Cyberfaking: I Can,So I Will? Intentions to Fake in Online Psychological Testing

Abstract

The aim of this study was to investigate whether intentions to fake online (cyberfaking) or in pencil-and-paper psychological testing differ. Participants (N=154) completed online questionnaires measuring attitudes toward faking, perceived behavioral control over faking, subjective norms regarding faking, and intentions to fake in future psychological assessment, with online and pencil-and-paper test administration scenarios compared. Participants showed similar intentions toward cyberfaking and faking in pencil-and-paper testing. However, participants held more positive attitudes toward cyberfaking than faking offline, greater perceived behavioral control over cyberfaking than offline faking, and more favorable subjective norms toward cyberfaking compared to offline faking. Analysis via multiple regression revealed that more positive attitudes toward cyberfaking, greater perceived behavioral control over cyberfaking, and more favorable subjective norms regarding cyberfaking were significantly related to the intention to cyberfake. In addition, more positive attitudes toward faking offline and greater perceived behavioral control over faking offline were significantly related to the intention to fake in offline tests. Overall, results indicated a similar pattern of relationship in the prediction of intentions to engage in faking regardless of the test administration modality scenario. Subjective norm, however, was not a significant predictor for faking offline. Future research could aim to include a behavioral faking outcome measure, as well as examine intentions to cyberfake in specific scenarios (for example, faking good or faking bad).

Introduction

The Internet is widely usedfor psychological research and assessment in a number of domains.¹ Examination of the equivalence of pencil-and-paper and Internet-based versions of many psychological measures has been undertaken (e.g.^2,3). Most recently, research has examined the fakeability of psychological measures completed online in comparison with pencil-and-paper measures.⁴ However, to date, individuals' willingness to engage in faking in the online environment (cyberfaking) has not been examined. This study investigated the influence of computer-based test administration on faking behavior by examining and comparing for the first time the predictors of intentions to fake psychological questionnaires in both online and offline (pencil-and-paper) test administration scenarios.

Equivalence in online and pen-and-paper psychological testing

Buchanan⁵ recommended that the equivalence of online and pencil-and-paper administered psychological questionnaires must be established. Most studies have concluded that administration modes are generally comparable (e.g.^1–3). It has also been argued that investigation of the factors that might interact with testing in an online environment should also be considered.⁶ However, to date, no research has investigated whether an individual's intention to fake psychological questionnaires differs depending on online or offline administration.

Cyberfaking versus pencil-and-paper faking

When an individual strategically modifies his/her self-presentation in specific ways as a part of a psychological assessment, this is said to be classified as faking or malingering.⁷ Numerous studies have demonstrated that individuals are able to alter their responses on psychological tests to present themselves more favorably, such as for a job application (e.g.⁸), or less favorably, so as to be given a diagnosis of a mental illness (e.g.⁷). Because distorting the responses to psychological questionnaires may influence a number of important decisions in a range of contexts, as well as the validity of data collection conducted online, identifying the characteristics that may influence the faking behavior may prove valuable.

The influence that mode of delivery has on the fakeability of self-report psychological tests was recently examined.⁴ Personality and depression were measured using the HEXACO-60 and the DASS-21. After initial administration using standard instructions, the HEXACO-60 and DASS-21 were then readministered with instructions to “fake good” (as if applying for a job) on the HEXACO-60 and “fake bad” (as if experiencing debilitating depression) on the DASS-21. There were no significant differences in scores obtained for Internet or pen-paper conditions. Importantly, calculation of effect sizes revealed that the administration mode had a limited influence on faking. Grieve and de Groot concluded that the testing mode neither facilitates nor inhibits faking.

However, Grieve and de Groot⁴ also identified that a central issue in considering faking in online-versus-offline testing is whether the administration mode is likely to influence an individual's tendency or willingness to engage in faking. Specifically, the question remains as to whether an individual might be more willing to fake in online or in offline testing.

Predicting intentions to engage in behavior

According to the Theory of Planned Behavior (TPB), an individual's intentions to engage in a particular behavior can be predicted by three constructs: attitudes, subjective norm, and perceived behavioral control.⁹ Attitudes toward a behavior are influenced by our evaluation of whether engaging in the behavior is considered favorable or unfavorable. Subjective norm relates to the level of social pressure an individual feels to engage in the behavior. Perceived behavioral control involves the ease or difficulty perceived by the individual to perform the behavior. Together, more positive attitudes, stronger perceptions of positive social pressure (for example, subjective norms of approval), and greater perceived behavioral control will result in stronger intentions to engage in a particular behavior. Consequently, an individual will be more likely to perform that behavior.

The TPB has been used in a number of studies regarding the prediction of antisocial behaviors, including criminal activity,¹⁰ academic misconduct,¹¹ gambling,¹² and speeding.¹³ Importantly, the TPB has been used to predict dishonest actions (such as an individual's intentions to cheat on an exam, shoplift, or lie, and see¹⁴), suggesting that the TPB might be a useful approach to investigate intentions to deceive in psychological assessment.

McFarland and Ryan¹⁵ investigated the predictive validity of TPB variables in intentions to fake as a part of an employment selection process. More favorable attitudes toward faking, stronger favorable subjective norms regarding faking, and greater perceived behavioral control over faking behaviors were significantly associated with self-report intentions to fake in a selection context. Together, the TPB variables explained 45% of intentions to fake.

In addition, the TPB has shown good predictive validity toward intentions to engage in a variety of computer-mediated behaviors, such as social networking¹⁶ and podcasting.¹⁷ It follows that the TPB may prove to be a useful paradigm with which to investigate intentions to fake in an online psychological testing environment.

The current research

The current study aimed to examine the predictors of intentions to fake comparing intentions to fake in online and offline scenarios. Brennan and Gouvier¹⁸ suggested that analog studies of faking are appropriate. This is unsurprising, given that fakers rarely admit faking (e.g.¹⁹). Thus, a simulation design was selected.

Given the previous research on equivalence of measures (e.g.^1–3), and the equivalence of faking in online and offline environments,⁴ it was hypothesized that there would be no significant differences in attitudes, perceived behavioral control, subjective norms, and intentions to fake as a function of an online or offline administration mode. However, as testing a null hypothesis can be problematic,²⁰ in line with Grieve and de Groot's⁴ methodology, to more completely examine the influence of test administration mode, the effect size was also calculated.

It was hypothesized that a combination of attitudes toward faking, perceived behavioral control over faking, and subjective norm regarding faking behaviour would significantly predict the intention to fake on psychological questionnaires in both online and pen-and-paper testing scenarios. Specifically, it was anticipated that a favorable attitude toward faking, a high level of perceived behavioral control over faking, and a positive subjective norm regarding faking would increase the intention to fake. Again, given the previous evidence regarding the equivalence of psychological testing, it was anticipated that predictors of intention to fake would contribute similarly in both online and offline test administration multiple regression models.

Method

Participants

Participants (N=154; 51 men, 83 women) had a mean age of 30.55 (SD=12.82). The sample was comprised of 122 students (39 men, 83 women), recruited from a large, multicampus university, and 32 community members (12 men, 20 women), recruited via word of mouth, a student email distribution list, and a social networking site (Facebook). The only selection criterion was that participants were aged 18 or over. Preliminary analysis via independent t-test was conducted to investigate possible effects of participant sampling on the results. No significant difference as a function of student or community status was found for any of the variables (all p-values were between 0.11 and 0.76), suggesting that data from the student and community samples could be analyzed together.

Design

A within-group design was used to examine differences in attitudes, perceived behavioral control, subjective norms, and intentions regarding faking as a function of an online or offline presentation scenario. All participants completed all measures. A priori power analyses using G*Power²¹ suggested that a sample size of 45 would be required to reach 0.95 power to detect a small effect.

A correlational design was used, with analysis via multiple regressions conducted separately to assess intentions to fake online and intentions to fake offline. Predictor variables were attitude to faking (online and offline), perceived behavioral control over faking (online and offline), and subjective norm regarding faking behavior (online and offline). The outcome variable was a self-reported intention to fake, measured for both online and offline scenarios.

Control measures

Data were collected entirely online, to ensure that participants could not self-select into either pencil-and-paper or online response formats, whereby an inherent preference for either format may have influenced results. Further, by testing only online, we were able to recruit only participants who were comfortable with online formats in psychological testing. In addition, two versions of the same online survey were created to minimize the order effects. One version had all questions related to online testing scenarios asked first, followed by questions related to pencil-and-paper testing scenarios. In the second version, these were reversed. No order effects were seen in preliminary data analysis (p values were between 0.13 and 0.75).

Materials

Demographic information

Information for age, gender, and student versus community member status was requested.

TPB variables

Items assessing the TPB variables were developed based on McFarland and Ryan's¹⁵ methodology. However, rather than asking about generic attitudes toward faking, items were reworded to specifically identify faking as occurring online and offline. The words “online” and “pencil-and-paper” were capitalized to increase readability and to keep the participant focused on the faking context being assessed. Copies of all items are available from the corresponding author.

Intention to fake: Intention to fake was measured with four items using a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Two versions of the intentions items were presented: one for online-related items and one for pencil-and-paper (offline)-related items. A sample item is “I intend to fake on future ONLINE/PENCIL-AND-PAPER psychological tests”. One of the four items was reverse-scored. High intentions to fake are represented by high scores. In the current sample, good internal reliability was evident (see Table 1 for details of reliability for this and all other measures).

Table 1.

Means, Standard Deviations, and Internal Reliabilities

Variable	M	SD	Cronbach's α
Intention to fake—online	9.09	2.97	0.80
Intention to fake—offline	8.96	3.00	0.77
Attitude—online	12.68	4.44	0.84
Attitude—offline	12.10	4.57	0.86
Subjective norm online	5.89	1.78	0.77
Subjective norm offline	5.62	1.85	0.81
PBC online	15.78	4.19	0.80
PBC offline	14.25	4.64	0.86

PBC, perceived behavioral control.

Attitude toward faking: Attitudes toward faking were assessed using eight semantic differential items. Participants were asked to indicate the extent to which each of items accurately reflected their views about faking psychological tests online, with the options presented at each end of a 5-point scale. Two versions of the questionnaire were presented: one for online-related items and one for pencil-and-paper (offline)-related items. A sample statement is “Faking on ONLINE/PENCIL-AND-PAPER psychological tests is good-bad”. High scores indicate a favorable attitude toward faking, with some items reverse-scored. Internal reliability was very good in the current sample.

Perceived behavioral control over faking behavior: Perceived behavioral control was measured with five items using a 5-point Likert-type response format ranging from 1 (strongly disagree) to 5 (strongly agree). Again, two versions were presented for online and pencil-and-paper items. A sample item is “It would be easy for me to fake responses to an ONLINE/PENCIL-AND-PAPER psychological test”. One item was reverse-scored. High scores indicated high levels of perceived behavioral control over faking behavior. Internal reliability was very good.

Subjective norm concerning faking behavior: Subjective norm was measured with two items using a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree), with two versions, one online and one pencil-and-paper. A sample item is “No-one who is important to me would care if I faked on an ONLINE/PENCIL-AND-PAPER psychological test”. One item was reverse-scored. High scores indicate a favorable subjective norm regarding faking behavior. Good internal reliability was evident in the current sample.

Procedure

After obtaining ethics approval, research invitations were sent to participants via email and the social networking site, Facebook. Participants were asked to click on an electronic link, which took them to a secure online data collection service (SurveyMonkey), where they gave informed consent and then completed the questionnaires.

Results

Examination of regression diagnostics revealed two influential outliers (with standardized residuals >3), which were deleted for all analyses. After this deletion, all relevant assumptions were met.

Comparing ratings of online and offline faking

The means, standard deviations, and internal reliabilities are reported in Table 1. A series of paired sampled t-tests were conducted to explore whether the test administration mode influenced attitudes toward faking, perceived behavioral control over faking, subjective norms regarding faking, and intentions to fake in psychological testing. Participants held significantly more positive attitudes toward faking online than faking offline, t(152)=2.56, p<0.05, r=0.20, with the test administration mode explaining 4% of the variance in attitudes. Participants also reported significantly greater perceived behavioral control over online faking than offline faking, t(152)=8.32, p<0.001, r=0.55, with an administration mode explaining 30% of the variance in perceived behavioral control. Significantly more favorable subjective norms were also evident for online psychological testing compared to offline testing, t(152)=3.17, p<0.005, r=0.25, with 6% of variance explained. However, there were no significant differences in intentions to fake in psychological testing as a function of test administration mode, t(152)=1.09, p=0.28 r=0.08, explaining 0.64% of variance.

Intentions to cyberfake

The combination of attitude online, perceived behavioral control online, and subjective norm online accounted for 52% (51% adjusted) of variability in intention to fake online, and significantly predicted intention to fake, R=0.72, R²=0.52, F (3, 150)=54.86, p<0.001. These results represent a large effect, f²=1.09.²² Within the model, more positive attitudes toward faking online, greater perceived behavioral control online, and more positive subjective norm online were all significant predictors of intention to fake. Table 2 shows the correlations between online variables, and full details of the regression are presented in Table 3.†

Table 2.

Correlations Between Predictor Variables and Intention to Cyberfake

	Intention to fake online	Attitude online	PBC online	Subjective norm online
Intention to fake online	-
Attitude online	0.56^**	-
PBC online	0.59^**	0.32^**	-
Subjective norm online	0.45^**	0.40^**	0.33^**	-

p<0.01.

Table 3.

Multiple Regression: Cyberfaking

	B	SE B	β	t
Constant	−0.25	0.769		−0.32
Attitude online	0.25	0.04	0.37	5.80^***
PBC online	0.30	0.04	0.42	6.83^***
Subjective norm online	0.26	0.12	0.16	2.49^*

p<0.05.

***

p<0.001.

Intentions to fake offline

The combination of attitude offline, perceived behavioral control offline, and subjective norm offline accounted for 54% (53.4% adjusted) of variability in intention to fake offline. The combination significantly predicted intention to fake, R=0.74, R²=0.54, F (3, 150)=59.40, p<0.001, representing a very large effect, f²=1.19.²² More positive attitudes toward and greater perceived behavioral control over faking offline were significant individual predictors of intention to fake. Table 4 shows correlations between variables. Table 5 shows the multiple regression.

Table 4.

Correlations Between Predictor Variables and Intention to Fake Offline

	Intention to fake offline	Attitude offline	PBC offline	Subjective norm offline
Intention to fake offline	-
Attitude offline	0.59^**	-
PBC offline	0.58^**	0.26^**	-
Subjective norm offline	0.40^**	0.41^**	0.32^**	-

p<0.01.

Table 5.

Multiple Regression: Offline

	B	SE B	β	T
Constant	−0.33	0.72		−0.45
Attitude offline	0.29	0.04	0.44	7.18^***
PBC offline	0.28	0.04	0.43	7.37^***
Subjective norm offline	0.14	0.10	0.09	1.38

***

p<0.001.

Discussion

The hypothesis that attitudes toward faking, perceived behavioral control over faking, subjective norms regarding faking, and intentions to fake would be similar for online and offline faking contexts received mixed support. In contrast to expectations, participants held more positive attitudes toward faking online, had stronger subjective norms (i.e., approval) toward faking online, and felt greater perceived behavioral control over online faking, with a test administration mode explaining 4%, 6%, and 30% of variance for these constructs, respectively. Although each of these differences was statistically significant, with such small effect sizes for attitudes and subjective norm, it would seem that consideration of perceived behavioral control in cyberfaking is primarily indicated. It has been suggested (e.g.,²³) that perceived behavioral control might be considered in terms of self-efficacy and controllability. It is possible that participants felt more confident in their ability to fake online (perhaps feeling that online testing might offer less opportunity for detection), or more able to control their responses in an online environment (perhaps because of perceived privacy). Interestingly, given the significant differences in attitudes, subjective norms, and perceived behavioral control, overall, there was no significant difference in intentions to fake as a function of the test administration mode.

As predicted, the combination of attitudes toward faking, perceived behavioral control over faking, and subjective norm regarding faking behavior significantly predicted intention to fake in both online and offline psychological questionnaire scenarios. For both the online and offline models, similar patterns of predictors were evident: with more positive attitudes and greater perceived behavioral control. However, subjective norm did not significantly predict intention to fake offline.

According to Ajzen,⁹ the importance of attitudes, subjective norm, and perceived behavioral control in the prediction of intention will vary across behaviors and situations. Subjective norm relates to how much social pressure an individual feels to engage in the behavior.⁹ It is an interesting finding that in the current study, subjective norm contributed significantly to intention to fake online, rather than offline. Kays, Gathercoal, and Buhrow²⁴ provide some possible insight into the mechanisms here. Kays et al. point out that increased use of social networking media may be related to more favorable attitudes when disclosing online. It is feasible that in the current study, favorable subjective norms around the online environment in general carried over into a specific, faking-related domain.

Additional considerations, limitations, and future directions

Self-report is commonly used when operationalizing the TPB.²⁵ Although the TPB constructs were largely successful in predicting individuals' intentions to fake in the current study, it remains that this style of measurement and the lack of an actual behavioral outcome may have been a limitation. Future research may use longitudinal modeling, initially testing faking intentions and then following up with behavioral assessment of faking. However, given some of the issues in the adequate detection of faking,²⁶ the fact that real fakers are unlikely to admit faking,²⁷ and the difficulties operationalizing faking,⁷ adequately assessing the behavioral dimension of the model may prove challenging.

It should be noted that the current study only addressed general faking intentions. Future research could explore intentions in specific contexts (such as faking good or faking bad) to identify any divergence in intentions in various scenarios. It is entirely possible that different faking scenarios may elicit different predictors of intentions to cyberfake. In addition, as all data were collected online in the current study, it would be useful to assess whether intentions to cyberfake and fake offline would be similar when reported in an offline environment.

There were no significant differences in any of the variables as a function of student or community status, and this is in line with Brennan and Gouvier's¹⁸ conclusion that analog studies of faking are appropriate. However, it should be noted that the current sample may not adequately reflect predictors of faking that may operate in organizational or clinical settings. The question remains as to whether intentions in this sample can be generalized to population-specific contexts (for example, faking good for a court case, or faking psychopathology to receive medication).

There is also a possibility that some participants faked their responses to the items in the current study. However, as a simulation design was used (specifically, participants were not in a testing environment where potentially high stakes were involved), and data were contributed anonymously and in a time and place of participants' own choosing, it would seem that the sample had little or no motivation to fake their responses. Indeed, simulation studies of faking are well supported due to the experimental control facilitated,^28–30 and research has used this approach to investigate the influence of personality on faking intentions previously.³¹ Therefore, it seems unlikely that faking within the study would have systematically affected responses.

Another consideration is that by only recruiting participants online, who were presumably comfortable with computer use, this may have influenced the results given the nature of the study. However, as access to the Internet is increasingly ubiquitous (via, for example, smartphones), and as many survey tools have been shown to be equivalent in online and offline formats (e.g.^1–4,32), and online surveys have previously been used to examine differences between online and offline psychosocial constructs (e.g.^33,34), it seems that any influence of computer-mediated assessment in the current research is most likely minimal. Still, while beyond the exploratory nature of the current research, it may be prudent to investigate this possibility in future research.

Summary and Conclusions

The research presented here was the first to examine whether individuals' intention to fake differed as a function of a test administration medium. Participants had more positive attitudes, higher levels of perceived behavioral control over faking, and higher subjective norm regarding faking behavior for cyberfaking, compared to offline faking. However, overall, participant's intentions for cyberfaking did not differ from intentions for offline faking. Predictors of intentions to fake online and offline were similar. A more positive attitude toward faking and greater perceived behavioral control over faking contributed significantly to the prediction of intention to fake psychological assessments in both online and offline environments. However, a high level of favorable subjective norm regarding the faking behavior was only significant in the model for online testing.

The current research uses an established theoretical paradigm within a previously unexamined context and in doing so provides a promising insight into the equivalence of attitudes and intentions toward online testing. Given recent growth in the provision of online psychological therapies where malingering or faking may be relevant (e.g.³⁵), gaining additional understanding into the influence of computer-mediated assessment is essential. The research presented here provides a framework for further research into the validity of a computer-mediated psychological assessment, specifically intentions toward engaging in cyberfaking.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

†

Based on the recommendation of an anonymous reviewer, we investigated possible effects of gender by adding gender as a final predictor in a hierarchical multiple regression for both intentions to fake online and intentions to fake offline. Adding gender did not significantly improve the prediction of faking intentions for either model.

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