts in solving this dilemma (Gaa, 1994). Ethical

2

experts will determine the audit quality especially in the condition where there is

no exact rules existed.

A considerable amount of the research on ethics within auditing

profession has focused on the second elements of Rest’s ethical reasoning process

model, those being the individuals’ ethical evaluation of a relevant situation.

Previous researchers have elaborated the model by exploring the various

individual and contextual factors influence on ethical evaluation. Several studies

investigate the association between auditors’ ethical judgment and demographic

characteristics: age, gender, and length of experience. Researchers also

investigate the influence of ethical work climate that is already designed by

Victor and Cullen on auditors’ ethical evaluation.

Researcher initiates to examine the impact of ethical work climate and

demographic variables on the ethical evaluation element of the Rest’s model

within the context of certain time pressure-related dysfunctional auditor behaviors

– especially URT and QTB. Other main purpose is to fulfill the requirement for

graduating from President University. The researcher is seeking the data by

distributing questionnaire to a group of experience level auditors in Java Island.

The methodology that is used in the research is explanatory quantitative to

explain the influence of one variable to other variables.

1.2. Problem Identification

Unethical behavior is the most of unwilling act in the audit field work;

however, it often occurs in the real world. Some study found that when

accountability pressure exists, auditors tend to tailor their message to the audience

when the audience is known (Buchman, Tetlock, & Reed, 1996; Cuccia,

Hackenbrack, & Nelson, 1995; Hackenbrack & Nelson, 1996) and auditors’

judgment variability decreases (Ashton, 1992; DeZoort, Horrison, & Taylor,

2006). Increasing the quality of an audit involves investing more time in the audit

3

that leads of further costs and aggressive audit fee competition (Beattie &

Fearnley, 1998).

Based on Rest’s reasoning process, there is the phase of auditor to be

aware of the situation or dilemma that may affect the welfare of others, evaluate

their ethical, decide what they will do ethically, and actual behavior. Once an

ethical dilemma is identified, the auditor will frame an ethical strategy for

resolving ethical dilemma. At this point, auditor judges whose line of action is

ethically justifiable and then decides how the dilemma ought to be resolved. A

connection between organizational climate and demographic variables influences

and individual ethical judgment have long been assumed. The sub-organizational

climate that we will talk about is ethical work climate. The types of ethical work

climate use the theoretical designed by Victor-Cullen that has nine dimensions.

Demographic variables that will be examined are age, gender, and length of

experience.

1.3. Statement of the Problem

This research is about determining the influence of ethical work climate

on auditors’ ethical evaluation of ethical issues in the context of time and budget

pressure. Researcher wants to find out the correlation between ethical evaluation

and ethical work climate whether ethical work climate has a significant influence

on ethical evaluation or not. This study will add knowledge about the ethical

evaluation and the influence of ethical work climate into it, giving suggestion to

the public accountant firms in order to maintain the ethical work climate.

Statement of the problem 1: Does ethical work climate have significant

influence on ethical evaluation?

Researcher wants to determine the influence of specified demographic

variables (age, gender, and length of experience) on auditors’ ethical evaluation

of ethical issues in the context of time and budget pressure. Researcher is seeking

the impact of age, gender, and length of experience on ethical evaluation to enrich

4

literature in auditing field by giving empirical evidence about the impact of work

ethical climate and demographic variables on auditors’ ethical evaluation.

Statement of the problem 2: Does age have significant influence on ethical

evaluation?

Statement of the problem 3: Does gender have significant influence on ethical

evaluation?

Statement of the problem 2: Does length of experience have significant

influence on ethical evaluation?

1.4. Research Objective

The objectives to meet in this research which has a title “The Influence of Ethical

Work Climate and Demographic Variables on Auditors’ Ethical Evaluation” are

as follows:

1. To fulfill the requirement of graduating from President University

2. To examine the influence of ethical work climate on auditors’ ethical

evaluation.

3. To examine the influence of age on auditors’ ethical evaluation.

4. To examine the influence of gender on auditors’ ethical evaluation.

5. To examine the influence of experience on auditors’ ethical evaluation.

1.5. Significance of the Study

Research is a study and analysis of factors, subject, or problem to find the

solution, discover information, and reach an understanding. The result of this

research is expected to give benefit to several parties, they are:

1. External auditors

External auditors have responsibilities to serve public interest. They

should have great competence, integrity in the practice. This research will

5

help them to maintain their ethics that suppose to be important and

concerned of being professional auditor.

2. Public accounting firm

Public accounting firm has an employment level such as: partner, director,

manager auditor, senior auditor, associate level, and staff. Above level of

employment in public accounting firms need to monitor and supervise

their co-worker in the audit team. This research will help them to make

right decision regarding the ethical matter in work field and control the

behavior of senior associate level and associates level.

3. Accountant’s professional organizations

Accountant’s professional organization will be greatly benefited from the

finding of this study as consideration in order to improve code of conduct

or rules that give strict punishment to any auditors who do unethical act.

The rules exist to maintain the auditors’ responsibility to serve public

interest.

4. Academician

Researcher expects this study will give additional knowledge in context of

Accountant Professional Ethics about the factors influence toward

auditors’ ethical evaluation. The finding of this research will enrich

literature in ethical field by giving empirical evidence about the impact of

work ethical climate and demographic variables on auditors’ ethical

evaluation.

5. Researchers

This research could be a reference for future researchers who conduct

research regarding auditor ethics in context of pressure.

6

1.6. Scope and Limitation of the Study Assumption

There are some points that should be clarified for the equalization

perception among reader and researcher regarding the matter of the research. This

research examines the evaluation of ethical issues in the context of time and

budgeting pressure such as underreporting of time (URT) and quality threatening

behavior (QTB). Thus, only one step of Rest’s ethical reasoning process is

discussed in this research without examining other phases. The variables used in

this research to examine auditors’ ethical evaluation are ethical work climate,

gender, length of experience, and age. The climate of accounting public firm will

examine based on theoretical ethical work climate type that developed by Victor

and Cullen. Researcher examines the public firm in Java Island due to time and

cost barrier.

1.7. Definition of Terms

The definitions provide a common frame of reference to improve the

understanding and usefulness of this study. Please refer to APPENDIX 1 to see

the terms.

7

CHAPTER II

LITERATURE REVIEW

2.1. Rest’s Ethical Reasoning Process

Kohlberg (1969) theory of cognitive development has provided a

framework for the majority of studies on auditors’ ethical reasoning (Jones,

Massey, & Thorne, An experimental examination of the effects of individual and

situational factors on unethical behavioral intentions in the workplace, 1996).

Drawing upon the Kohlberg’s theory, Rest & Narvaez (1994) identified four

sequential components of the ethical reasoning process: sensitivity in identifying

the existence of a moral question, ethical evaluation, intention to act morally and

actual moral behavior (Figure 2.1). Moral reasoning is much more than moral

judgment in producing moral behavior which included, but is not limited to moral

sensitivity, judgment, motivation, and character which are all interrelated in the

moral development of individual (Rest & Narvaez, Moral development in the

professions, 1994).

Figure 2.1 Rest's ethical reasoning process (Rest & Narvaez, Moral development in the

professions, 1994)

The first component (ethical sensitivity) refers to the recognition of an issue as

having ethical implications. Failure of an individual to recognize that a situation

may or may not contain a moral element would impede that individual from going

any further in the moral analysis of the situation (Wortman, 2006). Jones T. M.

(1991) identified that there is two necessary components to recognize a moral

dilemma: the individual must understand that his/her actions will affect others and

Ethical

sensitivity

Ethical

evaluation

Intention

to act

Actual

Behavior

8

that she/he has a choice in a matter. When person is able to be aware, then that

person will continue to the next steps. The second involves making a judgment if

an action is ethically correct. Once an individual makes a judgment about a moral

dilemma, s/he still has the opportunity to decide what behavior to adopt

(Wortman, 2006). The third deals with intention to act which is determined by the

value of an individual places on the ethical course of action versus the value of

other courses of action. The fourth distinguishes the intention from the actual

action. An intention may not result in an action and, therefore, Rest sees this as a

separate and distinct component.

2.2. Kohlberg Theory of Cognitive Development

Kohlberg postulates that cognitive structures and interpretative processes

precipitate an individual’s ethical decision choices (Trevino, Experimental

approaches to studying ethical-unethical behavior in organization, 1992).

Kohlberg theory is developmental in focus and proposes three broad levels of

sophistication in ethical reasoning. At first level, the pre-conventional level, the

individual decides what is right or wrong based upon consequences. At the

second level, the conventional level, the individual is concerned about

expectations of significant others and relies upon rules and laws to determine

what is right or wrong. At the third level, the post-conventional level, the

individual decides what is right or wrong in using universal ethical principles

such as common good and justice (Kohlberg, Stage and sequences: The cognitive

development approach to socialization, 1969; Rest, Narvaez, Bebeau, & Thoma,

1999). In specific, the stages of cognitive moral development are as follows.

(Kohlberg, Continuities in childhood and adult moral development revisited,

1973; Kohlberg, The philosophy of moral development 1, 1981; Logsdon &

Yuthas, 1997)

Pre-convential level: Behavioral norms are viewed as being external to the

individual.

9

Stage 1: Punishment-obidience orientation.

Stage 2: Instrumental hedonism and concrete reciprocity.

Conventional level: Externally validated norms are internalized by the individual.

Stage 3: Orientation to interpersonnal relation of mutuality.

Stage 4: Maintenance of social order; fixed rules and authority.

Post-conventional level: Individual recognition that external norms may not fully

encompass ethical behavior.

Stage 5: Social contract, with conscience orientation.

Stage 6: Universal ethical principle orientation.

At the pre-conventional level, a person views rules as imposed and

external to himself/herself. Moral decisions are justified in terms of one’s own

hedonistic interests and in terms of rewards and punishment. Stage one of

individuals form moral judgments guided by obedience for its own sake and to

avoid punishment. Stage two moral judgments are guided by a “you scratch my

back, I will scratch yours” reciprocity. (Arnaud, 2006).

At level two, the conventional level, the individual internalizes the shared

moral norms of the society or a group of the society (e.g. family). What is

considered morally right is explained in terms of living up to roles and what is

expected of the individual by others, and fulfilling duties, rules and laws. Stage

three individuals find ethical behavior to be what pleases and help others. Stage

four individuals’ perspective broaden to consider the society of which they are

part. At this stage, moral judgments consider the riles and laws of social, legal, or

religious systems that are designed to promote the common good. (Arnaud, 2006)

At level three, the post-conventional level, the individual has gone beyond

identification with others’ expectation, rules and laws. Stage five individuals

recognize the relativism of personal values. They still emphasize laws and rules

because they represent the social contract, but they understand the laws can be

10

changed for socially useful purposed. Stage six individuals guided by self chosen

ethical principles of justice and human right. Kohlberg claimed that higher stage

moral judgments are better and more desirable (Arnaud, 2006).

2.3. Ethical Work Climate Theory

Victor & Cullen (1987) define ethical work climate (EWC) as the shared

perceptions of what is ethically correct behavior and how ethical issue should be

handled. They noted “organization are social actors responsible for the ethical or

unethical behaviors of their employees,” and there is “…increasing concern for

understanding and managing organzational normative systems that may guide the

ethical behavior of employees,” (Victor & Cullen, The organizational bases of

ethical work climates, 1988). Ethical work climate is environment of company

that support ethical behavior as guidance for their employees. In regard forming

possible ethical climate types, Victor & Cullen (1987) combine two dimensions:

the ethical criteria used for organizational decision making and the loci of anaysis

as referent in ethical decision making.

2.3.1. The ethical criteria dimension

This dimension of EWC is grounded in Kohlberg’s theory of

cognitive moral development (Arnaud, 2006). Victor & Cullen (1987)

draw three levels of cognitive moral reasoning to define ethical dimension

of their model. They termed these criteria egoism, benevolence, and

principle, corresponding to Kohlberg’s preconventional, conventional, and

postconventional moral reasoning, respectively. The egoistic criterionis

characterized by employees’ desires to maximise self interest. The

benevolence ethical criterion is characterised by employees’ desires to

maximise the collective interest of the organization. The principle ethical

criterion is characterised by employees’ adhrence to broader principles of

society and humanity. Egoistic climate tends to lead less ethical decision

rather than benevolent and principle climate (Shafer, Poon, & Tjosvold,

2013).

11

2.3.2. Loci of analysis

The second dimension of Victor & Cullen (1987) framework

borrows from Kohlberg’s theory who defines three loci of concern at

which three ethical criterias are to be considered. The first two of

Kohlberg’s stages, the locus of concern is individual; in the third and

fourth stages the individual’s referent group becomes a larger social

system; and in the highest stages consideration is given to humanity and

other consideration as a whole (VanSandt, Shepard, & Zappe, 2006).

The crossing of these dimensions produces the 3x3 matrix of climate type shown

as below.

Figure 2.2 Theoretical ethical climate type (Victor & Cullen, A theory and measure of

ethical climate in organization, 1987)

The self-interest climate is only care about what is best for them. Individual tends

to protect his/her own interest above others. People in company profit climate are

expected to do anything of further the company’s profit. Profit organization is

mainly goal to achieve higher profit over year to year. They expect their people to

work efficiently in the company (egoism/cosmopolitan). Organization with

benevolence/individual is care about each individual decision making. The

benevolence/local is essential climate. This climate concerns for the good of all

12

people in the company or placing other people’s interest first. Organization

concerned the effect of decision to public in benevolence/cosmopolitan climate.

In principle/individual climate, organization members are expected to follow their

own personal and moral beliefs to identify and make ethical decisions (Wortman,

2006). Organizational with principle/local climate is described in terms of “it is

important to follow strictly the company’s rules and procedures”. Most behaviors

in this climate are directed towards the enforcement of policies and procedure that

build in company. Within principle/cosmopolitan climate, members of company

are primarily concerned with conformity to and abiding by professional standards

and laws (e.g. GAAP, IFRS, GAAS).

2.4. Ethical Behaviors Examined

Ethics research in accounting has focused on a multitude of unethical

behaviors and research on ethical decision making suggests an adverse effect of

time pressure on ethical principles (Moberg, 2000). A stream of literature exists

since the 1970s on time pressure-induced auditor behaviors which are considered

dysfunctional for audit firms and potentially damaging for the profession (e.g.

Buchheit, Pasewark Jr., & Strawser, 2003; Kelley & Margheim, 1987; Rhode,

1978; Sweeney & Pierce, 2006). The range of the behaviors examined in previous

research includes URT and various QTB such as biasing of sample selection,

premature sign-off (PSO) (where auditors sign-off work as completed without

actually completing the work), unauthorized reduction of sample size, greater

than appropriate reliance on client work, acceptance of weak client explanations,

and failure to properly document work (Pierce & Sweeney, Auditor responses to

cost control, 2003).

QTB has been associated with both time deadline and time budget

pressures (Kelley & Margheim, 1999; Pierce & Sweeney, 2004), whereas URT is

only relevant in the context of time budget pressure (Sweeney & Pierce, 2006).

Regarding the ethicality of these behaviors, QTB has been described as an ethical

issue, as it ‘has consequences for others and involves choice or volition on the

13

part of the auditor’ (Coram, Glavovic, Ng, & Woodliff, 2008) and this also

applies to URT.

The type of QTB behavior was considered important by audit seniors in

determining the consequences, with premature sign-off being much more serious

than small reductions in sample size (Pierce & Sweeney, 2006). Perceived

consequences of URT were less severe and included positive consequences such

as improved performance evaluations, increased firm profitability and negative

consequences such as pressure on the individual to maintain image of efficiency

and reduction in the quality of management information (Sweeney & Pierce,

2006). While QTB and URT are not specifically referred to in ethical guidelines

of the profession, the behaviors can be considered contrary to the spirit of the

guidelines. It would be expected that the perceived magnitude of consequences

and perceived social consensus regarding the unacceptability of the behaviors

would be highest for PSO and lowest for URT.

Yet, Pierce & Sweeney (2006) found that audit seniors expressed a low

level of concern over the ethicality of QTB, while Sweeney & Pierce (2006)

found that audit seniors’ concern over the ethicality of URT was virtually non-

existent. Sweeney, Arnold, & Pierce (2010) found URT can be perceived as an

ethical act and PSO is more unacceptable behavior than some other types of QTB.

2.5. Relationship between Ethical Evaluation and Ethical Work

Climate (EWC)

In general, egoistic climates tend to lead to less ethical decisions, while

benevolent and principled climates lead to more ethical decisions (Shafer, Poon,

& Tjosvold, 2013). Indeed, with their explicit focus on the pursuit of self-interest

(egoistic/individual) and narrowly defined firm interests such as profitability

(egoistic/local), it seems logical that egoistic climates should be associated with

less ethical behavior (Shafer, Poon, & Tjosvold, 2013). In the case of benevolent

climates, the welfare of individuals, organizational groups or members of society

14

at large are a primary focus of concern (Shafer, Poon, & Tjosvold, 2013).

Members within benevolent/local organizational climate are concerned about

what is best for everyone in the company. Often this ethical climate is manifested

through communication, employee inclusiveness, valuing people, and

demonstration of concern (Whitener, Brodt, Korsgaard, & Werner, 1998). These

factors all make up a sense of trust which has been linked to decision making

process (Gao, Sirgy, & Bird, 2005).

In such climates, employees perceive that decisions are made based on an

overarching concern for the well-being of these parties (Martin & Cullen, 2006);

thus, such decisions should generally be viewed as ethical in nature. It is

somewhat more difficult to generalize regarding the effects of principled climates

on ethical behavior. As noted by Trevino, Butterfield, & McCabe (1998), this

difficulty arises primarily due to the uncertain effects of principled/individual

climates. When the organizational climate encourages individuals to follow their

own moral principles, it is difficult to predict behavior and whether such behavior

will be viewed by others as ethical. The principled/local and

principled/cosmopolitan climates, however, should clearly promote relatively

ethical behavior due to their emphasis on following prescribed organizational or

professional rules and codes of conduct (Shafer, Poon, & Tjosvold, 2013).

Significant variation exists in the specific ethical climates identified across

studies (Trevino, Butterfield, & McCabe, The ethical context in organizations:

influences on employee attitudes and behaviors, 1998; Martin & Cullen, 2006).

Indeed, the nine theoretical climate types were intended only as a general

framework for the conceptualization of ethical climates and thus inconsistencies

across organizational settings should be expected (Victor & Cullen, A theory and

measure of ethical climate in organization, 1987). Nonetheless, recent studies of

ethical climate in public accounting firms have consistently found support for the

existence of benevolent/cosmopolitan (public interest) and

principled/cosmopolitan climates, as well as egoistic/individual and/or

egoistic/local climates (Cullen, Parboteeah, & Victor, 2003; Parboteeah, Cullen,

Victor, & Sakano, 2005; Shafer W. , 2009). Due to the emphasis on serving the

15

public interest and following professional codes of conduct in public accounting

(AICPA, 2009), it is not surprising that benevolent/cosmopolitan and

principled/cosmopolitan climates have consistently emerged in this context.

Codes of conduct (principle climate dimension) have been a common proxy for

the ethical environment in accounting and auditing literature, because

organization, including accounting firms, and their employees consider them to

be relevant and important (Lamberton, Mihalek, & Smith, 2005) in making

explicit ethical values, putting employees on notice as to what is ethical, and

shifting accountability for actions from firms to individuals.

Egoistic/individual and egoistic/local climates also appear highly relevant

to the public accounting context, since the pursuit of self-interest and firm

profitability are arguably among the primary obstacles to serving the public

interest and following the spirit of professional codes of conduct (Shafer, Poon, &

Tjosvold, 2013). The ethical climate construct has been quite influential in the

business ethics literature and the weight of the evidence suggests that employees’

perceptions of the prevailing climates in their organization affect ethical

decisions, and are also associated with work outcomes such as organizational

commitment and job satisfaction (Martin & Cullen, 2006).

2.6. Relationship between Ethical Evaluation and Demographics

Variables

This research examines the impact of specified demographic variables:

age, gender, and the length of experience towards ethical evaluation.

2.6.1. Age

Mixed findings have been reported on the relationship between age

and ethical decision making (Ford & Richardson, Ethical decision

making: A review of the empirical literature, 1994). Clarke, Hill, &

Stevens (1996) found that age and moral development were significantly

negatively related for Big 6 practitioners, while Ruegger & King (1992)

16

found that age was positively correlated to ethical attitudes. Lane (1995),

Loe, Ferrel, & Mansfield (2000), Longeneeker, McKinney, & Moore

(1989), and Yoo & Donthu (2002) found a possitive correlation between

age and ethical decision making indicating that older students are more

likely to act ethically than the younger students. Early reviews (Ford &

Richardson, Ethical decision making: A review of the empirical literature,

1994; Loe, Ferrel, & Mansfield, 2000) found seven out of eight studies

indicating older people are more ethical than younger people. Age showed

a significant positive relationship with ethical evaluation (Sweeney,

Arnold, & Pierce, 2010). Ethical judgment was associated with increased

age (Valentine & Rittenburg, 2007). O'Fallon & Butterfield (2005) further

found mixed results with eight of their 21 findings not producing

significant results. Six studies found a positive relationship between age

and ethical decision making, while five studies indicated a negative

relationship. In Lehnert, Park, & Singh (2015) review four findings that

reported a significant effect of age. Three findings reported that older

people tend to behave more ethically than younger people. Mixed results

in previous studies indicate that the role of age in ethical decision making

is not clear; therefore, researcher concludes age may have a significant

influence on auditors’ ethical evaluation.

2.6.2. Gender

A number of studies has examined the impact of gender on ethical

decision making, with some findings that females have higher ethical

decision making ability than males (Barnett, Bass, & Brown, 1994;

Bernardi & Arnold, 1997; Cohen, Pant, & Sharp, 1998; Clarke, Hill, &

Stevens, 1996; Eynon, Hill, & Stevens, 1997; Sweeney, Arnold, & Pierce,

2010) and others showing no difference between males and females

(Dubinsky & Levy, 1985; Radtke, 2000; Ponemon, Ethical reasoning and

selection socialization in accounting., 1992; Armstrong, 1987). Sweeney,

Arnold, & Pierce (2010) study indicated females reported significantly

17

higher ethical evaluation than males. O'Fallon & Butterfield (2005) noted

49 studies in gender category, with the majority or tose studies (23

studies) not finding significant differences. In the 16 studies found

significant gender differences, females were found to be more ethical than

males. Glover, Bumpus, Logan, & Ciesla (1997) found a significant

correlation between gender and ethiccal decision making. They found that

women’s decision about moral issues were more ethical than men’s.

Female seems to be more aware of ethical issues and more likely to act

ethically than their male counterparts (Robin & Babin, 1997; Borkowski

& Ugras, 1998). In (Craft, 2013), discussion of the impact of various

gender-specific variable on ethical decision making highlights that

females are more ethical than males, however, males are more consistent

in their decision making. As general, researcher concludes that gender has

significant influence on auditors’ ethical sensitivity.

2.6.3. Length of experience

Glover, Bumpus, Sharp, & Munchus (2002) found a positive

relationship between years of management experience and ethical choice.

They argued that greater experience may be linked with greater awareness

of acceptable ethics and a greater experience of dealing with similar

situations (Sweeney, Arnold, & Pierce, 2010). Otherwise, Thorne,

Massey, & Magnan (2003) found that there is a significant negative

corelation between years experience and ethical judgment. Eweje &

Brunton (2010) found that more experienced students appeared to be more

ethically oriented. A study found a positive and significant relationship

between work experience and ethical decisoin making with the other two

reporting non significant results (Lehnert, Park, & Singh, 2015). Ethical

judgement was associated incrased experience (Valentine & Rittenburg,

2007).

18

General auditing experience has been found to be positively

related to auditors’ judgment performance when the audit task requires

exercise of individual judgment (Martinov-Bennie & Pflugrath, 2009). For

more complex tasks requiring greater exercise of judgment, general

auditing experience can improve performance by providing the necessary

skills and/or knowledge required to complete these tasks (Anderson,

Koonce, & Marchant, 1994; Anderson & Maletta, 1994). Task specific

experience has been shown to be able to provide additional improvement

in the quality of auditors’ judgments for semi-structured and unstructured

tasks (Bonner & Lewis, 1990; Libby & Tan, 1994; O’Reilly, Leitch, &

Wedell, 2004; Pincus, 1991; Wright, 2001). Martinov-Bennie & Pflugrath

(2009) found greater task-specific experience provide more significantly

higher quality technical judgments than those with lower levels of task-

specific experience.

According to previous studies, researcher concludes that length of

experience has significant influence on auditors’ ethical evaluation.

2.7. Theoretical Framework

Rest (1986) has suggested that one needs to perform four basic

psychological processes in order to behave ethically. The four basic psychological

processes are called as Rest’s ethical reasoning. In short, the process begins with

the awareness of moral problem exists, and then individual decides the correct

moral and has willingness to behave ethically. The last phase is actual behavior.

The evaluation of ethical issue is really important for the professionals to

decide what is right or wrong, ethical or unethical act while they have extremely

pressure on audit. Some study found that when accountability pressure exists,

auditors tend to tailor their message to the audience when the audience is known

(Buchman, Tetlock, & Reed, 1996; Cuccia, Hackenbrack, & Nelson, 1995;

Hackenbrack & Nelson, 1996) and auditors’ judgment variability decreases

19

(Ashton, 1992; DeZoort, Horrison, & Taylor, 2006). Cost and time pressures are

also caused in ethical dilemma of auditor. Bigger cost of audit is probably caused

of extended time on audit for increasing audit quality. Imbalance in audit work

and staffing, client induces pressure, and complexity of business environment will

increase time pressure on auditor. The un-behaved actions will be arisen from

cost and time pressures are quality threatening behaviors (QTB) and

underreporting of time (URT). In specific, dysfunctional responses caused of time

and budget pressure are biasing the sample selection, too much reliance on client

work, phantom ticking, and an overall lower standard of work.

Some previous literature already expanded the Rest’s model by

recognizing influence of multiple variables and their interactions. They have

developed the relationship between individual factors into Rest’s model such as

general demographics characteristics (e.g. gender (Gilligan, 1982; Barnett, Bass,

& Brown, 1994; Bernardi & Arnold, 1997; Clarke, Hill, & Stevens, 1996; Cohen,

Pant, & Sharp, 1998; Browning & B., 1983; Dubinsky & Levy, 1985; Radtke,

2000), political orientation (Elmer, Renwick, & Malone, 1983)), ethical

development (Trevino, 1986), etc. Prior researchers also identified the influence

of contextual factors into the Rest’s model which include the immediate job

context (time and budget pressure, role pressure, rewards and sactions, and the

influence of significant others) (Trevino & Weaver, Managing ethics in the

business organization: Social scientific perspectives, 2003), the external context

(professional environment, organizational environment), and issue specific

factors. The ethical climate construct has been quite influential in the business

ethics literature and the weight of the evidence suggests that employees’

perceptions of the prevailing climates in their organization affect ethical

decisions, and are also associated with work outcomes such as organizational

commitment and job satisfaction (Martin & Cullen, 2006).

Organizational climate is conceptualized as the way individuals perceive

personal impact of their environment (James, James, & Ashe, 1990). Thus,

climate encompasses the set of characteristics, which the members of the

organization perceive and come to describe in a shared way (Verbeke, Volgering,

20

& Hessels, 1998). Victor & Cullen (1987) develop theoretical ethical work

climate types by combining the locus of analysis and ethical criterion that adopted

from Kohlberg theory. Indeed, the nine theoretical climate types were intended

only as a general framework for the conceptualization of ethical climates and thus

inconsistencies across organizational settings should be expected (Victor &

Cullen, A theory and measure of ethical climate in organization, 1987).

Nonetheless, recent studies of ethical climate in public accounting firms have

consistently found support for the existence of benevolent/cosmopolitan (public

interest) and principled/cosmopolitan climates, as well as egoistic/individual

and/or egoistic/local climates (Cullen, Parboteeah, & Victor, 2003; Parboteeah,

Cullen, Victor, & Sakano, 2005; Shafer W. , 2009). The ethical climate construct

has been quite influential in the business ethics literature and the weight of the

evidence suggests that employees’ perceptions of the prevailing climates in their

organization affect ethical decisions (Martin & Cullen, 2006).

The influence of auditors’ ethical evaluation may also come from

demographic variable. According to Cambridge advanced learner's dictionary

third edition (2008), demographic is the quantity and characteristics of the people

who live in particular area, especially in relation to their age, how much money

they have and what they spend it on. Demographic variables are talking about

gender, age, and length of experience that will be discussed in this study. Mixed

findings have been reported on the relationship between specified demographic

variables (age, gender, and length of experience) and ethical decision making.

Researcher concludes that those demographic variables may have significant

influence on ethical evaluation.

21

2.8. Assumption and Hypothesis

According to explanation of theories above and formulation of problems

from previous chapter, several hypotheses suggest as follows.

H1: Ethical work climate has significant influence on ethical evaluation.

H2: Age has significant influence on ethical evaluation.

H3: Gender has significant influence on ethical evaluation.

H4: Length of experience has significant influence on ethical evaluation.

22

Chapter III

Data Processing Method

3.1. Research Method

The research employed a quantitative methodology that uses statistical

measurement to obtain the result. The quantitative methodology is best suited for

study due to concrete result as the basis to prove the hypothesis. The procedures

of quantitative methodology will form the scientific foundation to explain the

relationship between independent variables and dependent variables in targeted

population that is called as explanatory approach. In explanatory approach,

statistical techniques are used to test the significant relationship among variables.

The variables of this study are as follows.

3.1.1. Dependent variable

Dependent variable is the main variable that researcher wants to

investigate deeply. Researcher is interested in quantifying and measuring

dependent variable to get solution of the problem. The dependent variable

of this research is auditors’ ethical evaluation. Auditors’ ethical evaluation

is auditors’ ability to evaluate the action.

3.1.2. Independent variable

Independent variable causes the change in dependent variable in

either positive or negative way. The independent variables that may have

influenced on auditors’ ethical evaluation are ethical work climate and

demographic variable.

23

3.2. Operational Variable Identification

Operational variable identification is to determine how measurement of

variables will be made. Each variable will be defined operationally.

1. Auditors’ ethical evaluation

To identify auditors’ ability to evaluate the action, the researcher prepares

the cases related to certain time and cost pressure-related dysfunctional

auditor behaviors – especially URT and QTB. Auditors is requested to

give their preference in five point scale (1 = favor the action; 5 = oppose

the action). The higher score given reflects a higher ethical evaluation as it

indicates that the behaviors represented in the cases are considered

unethical.

2. Ethical work climate

The nine theoretical climate types were intended to be evaluated. The nine

questions of ethical work climate are adopted from Victor & Cullen

(1987, 1988). Each indicator is to be rated by the respondents based on

how they are perceived it really in their organization, not how they prefer

it to be, in a five point scale, ranging from “strongly disagree” to :strongly

agree”.

3. Specified demographic variables

The demographic variables that are willing to execute in this research are

age, gender, and length of experience. A question is prepared for each

variable to determine how long their experience in professional field, their

age, and their gender.

The indicators of each variable are elaborated on table 3.1 below.

24

VARIABLE INDICATOR SYMBOL* SCALE

MEASUREMENT

Auditors'

ethical

evaluation

a. Auditors evaluate behavior about biasing

sample selection. EV1 Ordinal

b. Auditors evaluate behavior about over-reliance

on client work. EV2 Ordinal

c. Auditors evaluate behavior about URT. EV3 Ordinal

d. Auditors evaluate behavior about PSO. EV4 Ordinal

Ethical work

climate a. People in this company are very concerned

about what is best for them (egoism-individual

climate)

EWC1 Ordinal

b. People are expected to do anything o further

the company’s interest (egoism-local climate). EWC2 Ordinal

c. In this company, each person is expected,

above all, to work efficiently (egoism-

cosmopolitan climate).

EWC3 Ordinal

d. It is expected that each individual is cared for

when making decisions here (benevolence-

individual climate).

EWC4 Ordinal

e. Our major consideration is what is best for

everyone in this company (benevolence-local

climate).

EWC5 Ordinal

f. The effect of decisions on the customer and the

public are primary concerned in this company

(benevolence-cosmopolitan climate).

EWC6 Ordinal

g. Each person in this company decides for

himself what is right and wrong (principle-

individual climate).

EWC7 Ordinal

h. It is important to follow strictly the company’s

rules and procedures (principle-local climate). EWC8 Ordinal

i. In this company, people are expected to strictly

follow legal or professional standards (principle-

cosmopolitan climate).

EWC9 Ordinal

Specified Demographic

variables (age, gender,

length of

experience)

a. Auditors give their personal information about

their age. AGEM Ordinal

b. Auditors give personal information about their

gender. GENDERM Ordinal

c. Auditors give personal information about how

long they have worked at professional accounting

firm.

LEM Ordinal

* symbol in path diagram

Table 3.1 Operational variable identification

25

3.3. Data Collection Method

Data collection methods are designed to maintain the integrity of the

study. In order to provide sufficient information for the study, the author uses the

qualitative data. The qualitative data is used as the data that presents related

literatures and concepts to support this study. To address the research questions,

researcher collected data from several sources as below:

1. Questionnaire is one of three main data collections in survey research.

Due to the limitation of researcher, the questionnaire will be manually and

electronically distributed. Questionnaires were distributed to professional

auditor at big four public accounting firms and non big four public

accounting firms in Java Island. To measure auditors’ ethical evaluation,

researcher distributed questionnaires with cases that adopted and explored

from previous study. The respondents will give their opinion of each

statement in the 5 point scale. The questionnaire includes 3 parts: 1)

unethical action cases to measure ethical evaluation variable 2) ethical

work climate 3) personal information in regards to demographic variables

(see APPENDIX 5).

2. Literature review is secondary data to gather the data from source that

already exist. Literature review is a method of collecting theoretical data

by reading and studying some books and other writing materials which are

relevant to the topic that the author has chosen. The materials will be used

as guidance to develop conceptual framework and supporting argument of

the finding.

3.4. Sampling Design

The sample must represent the population interest and must be adequate

for subsequent analysis. Some form of random sampling is used in probability

sample design to enable researcher in using probability theory to determine the

accuracy of results through the computation of standard error. Two types that are

explained in probability theory are probability sampling designs and non

26

probability sampling designs. The auditor uses the purposive sampling, one of

non probability sampling design types. Researcher received back 283

questionnaires from 300 questionnaires distributed. Researcher is also

compromising the data from the auditor into four types of demographic variables

which are age, gender, firm size, and length of experience in table 3.2.

Demographic details Total data

Non big four 205

Big four 78

Female 147

Male 136

Mean length experience 2.5231

Mean age 24.9965

Table 3.2 Demographics details of the sample

3.5. Data Analysis

The research model is tested using structural equation modeling.

Structural equation modeling (SEM) is used to describe the causal relation among

the latent variables. The software for data processing is used LISREL. SEM is

also able to give information about factor loading and measurement errors of

variables. SEM is composed of two parts: measurement model and structural

model. Structural model describes relationship between latent variables.

Meanwhile, measurement model describes factor loading between observed

variables and latent variables.

There are six stages decisi

below.

Figure 3.1 Six stages process of SEM

Yes. Draw substantive conclusions and recommendations

Stage 6. Assess Structural Model Validity

Assess the GOF and significance, direction, and size of structural parameter estimates

Yes. Proceed to test structural model with stages 5 and 6

Stage 4. Assessing Measurement Model Validity

Assess line GOF and construct validity of measurement model

Stage 3. Designing a Study to Produce Empirical Results

Assess the adequacy of the sample size

Stage 2. Develop and Specify the Measurement Model

Make measured variables with constructs

Stage 1. Defining the Individual Constructs

six stages decision process of SEM that describe in

Six stages process of SEM (Hair, Black, Babin, Anderson, & Tatham, 2006)

Structural Model Valid?

Yes. Draw substantive conclusions and recommendations No. Refine model and test with new data.

Stage 6. Assess Structural Model Validity

Assess the GOF and significance, direction, and size of structural parameter estimates

Stage 5. Specify Structural Model

Convert measurement model to structural model

Measurement Model valid?

Yes. Proceed to test structural model with stages 5 and 6 No. Refine measures and design new study

Stage 4. Assessing Measurement Model Validity

Assess line GOF and construct validity of measurement model

Stage 3. Designing a Study to Produce Empirical Results

Assess the adequacy of the sample sizeSelect the estimation method (Weighted Least Squares

(WLS)) and missing data approach (LISTWISE method)

Stage 2. Develop and Specify the Measurement Model

Make measured variables with constructs Draw a path diagram for the measurement model

Stage 1. Defining the Individual Constructs

Pre-testing new scale development

27

in figure 3.1

(Hair, Black, Babin, Anderson, & Tatham, 2006)

No. Refine model and test with new data.

No. Refine measures and design new study

Select the estimation method (Weighted Least Squares

(WLS)) and missing data approach (LISTWISE method)

Draw a path diagram for the measurement model

28

The six stages process is consistent with two-step SEM process. By two-

step, we test the fit and construct validity of the proposed measurement model.

Once a satisfactory measurement model is achieved, the second step is to test the

structural theory. Both of steps are assessing fit and the validity. Researcher

illustrates the six stages in detail as below.

Stage 1: Defining individual constructs (Pretesting questionnaire)

The collected questionnaires are adopted and explored by researcher.

Cause of the new scale items, researcher should do pretesting questionnaire by

testing the reliability and validity among constructs. The reliability and validity

testing discussed as below.

1. Test reliability

Reliability testing is a measurement to test whether respondents could

answer the question consistently or not. There are two latent variables

such as ethical evaluation and ethical work climate. The measurement of

these variables is measured by statistics testing Cronbach Alpha (α).

Based on Nunnaly criterion, variables are reliable if their alpha is above

60% (Ghozali, 2006).

2. Test validity

Test validity is a measurement if the questions have already measured

what researcher wants to. Researcher uses Kaiser Meyer Oikin (KMO)

and Barlett’s Test to test validity among constructs. Based on the criteria

which is KMO should be more than 0.5 (Ghozali, 2006), the test factor

analysis could be done.

Stage 2: Developing and specifying the measurement model

In this stage, each latent construct to be included in the model is identified

and the measured indicator variables (items) are assigned to latent constructs.

This process will be represented with diagram in the next section.

29

Stage 3: Designing a study to produce empirical results

Designing a study to produce empirical results, we should concern about

two issues which are missing value method and estimation technique.

1. Weighted Least Square (WLS) Estimator

When assumption of multivariate normality is not met, researcher could

use alternative estimation technique which is Weighted Least Square

(WLS) (Hair, Black, Babin, Anderson, & Tatham, 2006). WLS is

estimation method that adapted from Asymptotically Distribution Free

(ADF). ADF is general estimation which does not depend on type of

distribution data. WLS needs ten respondents for each observed variable

(Wijayanto, 2008).

2. LISTWISE method

LISTWISE method or complete case approach is the simplest method for

dealing with missing data. Even though, it is easy for use, LISTWISE

method increases the likelihood of non-convergence (SEM program

cannot find any solution). But we can tackle this disadvantage because we

have large sample sizes (283 samples).

Stage 4: Assessing measurement model validity

Three measurements are using for assessing measurement model validity.

All the measurement should be valid to continue to the next stage.

1. Goodness-of-fit (GOF)

Hair, Black, Babin, Anderson, & Tatham (2006) categorize GOF indices

into three groups: absolute fit measures, incremental fit indices, and

parsimony fit indices. Absolute fit measures are direct measures of how

well the model specified the researcher reproduces the observed data.

Incremental fit indices assess how well a specified model fits relative to

some alternative baseline model. Parsimony fit indices are designed

30

specially to provide information about which model among sets if

competing models is the best, considering its fit relative to its complexity.

According to Hair, Black, Babin, Anderson, & Tatham (2006), using three

to four fit indices provides adequate evidence of model fit. The researcher

should report at least one incremental index and one absolute indices, in

addition to the χ2 and the associated degree of freedom. At least one of the

indices should be a badness of fit index. There are characteristics of

different fit indices used in demonstrating goodness of fit across different

model situation (Hair, Black, Babin, Anderson, & Tatham, 2006).

N<250 N>250

m≤12 12<m<30 m≥30 m≤12 12<m<30 m≥30

χ2 Insignificant

ρ-values

expected.

Significant

ρ-values

can result

even with

good fit.

Significant

ρ-values

expected.

Insignificant

ρ-values

expected.

Significant

ρ-values

expected.

Significant

ρ-values

expected.

CFI or

TLI

0.97 or

better.

0.95 or

better.

Above

0.92.

0.95 or

better.

Above

0.92.

Above

0.90.

SRMR Could be

biased

upward, use

other

indices.

0.80 or

less (with

CFI of

0.95 or

higher)..

Less than

0.09 (with

CFI above

0.92)

Could be

biased

upward, use

other

indices.

0.80 or

less (with

CFI of

0.92 or

higher).

0.80 or

less (with

CFI of

0.92 or

higher).

RMSEA Values <

0.08 with

CFI ≥ 0.97.

Values <

0.08 with

CFI ≥

0.95.

Values <

0.08 with

CFI ≥

0.92.

Values <

0.07 with

CFI ≥ 0.97.

Values <

0.07 with

CFI ≥

0.92.

Values <

0.07 with

CFI ≥

0.90.

Table 3.3 Goodness-of-fit indices based on situational criterion (Hair, Black, Babin,

Anderson, & Tatham, 2006)

31

Beside the above criteria, researcher could use indices below to

accept the overall fit.

a. Goodness-of-Fit Index (GFI) is less sensitive to sample size. Its

range value is 0 to 1 with higher values indicating better fit. In the

past, GFI values of greater than 0.90 typically were considered good.

Others argue 0.95 should be used (Hair, Black, Babin, Anderson, &

Tatham, 2006). Meanwhile, 0.80 ≤ GFI ≤ 0.90 is called marginal fit

(Wijayanto, 2008).

b. Normed Fit Index (NFI) is ratio of the difference in the χ2

value for

the fitted model and a null model divided by χ2

value for null model. A

model with perfect ft would produce an NFI of 1 (Hair, Black, Babin,

Anderson, & Tatham, 2006). NFI value ≥ 0.90 shows good fit and

0.80 ≤ NFI < 0.90 is referred to marginal fit (Wijayanto, 2008).

c. Adjusted Goodness of Fit Index (AGFI) is extended of GFI that

adjusted with ratio between degree of freedom of null or independence

or baseline model and degree of freedom of estimated model. Like as

GFI, AGFI value is around 0 to 1 and AGFI value that is higher than

or equal to 0.90 shows good fit. Meanwhile, 0.80 ≤ GFI < 0.90 is

usually called as marginal fit.

d. Parsimonious Normal Fit Index (PNFI) and Parsimony Goodness-

of-fit Index (PGFI) consider the number of degree of freedom to

achieve a good fit (Wijayanto, 2008). The values of the PNFI and

PGFI are meant to be used in comparing one model to another with the

highest PNFI and PGFI value being most supported with respect to the

criteria captured by these indices (Hair, Black, Babin, Anderson, &

Tatham, 2006).

2. Construct validity

To assess construct validity, researcher examines validity testing and

reliability testing as follows.

32

a. Validity testing of measurement model.

According to Ridgon & Ferguson (1991) and Doll, Xia, & Torkzadeh

(1994), a variable is valid if t-value of factor loading should be equal or

more than its critical value (or 1.96) and its standardized factor loading is

equal and higher than 0.70. Hair, Black, Babin, Anderson, & Tatham

(2006) state that standardized factor loading ≥ 0.50 is very significant.

b. Reliability testing of measurement model

Two measurements that could measure the reliability of measurement

model are:

Construct reliability measure

�� = �∑ ��

�∑ ��

+ ∑ �

Note:

Std. loading = standardized loading

ej = measurement error.

Variance extracted measure

Variance extracted = ∑ ��. ��

�

∑ ��. ��

+ ∑ �

Note:

Std. loading = standardized loading

ej = measurement error.

Hair, Black, Babin, Anderson, & Tatham (2006) stated that a construct is

reliable if construct reliability (CR) ≥ 0.07 and variance extracted (VE) ≥

0.05.

33

Stage 5: Specifying the structural model

Stage 5 involves specifying the structural model by assigning relationships

from one construct to another based on the proposed theoretical model (Hair,

Black, Babin, Anderson, & Tatham, 2006). In detail, it will be illustrated in the

next chapter.

Stage 6: Assessing the structural model validity

The fit of structural model can be tested by structural model GOF. The

overall fit can be assessed using the same criteria as the measurement model:

using the χ2 value for structural model, one other absolute index, one incremental

index, one goodness-of-fit indicator, and badness-of-fit indicator (Hair, Black,

Babin, Anderson, & Tatham, 2006). The model of this researcher is considered as

saturated structural model then, the fit for saturated theoretical model should be

the same as those obtained for the CFA model. Researcher does not need to test

goodness-of-fit of structural model.

Good model fit alone is insufficient to support a proposed theory.

Researcher also examines the individual estimation to test specific hypothesis. A

theoretical model is considered valid to the extent that the parameter estimates are

statistically significant and in the predicted direction (Hair, Black, Babin,

Anderson, & Tatham, 2006). As other multivariate techniques, the significant

influence of the exogenous variables on endogenous variable can be shown from

its t-value. Researcher compares the t-value of each parameter to its critical value.

The higher absolute t-value than critical value indicates significant influence. If

the significant value is 0.05, its critical value is ±1.96 (two-tail). Therefore,

absolute t-test should be higher than 1.96 to get significant correlation and accept

the hypothesis. The standardized factor loading of structural model (γ) indicates

nature of relationship. If they are greater than zero, it means positive relationship.

If they are less than zero, it means negative relationships.

34

3.6. Refining measures model

While researcher assesses fit and validity of measurement in fourth stage

of base model, we get invalid measurement model. Then, researcher has to do

repetitive refining measures model. Repetitive refining measures model is done in

three times to get the most fit and valid model. We illustrate the processes into

four sections: Model I, Model II, Model III, and Model IV. For detailed, please

refer to APPENDIX 2A-D.

3.6.1. Model I

Model I is basic model which will be refined to be Model II

because of an invalid observed variable. Researcher is only doing stage 1-

4 in model I.

Stage 1: Pretesting

Before distributing the questionnaire to actual respondents,

researcher tested the reliability and validity of questionnaire. Researcher

distributed pre-test questionnaire to 22 President University accounting

students who already had internship in public accounting firm. Researcher

could not test reliability and validity of demographic variables because the

respondents have the similar age category and length of experience.

Reliability Statistics

Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.854 .853 4

Table 3.4 Pre-testing of reliability ethical valuation variables model I

35


Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.755 .800 9

Table 3.5 Pre-testing of reliability ethical work climate variables model I

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .567

Bartlett's Test of

Sphericity

Approx. Chi-Square 182.470

Df 78

Sig. .000

Table 3.6 Pre-testing of validity model I

According to the result, ethical evaluation and ethical work climates

constructs show Conbach Alpha 85.3%, and 80%, respectively. Based on

Nunnaly criteria, the constructs are reliable. KMO = 0.567 indicates that

researcher could be continuing to do factor analysis.

36

Stage 2: Measurement model I

Figure 3.2 x-measurement model I

Figure 3.3 y-measurement model I

37

The fit from path diagram shows chi square as amounted to 308.50

(p value = 0.00) and RMSEA value as amounted to 0.088 that indicate bad

fit model.

Stage 3: Missing data and model estimation

There is no missing information available. Researcher has 283

effective samples. Researcher could not examine the model by using the

common SEM estimation procedure (maximum likelihood estimation)

because the data type is ordinal. Researcher uses alternative procedure

(weighted least squares) which needs ten respondents for each observed

variables. The observed variables are 16; so, we need 160 samples.

Researcher concludes that we can adequately estimate with the sample we

have.

Stage 4: Assessing measurement model I validity

From goodness-of-fit table, we could see two indices indicate the

bad fit and the rest of indices indicate good fit. The overall goodness-of-fit

of measurement model is a good fit of the data. Its construct reliability and

variance extracted measures for each of constructs are good. The

reliability values are 0.904, 0.91, 1, 1, and 1 for ethical evaluation, ethical

work climate, age, gender, and length of experience. In order, variances

extracted for five constructs are 0.704, 0.548, 1, 1, and 1. As the criteria

that standardized factor loading should be higher than 0.5, so the observed

variable is valid. There is one observed variable that is invalid (EWC

7/principle-individual) which has standardized factor loading as amounted

to 0.291. Therefore, researcher is going to refine measures by deleting the

invalid observed variable and conducting analysis of model II in the next

section 3.6.2. For the detail information of assessing measurement model I

validity is conducting by three tests as below.

38

1. Goodness-of-fit

INDICATOR CRITERIA RESULT CONCLUSION

BASED ON SITUATION CRITERION (N=283;16)

χ2

Significant ρ value (ρ value <

0.05) 0 Good fit

CFI ≥0.92 0.979 Good fit

NNFI/TLI ≥0.92 0.974 Good fit

RMSEA ≤0.07 0.0879 Bad fit

SRMR ≤0.08 0.164 Bad fit

OTHER INDICES

GFI ≥0.90/0.95 0.979 Good fit

NFI ≥0.90 0.969 Good fit

AGFI ≥0.90 0.97 Good fit

Table 3.7 GOF measurement model I

2. Reliability test

Latent

variables

Tot.

SFL

Tot.

SFL^2

Tot.

Measurement

error

CR VE Conclusion

EV 3.344 2.815322 1.184 0.90426 0.70395 Reliable

EWC 6.414 4.934722 4.063 0.91012 0.54844 Reliable

AGE 1 1 0 1 1 Reliable

GENDER 1 1 0 1 1 Reliable

LE 1 1 0 1 1 Reliable

Table 3.8 Reliability test of model I

39

3. Validity test

LATENT VARIABLES EV EWC AGE GENDER LENGTH OF

EXPERIENCE CONCLUSION

OBSERVED

VARIABLES SFL* t- value SFL* t- value SFL* t- value SFL* t- value SFL* t- value

EV1 0.796 ** Valid

EV2 0.903 26.999 Valid

EV3 0.741 20.939 Valid

EV4 0.904 26.888 Valid

EWC1 0.572 17.066 Valid

EWC2 0.528 13.764 Valid

EWC3 0.873 36.385 Valid

EWC4 0.845 30.665 Valid

EWC5 0.753 27.435 Valid

EWC6 0.838 31.719 Valid

EWC7 0.291 7.632 Invalid

EWC8 0.735 27.923 Valid

EWC9 0.979 45.091 Valid

AGEM 1 ** Valid

GENDERM 1 ** Valid

LEM 1 ** Valid

* SFL = Standardized Factor Loading. SFL target is ≥ 0.70 or 0.50

** Determined as default by LISREL, t-value could not be estimated. t-values target is ≥ 1.96 when sig. 0.05.

Table 3.9 Validity test of model I

40

3.6.2. Model II

Due to time and cost barrier, the researcher uses the available data

and does refining measures model by deleting the invalid observed

variable (EWC 7/principle-individual) from model I.

Stage 1: Pretesting

Like as the model I, researcher does pretesting questionnaire to 22

accounting students in President University who have similar

demographic background.


Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.854 .853 4

Table 3.10 Pre-testing of reliability ethical evaluation variables model II


Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.827 .832 8

Table 3.11 Pre-testing of reliability ethical work climate variables model II

41



Adequacy. .609

Bartlett's Test of

Sphericity


Df 66

Sig. .000

Table 3.12 Pretesting of validity model II

According to the result, ethical evaluation and ethical work

climates constructs show Conbach Alpha 85.3%, and 83.2%, respectively.

Based on Nunnaly criteria, the constructs are reliable. KMO = 0.609

indicates that researcher could be continuing to do factor analysis.

Stage 2: Measurement model II

Figure 3.4 x-measurement model II

42

Figure 3.5 y-measurement model II

The goodness-of-fit from path diagram indicates marginal fit

because RMSEA value as amounted to 0.081. The chi-square is big with

the p-value is equal to 0 indicate the bad fit.



effective samples. Researcher could not examine the model by using the

common SEM estimation procedure (maximum likelihood estimation)

because the data type is ordinal. Researcher uses alternative procedure

(weighted least squares) which needs ten respondents for each observed

variables. The observed variables are 15; so, we need 150 samples.

Researcher concludes that we can adequately estimate with the sample we

have.

43

Stage 4: Assessing measurement model II validity

We could see from goodness-of-fit table that two indices indicate

the bad fit and six indices indicate good fit. The overall goodness-of-fit of

measurement model II is good. The measurement model is reliable. It can

be shown from construct reliability and variance extracted which have

value above 0.7 and 0.5, respectively. However, there is one observed

variable (EWC 2/egoism-local) that is invalid because its standardized

factor loading as amounted 0.489 is lower than standardized factor loading

criteria which is 0.5. Therefore, researcher is going to refine measures by

deleting the invalid observed variable (EWC 2/egoism-local) and

conducting analysis of model III in the next section 3.6.3. For the detail

information of assessing measurement model II validity is conducted by

three tests as below.

1. Goodness-of-fit



χ2

Significant p value (p value <

0.05) 0.00 Good fit





OTHER INDICES

GFI ≥0.90/0.95 0.981 Good fit



Table 3.13 Goodness-of-fit measurement model II

44

2. Reliability test

Latent

variables

Tot.

SFL

Tot.

SFL^2

Tot.

Measureme

nt error

CR VE Conclusio

n

EV 3.323 2.77908

9

1.221 0.90043

5

0.69475

7

Reliable

EWC 5.891 4.51023

1

3.488 0.90867

2

0.56390

4

Reliable




Table 3.14 Reliability test of measurement model II

45

3. Validity test

LATENT

VARIABLES EV EWC AGE GENDER

LENGTH OF

EXPERIENCE CONCLUSION

OBSERVED

VARIABLES SFL* t- value SFL* t- value SFL* t- value SFL* t- value SFL* t- value

EV1 0.792 ** Valid

EV2 0.902 23.324 Valid

EV3 0.739 17.913 Valid

EV4 0.89 22.504 Valid

EWC1 0.56 15.234 Valid


EWC3 0.866 31.432 Valid

EWC4 0.818 26.155 Valid

EWC5 0.739 24.634 Valid

EWC6 0.802 25.685 Valid

EWC8 0.664 20.896 Valid

EWC9 0.953 38.606 Valid

AGEM 1 ** Valid

GENDERM 1 ** Valid

LEM - 1 ** Valid

* SFL = Standardized Factor Loadings. SFL target is ≥ 0.70 or 0.50


Table 3.15 Validity test of model II

46

3.6.3. Model III

Model III is as the result of refining model II which has invalid

observed variable of ethical work climate constructs (EWC 2/egoism-

local).

Step 1: Pretesting

Pretesting is conducting for testing validity and reliability of

variables before distributing to actual respondents. Researcher uses the

pretest data from model I and deletes the invalid variables from previous

model (EWC 2/egoism-local and EWC 7/principle-cosmopolitan).


Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.854 .853 4

Table 3.16 Pre-testing of reliability ethical evaluation variables model III


Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.832 .845 7

Table 3.17 Pre-testing of reliability ethical work climate variables model III

47



Adequacy. .617

Bartlett's Test of

Sphericity


Df 55

Sig. .000

Table 3.18 Pre-testing of validity model III

From the results above, Cronbach Alpha of ethical evaluation variables

and ethical work climates variables are 85.3% and 84.5%, respectively. It

means those variables are valid to determine the latent variable. KMO =

0.617 indicates that the analysis could be continuing.

Stage 2: Measurement model III

Figure 3.6 x-measurement model III

48

Figure 3.7 y-measurement model III

The chi square of path diagram is still big but it is smaller than the

chi square of previous models (model I and model II) indicates that the

model is better than previous models. The RMSEA as amounted to 0.0072

indicates good fit model.



effective samples. Researcher uses alternative procedure (weighted least

squares) which needs ten respondents for each observed variables. The

observed variables are 14; so, we need 140 samples. Researcher concludes

that we can adequately estimate with the sample we have.

Stage 4: Assessing measurement model III validity

We could see from goodness-of-fit table that two indices indicate

bad fit and six indices indicate good fit. Researcher concludes that the

49

overall goodness-of-fit of measurement model III is good. The reliability

and variance extracted measures for each latent variables are good. The

reliability values are 0.887 and 0.882 for ethical evaluation and ethical

work climate. Variance extracted for ethical evaluation and ethical work

climate is 0.666 and 0.526, respectively. All the demographic variables

have construct reliability and variance extracted as amounted to 1.

Meanwhile, there is still an invalid observed variable (EWC 1/egoism-

individual). Therefore, researcher is going to refine measures by deleting

the invalid observed variable (EWC 1/egoism-individual) and conducting

analysis of model IV in the next section 3.6.4. For the detail information

of assessing measurement model III validity is conducting by three tests as

below.

1. Goodness-of-fit of measurement model



χ2

Significant p value (p value <

0.05) 0.00534 Good fit





OTHER INDICES

GFI ≥0.90/0.95 0.984 Good fit



Table 3.19 GOF measurement model III

50

2. Reliability test

Latent

variables

Tot.

SFL

Tot.

SFL^2

Tot.

Measurement

error

CR VE Conclusion

EV 3.242 2.663834 1.335 0.8873 0.666153 Reliable

EWC 4.983 3.684261 3.318 0.882124 0.526153 Reliable




Table 3.20 Reliability test model III

51

3. Validity test


EXPERIENCE

CONCLUSION

OBSERVED

VARIABLES

SFL* t- value SFL* t- value SFL* t- value SFL* t- value SFL* t- value

EV1 0.763 ** Valid

EV2 0.891 21.863 Valid

EV3 0.678 15.903 Valid

EV4 0.91 20.482 Valid


EWC3 0.84 27.747 Valid

EWC4 0.78 21.825 Valid

EWC5 0.629 16.197 Valid

EWC6 0.749 21.109 Valid

EWC8 0.603 16.282 Valid

EWC9 0.909 32.216 Valid

AGEM 1 ** Valid

GENDERM 1 ** Valid

LEM 1 ** Valid



Table 3.21 Validity test of measurement model III

52

3.6.4. Model IV

Researcher uses model IV for further analysis. This model is

refining model from model III which has invalid observed variable.

Stage 1: Pretesting

Researcher tests the validity and reliability of variables by using

similar available data with previous model (model I, model II, and model

III).


Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.854 .853 4

Table 3.22 Pre-testing of ethical evaluation variables model IV


Cronbach's

Alpha

Cronbach's

Alpha Based

on

Standardized

Items

N of Items

.872 .881 6

Table 3.23 Pre-testing of reliability EWC variables model IV

53



Adequacy. .648

Bartlett's Test of

Sphericity


Df 45

Sig. .000

Table 3.24 Pre-testing validity model IV

By comparing the result with prior model, the Cronbach Alpha of

ethical work climate variables (EWC 3/egoism-cosmopolitan, EWC

4/benevolence-individual, EWC 5/benevolence-local, EWC

6/benevolence-cosmopolitan, EWC 8/principle-local, and EWC

9/principle-cosmopolitan) is increasing than the Cronbach Alpha of

ethical work climate in the previous models. That means the reliability of

construct ethical work climate is higher than previous construct which is

88.1%. Because the ethical evaluation uses similar indicator with previous

constructs, the Cronbach Alpha is same as amounted to 85.3% that

indicates the reliability among indicators. According to KMO result which

is 0.648, it indicates the analysis could be analyzed further.

54

Stage 2: Measurement model IV

Figure 3.8 x-measurement model IV

Figure 3.9 y-measurement model IV

55

Figure 3.8 and 3.9 shows chi square as amounted 117.02 (p-value

= 0.00001) is smaller than the chi square in the model I, II, III. The

RMSEA value as amounted to 0.06 which is below the criteria (0.05 <

RMSEA < 0.08) indicates good fit model.



effective samples. Researcher uses alternative procedure (weighted least

squares) which needs ten respondents for each observed variables. The

observed variables are 13; so, we need 130 samples. Researcher concludes

that we can adequately estimate with the sample we have.

Stage 4: Assessing measurement model IV validity

Researcher found that two goodness-of-fit indices show bad fit and

the six indices show good fit. All indicators of latent variables are valid

and reliable. It can be shown that reliability values are 0.878 and 0.893 for

ethical evaluation and ethical work climate. Variance extracted for both

variables are 0.647 and 0.585, respectively. All standardized factor

loadings of observed variables are higher than 0.5 and their absolute t-

value is above the positive critical value (1.96) at 0.05 significance level.

We also compare the parsimony fit indices of model IV with parsimony fit

indices of the model which is deleted the demographic variables. The

PGFI and PNFI values of model IV as amounted to 0.630 and 0.730,

respectively, are higher than PGFI and PNFI values of model which has

been deleted the demographic variables as amounted 0.580 and 0.548,

respectively (see APPENDIX 3). Researcher concludes that model IV is

the most supported with respect to the criteria captured by these indices.

So, the measurement model validity is met, researcher could continue to

56

stage 5 and stage 6 to analyze structural model. Stage 5 and stage 6 will be

explained in the next chapter.

1. Goodness-of-fit



χ2 Significant p value (p value <

0.05) 0.14 Bad fit



RMSEA ≤0.07 0.0601 Good fit


OTHER INDICES

GFI ≥0.90/0.95 0.988 Good fit



Table 3.25 Goodness-of-fit measurement model IV

2. Reliability test

Latent

variables Tot.

SFL Tot.

SFL^2 Tot.

Measurement

error

CR VE Conclusio

n

EV 3.187 2.58638

1 1.413 0.8778

7 0.6467 Reliable

EWC 4.552 3.50875 2.492 0.8926

4 0.5847

2 Reliable




Table 3.26 Reliability test of measurement model IV

57

3. Validity test


EXPERIENCE

CONCLUSION

OBSERVED

VARIABLES

SFL* t- value SFL* t- value SFL* t- value SFL* t- value SFL* t- value

EV1 0.692 ** VALID

EV2 0.916 16.655 VALID

EV3 0.685 13.574 VALID

EV4 0.894 16.411 VALID

EWC3 0.826 25.159 VALID

EWC4 0.775 20.726 VALID

EWC5 0.678 15.619 VALID

EWC6 0.735 19.793 VALID

EWC8 0.622 16.221 VALID

EWC9 0.916 31.029 VALID

AGEM 1 ** VALID

GENDERM 1 ** VALID

LEM 1 ** VALID



Table 3.27 Validity test of measurement model IV

58

3.7. Hypothesis Testing

As discussed in the section 3.5., researcher tests the hypothesis by using t-

test after measurement model and structural model is valid.

3.6.1 T-test

The t-value is the value of the t-test statistic that the corresponding

parameter is equal to zero. Practically, if the sample size is large,

researcher can evaluate the obtained t-value, shown on LISREL printout,

against the critical value of z researcher selects based upon researcher

choice of significant level. If absolute t-value is larger than the positive

critical value, the null hypothesis is rejected and the conclusion is that the

parameter is significantly different from zero. (Output question)

3.8. Limitations

Researcher experiences difficulties while gathering data. Several public

accounting firms rejected my questionnaire and got non-responses of few

questionnaires due to high season and their policies to not receive any

questionnaires. Many of public accounting firms gave positive feedback.

Researcher can get sufficient data for doing analysis of model although it is

taking time so long.

59

CHAPTER IV

ANALYSIS OF DATA AND INTERPRETATION OF

RESULTS

4.1. Structural Model

All observed variables in model measurement IV are valid, good fit

model, and the most supported with respect to the criteria captured by parsimony

fit indices in comparing with model which has deleted demographic variables.

We formulate the structural model as below.

Figure 4.1 Structural model

The structural model can be computed in mathematical equation as below.

$% = &1 $'� + &2 ()$ + &3)$*+$, + &4 -$

$% = 0.56 $'� − 0.85 ()$ + 0.24 )$*+$, + 0.73 -$

4.2. Testing of Structural Model Validity

The model is considered as saturated structural model. The goodness-of-

fit of structural model result is as same as the goodness-of-fit for measurement

60

model result. The goodness-of-fit for measurement results good fit model, so does

structural model.

4.3. Hypothesis Testing

T-test on principals shows how far the influence of one exogenous

variable toward endogenous variable. The criteria to take the decision is if the

absolute obtained t-value is higher than the positive critical value of z researcher

selects based upon the choice of significance level, the null hypothesis is rejected

and the parameter is significant. Researcher chooses the significant level at 0.05

which has critical value ±1.96. The absolute t-value of all parameters should be

above 1.96. Table 4.1 below shows the t-value each parameters and its gamma (γ)

coefficients for each relationship between an exogenous variable and an

endogenous variable.

PARAMETERS γ t-value

EWC → EV 0.559 8.094

AGE → EV -0.847 -1.127

GENDER → EV 0.238 1.604

LE → EV 0.732 1.052

Table 4.1 t-value result

The table above shows only ethical work climate as exogenous variable

has significant influence on ethical evaluation. The researcher presents the result

as follows.

1. First hypothesis

H0 : Ethical work climate has insignificant influence on ethical

evaluation.

Ha/H1 : Ethical work climate has significant influence on ethical

evaluation.

EWC t statistic value (8.094) is on rejected area when the critical

value at 1.96. Statistically, hypothesis H0 is rejected. It means that ethical

61

work climate has significant influence on auditors’ ethical evaluation in

context of time and cost pressure.

2. Second hypothesis

H0 : Age has insignificant influence on ethical evaluation.

Ha/H2 : Age has significant influence on ethical evaluation.

AGE t statistic value (-1.127) is on accepted area when the critical

value at 1.96. Statistically, hypothesis H0 is accepted. It means that age

has insignificant influence on auditors’ ethical evaluation in context of

time and cost pressure.

3. Third hypothesis

H0 : Gender has insignificant influence on ethical evaluation.

Ha/H3 : Gender has significant influence on ethical evaluation.

GENDER t statistic value (1.604) is on accepted area when the

critical value at 1.96. Statistically, hypothesis H0 is accepted. It means that

gender has insignificant influence on auditors’ ethical evaluation in


4. Fourth hypothesis

H0 : Length of experience has insignificant influence on ethical

evaluation.

Ha/H4 : Length of experience has significant influence on ethical

evaluation.

LE t statistic value (1.052) is on accepted area when the critical

value at 1.96. Statistically, hypothesis H0 is accepted. It means that length

of experience has insignificant influence on auditors’ ethical evaluation in


62

4.4. Data Interpretation

4.4.1. The influence ethical work climate on ethical evaluation

The public accounting firms in Java Island identified six climates

that were statistically significant – efficiency, friendship, team interest,

social responsibility/public interest, company rules and procedures, and

laws and professional codes. Laws and professional codes climate is the

most strongly perceived ethical climate that emerged as the predominant

factor. It is not surprising because accounting firms and their employees

(auditors) consider law and professional code to be important in their

practice. Auditors considered company’s rules and procedures to

implement in their practice. The auditors in Java area perceived

benevolent ethical climate that employees perceive that decisions are

made based on an overarching concern for the well-being of their

company. Egoism/individual (self interest) and egoism/local (company

profit) climates do not exist in public accounting firms reflects the

auditors to not focus on auditors’ self interest and public accounting firm’s

interest. Moreover, the employees of public accounting firms are expected

to work efficiently.

Based on the hypothesis testing result, factor loading between

ethical work climate and auditors’ ethical evaluation is 0.559 and t-value

is 8.094. The result indicates ethical work climate with six dimensions has

significant influence positively on auditors’ ethical evaluation in the

context of time and cost pressure. When auditors are more perceived the

six ethical work climates, the auditors will be better to evaluate what is

right or wrong ethically.

63

4.4.2. The influence specified demographic variables (age, gender,

and length of experience) on ethical evaluation

In hypothesis result, the parameter of the influence age on

auditors’ ethical evaluation has factor loading as amounted to -0.847 and

t-value as amounted to -1.127. It indicates that age has insignificant

influence negatively on auditors’ ethical evaluation in context of cost and

time pressure. Other words, increasing age will decrease insignificantly

the ability to evaluate ethical action. It is contradictive with the previous

researches that stated older people are more ethical than younger people.

For further analysis, we can see in APPENDIX 4, auditors who are

younger than 25 years old have higher ethical evaluation in case 2 and

case 4, meanwhile, auditors who are older than or equal to 25 years old

have higher ethical evaluation in case 1 and 4. Thus, age gives no impact

on auditors’ ethical evaluation.

Hypothesis 3 stated that gender has significant influence on

auditors’ ethical evaluation. But, the hypothesis is rejected because its

factor loading is 0.238 and its t-value is 1.604 which is below than critical

value at 0.05 significant levels. Researcher concludes that gender has

insignificant positively influence on ethical evaluation statistically. From

table in APPENDIX 4, we could see that males have higher ethical

evaluation ability than females in case 1, 2, and 4. This finding is

supported by Dubinsky & Levy (1985), Radtke (2000) and Armstrong

(1987). They also found that males have greater ethical evaluation ability

than females. In APPENDIX 4, we can see females have greater ethical

evaluation than females in case 3. It is consistent with numbers of

previous researches that found females have higher ethical decision

making than males. The inconsistency of ethical evaluation among cases

indicates males and females may have great ethical evaluation.

Researcher hypothesize that length of experience will have

significant influence on auditors’ ethical evaluation. However, the result

has different answer for the fourth hypothesis. In table 4.1, the factor

64

loading of the parameter is 0.732 and its t-value is 1.052. It means length

of experience has insignificant positively influence on ethical evaluation.

The greater experience is associated with increasing ability to evaluate

ethical action insignificantly in context of cost and time pressure. The

result is consistent with the Glover, Bumpus, Sharp, & Munchus (2002)

and Lehnert, Park, & Singh (2015) finding that there is a positive

relationship between years of management experience and ethical choice.

Researcher also analyzes the impact of experience on ethical evaluation by

dividing sample into two groups (experience less than 2 years and

experience more than or equal to 2 years). It shows on table in

APPENDIX 4 that auditors who have experience more than 2 years have

higher ethical evaluation ability than auditors who have experience less

than 2 years. It is consistent with Martinov-Bennie & Pflugrath (2009)

finding. They found greater task-specific experience provides more

significantly higher quality technical judgments than those with lower

levels of task-specific experience.

In the age categorical, case 4 has the highest means (4.023 for age

< 25 years old and 4.019 for age ≥ 25 years old) and case 3 has the lowest

means (3.32 for age < 25 years old and 3.343 for age ≥ 25 years old) than

the other cases. By observing the highest and lowest score of cases among

gender groups, case 4 shows the highest means (3.986 for female and

4.059 for male) and case 3 shows the lowest means (3.34 for female and

3.316 for male). We can conclude that female and male auditors evaluate

that PSO is the most unethical act and URT is the least unethical act than

other cases. The auditors who have length of experience is less than 2

years and more than 2 years also evaluate that PSO is the most unethical

act and URT is the least unethical act by observing the lowest and highest

score of the groups. As overall, auditors evaluate that PSO is the most

unethical act and URT is the least unethical act than other cases (biasing

sample selection and over-reliance on client work cases). It is consistent

with previous studies which suggest that auditors evaluate PSO as being a

65

more unacceptable behavior than some other types of QTB (Sweeney,

Arnold, & Pierce, 2010) and URT were less severe (Sweeney & Pierce,

2006).

66

CHAPTER V

CONCLUSIONS AND RECOMMENDATIONS

5.1. Conclusion

Based on the analysis, test result, and discussion in chapter IV, researcher

summarizes the conclusion as follows.

1. Efficiency, friendship, team interest, social responsibility/public interest,

company rules and procedures, and laws and professional codes climates

have significant influence on auditors’ ethical evaluation. The auditors

should perceived this six climates in their company to increase their

ethical evaluation.

2. Age has no influence on auditors’ ethical evaluation. Ethical evaluation is

not affected of auditors’ age.

3. Gender has no influence on auditors’ ethical evaluation. Ethical evaluation

is not affected of auditors’ gender type.

4. Length of experience has insignificant influence on auditors’ ethical

evaluation. Even though it is insignificant, auditors who have more

experience will have greater ethical evaluation than auditors who have less

experience.

5.2. Recommendations

For future improvement, researcher proposes some recommendations to

several parties as follows.

5.2.1. Future researchers

The findings indicate a need for future research, including

consideration of ethical sensitivity of the behaviors. The ethical sensitivity

is the important key to recognize the existence of ethical issues. Once

people can be aware of ethical issues, they will be able to evaluate the

67

issues. Finding in this study are based on responses to hypothetical

scenarios, and this method of data collection does not capture the pressure

of ethical evaluation in the real audit environment. Future research should

conduct the research by using qualitative method (interview) for capturing

the real condition. Researcher suggests comparing of the variables

impacting on ethical decision making for multiple employment levels

within accounting firms. Future researches are needed on other factors

which impact ethical evaluation such as perceived ethical intensity and

training. Furthermore, interaction between independents variables should

be considered.

5.2.2. External auditors

Based on the result, auditor should perceive higher ethical work

climate which composed by efficiency, friendship, team interest, social

responsibility/public interest, company rules and procedures, and laws and

professional codes because these climates have significantly influence in

their ability to evaluate ethical act statistically. Auditors have moderate

ability to evaluate ethical act in case 1, 2, and 3. They suppose to increase

their ethical evaluation by ethical training.

5.2.3. Public accounting firms

Public accounting also should provide training for their auditors

for increasing their technical and ethical skills. Technical and ethical skills

are considered to be important in solving ethical dilemma.

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15

APPENDIX 1

DEFINITION OF TERMS

1. Auditors : someone whose job to carry out an

official examination of the accounts of the business and to produce a

report (Cambridge advanced learner's dictionary third edition, 2008).

2. Ethics : That branch of philosophy dealing

with the values relating to human conduct, with respect to the Tightness

and wrongness of certain actions and to the goodness and boldness of the

motives and ends of such actions (Nichols, 2001).

3. Rest’s ethical reasoning process : the sequential steps in decision

making that designed by Rest.

4. Ethical sensitivity : the awareness of ethical issues.

5. Ethical evaluation : judgment which action is ethically

justifiable.

6. Intention to act : one must be motivated to prioritize

moral values.

7. Actual behavior : one has the courage to act ethically.

8. QTB : any intentional action taken by the

auditor during and engagement that reduces evidence gathering

inappropriately as consequences of time deadlines and time budget

pressures.

9. Biasing sample selection : bias that exists in sample selection

process, where data is systematically excluded due to a particular attribute

(Investopedia, 2015).

10. Overreliance on client work : accepting client work without

certain and further explanation.

11. Premature sign off : auditors sign off work as completed

without actually completing the work (Pierce & Sweeney, Auditor

responses to cost control, 2003).

16

12. Underreporting of time (URT) : any intentional act taken by auditor

that does not directly affect to financial statements. as consequences of

time budget pressures.

13. Ethical work climate : the shared perceptions of what is

ethically correct behavior and how ethical issues should be handled

(Victor & Cullen, A theory and measure of ethical climate in organization,

1987).

14. Demographic variables : the quantity and characteristics of

the people who live in particular area, especially in relation to their age,

how much money they have and what they spend it on (Cambridge

advanced learner's dictionary third edition, 2008).

APPENDIX 2A

MODEL I

DATE: 1/15/2016 TIME: 3:14 L I S R E L 8.72 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2005 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file F:\PRESUNIV - ENA\thesis - ena\LISREL - TESTING\NEW TRIAL\LISREL\BASIC\EV SFL ABOBE 0.5 ALL EWC\1 SYNTAX CFA.PR2: OBSERVED VARIABLES EV1 EV2 EV3 EV4 EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 EWC7 EWC8 EWC9 AGEM GENDERM LEM COVARIANCE MATRIX FROM FILE EVALL.COV ASYMPTOTIC COVARIANCE MATRIX FROM FILE EVALL.ACM SAMPLE SIZE = 283 LATENT VARIABLES EV EWC AGE GENDER LE EV1-EV4 = EV EWC1-EWC9 = EWC AGEM = 1*AGE GENDERM = 1*GENDER LEM = 1*LE EV = EWC AGE GENDER LE SET ERROR VARIANCE OF AGEM TO 0 SET ERROR VARIANCE OF GENDERM TO 0 SET ERROR VARIANCE OF LEM TO 0 LISREL OUTPUT: ND=3 SC ME=WLS PATH DIAGRAM END OF PROBLEM

Covariance Matrix

19

EV1 EV2 EV3 EV4 EWC1 EWC2 -------- -------- -------- -------- -------- -------- EV1 1.268 EV2 0.980 2.068 EV3 0.500 0.806 1.034 EV4 1.046 1.586 0.761 2.133 EWC1 0.143 0.101 0.093 0.085 1.008 EWC2 0.057 0.075 0.063 0.058 0.680 2.323 EWC3 0.566 0.660 0.454 1.011 0.444 0.976 EWC4 0.185 0.269 0.203 0.388 0.395 0.568 EWC5 0.179 0.148 0.117 0.171 0.376 0.651 EWC6 0.196 0.280 0.132 0.380 0.231 0.426 EWC7 0.079 -0.119 0.036 -0.080 0.325 0.349 EWC8 0.225 0.324 0.266 0.370 0.360 0.784 EWC9 1.055 1.777 0.737 1.535 0.641 0.822 AGEM 0.063 -0.057 0.011 -0.069 -0.171 -0.132 GENDERM 0.110 0.044 -0.012 0.052 -0.043 0.088 LEM 0.106 0.060 0.084 -0.001 -0.288 -0.317 Covariance Matrix EWC3 EWC4 EWC5 EWC6 EWC7 EWC8 -------- -------- -------- -------- -------- -------- EWC3 3.385 EWC4 1.086 1.339 EWC5 0.849 0.714 1.439 EWC6 0.743 0.399 0.512 0.862 EWC7 0.170 -0.042 0.326 0.255 1.773 EWC8 0.866 0.665 0.457 0.459 0.438 1.871 EWC9 2.969 1.598 1.398 1.248 -0.094 1.546 AGEM -0.202 -0.051 0.038 -0.095 -0.087 -0.207 GENDERM -0.209 -0.109 0.018 -0.015 -0.115 -0.142 LEM -0.101 -0.090 -0.002 -0.132 -0.140 -0.354 Covariance Matrix

20

EWC9 AGEM GENDERM LEM -------- -------- -------- -------- EWC9 8.624 AGEM 0.399 1.000 GENDERM -0.293 0.430 1.000 LEM 0.406 0.937 0.375 1.000

Parameter Specifications LAMBDA-Y EV -------- EV1 0 EV2 1 EV3 2 EV4 3 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 4 0 0 0 EWC2 5 0 0 0 EWC3 6 0 0 0 EWC4 7 0 0 0 EWC5 8 0 0 0 EWC6 9 0 0 0 EWC7 10 0 0 0 EWC8 11 0 0 0 EWC9 12 0 0 0 AGEM 0 0 0 0 GENDERM 0 0 0 0 LEM 0 0 0 0 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 13 14 15 16 PHI EWC AGE GENDER LE -------- -------- -------- -------- EWC 0 AGE 17 18 GENDER 19 20 21 LE 22 23 24 25 PSI EV

21

-------- 26 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 27 28 29 30 THETA-DELTA EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 31 32 33 34 35 36 THETA-DELTA EWC7 EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- -------- 37 38 39 0 0 0

Number of Iterations = 26 LISREL Estimates (Weighted Least Squares) LAMBDA-Y EV -------- EV1 0.897 EV2 1.298 (0.048) 26.999 EV3 0.753 (0.036) 20.939 EV4 1.321 (0.049) 26.888 LAMBDA-X

22

EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.574 - - - - - - (0.034) 17.066 EWC2 0.805 - - - - - - (0.059) 13.764 EWC3 1.606 - - - - - - (0.044) 36.385 EWC4 0.978 - - - - - - (0.032) 30.665 EWC5 0.904 - - - - - - (0.033) 27.435 EWC6 0.778 - - - - - - (0.025) 31.719 EWC7 0.387 - - - - - - (0.051) 7.632 EWC8 1.005 - - - - - - (0.036) 27.923 EWC9 2.876 - - - - - - (0.064) 45.091 AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.551 -1.087 0.236 1.016 (0.054) (1.974) (0.166) (1.905) 10.268 -0.551 1.422 0.533

23

Covariance Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.557 1.000 AGE -0.004 -0.029 1.000 GENDER 0.109 -0.063 0.466 1.000 LE 0.048 -0.010 0.974 0.408 1.000 PHI EWC AGE GENDER LE -------- -------- -------- -------- EWC 1.000 AGE -0.029 1.000 (0.051) (0.060) -0.571 16.793 GENDER -0.063 0.466 1.000 (0.050) (0.064) (0.060) -1.250 7.325 16.793 LE -0.010 0.974 0.408 1.000 (0.050) (0.013) (0.066) (0.060) -0.210 73.592 6.208 16.793 PSI EV -------- 0.614 (0.119) 5.170 Squared Multiple Correlations for Structural Equations EV -------- 0.386 Squared Multiple Correlations for Reduced Form EV -------- 0.386 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.464 0.382 0.466 0.389

24

(0.092) (0.142) (0.080) (0.154) 5.055 2.687 5.856 2.528 Squared Multiple Correlations for Y - Variables EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.634 0.815 0.549 0.818 THETA-DELTA EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 0.678 1.675 0.806 0.383 0.622 0.256 (0.071) (0.167) (0.246) (0.101) (0.104) (0.064) 9.504 10.005 3.272 3.781 5.963 4.002 THETA-DELTA EWC7 EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- -------- 1.622 0.861 0.352 - - - - - - (0.113) (0.133) (0.631) 14.403 6.476 0.557 Squared Multiple Correlations for X - Variables EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 0.327 0.279 0.762 0.714 0.568 0.703 Squared Multiple Correlations for X - Variables EWC7 EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- -------- 0.085 0.540 0.959 1.000 1.000 1.000 Goodness of Fit Statistics

25

Degrees of Freedom = 97 Minimum Fit Function Chi-Square = 308.505 (P = 0.0) Estimated Non-centrality Parameter (NCP) = 211.505 90 Percent Confidence Interval for NCP = (162.220 ; 268.406) Minimum Fit Function Value = 1.094 Population Discrepancy Function Value (F0) = 0.750 90 Percent Confidence Interval for F0 = (0.575 ; 0.952) Root Mean Square Error of Approximation (RMSEA) = 0.0879 90 Percent Confidence Interval for RMSEA = (0.0770 ; 0.0991) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.000 Expected Cross-Validation Index (ECVI) = 1.371 90 Percent Confidence Interval for ECVI = (1.196 ; 1.572) ECVI for Saturated Model = 0.965 ECVI for Independence Model = 35.879 Chi-Square for Independence Model with 120 Degrees of Freedom = 10085.851 Independence AIC = 10117.851 Model AIC = 386.505 Saturated AIC = 272.000 Independence CAIC = 10192.178 Model CAIC = 567.677 Saturated CAIC = 903.781 Normed Fit Index (NFI) = 0.969 Non-Normed Fit Index (NNFI) = 0.974 Parsimony Normed Fit Index (PNFI) = 0.784 Comparative Fit Index (CFI) = 0.979 Incremental Fit Index (IFI) = 0.979 Relative Fit Index (RFI) = 0.962 Critical N (CN) = 121.945 Root Mean Square Residual (RMR) = 0.398 Standardized RMR = 0.164 Goodness of Fit Index (GFI) = 0.979 Adjusted Goodness of Fit Index (AGFI) = 0.970 Parsimony Goodness of Fit Index (PGFI) = 0.698

Standardized Solution

26

LAMBDA-Y EV -------- EV1 0.897 EV2 1.298 EV3 0.753 EV4 1.321 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.574 - - - - - - EWC2 0.805 - - - - - - EWC3 1.606 - - - - - - EWC4 0.978 - - - - - - EWC5 0.904 - - - - - - EWC6 0.778 - - - - - - EWC7 0.387 - - - - - - EWC8 1.005 - - - - - - EWC9 2.876 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.551 -1.087 0.236 1.016 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.557 1.000 AGE -0.004 -0.029 1.000 GENDER 0.109 -0.063 0.466 1.000 LE 0.048 -0.010 0.974 0.408 1.000 PSI EV -------- 0.614 Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.551 -1.087 0.236 1.016

27

Completely Standardized Solution LAMBDA-Y EV -------- EV1 0.796 EV2 0.903 EV3 0.741 EV4 0.904 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.572 - - - - - - EWC2 0.528 - - - - - - EWC3 0.873 - - - - - - EWC4 0.845 - - - - - - EWC5 0.753 - - - - - - EWC6 0.838 - - - - - - EWC7 0.291 - - - - - - EWC8 0.735 - - - - - - EWC9 0.979 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.551 -1.087 0.236 1.016 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.557 1.000 AGE -0.004 -0.029 1.000 GENDER 0.109 -0.063 0.466 1.000 LE 0.048 -0.010 0.974 0.408 1.000 PSI EV -------- 0.614 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.366 0.185 0.451 0.182

28

THETA-DELTA EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 0.673 0.721 0.238 0.286 0.432 0.297 THETA-DELTA EWC7 EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- -------- 0.915 0.460 0.041 - - - - - - Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.551 -1.087 0.236 1.016 Time used: 0.828 Seconds

APPENDIX 2B

MODEL II

DATE: 1/15/2016 TIME: 3:18 L I S R E L 8.72 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2005 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file F:\PRESUNIV - ENA\thesis - ena\LISREL - TESTING\NEW TRIAL\LISREL\BASIC\EV SFL ABOBE 0.5 ALL EWC\2 SYNTAX CFA.PR2: OBSERVED VARIABLES EV1 EV2 EV3 EV4 EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 AGEM GENDERM LEM COVARIANCE MATRIX FROM FILE EVALL2.COV ASYMPTOTIC COVARIANCE MATRIX FROM FILE EVALL2.ACM SAMPLE SIZE = 283 LATENT VARIABLES EV EWC AGE GENDER LE EV1-EV4 = EV EWC1-EWC9 = EWC AGEM = 1*AGE GENDERM = 1*GENDER LEM = 1*LE EV = EWC AGE GENDER LE SET ERROR VARIANCE OF AGEM TO 0 SET ERROR VARIANCE OF GENDERM TO 0 SET ERROR VARIANCE OF LEM TO 0 LISREL OUTPUT: ND=3 SC ME=WLS PATH DIAGRAM END OF PROBLEM

Covariance Matrix

31

EV1 EV2 EV3 EV4 EWC1 EWC2 -------- -------- -------- -------- -------- -------- EV1 1.268 EV2 0.980 2.068 EV3 0.500 0.806 1.034 EV4 1.046 1.586 0.761 2.133 EWC1 0.143 0.101 0.093 0.085 1.008 EWC2 0.057 0.075 0.063 0.058 0.680 2.323 EWC3 0.566 0.660 0.454 1.011 0.444 0.976 EWC4 0.185 0.269 0.203 0.388 0.395 0.568 EWC5 0.179 0.148 0.117 0.171 0.376 0.651 EWC6 0.196 0.280 0.132 0.380 0.231 0.426 EWC8 0.225 0.324 0.266 0.370 0.360 0.784 EWC9 1.055 1.777 0.737 1.535 0.641 0.822 AGEM 0.063 -0.057 0.011 -0.069 -0.171 -0.132 GENDERM 0.110 0.044 -0.012 0.052 -0.043 0.088 LEM 0.106 0.060 0.084 -0.001 -0.288 -0.317 Covariance Matrix EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- EWC3 3.385 EWC4 1.086 1.339 EWC5 0.849 0.714 1.439 EWC6 0.743 0.399 0.512 0.862 EWC8 0.866 0.665 0.457 0.459 1.871 EWC9 2.969 1.598 1.398 1.248 1.546 8.624 AGEM -0.202 -0.051 0.038 -0.095 -0.207 0.399 GENDERM -0.209 -0.109 0.018 -0.015 -0.142 -0.293 LEM -0.101 -0.090 -0.002 -0.132 -0.354 0.406 Covariance Matrix AGEM GENDERM LEM -------- -------- -------- AGEM 1.000 GENDERM 0.430 1.000

32

LEM 0.937 0.375 1.000

Parameter Specifications LAMBDA-Y EV -------- EV1 0 EV2 1 EV3 2 EV4 3 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 4 0 0 0 EWC2 5 0 0 0 EWC3 6 0 0 0 EWC4 7 0 0 0 EWC5 8 0 0 0 EWC6 9 0 0 0 EWC8 10 0 0 0 EWC9 11 0 0 0 AGEM 0 0 0 0 GENDERM 0 0 0 0 LEM 0 0 0 0 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 12 13 14 15 PHI EWC AGE GENDER LE -------- -------- -------- -------- EWC 0 AGE 16 17 GENDER 18 19 20 LE 21 22 23 24 PSI EV -------- 25 THETA-EPS EV1 EV2 EV3 EV4

33

-------- -------- -------- -------- 26 27 28 29 THETA-DELTA EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 30 31 32 33 34 35 THETA-DELTA EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- 36 37 0 0 0

Number of Iterations = 22 LISREL Estimates (Weighted Least Squares) LAMBDA-Y EV -------- EV1 0.892 EV2 1.297 (0.056) 23.324 EV3 0.751 (0.042) 17.913 EV4 1.300 (0.058) 22.504 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.562 - - - - - - (0.037) 15.234 EWC2 0.745 - - - - - - (0.064)

34

11.699 EWC3 1.594 - - - - - - (0.051) 31.432 EWC4 0.947 - - - - - - (0.036) 26.155 EWC5 0.886 - - - - - - (0.036) 24.634 EWC6 0.745 - - - - - - (0.029) 25.685 EWC8 0.908 - - - - - - (0.043) 20.896 EWC9 2.799 - - - - - - (0.073) 38.606 AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.576 -0.916 0.223 0.882 (0.051) (1.246) (0.124) (1.201) 11.324 -0.735 1.808 0.734 Covariance Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.563 1.000 AGE 0.001 -0.068 1.000 GENDER 0.088 -0.123 0.472 1.000 LE 0.060 -0.054 0.965 0.418 1.000 PHI

35

EWC AGE GENDER LE -------- -------- -------- -------- EWC 1.000 AGE -0.068 1.000 (0.055) (0.060) -1.249 16.793 GENDER -0.123 0.472 1.000 (0.055) (0.067) (0.060) -2.243 7.090 16.793 LE -0.054 0.965 0.418 1.000 (0.053) (0.014) (0.069) (0.060) -1.034 68.558 6.066 16.793 PSI EV -------- 0.604 (0.104) 5.803 Squared Multiple Correlations for Structural Equations EV -------- 0.396 Squared Multiple Correlations for Reduced Form EV -------- 0.396 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.472 0.387 0.469 0.442 (0.098) (0.146) (0.081) (0.154) 4.827 2.659 5.794 2.862 Squared Multiple Correlations for Y - Variables EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.627 0.813 0.546 0.793 THETA-DELTA

36

EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 0.692 1.767 0.844 0.443 0.654 0.307 (0.073) (0.168) (0.258) (0.105) (0.107) (0.067) 9.493 10.532 3.266 4.209 6.118 4.581 THETA-DELTA EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- 1.046 0.789 - - - - - - (0.137) (0.655) 7.661 1.205 Squared Multiple Correlations for X - Variables EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 0.313 0.239 0.751 0.669 0.546 0.644 Squared Multiple Correlations for X - Variables EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- 0.441 0.909 1.000 1.000 1.000 Goodness of Fit Statistics Degrees of Freedom = 83 Minimum Fit Function Chi-Square = 234.781 (P = 0.00) Estimated Non-centrality Parameter (NCP) = 151.781 90 Percent Confidence Interval for NCP = (109.817 ; 201.393) Minimum Fit Function Value = 0.833 Population Discrepancy Function Value (F0) = 0.538 90 Percent Confidence Interval for F0 = (0.389 ; 0.714) Root Mean Square Error of Approximation (RMSEA) = 0.0805 90 Percent Confidence Interval for RMSEA = (0.0685 ; 0.0928)

37

P-Value for Test of Close Fit (RMSEA < 0.05) = 0.000 Expected Cross-Validation Index (ECVI) = 1.095 90 Percent Confidence Interval for ECVI = (0.946 ; 1.271) ECVI for Saturated Model = 0.851 ECVI for Independence Model = 29.044 Chi-Square for Independence Model with 105 Degrees of Freedom = 8160.488 Independence AIC = 8190.488 Model AIC = 308.781 Saturated AIC = 240.000 Independence CAIC = 8260.169 Model CAIC = 480.662 Saturated CAIC = 797.454 Normed Fit Index (NFI) = 0.971 Non-Normed Fit Index (NNFI) = 0.976 Parsimony Normed Fit Index (PNFI) = 0.768 Comparative Fit Index (CFI) = 0.981 Incremental Fit Index (IFI) = 0.981 Relative Fit Index (RFI) = 0.964 Critical N (CN) = 140.186 Root Mean Square Residual (RMR) = 0.354 Standardized RMR = 0.149 Goodness of Fit Index (GFI) = 0.981 Adjusted Goodness of Fit Index (AGFI) = 0.973 Parsimony Goodness of Fit Index (PGFI) = 0.679

Standardized Solution LAMBDA-Y EV -------- EV1 0.892 EV2 1.297 EV3 0.751 EV4 1.300 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.562 - - - - - - EWC2 0.745 - - - - - - EWC3 1.594 - - - - - - EWC4 0.947 - - - - - -

38

EWC5 0.886 - - - - - - EWC6 0.745 - - - - - - EWC8 0.908 - - - - - - EWC9 2.799 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.576 -0.916 0.223 0.882 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.563 1.000 AGE 0.001 -0.068 1.000 GENDER 0.088 -0.123 0.472 1.000 LE 0.060 -0.054 0.965 0.418 1.000 PSI EV -------- 0.604 Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.576 -0.916 0.223 0.882

Completely Standardized Solution LAMBDA-Y EV -------- EV1 0.792 EV2 0.902 EV3 0.739 EV4 0.890 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.560 - - - - - - EWC2 0.489 - - - - - - EWC3 0.866 - - - - - -

39

EWC4 0.818 - - - - - - EWC5 0.739 - - - - - - EWC6 0.802 - - - - - - EWC8 0.664 - - - - - - EWC9 0.953 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.576 -0.916 0.223 0.882 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.563 1.000 AGE 0.001 -0.068 1.000 GENDER 0.088 -0.123 0.472 1.000 LE 0.060 -0.054 0.965 0.418 1.000 PSI EV -------- 0.604 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.373 0.187 0.454 0.207 THETA-DELTA EWC1 EWC2 EWC3 EWC4 EWC5 EWC6 -------- -------- -------- -------- -------- -------- 0.687 0.761 0.249 0.331 0.454 0.356 THETA-DELTA EWC8 EWC9 AGEM GENDERM LEM -------- -------- -------- -------- -------- 0.559 0.091 - - - - - - Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.576 -0.916 0.223 0.882

APPENDIX 2C

MODEL III

DATE: 1/15/2016 TIME: 3:22 L I S R E L 8.72 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2005 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file F:\PRESUNIV - ENA\thesis - ena\LISREL - TESTING\NEW TRIAL\LISREL\BASIC\EV SFL ABOBE 0.5 ALL EWC\3 SYNTAX CFA.PR2: OBSERVED VARIABLES EV1 EV2 EV3 EV4 EWC1 EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 AGEM GENDERM LEM COVARIANCE MATRIX FROM FILE EVALL3.COV ASYMPTOTIC COVARIANCE MATRIX FROM FILE EVALL3.ACM SAMPLE SIZE = 283 LATENT VARIABLES EV EWC AGE GENDER LE EV1-EV4 = EV EWC1-EWC9 = EWC AGEM = 1*AGE GENDERM = 1*GENDER LEM = 1*LE EV = EWC AGE GENDER LE SET ERROR VARIANCE OF AGEM TO 0 SET ERROR VARIANCE OF GENDERM TO 0 SET ERROR VARIANCE OF LEM TO 0 LISREL OUTPUT: ND=3 SC ME=WLS PATH DIAGRAM END OF PROBLEM

Covariance Matrix

42

EV1 EV2 EV3 EV4 EWC1 EWC3 -------- -------- -------- -------- -------- -------- EV1 1.268 EV2 0.980 2.068 EV3 0.500 0.806 1.034 EV4 1.046 1.586 0.761 2.133 EWC1 0.143 0.101 0.093 0.085 1.008 EWC3 0.566 0.660 0.454 1.011 0.444 3.385 EWC4 0.185 0.269 0.203 0.388 0.395 1.086 EWC5 0.179 0.148 0.117 0.171 0.376 0.849 EWC6 0.196 0.280 0.132 0.380 0.231 0.743 EWC8 0.225 0.324 0.266 0.370 0.360 0.866 EWC9 1.055 1.777 0.737 1.535 0.641 2.969 AGEM 0.063 -0.057 0.011 -0.069 -0.171 -0.202 GENDERM 0.110 0.044 -0.012 0.052 -0.043 -0.209 LEM 0.106 0.060 0.084 -0.001 -0.288 -0.101 Covariance Matrix EWC4 EWC5 EWC6 EWC8 EWC9 AGEM -------- -------- -------- -------- -------- -------- EWC4 1.339 EWC5 0.714 1.439 EWC6 0.399 0.512 0.862 EWC8 0.665 0.457 0.459 1.871 EWC9 1.598 1.398 1.248 1.546 8.624 AGEM -0.051 0.038 -0.095 -0.207 0.399 1.000 GENDERM -0.109 0.018 -0.015 -0.142 -0.293 0.430 LEM -0.090 -0.002 -0.132 -0.354 0.406 0.937 Covariance Matrix GENDERM LEM -------- -------- GENDERM 1.000 LEM 0.375 1.000

43

Parameter Specifications LAMBDA-Y EV -------- EV1 0 EV2 1 EV3 2 EV4 3 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 4 0 0 0 EWC3 5 0 0 0 EWC4 6 0 0 0 EWC5 7 0 0 0 EWC6 8 0 0 0 EWC8 9 0 0 0 EWC9 10 0 0 0 AGEM 0 0 0 0 GENDERM 0 0 0 0 LEM 0 0 0 0 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 11 12 13 14 PHI EWC AGE GENDER LE -------- -------- -------- -------- EWC 0 AGE 15 16 GENDER 17 18 19 LE 20 21 22 23 PSI EV -------- 24 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 25 26 27 28 THETA-DELTA

44

EWC1 EWC3 EWC4 EWC5 EWC6 EWC8 -------- -------- -------- -------- -------- -------- 29 30 31 32 33 34 THETA-DELTA EWC9 AGEM GENDERM LEM -------- -------- -------- -------- 35 0 0 0

Number of Iterations = 24 LISREL Estimates (Weighted Least Squares) LAMBDA-Y EV -------- EV1 0.859 EV2 1.282 (0.059) 21.863 EV3 0.690 (0.043) 15.903 EV4 1.328 (0.065) 20.482 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.474 - - - - - - (0.046) 10.235 EWC3 1.546 - - - - - - (0.056) 27.747 EWC4 0.903 - - - - - - (0.041) 21.825

45

EWC5 0.754 - - - - - - (0.047) 16.197 EWC6 0.695 - - - - - - (0.033) 21.109 EWC8 0.825 - - - - - - (0.051) 16.282 EWC9 2.668 - - - - - - (0.083) 32.216 AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.588 -0.742 0.202 0.681 (0.055) (0.658) (0.101) (0.629) 10.721 -1.128 1.997 1.083 Covariance Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.563 1.000 AGE -0.091 -0.139 1.000 GENDER 0.025 -0.180 0.438 1.000 LE -0.025 -0.135 0.946 0.372 1.000 PHI EWC AGE GENDER LE -------- -------- -------- -------- EWC 1.000 AGE -0.139 1.000 (0.061) (0.060) -2.289 16.793 GENDER -0.180 0.438 1.000

46

(0.061) (0.069) (0.060) -2.962 6.323 16.793 LE -0.135 0.946 0.372 1.000 (0.059) (0.015) (0.071) (0.060) -2.298 63.447 5.242 16.793 PSI EV -------- 0.614 (0.091) 6.761 Squared Multiple Correlations for Structural Equations EV -------- 0.386 Squared Multiple Correlations for Reduced Form EV -------- 0.386 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.529 0.425 0.558 0.368 (0.098) (0.147) (0.081) (0.161) 5.401 2.886 6.903 2.286 Squared Multiple Correlations for Y - Variables EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.583 0.795 0.460 0.827 THETA-DELTA EWC1 EWC3 EWC4 EWC5 EWC6 EWC8 -------- -------- -------- -------- -------- -------- 0.783 0.996 0.524 0.870 0.379 1.190 (0.074) (0.265) (0.109) (0.111) (0.069) (0.139) 10.519 3.759 4.801 7.852 5.509 8.544

47

THETA-DELTA EWC9 AGEM GENDERM LEM -------- -------- -------- -------- 1.503 - - - - - - (0.678) 2.218 Squared Multiple Correlations for X - Variables EWC1 EWC3 EWC4 EWC5 EWC6 EWC8 -------- -------- -------- -------- -------- -------- 0.223 0.706 0.608 0.395 0.560 0.364 Squared Multiple Correlations for X - Variables EWC9 AGEM GENDERM LEM -------- -------- -------- -------- 0.826 1.000 1.000 1.000 Goodness of Fit Statistics Degrees of Freedom = 70 Minimum Fit Function Chi-Square = 170.942 (P = 0.00) Estimated Non-centrality Parameter (NCP) = 100.942 90 Percent Confidence Interval for NCP = (66.374 ; 143.208) Minimum Fit Function Value = 0.606 Population Discrepancy Function Value (F0) = 0.358 90 Percent Confidence Interval for F0 = (0.235 ; 0.508) Root Mean Square Error of Approximation (RMSEA) = 0.0715 90 Percent Confidence Interval for RMSEA = (0.0580 ; 0.0852) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.00534 Expected Cross-Validation Index (ECVI) = 0.854 90 Percent Confidence Interval for ECVI = (0.732 ; 1.004) ECVI for Saturated Model = 0.745 ECVI for Independence Model = 24.141

48

Chi-Square for Independence Model with 91 Degrees of Freedom = 6779.824 Independence AIC = 6807.824 Model AIC = 240.942 Saturated AIC = 210.000 Independence CAIC = 6872.860 Model CAIC = 403.533 Saturated CAIC = 697.772 Normed Fit Index (NFI) = 0.975 Non-Normed Fit Index (NNFI) = 0.980 Parsimony Normed Fit Index (PNFI) = 0.750 Comparative Fit Index (CFI) = 0.985 Incremental Fit Index (IFI) = 0.985 Relative Fit Index (RFI) = 0.967 Critical N (CN) = 166.671 Root Mean Square Residual (RMR) = 0.269 Standardized RMR = 0.116 Goodness of Fit Index (GFI) = 0.984 Adjusted Goodness of Fit Index (AGFI) = 0.976 Parsimony Goodness of Fit Index (PGFI) = 0.656

Standardized Solution LAMBDA-Y EV -------- EV1 0.859 EV2 1.282 EV3 0.690 EV4 1.328 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.474 - - - - - - EWC3 1.546 - - - - - - EWC4 0.903 - - - - - - EWC5 0.754 - - - - - - EWC6 0.695 - - - - - - EWC8 0.825 - - - - - - EWC9 2.668 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE

49

-------- -------- -------- -------- EV 0.588 -0.742 0.202 0.681 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.563 1.000 AGE -0.091 -0.139 1.000 GENDER 0.025 -0.180 0.438 1.000 LE -0.025 -0.135 0.946 0.372 1.000 PSI EV -------- 0.614 Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.588 -0.742 0.202 0.681

Completely Standardized Solution LAMBDA-Y EV -------- EV1 0.763 EV2 0.891 EV3 0.678 EV4 0.910 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC1 0.473 - - - - - - EWC3 0.840 - - - - - - EWC4 0.780 - - - - - - EWC5 0.629 - - - - - - EWC6 0.749 - - - - - - EWC8 0.603 - - - - - - EWC9 0.909 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE

50

-------- -------- -------- -------- EV 0.588 -0.742 0.202 0.681 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.563 1.000 AGE -0.091 -0.139 1.000 GENDER 0.025 -0.180 0.438 1.000 LE -0.025 -0.135 0.946 0.372 1.000 PSI EV -------- 0.614 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.417 0.205 0.540 0.173 THETA-DELTA EWC1 EWC3 EWC4 EWC5 EWC6 EWC8 -------- -------- -------- -------- -------- -------- 0.777 0.294 0.392 0.605 0.440 0.636 THETA-DELTA EWC9 AGEM GENDERM LEM -------- -------- -------- -------- 0.174 - - - - - - Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.588 -0.742 0.202 0.681 Time used: 0.531 Seconds

APPENDIX 2D

MODEL IV

DATE: 1/15/2016 TIME: 3:25 L I S R E L 8.72 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2005 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file F:\PRESUNIV - ENA\thesis - ena\LISREL - TESTING\NEW TRIAL\LISREL\BASIC\EV SFL ABOBE 0.5 ALL EWC\4 SYNTAX STRUCTURAL.PR2: OBSERVED VARIABLES EV1 EV2 EV3 EV4 EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 AGEM GENDERM LEM COVARIANCE MATRIX FROM FILE EVALL4.COV ASYMPTOTIC COVARIANCE MATRIX FROM FILE EVALL4.ACM SAMPLE SIZE = 283 LATENT VARIABLES EV EWC AGE GENDER LE EV1-EV4 = EV EWC3-EWC9 = EWC AGEM = 1*AGE GENDERM = 1*GENDER LEM = 1*LE EV = EWC AGE GENDER LE SET ERROR VARIANCE OF AGEM TO 0 SET ERROR VARIANCE OF GENDERM TO 0 SET ERROR VARIANCE OF LEM TO 0 LISREL OUTPUT: ND=3 SC ME=WLS PATH DIAGRAM END OF PROBLEM

Covariance Matrix

53

EV1 EV2 EV3 EV4 EWC3 EWC4 -------- -------- -------- -------- -------- -------- EV1 1.268 EV2 0.980 2.068 EV3 0.500 0.806 1.034 EV4 1.046 1.586 0.761 2.133 EWC3 0.566 0.660 0.454 1.011 3.385 EWC4 0.185 0.269 0.203 0.388 1.086 1.339 EWC5 0.179 0.148 0.117 0.171 0.849 0.714 EWC6 0.196 0.280 0.132 0.380 0.743 0.399 EWC8 0.225 0.324 0.266 0.370 0.866 0.665 EWC9 1.055 1.777 0.737 1.535 2.969 1.598 AGEM 0.063 -0.057 0.011 -0.069 -0.202 -0.051 GENDERM 0.110 0.044 -0.012 0.052 -0.209 -0.109 LEM 0.106 0.060 0.084 -0.001 -0.101 -0.090 Covariance Matrix EWC5 EWC6 EWC8 EWC9 AGEM GENDERM -------- -------- -------- -------- -------- -------- EWC5 1.439 EWC6 0.512 0.862 EWC8 0.457 0.459 1.871 EWC9 1.398 1.248 1.546 8.624 AGEM 0.038 -0.095 -0.207 0.399 1.000 GENDERM 0.018 -0.015 -0.142 -0.293 0.430 1.000 LEM -0.002 -0.132 -0.354 0.406 0.937 0.375 Covariance Matrix LEM -------- LEM 1.000

Parameter Specifications LAMBDA-Y EV

54

-------- EV1 0 EV2 1 EV3 2 EV4 3 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC3 4 0 0 0 EWC4 5 0 0 0 EWC5 6 0 0 0 EWC6 7 0 0 0 EWC8 8 0 0 0 EWC9 9 0 0 0 AGEM 0 0 0 0 GENDERM 0 0 0 0 LEM 0 0 0 0 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 10 11 12 13 PHI EWC AGE GENDER LE -------- -------- -------- -------- EWC 0 AGE 14 15 GENDER 16 17 18 LE 19 20 21 22 PSI EV -------- 23 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 24 25 26 27 THETA-DELTA EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- 28 29 30 31 32 33 THETA-DELTA

55

AGEM GENDERM LEM -------- -------- -------- 0 0 0

Number of Iterations = 15 LISREL Estimates (Weighted Least Squares) LAMBDA-Y EV -------- EV1 0.779 EV2 1.317 (0.079) 16.655 EV3 0.696 (0.051) 13.574 EV4 1.306 (0.080) 16.411 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC3 1.519 - - - - - - (0.060) 25.159 EWC4 0.896 - - - - - - (0.043) 20.726 EWC5 0.813 - - - - - - (0.052) 15.619 EWC6 0.682 - - - - - - (0.034) 19.793 EWC8 0.851 - - - - - - (0.052) 16.221

56

EWC9 2.690 - - - - - - (0.087) 31.029 AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.559 -0.847 0.238 0.732 (0.069) (0.752) (0.148) (0.696) 8.094 -1.127 1.604 1.052 Covariance Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.510 1.000 AGE -0.059 -0.025 1.000 GENDER 0.015 -0.162 0.475 1.000 LE -0.002 -0.043 0.941 0.369 1.000 PHI EWC AGE GENDER LE -------- -------- -------- -------- EWC 1.000 AGE -0.025 1.000 (0.064) (0.060) -0.392 16.793 GENDER -0.162 0.475 1.000 (0.063) (0.073) (0.060) -2.554 6.528 16.793 LE -0.043 0.941 0.369 1.000 (0.061) (0.016) (0.074) (0.060) -0.702 57.055 4.953 16.793 PSI EV -------- 0.662

57

(0.109) 6.048 Squared Multiple Correlations for Structural Equations EV -------- 0.338 Squared Multiple Correlations for Reduced Form EV -------- 0.338 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.661 0.333 0.549 0.427 (0.102) (0.152) (0.083) (0.163) 6.502 2.196 6.645 2.616 Squared Multiple Correlations for Y - Variables EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.479 0.839 0.469 0.800 THETA-DELTA EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- 1.076 0.536 0.778 0.396 1.148 1.386 (0.273) (0.111) (0.120) (0.070) (0.143) (0.694) 3.948 4.818 6.454 5.697 8.041 1.998 THETA-DELTA AGEM GENDERM LEM -------- -------- -------- - - - - - - Squared Multiple Correlations for X - Variables EWC3 EWC4 EWC5 EWC6 EWC8 EWC9

58

-------- -------- -------- -------- -------- -------- 0.682 0.600 0.460 0.540 0.387 0.839 Squared Multiple Correlations for X - Variables AGEM GENDERM LEM -------- -------- -------- 1.000 1.000 1.000 Goodness of Fit Statistics Degrees of Freedom = 58 Minimum Fit Function Chi-Square = 117.019 (P = 0.000) Estimated Non-centrality Parameter (NCP) = 59.019 90 Percent Confidence Interval for NCP = (31.973 ; 93.843) Minimum Fit Function Value = 0.415 Population Discrepancy Function Value (F0) = 0.209 90 Percent Confidence Interval for F0 = (0.113 ; 0.333) Root Mean Square Error of Approximation (RMSEA) = 0.0601 90 Percent Confidence Interval for RMSEA = (0.0442 ; 0.0757) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.140 Expected Cross-Validation Index (ECVI) = 0.649 90 Percent Confidence Interval for ECVI = (0.553 ; 0.772) ECVI for Saturated Model = 0.645 ECVI for Independence Model = 22.167 Chi-Square for Independence Model with 78 Degrees of Freedom = 6225.127 Independence AIC = 6251.127 Model AIC = 183.019 Saturated AIC = 182.000 Independence CAIC = 6311.517 Model CAIC = 336.318 Saturated CAIC = 604.736 Normed Fit Index (NFI) = 0.981 Non-Normed Fit Index (NNFI) = 0.987 Parsimony Normed Fit Index (PNFI) = 0.730 Comparative Fit Index (CFI) = 0.990 Incremental Fit Index (IFI) = 0.990 Relative Fit Index (RFI) = 0.975 Critical N (CN) = 208.131

59

Root Mean Square Residual (RMR) = 0.254 Standardized RMR = 0.104 Goodness of Fit Index (GFI) = 0.988 Adjusted Goodness of Fit Index (AGFI) = 0.981 Parsimony Goodness of Fit Index (PGFI) = 0.630

Standardized Solution LAMBDA-Y EV -------- EV1 0.779 EV2 1.317 EV3 0.696 EV4 1.306 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC3 1.519 - - - - - - EWC4 0.896 - - - - - - EWC5 0.813 - - - - - - EWC6 0.682 - - - - - - EWC8 0.851 - - - - - - EWC9 2.690 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.559 -0.847 0.238 0.732 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.510 1.000 AGE -0.059 -0.025 1.000 GENDER 0.015 -0.162 0.475 1.000 LE -0.002 -0.043 0.941 0.369 1.000 PSI EV -------- 0.662

60

Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.559 -0.847 0.238 0.732

Completely Standardized Solution LAMBDA-Y EV -------- EV1 0.692 EV2 0.916 EV3 0.685 EV4 0.894 LAMBDA-X EWC AGE GENDER LE -------- -------- -------- -------- EWC3 0.826 - - - - - - EWC4 0.775 - - - - - - EWC5 0.678 - - - - - - EWC6 0.735 - - - - - - EWC8 0.622 - - - - - - EWC9 0.916 - - - - - - AGEM - - 1.000 - - - - GENDERM - - - - 1.000 - - LEM - - - - - - 1.000 GAMMA EWC AGE GENDER LE -------- -------- -------- -------- EV 0.559 -0.847 0.238 0.732 Correlation Matrix of ETA and KSI EV EWC AGE GENDER LE -------- -------- -------- -------- -------- EV 1.000 EWC 0.510 1.000 AGE -0.059 -0.025 1.000 GENDER 0.015 -0.162 0.475 1.000 LE -0.002 -0.043 0.941 0.369 1.000 PSI EV -------- 0.662

61

THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.521 0.161 0.531 0.200 THETA-DELTA EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- 0.318 0.400 0.540 0.460 0.613 0.161 THETA-DELTA AGEM GENDERM LEM -------- -------- -------- - - - - - - Regression Matrix ETA on KSI (Standardized) EWC AGE GENDER LE -------- -------- -------- -------- EV 0.559 -0.847 0.238 0.732 Time used: 0.438 Seconds

APPENDIX 3

MODEL THAT HAS DELETED DEMOGRAPHIC VARIABLES

DATE: 1/15/2016 TIME: 3:28 L I S R E L 8.72 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2005 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file F:\PRESUNIV - ENA\thesis - ena\LISREL - TESTING\NEW TRIAL\LISREL\BASIC\EV SFL ABOBE 0.5 ALL EWC\6 SYNTAX STR CFA.PR2: OBSERVED VARIABLES EV1 EV2 EV3 EV4 EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 COVARIANCE MATRIX FROM FILE EVALL6.COV ASYMPTOTIC COVARIANCE MATRIX FROM FILE EVALL6.ACM SAMPLE SIZE = 283 LATENT VARIABLES EV EWC EV1-EV4 = EV EWC3-EWC9 = EWC EV = EWC LISREL OUTPUT: ND=3 SC ME=WLS PATH DIAGRAM END OF PROBLEM

Covariance Matrix EV1 EV2 EV3 EV4 EWC3 EWC4 -------- -------- -------- -------- -------- -------- EV1 1.419 EV2 0.731 1.265

64

EV3 0.494 0.601 1.129 EV4 0.733 0.856 0.543 1.234 EWC3 0.219 0.180 0.173 0.282 0.664 EWC4 0.083 0.083 0.122 0.136 0.285 0.717 EWC5 0.106 0.056 0.074 0.067 0.240 0.294 EWC6 0.148 0.145 0.103 0.209 0.262 0.223 EWC8 0.108 0.110 0.139 0.120 0.189 0.295 EWC9 0.273 0.298 0.201 0.256 0.357 0.284 Covariance Matrix EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- EWC5 0.641 EWC6 0.254 0.640 EWC8 0.175 0.213 0.787 EWC9 0.265 0.275 0.217 0.720

Parameter Specifications LAMBDA-Y EV -------- EV1 0 EV2 1 EV3 2 EV4 3 LAMBDA-X EWC -------- EWC3 4 EWC4 5 EWC5 6 EWC6 7 EWC8 8 EWC9 9 GAMMA EWC -------- EV 10 PSI

65

EV -------- 11 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 12 13 14 15 THETA-DELTA EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- 16 17 18 19 20 21

Number of Iterations = 11 LISREL Estimates (Weighted Least Squares) LAMBDA-Y EV -------- EV1 0.720 EV2 0.887 (0.093) 9.527 EV3 0.574 (0.071) 8.064 EV4 0.805 (0.084) 9.591 LAMBDA-X EWC -------- EWC3 0.551 (0.045) 12.136 EWC4 0.553

66

(0.051) 10.813 EWC5 0.403 (0.051) 7.968 EWC6 0.446 (0.044) 10.216 EWC8 0.443 (0.046) 9.734 EWC9 0.629 (0.048) 13.143 GAMMA EWC -------- EV 0.464 (0.076) 6.108 Covariance Matrix of ETA and KSI EV EWC -------- -------- EV 1.000 EWC 0.464 1.000 PHI EWC -------- 1.000 PSI EV -------- 0.784 (0.164) 4.776 Squared Multiple Correlations for Structural Equations EV

67

-------- 0.216 Squared Multiple Correlations for Reduced Form EV -------- 0.216 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.742 0.253 0.706 0.328 (0.093) (0.063) (0.062) (0.057) 7.973 4.001 11.367 5.721 Squared Multiple Correlations for Y - Variables EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.411 0.756 0.318 0.664 THETA-DELTA EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- 0.313 0.300 0.382 0.386 0.483 0.254 (0.036) (0.045) (0.035) (0.038) (0.049) (0.037) 8.671 6.624 10.846 10.204 9.789 6.819 Squared Multiple Correlations for X - Variables EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- 0.492 0.505 0.299 0.340 0.289 0.609 Goodness of Fit Statistics Degrees of Freedom = 34 Minimum Fit Function Chi-Square = 70.416 (P = 0.000241) Estimated Non-centrality Parameter (NCP) = 36.416 90 Percent Confidence Interval for NCP = (16.160 ; 64.432)

68

Minimum Fit Function Value = 0.250 Population Discrepancy Function Value (F0) = 0.129 90 Percent Confidence Interval for F0 = (0.0573 ; 0.228) Root Mean Square Error of Approximation (RMSEA) = 0.0616 90 Percent Confidence Interval for RMSEA = (0.0411 ; 0.0820) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.163 Expected Cross-Validation Index (ECVI) = 0.399 90 Percent Confidence Interval for ECVI = (0.327 ; 0.498) ECVI for Saturated Model = 0.390 ECVI for Independence Model = 0.978 Chi-Square for Independence Model with 45 Degrees of Freedom = 255.812 Independence AIC = 275.812 Model AIC = 112.416 Saturated AIC = 110.000 Independence CAIC = 322.267 Model CAIC = 209.970 Saturated CAIC = 365.500 Normed Fit Index (NFI) = 0.725 Non-Normed Fit Index (NNFI) = 0.771 Parsimony Normed Fit Index (PNFI) = 0.548 Comparative Fit Index (CFI) = 0.827 Incremental Fit Index (IFI) = 0.836 Relative Fit Index (RFI) = 0.636 Critical N (CN) = 225.514 Root Mean Square Residual (RMR) = 0.0834 Standardized RMR = 0.0983 Goodness of Fit Index (GFI) = 0.938 Adjusted Goodness of Fit Index (AGFI) = 0.899 Parsimony Goodness of Fit Index (PGFI) = 0.580

Standardized Solution LAMBDA-Y EV -------- EV1 0.720 EV2 0.887 EV3 0.574

69

EV4 0.805 LAMBDA-X EWC -------- EWC3 0.551 EWC4 0.553 EWC5 0.403 EWC6 0.446 EWC8 0.443 EWC9 0.629 GAMMA EWC -------- EV 0.464 Correlation Matrix of ETA and KSI EV EWC -------- -------- EV 1.000 EWC 0.464 1.000 PSI EV -------- 0.784 Regression Matrix ETA on KSI (Standardized) EWC -------- EV 0.464

Completely Standardized Solution LAMBDA-Y EV -------- EV1 0.641 EV2 0.870 EV3 0.564 EV4 0.815 LAMBDA-X EWC -------- EWC3 0.702

70

EWC4 0.711 EWC5 0.546 EWC6 0.583 EWC8 0.538 EWC9 0.781 GAMMA EWC -------- EV 0.464 Correlation Matrix of ETA and KSI EV EWC -------- -------- EV 1.000 EWC 0.464 1.000 PSI EV -------- 0.784 THETA-EPS EV1 EV2 EV3 EV4 -------- -------- -------- -------- 0.589 0.244 0.682 0.336 THETA-DELTA EWC3 EWC4 EWC5 EWC6 EWC8 EWC9 -------- -------- -------- -------- -------- -------- 0.508 0.495 0.701 0.660 0.711 0.391 Regression Matrix ETA on KSI (Standardized) EWC -------- EV 0.464 Time used: 0.125 Seconds

71

APPENDIX 4

ETHICAL EVALUATION: MEANS

Case 1 Case 2 Case 3 Case 4

Less than 25 years old 3.509 3.811 3.32 4.023

More than or equal to 25 years

old 3.602 3.759 3.343 4.019

Female 3.463 3.769 3.34 3.986

Male 3.632 3.816 3.316 4.059

Less than 2 years 3.477 3.762 3.273 4.000

More than or equal to 2 years 3.649 3.838 3.414 4.054

Note:

Case 1: biasing sample selection

Case 2: over-reliance on client work

Case 3: URT

Case 4: PSO

72

APPENDIX 5

QUESTIONNAIRES

This questionnaire prepared in regards to my data collection in thesis. The topic

that arises is about evaluating the ethical issues.Your response will strictly

confidential.

Background information

Keith is the audit senior assigned to the audit of J company. This is Keith's second

year on this audit, and there were no particular problems found in the last year's

audit. The general review of the accounting and internal control systems did not

indicate anything particularly worrisome, but the audit partner instructed the audit

team to take the normal precautionary measures when doing the audit.

The audit team members are under considerable time pressure on this audit. Keith

has had previous time problems on audits and has received poor evaluations. He

believes that it is important that he meets the time budget and deadline for this

audit to get a good evaluation. While the audit manager will generally listen to

requests to go over the time budget and deadline, he generally expects that the

audit staff will do the work in the time allocated, unless extraordinary

circumstances dictate otherwise.

According to the audit program for J Company, one of the audit tests specified

that Keith was to select 10 stock items from the stock sheets and check that they

had been correctly valued by comparing cost prices on the stock sheets with those

on the supplier invoices. The company has a large number of stock items with

different serial codes, and the act of locating the invoice and comparing the price

can be time consuming.

73

Instruction: Please state your preference for what action to take in the case on

five point scale. (1 = strongly favor the action; 5 = strongly oppose the action)

Case 1 2 3 4 5

Case 1: Keith selects 10 stock items, and for 9 of the stock

items has no problem while comparing and agreeing the cost

prices to the invoices. However, for one of the stock items, he

finds a discrepancy between the cost price on the stock sheet

and the price on the invoice. Since he is already under time

pressure, he decides to ignore that stock item and selects

another stock item for which the price agrees with the invoice.

He noted on the audit file that all prices were checked and

found to be correct for all the 10 stock items selected.

Case 2: The client presents Keith with a sample of 15 stock

items which the client has already matched with invoices to

save Keith’s time. Keith relies on the client work and signs off

the test without noting the reliance on the client work.

Case 3: The audit manager informs Keith that meeting the time

budget is more important than meeting the time deadline. Keith

completes all his work but does not charge the total time he has

spent on the audit work on his timesheet. This allows him to

meet the budget for the job.

Case 4: In order to save time, Keith does not attempt this test at

all but signs it off on the audit program as completed.

Questions asked regarding your firm (ethical work climate)

74

Instructions: Please answer the following questions about the general climate in

your company in terms of how it really is in your company, not how you would

prefer it to be by using five point scale. (1 = strongly disagree; 5 = strongly

agree)

Statements 1 2 3 4 5

(EI) People in this company are very concerned about what is

best for them.

(EL) People are expected to do anything o further the

company’s interest.

(EC) In this company, each person is expected, above all, to

work efficiently.

(BI) It is expected that each individual is cared for when

making decisions here.

(BL) Our major consideration is what is best for everyone in

this company.

(BC) The effect of decisions on the customer and the public are

primary concerned in this company.

(PI) Each person in this company decides for himself what is

right and wrong.

(PL) It is important to follow strictly the company’s rules and

procedures.

(PC) In this company, people are expected to strictly follow

legal or professional standards.

Personal information

How old are you?

75

______ years

Are you male or female? Please use checklist for the answer ()

( ) Male ( ) Female

How long have you worked in public accounting firm?

_____ year _____ month

Which of the following describes your audit firm? Please use checklist for the

answer ()

( ) Big Four ( ) 0-5 partners ( ) 6-15

partners

( ) 16+ audit partners but not Big Four

What is your current position? Please use checklist for the answer ()

( ) Partner ( ) Director ( ) Manager

( ) Assistant Manager ( ) Senior Associate ( ) Associate

76

APPENDIX 6

LIST OF PUBLIC ACCOUNTING FIRMS

NO. NAME ADDRESS

1 KAP HENDRAWINATA EDDY

SIDDHARTA & TANZIL

Intiland Tower 18 Floor. Jl. Jend. Sudirman

Kav.32, Jakarta Pusat 10220

2 KAP MULYAMIN SENSI

SURYANTO & LIANNY

Intiland Tower 7 Floor. Jl. Jend. Sudirman

Kav.32, Jakarta Pusat 10220

3 KAP JOACHIM POLTAK LIAN

MICHELL DAN REKAN

Graha Mandiri Lantai 24. Jl. Imam Bonjol

No.61, Jakarta Pusat 10310.

4 KAP MEIDINA, RATNA Gedung Thamrin City Lantai 7 Blok O5

No.35. Jl. Thamrin Boulevard, Jakarta Pusat

10230

5 KAP JANSEN & RAMDAN Gedung Jaya 7th Floor. Jl. M. H. Thamrin

No.12, Jakarta Pusat 10340.

6 KAP Drs. BERNARDI & REKAN Jl. Cikini Raya No.9, Jakarta Pusat 10330.

7 KAP JOJO SUNARJO & REKAN Gedung Dewan Pers Lantai 5. Jl. Kebon Sirih

No.32 - 34, Jakarta Pusat 10110.

8 KAP Drs. SELAMAT, Ak., BAP Wisma Tigris Lantai 4. Jl. Batu Ceper No.19

DEF, Jakarta Pusat 10120.

9 KAP DRS. BAMBANG

MUDJIONO & WIDIARTO

Gedung Sarana Jaya Lantai III R. 301. Jl.

Tebet Barat IV No. 20, Jakarta Selatan 12810.

10 KAP MAURICE GANDA

NAINGGOLAN

Epiwalk Office Suites Lantai 6 Unit B640.

Komplek Rasuna Epicentrum, Jl. H.R. Rasuna

Said, Kuningan, Jakarta Selatan 12430.

11 KAP RAMA WENDRA The Manhattan Square Mid tower 18th floor.

Jalan TB Simatupang Kav. 15, Jaksel 12560.

12 KAP ARIA KANAKA & REKAN Gedung Sona Topaz Tower lt.7. Jl. Jenderal

Sudirman kav.26, Jakarta Selatan 12920.

13 KAP BASYIRUDDIN & WILDAN MT. Haryono Square Building 3 Floor, No.23.

Jl. MT. Haryono Kav.10, Jakarta Timur 13330

14 KAP ABDUL AZIZ FIBY ARIZA Komplek Bumi Malaka Asri 3. Jl. Flamboyan

Raya H1/9, Malakasari, Duren Sawit, Jakarta

Timur 13460.

15 KAP WARNOYO, S.E., M.Si. Metland Menteng Blok G-1 Nomor 2, Kel.

Ujung Menteng, Cakung, Jakarta Timur

77

13950.

16 KAP YUWONO H Jl. Arabika VIII Blok AA.2 No. 2, Pondok

Kopi, Jakarta Timur 13350.

17 KAP Drs. BAMBANG

SUDARYONO & REKAN

Jl. Wisma Jaya No.2, Rawamangun, Jakarta

Timur 13220.

18 KAP EFFENDY & REKAN Grand Galaxy City. Jl. Grand Galaxy

Boulevard Blok FE - 525, Bekasi Selatan,

Bekasi 17147.

19 KAP HELIANTONO & REKAN

(CABANG)

Komplek Ruko Fajar. Jl. Kalimalang Raya

No.59 A, Jakasampurna, Bekasi 17145.

20 KAP Drs. MOHAMMAD

YOESOEF DAN REKAN

Jl. Damar IV No.15, Jatibening 2, Pondok

Gede, Bekasi 17412

21 KAP MOH. MAHSUN, Ak, M.Si,

CPA

Jl. Prof. Dr. Soepomo Gg. Lucida No.02

Janturan Umbulharjo Yogyakarta 55164,

Indonesia.

22 KAP TANUDIREDJA,

WIBISANA & REKAN

Plaza 89 Lantai 11, 12 & 12 M. Jl. H.R.

Rasuna Said X-7 No.6, Jakarta Selatan 12940.

23 KAP. SIDDHARTA WIDJAJA &

REKAN

Wisma GKBI Lantai 33. Jl. Jend. Sudirman

Kav.28, Jakarta Pusat 10210.

24 KAP OSMAN BING SATRIO &

ENY

The Plaza Office Tower, Lantai 32. Jl. M. H.

Thamrin Kav.28 - 30, Jakarta Pusat 10350.

25 KAP ARYANTO, AMIR JUSUF,

MAWAR & SAPTOTO

Plaza ASIA Lantai 10 & 11. Jl. Jendral

Sudirman Kav.59, Jakarta Selatan 12190.

26 KAP TANUBRATA SUTANTO

FAHMI DAN REKAN

Prudential Tower Lantai 17. Jl. Jend.

Sudirman Kav.79, Jakarta Selatan 12910.

27 KAP TOTON SUCIPTO Metland Transyogi, Gandaria XII No. 40

Cileungsi Bogor, Indonesia 16820

the influence of ethical work climate (ewc) and

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