Gender, Overconfidence, and Impression Management

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BSc Business Administration

Specialization Management in the Digital Age

Bachelor Thesis

Gender, Overconfidence, and Impression

Management

Maria Halberg-Holmgaard : 1160489

Word Count : 3842

Submission Date : 8th July 2020

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With the staggering concerns of validity of interviews and candidates use of deceptive impression management, and the implications on organizations thereof it has given way to the research the predictors of deceptive. To attempt to explain the main contributors to deceptive impression management. In the present paper I investigate the role that overconfidence might play in explaining gender differences in deceptive impression management. Specifically, I suggest that there is a positive relationship between

overconfidence and deceptive impression management, such that under high overconfidence more deceptive impression management will occur. Additionally, it is suggested that

overconfidence mediates the relationship between gender and use of deceptive impression management. These hypotheses were tested with a sample of 70 participants. Hypothesis 1 was supported suggesting that overconfidence does marginally lead to deceptive impression management. Hypothesis 2 was not supported indicating that no distinction between genders was identified in relation to their use of overconfidence and the outcome of deceptive

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1. Introduction

Organizations devote considerable resources to gaining a competitive advantage through their workforce, as finding applicants that have a good job fit and organization fit is vital. Effective personnel selection procedures are critical to this goal. The interview is the most widely used method for the assessment of candidates (Adams & Elacqua, 1994; Bureau of National Affairs, 1988).However, compared with other selection methods, its reliability and validity are rather low (e.g., Macan, 2009; Marchese & Muchinsky, 1993; Posthuma, Morgeson, & Campion, 2002). One factor contributing to this is the pervasive use of

impression management by candidates (Stevens & Kristof, 1995; Fletcher, 1992). Impression management is especially problematic when it relies on deception, since this might invalidate the interview process and potentially lead to bias in selection decisions (Delery & Kacmar, 1998). Indeed, prior studies indicate that the detection of impression management during job interviews is a difficult task for interviewers (Roulin, Bangerter, Levashina, 2014). This is problematic for a number of reasons. For example, it can potentially lead to the selection of less competent applicants, therefore paving the way for suboptimal organizational

performance. Furthermore, this form of deceptive communication can also lead to decreased task performance of work teams, as it impairs trustworthiness and mutuality (Fuller, Marett, & Twitchell, 2012).

Scholars have devoted considerable efforts to investigating individual differences that might predict the use of deceptive impression management (e.g., Roulin & Bourdage, 2017; van Iddekinge, McFarland, & Raymark, 2007). One consistent finding is that men are more prone to deceptive impression management than women (e.g., Guadagno & Cialdini, 2007; Singh, Kumra, & Vinnicombe, 2002; Tyler & Feldman, 2004). To date, however, there is no clear understanding of why men and women differ in their use of deception. To address this gap in the literature, in the present paper I investigate the role that overconfidence might play

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in explaining gender differences in deceptive impression management. Specifically, I suggest that overconfidence mediates the relationship between gender and use of deceptive

impression management.

2. Theoretical Background 2.1 Impression Management and Gender

Impression Management refers to the process by which people attempt to control the information others have of them in order to create a favorable image of themselves (Leary & Kowalski, 1990; Schlenker, 2006). Impression management can manifest in honest or deceptive forms. Honest impression management involves illustrating and emphasizing actual competencies and job-related abilities (Hogue, Levashina, & Hang, 2013). For example, “I made sure the interviewer was aware of my skills and abilities” taken from the honest impression management scale created by Bourdage, et al. (2018).Deceptive

impression management, on the other hand, refers to the embellishment, exaggeration, and sometimes even invention of competencies or abilities that the interviewee thinks the interviewers is looking for (Hogue, Levashina, & Hang, 2013). For example, “I exaggerated my responsibilities on my previous jobs” taken from the deceptive impression management scale created by Bourdage, et al. (2018).Impression management behaviors can therefore fall on a continuum between honest and deceptive.

Impression management is common within job applicants (Tsai, Chen, & Chiu, 2005; Kacmar, Delery, & Ferris, 1992). However, several studies indicate that men and women differ in the extent and the form in which they utilize impression management (Bolino, Long, & Turnley, 2016). For instance, men more so than women have an increased likely hood of using comparatively aggressive and self-serving forms of impression management. Opposed

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interpersonally sensitive, and other-oriented forms of impression management (Guadagno & Cialdini, 2007). In addition, men and women use impression management for different purposes, men generally attempt to acquire instrumental rewards and want to stand out when using impression management while women are more likely to balance the needs of all involved and use their effort to achieve a fair outcome (Tannen, 1994). Together, these findings suggest that men might be more prone than women to engage in deceptive forms of impression management during job interview contexts, as deception represents a riskier and more aggressive behavioral choice. Consistent with this, Lohse and Qari (2019) found that women’s deceptive impression management decreases in face-to-face interactions, which does not occur in men. Prior explanations for these differences have been grounded in gender role expectations (e.g., Hogue et al., 2013). In particular, scholars have argued that gender stereotypes, which describe men as more goal-oriented, assertive, and competitive than women, might promote deceptive impression management in men. However, several studies have also found no gender differences in deceptive impression management (e.g., Childs, 2012; Pate, 2018; Ezquerra, Kolev, Rodriguez-Lara, 2018). This has led researchers to consider potential alternative reasons that could explain such gender differences. In the next sections, I point to overconfidence as a possible explanatory mechanism for gender

differences in deceptive impression management.

2.2 Overconfidence and Deceptive Impression Management

Overconfidence can be defined as an exaggerated perception of one’s knowledge, competence, or abilities (see Moore & Healy, 2008 for a review). Overconfidence can manifest in three different forms. First, overprecision, which involves excessive assurance concerning the accuracy of one’s beliefs. Second, overestimation, which denotes

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overplacement, which refers to the better than average effect; the belief that one is better than

others.

These exaggerated self-views have a strong influence on individuals decision-making and behavioral choices. For example, overconfident executives tend to incur in excessive trading (Odean, 1999; Barber and Odean, 2002; Glaser and Weber, 2007) and are more likely to choose higher-risk investments (Barber & Odean, 2001). Overconfident CEOs also tend to overestimate the returns that these investments will generate, resulting in discrepancies between organizations’ performance and expected earnings (Hsieh, Wang, & Demirkan, 2018). To alleviate these discrepancies, overconfident executives often engage in unethical earnings management strategies, such as tax avoidance (Desai and Dharmapala, 2009; Halon, 2005; Olsen & Stekelberg, 2015). Relatedly, Schram and Zechman (2012) found that

overconfident executive as more likely to commit accounting fraud. Altogether, these findings indicate that overconfident individuals are more prone to risk-taking behavior and unethical behavior. These tendencies should promote deceptive impression management for several reasons. First, as risk-taking can lead to successful and valuable outcomes it would advocate using deceptive impression management to achieve these outcomes. For example, the expectancy-value model (Barron & Hulleman, 2015) would support risk-taking under situations where one perceives the outcome greater than the risk. However, candidates in the interview stage that deceive run a risk of getting caught, they are gambling with the

possibility of a negative appearance and being disqualified from the selectin process.Second, unethical behavior and deceptive impression management correlate as they involve lying and inventing information to promote one self.

Researchers have also documented a relationship between overconfidence and

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confidence (Jain & Bearden, 2011). Individuals that score high on machiavellianism also tend to engage in interpersonal manipulation and exhibit immoral behaviors such as stealing and cheating (Christie and Geis 1970; Harrell and Hartnagel 1976; Jones and Paulhus 2009). These behaviors follow from the maquiavelli’s emphasis on their personal goals and benefits (Ain et al., 2013; O’Boyle et al., 2012). Because deceptive impression management

constitutes a form of interpersonal manipulation to achieve a personal goal (i.e., increase perceptions of competence during job interviews), the association between overconfidence and maquiavellianism suggests that overconfident individuals might be more prone to engage in deceptive impression management.

In sum, because overconfidence is associated with risk-taking, unethical behavior, and interpersonal manipulation to serve personal goals, I expect overconfidence to predict

deception impression management in job interview contexts.

H1: There is a positive relationship between overconfidence and deceptive impression

management, such that under high overconfidence more deceptive impression management will occur.

While overconfidence is widespread bias (e.g., Alicke & Govorun, 2005; Dunning, Heath, & Suls, 2004), several studies indicate men are often more overconfident than women. For instance, Niederle and Vesterlund (2007) found that men are considerably more

overconfident about their own relative performance in comparison to women. Additionally, it was found that the lack in overconfidence in women could be contributed to a tendency of women to underestimate their own abilities, while this does not occur as frequently in men (Beyer & Bowden, 1997; Croson & Gneezy, 2009). As demonstrated by Duabman,

Heatherington and Ahn (1992) were women show low confidence compared to men, this was depicted in men’s overestimation of their actual grade point averages compared to women which appear to protect their self-presentational image and rate themselves lower than the

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average. Estes and Hosseini (1988) investigated investor confidence and found that women were substantially less confident then men when regarding investment decisions, even if their competence was at the same degree. In sum, there is consistent evidence that men tend to exhibit higher overconfidence compared to women, which leads me to establish the following hypothesis.

H2: Overconfidence mediates the relationship between gender and deceptive impression management.

3. Method 3.1 Participants and procedure

Four bachelor students at a large Dutch university recruited participants through their personal and professional networks. Participants were 154 individuals who had completed a job interview within the last 12 months. However, 84 responses were removed before the analyses due to incomplete responses, missed attention checks, or not meeting the 12-month criteria. This resulted in a final sample of 70 (35, 51.5% women, Mage =24.69, SD = 7.36)

Of the participants, 51.5% were female, with an age range between 18 and 53 years

(M = 24.69, SD = 7.363), and their last interview ranged between 1 and 12 months ago (M = 5.61, SD = 3.797) within their professional career they ranged between 1 and 50 interviews (M = 5.81, SD = 8.691). There as, 48.6% of the participants were male, with an age range

between 19 and 62 years (M = 27.52, SD = 10.097), and their last interview ranged between 1 and 12 months ago (M = 9.14, SD = 3.758) within their professional career they ranged between 1 and 100 interviews (M = 12.44, SD = 19.435). Participants also reported their ethnicity, work experience, and education.

Participants accessed an online survey through a provided link. First, answered a few demographic questions (Age, Gender, Race, Education, Work experience, Work experience

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questionnaires aimed to capture overconfidence and deceptive impression management. Because the current survey was part of a larger study with additional research questions, participants also completed other questionnaires (e.g. entitlement, honesty-humility) that are not used for this research project.

3.2 Measures

Overconfidence. All questions were self-rated, overconfidence was measured using the General Knowledge Questionnaire (GKQ) by Michailova & Katter (2014). Each question had three short (one or two-word) multiple choice answers and then participants had to answer all the questions and state their confidence in the correctness of each of their answers (any number between 33% and 100%). Individual overconfidence was measured as a bias score (equation above), which was calculated as the difference between the average

confidence level across all questions and the proportion of correct answers. Example items include “What is the name for an instant camera? canon camera, polaroid camera, Minolta camera” followed by “How confident are you that your answer is correct?”. Overconfidence was recorded on a bias score;

(

)



= − = N i i i a c N score bias 1 1

Deceptive Impression Management. All questions were self-rated, deceptive Impression Management was recorded on a 5-point Likert scale, ranging from 1 = to no

extent to 5 = to a very great extent. Deceptive impression management was measured with

the 5-point Likert scale of Bourdage, Roulin & Tarraf (2018). Example items include “I made sure to let the interviewer know about my job credentials.” and “I exaggerated my

responsibilities on my previous jobs.”. The scale showed sufficient reliability as the Cronbach’s alpha = 0.88.

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Control Variables. To rule out other possible effects on the hypothesis, some control variables were taken into account for this research. Six potentially relevant control variables were used throughout the tests including; age, last interview, number of interviews within professional career, ethnicity, education and if they have attended a workshop for job application skills. These variables will be kept constant throughout the experiment to help test the relative relationship of the underlying research between the main dependent and independent variables.

3.3 Analytical Plan

To test Hypothesis 1, the relationship between Overconfidence and Deceptive Impression Management, linear regression will be utilized with Deceptive Impression Management as the dependent variable and Overconfidence as the independent variable. For Hypothesis 2, the mediation effect between Overconfidence and Gender on Deceptive Impression

Management, the PROCESS macro of Hayes (2018) Model 4 will be utilized with overconfidence as the independent, gender as the mediating variable, and deceptive impression management as the dependent variable.

4. Results 4.1 Correlations

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Table 1 contains means, standard deviation and bivariate correlations of the main variables Deceptive Impression Management, Overconfidence and Gender. The table also contains the control variables, Age, Ethnic Origin, Highest Academic Level, Attendance of Job Application Skills Workshop, Number of interviews during professional career and Last Interview. It can be identified that there is a significant positive moderate correlation between Deceptive Impression Management and Overconfidence (r = 0.243, p < .05). Two unexpected findings are that there seems to be a significant positive strong relationship between

Overconfidence and Age (r = 0.397, p < .01) and a significant positive moderate relationship between Deceptive Impression Management and the number of interviews during

professional career (r = 0.283, p < .05).

4.2 Assumptions

To test the hypothesis linear regression will be utilized, to allow valid results some regression assumptions are tested to make sure that the predictive power of the model and the reliability is not affected. Normality is checked by examining the residual of the main

variables via the frequency distribution and P-P plot (see appendix 3.1). Normality can be assumed as the results showed that the bars in the histogram are bell-shaped and that the P-P plot distribution is in line. The scatter plot (appendix 3.1) for homoscedasticity, indicates assumptions are met therefore there is no systematic relationship between the errors in our model and what the model predicts; in relation to the independent and dependent variables. Durbin-Waston statistic was used to test the independence of observations, results were 2.232 this assumption is therefore met as it indicates positive autocorrelation. To check for

Multicollinearity both VIF and tolerance are observed these statistics indicate that all

variables have no collinearity (Tolerance > .20, VIF < 5). Lastly to check for outliers, cook’s distance is interpreted which reveals five data points are above 0.0571 calculated by the rule

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of thumb (4/N, 4/70), these values are therefore considered potential outliers. However, the data did not get adjusted as these data points are relatively below 1.

4.3 Hypotheses Testing

To test hypothesis 1, I regressed participants use of deceptive impression

management onto their overconfidence scores. This showed a moderately significant and positive association between overconfidence and deceptive impression management, b = .024, SE =.012, 95%CI [.001, .048], t = 2.06, p = .043. Table 2 shows results from regression analyses for overconfidence (model 1), control variables (model 2) and together (model 3). The results showed that the R-Square of model three is 13.6%, interpreted as the proportion of the variance for the dependent variable that's explained by the independent variable and control variables in the linear model. The unstandardized coefficient of the independent variable overconfidence is β = .024 with t = 1.88 and p = .06. To indicate the improvement that the overconfidence variable added to the model the R-Square change in comparison to model 1 is examined resulting in .05 with p = 0.06. This illustrates that per one-unit change within the independent variable overconfidence, the dependent variable deceptive impression management increases by .236. As for this study Model 1 will be utilized, therefore, since

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these results are shown to be significant (p < 0.05) we reject the null hypothesis of hypothesis 1.

To test hypothesis 2, I used Model 4 of PROCESS macro of Hayes (2018),

introducing gender as independent variable, deceptive impression management as dependent variable, and overconfidence as the mediator. The direct path from gender to overconfidence was positive but not statistically significant (β = .3611, SE β = 1.1763, p = .7599). The path (direct effect) from gender to deceptive impression management is positive and not

marginally statistically significant (β = .0007, SE β = .1296, p = .9960). The direct effect of overconfidence on deceptive impression management is positive and marginally statistically significant (β = .0249, SE β = .0137, p = .0750). The indirect effect is tested using non-parametric bootstrapping. The indirect effect (EI = .0090) is statistically not significant: 95% CI = (-.0470, .1163) concluding that the indirect effect is zero as it falls between the lower and upper bound of the 95% confidence interval. Therefore, overconfidence does not act as a mediator in the model concluding that hypothesis 2 is not supported and we cannot reject the null hypothesis.

5. Discussion 5.1 Summary

This study examined the relationship between gender and overconfidence on deceptive impression management. In particular, I investigated whether overconfidence

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would mediate a positive association between gender and use of deceptive impression management. The main results of this study include, findings of a marginal association between overconfidence and deceptive impression management, but no mediation of overconfidence between gender and deceptive impression management, and also no direct effect of gender on deceptive impression management.

The first hypothesis, whether there is a positive relationship between overconfidence and deceptive impression management, such that under high overconfidence more deceptive impression management will occur. There was a moderate positive significant relation found to support this hypothesis. This result is in line with previous research conducted on this topic (Desai and Dharmapala, 2009; Halon, 2005; Olsen & Stekelberg, 2015). An possible way to strengthen this relation, could be that overconfidence is a fraction of a personality type. As discussed, earlier overconfidence is in accordance to the expectancy-value model (Barron & Hulleman, 2015) that supports risk-taking under situations where one perceives the outcome greater than the risk, it could be that participants didn’t assert confidence under the construct used in this research (survey). Hypothesis 1 was supported, as there is limited literature concerning the interaction between these variables this research contributes to the growing literature on deceptive impression management.

The second hypothesis, whether overconfidence mediates the relationship between gender and deceptive impression management, did not have a significant relationship and thereby hypothesis 2 was not supported. However, these results are consistent with a selected amount of previous research conducted on this topic some examples are (Childs, 2012; Pate, 2018; Ezquerra, Kolev, Rodriguez-Lara, 2018) which conclude similar results regarding gender and their effect on deceptive impression management. Nonetheless, there are also studies showing that men use of deceptive IM therefore further research is needed to clarify

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this existence and strength of this relationship.

5.2 Practical Implication

Analyzing these relationships leading to deceptive impression management as described in the objective of this paper is relevant because it can be very problematic in the selection process of job candidates as it can lead to biases. Especially since the interview compared with other methods is the most widely used. There is considerable research

conducted to explore alternative that lead to deceptive impression management however there is no clear understanding of why men and women differ in their use of deception. The

selection of unqualified candidates can lead to decreased task performance of work teams, as it impairs trustworthiness and mutuality, this is of high importance to avoid for any

organization. Therefore, it has a very high practical implications to many industries. The findings of a marginally significant relationship between overconfidence and deceptive impression management can help tailor the interview process as such to identify

overconfidence at an earlier stage to avoid an problematic future implications for organizations.

5.3 Limitations and Future Research

Due to the convenience sampling all data was collected in the Netherlands, this method of data collection might limit the validity of the current research. The final sample used was 70 participants this amount should preferability be higher for a more accurate representation of the population. However, the literature used as a base for this research project was primarily based/obtained in America, and this could lead to geographic and behavioral differences. Additionally, the surveys collected covered a range of variables than those examined in this study resulting in a rather long survey, this could have led to possible

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lack of attention, out of the participants that completed the survey 15 missed attention check one and 11 missed attention check two.

In future research a more specific collection method would be recommended this includes a shorter survey and wider reach of participants (culturally), additionally a survey focused more purely on various aspects of confidence and deceptive impression management could draw vastly different results. For example; specific personality traits that are prone to overconfidence. Deceptive impression management was examined from the perspective of an interview, this variable should also be examined in other situations such as financial investing (stocks) as it has been seen in previous research in this construct.

While conducting the analysis particular variables stood out includes age and attendance of applicates to skills workshops, these variables should be further studied in combination. Therefore, for future research it would be beneficial to identify a clearer population and variables that are influencing participants behavior.

6. Conclusion

Previous research has focused on different alternatives that influence the use of deceptive impression management however is was left unclear whether women and men truly differ in their use of deception and if this relationship had causal links with external

variables. Thus, the present study examined the relationship between overconfidence and gender on the use of deceptive impression management. Results show that overconfidence marginally leads to deceptive impression management. Additionally, no distinction between genders was identified in relation to their use of overconfidence and the outcome of deceptive impression management.

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8. Appendices: 8.1 Appendix 1: Reliability Analysis

RELIABILITY

/VARIABLES=HDIM1 HDIM2 HDIM3 HDIM4 HDIM5 HDIM6 HDIM7 HDIM8 HDIM9 HDIM10 HDIM11 HDIM12 HDIM13

HDIM14 HDIM15 HDIM16 HDIM17 HDIM18 HDIM19 HDIM20 HDIM21 HDIM22 HDIM23 HDIM24 HDIM25 HDIM26 HDIM27

HDIM28 AttentionCheck1Select5 /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA

/STATISTICS=SCALE CORR /SUMMARY=TOTAL.

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Correlations Age (in number): Ethnic origin: - Selected Choice Highest academic level you have completed:

Have you ever attended a course or a workshop on job application

skills?

How many job interviews have you gone through during

your professional

career?

When did you have your last job interview (in months)? Gender: - Selected Choice Overconfidenc e DIM

Age (in number): Pearson Correlation 1 -.149 .241* -.299* .335** -.045 -.198 .397** .186

Sig. (2-tailed) .217 .045 .012 .005 .709 .101 .001 .124

N 70 70 70 70 70 70 70 70 70

Ethnic origin: - Selected Choice

Pearson Correlation -.149 1 -.281* -.024 -.074 .062 .181 .186 -.010

Sig. (2-tailed) .217 .019 .845 .542 .611 .134 .123 .932

N 70 70 70 70 70 70 70 70 70

Highest academic level you have completed:

Pearson Correlation .241* -.281* 1 -.136 .036 .071 .077 .083 -.008

Sig. (2-tailed) .045 .019 .261 .770 .557 .527 .495 .945

N 70 70 70 70 70 70 70 70 70

Have you ever attended a course or a workshop on job application skills?

Pearson Correlation -.299* -.024 -.136 1 -.338** .228 .000 -.209 -.137

Sig. (2-tailed) .012 .845 .261 .004 .057 1.000 .083 .258

N 70 70 70 70 70 70 70 70 70

How many job interviews have you gone through during your professional career?

Pearson Correlation .335** -.074 .036 -.338** 1 -.110 -.220 .023 .283*

Sig. (2-tailed) .005 .542 .770 .004 .363 .067 .847 .018

N 70 70 70 70 70 70 70 70 70

When did you have your last job interview (in months)?

Pearson Correlation -.045 .062 .071 .228 -.110 1 .038 -.072 -.085

Sig. (2-tailed) .709 .611 .557 .057 .363 .752 .556 .483

N 70 70 70 70 70 70 70 70 70

Gender: - Selected Choice Pearson Correlation -.198 .181 .077 .000 -.220 .038 1 .024 -.075

Sig. (2-tailed) .101 .134 .527 1.000 .067 .752 .843 .535

N 70 70 70 70 70 70 70 70 70

Overconfidence Pearson Correlation .397** .186 .083 -.209 .023 -.072 .024 1 .243*

Sig. (2-tailed) .001 .123 .495 .083 .847 .556 .843 .043

N 70 70 70 70 70 70 70 70 70

DIM Pearson Correlation .186 -.010 -.008 -.137 .283* -.085 -.075 .243* 1

Sig. (2-tailed) .124 .932 .945 .258 .018 .483 .535 .043

N 70 70 70 70 70 70 70 70 70

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

(26)

8.3 Appendix 3: Assumptions

REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE

/STATISTICS COEFF OUTS CI(95) R ANOVA COLLIN TOL CHANGE /CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DIM

/METHOD=ENTER Age Race Education Workshop NumerInterviews LastInterview

/METHOD=ENTER Age Race Education Workshop NumerInterviews LastInterview Overconfidence

/SCATTERPLOT=(*ZRESID ,*ZPRED)

/RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID) /SAVE COOK.

Regression

Notes

Output Created 12-JUN-2020 13:07:16

Comments

Input Data

/Users/mariahalberg-holmgaard/Desktop/Thesis data.sav

Active Dataset DataSet1

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data File

70

Missing Value Handling Definition of Missing User-defined missing

values are treated as missing.

(27)

Cases Used Statistics are based on cases with no missing values for any variable used.

Syntax REGRESSION

/DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA COLLIN TOL CHANGE /CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DIM /METHOD=ENTER Age Race Education Workshop NumerInterviews

LastInterview

/METHOD=ENTER Age Race Education Workshop NumerInterviews LastInterview Overconfidence /SCATTERPLOT=(*ZRESI D ,*ZPRED) /RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID) /SAVE COOK.

Resources Processor Time 00:00:00.47

Elapsed Time 00:00:00.00

Memory Required 50624 bytes

Additional Memory

Required for Residual Plots

800 bytes

Variables Created or Modified

COO_1 Cook's Distance

(28)

DIM 2.6291 .50585 70

Age (in number): 26.56 9.759 70

Ethnic origin: - Selected Choice

1.40 1.209 70

Highest academic level you have completed:

3.74 .846 70

Have you ever attended a course or a workshop on job application skills?

1.50 .504 70

9.03 15.170 70

When did you have your last job interview (in months)? 5.53 3.721 70 Overconfidence 34.3949 5.04422 70

Correlations

DIM Age (in number) : Ethnic origin: - Selecte d Choice Highest academ ic level you have complet ed: Have you ever attende d a course or a worksho p on job applicati on skills? How many job intervie ws have you gone through during your professi onal career? Pearson Correlation DIM 1.00 0 .186 -.010 -.008 -.137 .283 Age (in number): .186 1.000 -.149 .241 -.299 .335 Ethnic origin: - Selected -.010 -.149 1.000 -.281 -.024 -.074

(29)

Highest

academic level you have completed:

-.008 .241 -.281 1.000 -.136 .036

Have you ever attended a course or a workshop on job application skills? -.137 -.299 -.024 -.136 1.000 -.338

How many job interviews have you gone through during your

professional career?

.283 .335 -.074 .036 -.338 1.000

When did you have your last job interview (in months)? -.085 -.045 .062 .071 .228 -.110 Overconfidenc e .243 .397 .186 .083 -.209 .023 Sig. (1-tailed) DIM . .062 .466 .472 .129 .009 Age (in number): .062 . .109 .022 .006 .002 Ethnic origin: - Selected Choice .466 .109 . .009 .422 .271 Highest academic level you have completed: .472 .022 .009 . .131 .385

Have you ever attended a course or a workshop on job application skills? .129 .006 .422 .131 . .002

(30)

.009 .002 .271 .385 .002 .

When did you have your last job interview (in months)? .242 .355 .305 .278 .029 .181 Overconfidenc e .022 .000 .061 .248 .041 .424 N DIM 70 70 70 70 70 70 Age (in number): 70 70 70 70 70 70 Ethnic origin: - Selected Choice 70 70 70 70 70 70 Highest academic level you have completed: 70 70 70 70 70 70

Have you ever attended a course or a workshop on job application skills? 70 70 70 70 70 70

70 70 70 70 70 70

(31)

Variables Entered/Removed

a Model Variables Entered Variables Removed Method

1 When did you

have your last job interview (in months)?, Age (in number):, Ethnic origin: - Selected Choice, How many job interviews have you gone through during your professional career?, Highest academic level you have completed:, Have you ever attended a course or a workshop on job application skills?b . Enter 2 Overconfidence b . Enter

a. Dependent Variable: DIM

b. All requested variables entered.

Model Summary

c Mod R Squar Adjusted Std. Error of the Change Statistics R Square F Chang

(32)

1 .307a .094 .008 .50380 .094 1.094 6 63

2 .375b .141 .043 .49473 .046 3.330 1 62

ANOVA

a

Model

Sum of

Squares df Mean Square F Sig.

1 Regression 1.666 6 .278 1.094 .376b Residual 15.990 63 .254 Total 17.656 69 2 Regression 2.481 7 .354 1.448 .203c Residual 15.175 62 .245 Total 17.656 69

b. Predictors: (Constant), When did you have your last job interview (in months)?, Age (in number):, Ethnic origin: - Selected Choice, How many job interviews have you gone through during your professional career?, Highest academic level you have completed:, Have you ever attended a course or a workshop on job application skills?

c. Predictors: (Constant), When did you have your last job interview (in months)?, Age (in number):, Ethnic origin: - Selected Choice, How many job interviews have you gone through during your professional career?, Highest academic level you have completed:, Have you ever attended a course or a workshop on job application skills?, Overconfidence

Coefficients

a Model Unstandardized Coefficients Standard ized Coefficie nts t Sig. 95.0% Confiden ce Interval for B B Std. Error Beta Lower Bound 1 (Constant) 2.545 .440 5.787 .000 1.666 Age (in number): .006 .007 .110 .822 .414 -.008 Ethnic origin: - Selected Choice .007 .053 .016 .123 .902 -.100

(33)

Highest

academic level you have completed:

-.023 .078 -.038 -.293 .771 -.178

Have you ever attended a course or a workshop on job application skills? -.018 .136 -.018 -.132 .896 -.289

How many job interviews have you gone through during your professional career? .008 .004 .237 1.790 .078 -.001

When did you have your last job interview (in months)? -.007 .017 -.048 -.386 .700 -.040 2 (Constant) 1.842 .579 3.183 .002 .685 Age (in number): 5.314E-5 .007 .001 .007 .994 -.015 Ethnic origin: - Selected Choice -.020 .054 -.048 -.368 .714 -.128 Highest academic level you have completed: -.029 .076 -.049 -.384 .702 -.182

Have you ever attended a course or a workshop on job application skills? .007 .134 .007 .053 .958 -.261

(34)

How many job interviews have you gone through during your professional career? .009 .004 .274 2.079 .042 .000

-.004 .017 -.032 -.263 .793 -.038

Overconfidence .025 .014 .248 1.825 .073 -.002

Excluded Variables

a

Model Beta In t Sig.

Partial Correlation Collinearity Statistics Tolerance VIF Minimum Tolerance 1 Overconfidenc e .248b 1.825 .073 .226 .752 1.330 .663

b. Predictors in the Model: (Constant), When did you have your last job interview (in months)?, Age (in number):, Ethnic origin: - Selected Choice, How many job interviews have you gone through during your professional career?, Highest academic level you have completed:, Have you ever attended a course or a workshop on job application skills?

Collinearity Diagnostics

a Mo del Dimen sion Eigen value Conditi on Index Variance Proportions (Cons tant) Age (in number ): Ethnic origin: - Selecte d Choice Highest academ ic level you have complet ed: 1 1 5.417 1.000 .00 .00 .01 .00 2 .766 2.660 .00 .00 .02 .00

(35)

5 .110 7.002 .00 .33 .01 .00 6 .052 10.200 .01 .54 .00 .41 7 .014 19.818 .98 .08 .17 .57 2 1 6.383 1.000 .00 .00 .01 .00 2 .767 2.884 .00 .00 .02 .00 3 .400 3.994 .00 .00 .73 .00 4 .255 4.999 .00 .02 .00 .01 5 .111 7.575 .00 .24 .01 .00 6 .053 11.019 .01 .51 .00 .35 7 .024 16.415 .06 .18 .23 .50 8 .007 29.884 .94 .04 .00 .13

Residuals Statistics

a

Minimum Maximum Mean Std. Deviation N

Predicted Value 2.3468 3.4461 2.6291 .18963 70

Std. Predicted Value -1.489 4.309 .000 1.000 70

Standard Error of Predicted Value

.098 .401 .158 .054 70

Adjusted Predicted Value 2.3011 3.6328 2.6271 .20181 70

Residual -.97060 1.04725 .00000 .46897 70

Std. Residual -1.962 2.117 .000 .948 70

Stud. Residual -2.164 2.527 .001 1.012 70

Deleted Residual -1.18049 1.49246 .00201 .53863 70

Stud. Deleted Residual -2.232 2.647 .003 1.026 70

Mahal. Distance 1.711 44.269 6.900 6.547 70

Cook's Distance .000 .339 .020 .045 70

Centered Leverage Value .025 .642 .100 .095 70

(36)

(37)

SORT CASES BY COO_1 (D).

8.4 Appendix 4: Regression Hypothesis 1

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DIM

/METHOD=ENTER Age NumerInterviews /METHOD=ENTER Overconfidence /SAVE PRED ZPRED RESID ZRESID.

(38)

Notes

Comments

Input Data

/Users/mariahalberg-holmgaard/Desktop/spss data/Thesis data.sav

Filter <none>

Weight <none>

Split File <none>

70

Missing Value Handling Definition of Missing User-defined missing

values are treated as missing.

Cases Used Statistics are based on

cases with no missing values for any variable used.

Syntax REGRESSION

/MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT DIM /METHOD=ENTER Age NumerInterviews /METHOD=ENTER Overconfidence

/SAVE PRED ZPRED RESID ZRESID.

(39)

Additional Memory Required for Residual Plots

0 bytes

PRE_3 Unstandardized Predicted

Value

RES_3 Unstandardized Residual

ZPR_3 Standardized Predicted

Value

ZRE_3 Standardized Residual

Variables Entered/Removed

a Model Variables Entered Variables Removed Method

1 How many job

interviews have you gone through during your professional career?, Age (in number):b . Enter 2 Overconfidenc eb . Enter a. Dependent Variable: DIM

b. All requested variables entered.

Model Summaryc Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square

Change F Change df1 df2 Sig. F Change

1 .299a .089 .062 .48985 .089 3.291 2 67 .043

2 .369b .136 .097 .48082 .046 3.541 1 66 .064

a. Predictors: (Constant), How many job interviews have you gone through during your professional career?, Age (in number): b. Predictors: (Constant), How many job interviews have you gone through during your professional career?, Age (in number):, Overconfidence

(40)

Model

Sum of

1 Regression 1.579 2 .790 3.291 .043b Residual 16.077 67 .240 Total 17.656 69 2 Regression 2.398 3 .799 3.458 .021c Residual 15.258 66 .231 Total 17.656 69

b. Predictors: (Constant), How many job interviews have you gone through during your professional career?, Age (in number):

c. Predictors: (Constant), How many job interviews have you gone through during your professional career?, Age (in number):, Overconfidence

Coefficients

a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.413 .172 14.033 .000

Age (in number): .005 .006 .102 .828 .411

.008 .004 .249 2.011 .048

2 (Constant) 1.731 .400 4.331 .000

Age (in number): -5.886E-5 .007 -.001 -.009 .993

.009 .004 .278 2.270 .026

Overconfidence .024 .013 .236 1.882 .064

Excluded Variables

a

Model Beta In t Sig.

Partial Correlation

Collinearity Statistics Tolerance

(41)

b. Predictors in the Model: (Constant), How many job interviews have you gone through during your professional career?, Age (in number):

Residuals Statistics

a

Predicted Value 2.3529 3.4482 2.6291 .18643 70

Residual -.92127 1.09841 .00000 .47025 70

Std. Residual -1.916 2.284 .000 .978 70

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI(95) R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT DIM

/METHOD=ENTER Overconfidence /SAVE PRED ZPRED RESID ZRESID.

Regression

Notes

Comments

Input Data

/Users/mariahalberg-holmgaard/Desktop/spss data/Thesis data.sav

Filter <none>

Weight <none>

Split File <none>

(42)

Missing Value Handling Definition of Missing User-defined missing values are treated as missing.

Cases Used Statistics are based on

cases with no missing values for any variable used.

Syntax REGRESSION

/MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT DIM /METHOD=ENTER Overconfidence

/SAVE PRED ZPRED RESID ZRESID.

Memory Required 46768 bytes

Additional Memory Required for Residual Plots

0 bytes

PRE_5 Unstandardized Predicted

Value

RES_5 Unstandardized Residual

ZPR_5 Standardized Predicted

Value

ZRE_5 Standardized Residual

Variables Entered/Removed

a Model Variables Entered Variables Removed Method 1 Overconfidenc eb . Enter

(43)

Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square

Change F Change df1 df2 Sig. F Change

1 .243a .059 .045 .49435 .059 4.250 1 68 .043

a. Predictors: (Constant), Overconfidence b. Dependent Variable: DIM

ANOVA

a

Model

Sum of

1 Regression 1.039 1 1.039 4.250 .043b

Residual 16.618 68 .244

Total 17.656 69

b. Predictors: (Constant), Overconfidence

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig.

95.0% Confidence Interval for B

B Std. Error Beta Lower Bound Upper Bound

1 (Constant) 1.792 .410 4.371 .000 .974 2.611

Overconfidence .024 .012 .243 2.062 .043 .001 .048

Residuals Statistics

a

Predicted Value 2.3933 3.0155 2.6291 .12269 70

Residual -.93596 1.09760 .00000 .49075 70

Std. Residual -1.893 2.220 .000 .993 70

(44)

8.5 Appendix 4: PROCESS Hypothesis 2 Warning # 14324

MATRIX cannot do split-file processing in interactive mode. Run the job in batch mode or process each split-file group separately.

Run MATRIX procedure:

************* PROCESS Procedure for SPSS Release 2.16.3 ****************** Written by Andrew F. Hayes, Ph.D. www.afhayes.com

************************************************************************** Model = 4 Y = DIM X = Gender M = Overconf Statistical Controls:

CONTROL= Age Race Educatio Workshop NumerInt LastInte Sample size 70 ************************************************************************** Outcome: Overconf Model Summary R R-sq MSE F df1 df2 p .4992 .2492 21.2602 2.9399 7.0000 62.0000 .0101 Model coeff se t p constant 27.7577 4.3768 6.3420 .0000 Gender .3611 1.1763 .3069 .7599 Age .2310 .0645 3.5843 .0007 Race 1.0368 .4945 2.0964 .0401 Educatio .2306 .7195 .3205 .7496 Workshop -.9705 1.2456 -.7792 .4389 NumerInt -.0469 .0411 -1.1404 .2585 LastInte -.0871 .1550 -.5616 .5764 ************************************************************************** Outcome: DIM Model Summary R R-sq MSE F df1 df2 p .3750 .1406 .2488 1.2474 8.0000 61.0000 .2878

(45)

constant 1.8530 .6079 3.0482 .0034 Overconf .0249 .0137 1.8113 .0750 Gender -.0083 .1273 -.0654 .9481 Age .0000 .0077 -.0039 .9969 Race -.0193 .0554 -.3487 .7285 Educatio -.0285 .0779 -.3659 .7157 Workshop .0063 .1354 .0467 .9629 NumerInt .0091 .0045 2.0228 .0475 LastInte -.0044 .0168 -.2606 .7953

************************** TOTAL EFFECT MODEL **************************** Outcome: DIM Model Summary R R-sq MSE F df1 df2 p .3072 .0944 .2579 .9229 7.0000 62.0000 .4952 Model coeff se t p constant 2.5437 .4821 5.2767 .0000 Gender .0007 .1296 .0051 .9960 Age .0057 .0071 .8056 .4236 Race .0065 .0545 .1193 .9055 Educatio -.0228 .0792 -.2872 .7749 Workshop -.0178 .1372 -.1299 .8971 NumerInt .0079 .0045 1.7497 .0851 LastInte -.0065 .0171 -.3834 .7027

***************** TOTAL, DIRECT, AND INDIRECT EFFECTS ******************** Total effect of X on Y Effect SE t p .0007 .1296 .0051 .9960 Direct effect of X on Y Effect SE t p -.0083 .1273 -.0654 .9481 Indirect effect of X on Y

Effect Boot SE BootLLCI BootULCI Overconf .0090 .0401 -.0470 .1163 Partially standardized indirect effect of X on Y Effect Boot SE BootLLCI BootULCI Overconf .0178 .0826 -.0979 .2309 Completely standardized indirect effect of X on Y

(46)

Overconf .0088 .0383 -.0449 .1068 Ratio of indirect to total effect of X on Y

Effect Boot SE BootLLCI BootULCI Overconf 13.6641 302.3717 143.0443 19012.2802 Ratio of indirect to direct effect of X on Y

Effect Boot SE BootLLCI BootULCI Overconf -1.0790 37.6133 -201.7154 -.3615

******************** ANALYSIS NOTES AND WARNINGS *************************

Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000

WARNING: Bootstrap CI endpoints below not trustworthy. Decrease confidence or increase bootstraps

-201.7154

Level of confidence for all confidence intervals in output: 95.00

Gender, Overconfidence, and Impression Management

Gender, Overconfidence, and Impression

Management

Table of Contents

(

)



Regression

Notes

Correlations

Variables Entered/Removed

Model Summary

ANOVA

Coefficients

Excluded Variables

Collinearity Diagnostics

Residuals Statistics

Notes

Variables Entered/Removed

Coefficients

Excluded Variables

Residuals Statistics

Regression

Notes

Variables Entered/Removed

ANOVA

Residuals Statistics