Predictors of Multi-Tasking Ability for Selection: Attitudes versus Ability. Richard T. Cober. Alana T. Cober. University of Akron. Amie D.

Transcription

1 Polychronicity and Selection 1 Predictors of Multi-Tasking Ability for Selection: Attitudes versus Ability Richard T. Cober Alana T. Cober University of Akron Amie D. Lawrence Matthew S. O'Connell Select International Cober, R. T., Cober, A. T., Lawrence, A. D., & O Connell, M. S. (2003). Predictors of multitasking ability for selection: Attitudes versus ability. Paper presented at the 18 th annual meeting of the Society for Industrial and Organizational Psychology, Orlando, FL.

2 Polychronicity and Selection 2 Predictors of Multi-Tasking Ability for Selection: Attitudes versus Ability Technology and globalization have changed the stressors individuals encounter at work. As the complexity and scope of jobs has increased, time has become an increasingly valuable organizational resource (Persing, 1999). In order to most productively use time to meet performance goals, employees often must handle multiple tasks related to multiple projects (Anderson, 2002; Rubinstein, Meyer, & Evans, 2002). Researchers refer to individuals who enjoy work that requires multi-tasking as possessing a polychronic preference. Research on multi-tasking suggests that such job-related demands lead not only to detriments in performance, but also to stress and role overload (Bluedorn, 2000; Rubinstein et al., 2002). Recently, however, this focus of inquiry has shifted to an examination of variables that may facilitate performance on work requiring multi-tasking behavior. These studies suggest that with practice, people can handle tasks simultaneously with little difference in cognitive load than if they were working on a single task (Hazeltine, Teague, & Ivry, 2002; Schumacher et al., 2001). An important part of this literature has found that individuals differ on their preference for polychronic work and that those individuals who are polychronic, as opposed to monochromic, are more willing and better able to handle the demands of multi-tasking (Bluedorn, Kalliath, Strube, & Martin, 1999; Bluedorn, Kaufman, & Lane, 1992; Hall, 1983; Kaufman, Lane, & Lindquist, 1991b). Since the pace of today s organizations requires the ability to multi-task, it seems appropriate to consider polychronicity as a criterion for selection. This paper examines the utility of a computer-based simulation assessing polychronic ability versus the most widely used measure of polychronic preference (Inventory of Polychronic Values (IPV); Bluedorn et al., 1999) and the most robust criterion for selection (Cognitive Ability; Schmidt & Hunter, 1977) for predicting task performance in a manufacturing organization.

3 Polychronicity and Selection 3 Polychronicity Defined Polychronicity has been treated as an individual difference variable related to both Type A behavior and cultural predispositions (Slocombe, 1999). Polychronicity relates to both individual preferences to be engaged in two or more tasks simultaneously and a belief that such organization of work is the most effective way to get things done (Bluedorn et al., 1999; Hall, 1983). Individuals can then be classified as monochronic or polychronic. Monochronic individuals perform tasks one at a time, are more schedule driven, engage in more detailed planning, are task-oriented, and pay close attention to promptness (Bluedorn et al., 1999; Bluedorn et al., 1992; Hall, 1983). On the other hand, polychronic individuals prefer to approach tasks simultaneously, are more likely to change plans, tend to be less organized and aware of time, more likely to report that they have completed daily goals, and feel less stress under time pressure (Bluedorn et al., 1999; Bluedorn et al., 1992; Conte, Rizzuto, & Steiner, 1998; Hall, 1983). Polychronic time use has been negatively related to role overload, and positively related to education, hours worked, and number of social activities in which a person is involved (Bluedorn et al., 1992; Kaufman, Lane, & Lindquist, 1991a). Kauffman, Lane, and Lindquist (1991b) were the first to formally suggest that polychronicity should be considered within the context of selection and placement of employees. Polychronicity and Selection To date, only one article has directly assessed the potential utility of polychronicity as a selection variable by assessing the extent to which fit between employee preferences and organizational requirements for polychronic behavior affected organizational outcomes. Slocombe and Bluedorn (1999) found that employee-organization polychronic fit positively related to organizational commitment, willingness to exert effort, and the perceived fairness of

4 Polychronicity and Selection 4 performance evaluations. Other studies have found polychronic individuals were attracted to organizations they perceived as having polychronic jobs available (Bluedorn, 2000), as well as avoidant of organizations that are highly controlled and emphasize adherence to deadlines and punctuality (Benabou, 1999). These results suggest that it may be suitable to consider individuals polychronic preference in the selection process, especially for jobs that require a significant amount of multi-tasking. Bluedorn and colleagues (1999) developed a ten-item scale to measure polychronic preference (IPV). They used data from eleven samples to support the factor structure, construct validity, and nomological network for this measure, and suggest that it be used to examine research questions relating to polychronicity s effect on organizational life. However, significant questions remain regarding the relationship between the polychronicity construct (both preference and ability) and work performance (O'Connell, Lawrence, & Klinvex, 2002a). Such a linkage would significantly affect the utility of the construct of polychronicity as a variable for use in personnel selection. The only study examining the relationship between polychronic preference and actual behavior found that scores of polychronic preference related to engagement in only three of eleven polychronic behaviors (Kaufman et al., 1991a). Therefore, just because an individual indicates that they prefer polychronic work does not mean that they will succeed in polychronic jobs. In their discussion of findings relating polychronic preference to work outcomes, Kaufman et al. (1991a) questioned the assumption that preference actually relates to polychronic behavior, which has been a pervasive supposition in polychronic research. With regards to selection, screening candidates for their ability to multi-task (polychronic behavior) may be an effective way to select candidates for polychronic jobs. To assess the extent to which individuals might succeed in jobs requiring polychronic behavior, O Connell and

5 Polychronicity and Selection 5 colleagues (2002a) developed and validated a multi-tasking simulation to be administered via computer. They found that scores on the multi-tasking simulation positively related to task performance and cognitive ability in a manufacturing organization. In addition, they found evidence for discriminant validity, as performance on the simulation did not relate to contextual performance (e.g., Organ, 1997; Podsakoff, MacKenzie, Paine, & Bachrach, 2000), locus of control (e.g., Spector, 1988), or the Big Five Personality variable Conscientiousness. A limitation of the O Connell et al. (2002a) study was that they did not establish a relationship between polychronic preference and performance on the simulation of polychronic behavior. It seems that a positive relationship should exist between an individual s preference to be polychronic and their actual ability to be polychronic. As previously mentioned, Kauffman, et al. (1992a) found, using only a four item Polychronic Attitude Index, that polychronic preference significantly related to responses on three of eleven polychronic activity statements. In this study, we used Bluedorn, et al. s (1999) IPV measure expecting to find a relationship between that measure of polychronic preference and performance on the multi-tasking simulation. Therefore: Hypothesis 1: Polychronic preference positively relates to performance on a multi-tasking simulation designed to measure polychronic behavior. In order for a measure to be useful in a selection context, it must be job-related and should be a good predictor of job performance (Gatewood & Field, 1994). Kauffman, Lane, and Lindquist (1992b) suggested that measures of polychronicity could be used in the selection and placement of employees. The most appropriate measure of polychronicity for use in selection is still open for debate. The IPV assesses polychronicity as a personality construct. Research on the use of personality in selection has found that personality variables are typically not as valid predictors as measures of cognitive ability or work samples (Hunter & Hunter, 1984; Schmidt &

6 Polychronicity and Selection 6 Hunter, 1998). O Connell et al s (2002a) simulation is similar to a work sample that may be traditionally collected via an assessment center, but because of the ability to deliver the assessment via computer (e.g. kiosk or Internet), it seems a more practical type of work sample assessment. We would expect that for jobs requiring high levels of multi-tasking behavior, a test that most directly assesses that behavior, should account for more criterion-related variance than a personality measure assessing one s preference to engage in that behavior. Therefore: Hypothesis 2: Performance on the multi-tasking simulation will be more predictive of task-related job performance than the measurement of one s preference to engage in polychronic behaviors. Cognitive ability tests have been found to be a robust predictor of performance (Schmidt & Hunter, 1977), and are, therefore, widely used for selection. However, for jobs requiring special skills, like mechanical ability, work samples have been found to be a comparable or even more valid predictor of performance than cognitive ability tests (Barrett, Polomsky, & McDaniel, 1999; Hunter & Hunter, 1984). O Connell, et al. (2002a) demonstrated that the multi-tasking simulation used in this study positively related to scores on a cognitive ability test. In this study, we wanted to determine whether the multi-tasking simulation was a simple proxy for cognitive ability, or whether it actually accounted for variance in performance beyond scores on cognitive ability tests. Therefore: Hypothesis 3: Performance on the multi-tasking simulation will account for variance in task-related job performance beyond that accounted for by cognitive ability. The Present Study A sample of manufacturing employees was used to test the comparative utility of a measure of polychronic preference (IPV), polychronic ability (multi-tasking simulation), and

7 Polychronicity and Selection 7 cognitive ability for predicting task-related performance. The jobs employees in this sample held required a significant amount of multi-tasking behavior in order to meet productivity goals. The simulation was designed specifically for use in the selection of manufacturing employees, but requires no previous knowledge of manufacturing systems or equipment in its measurement of performance. More information on the simulation can be found in O Connell, et al. (2002a). Method Participants Participants were 156 employees at a large manufacturer of agricultural equipment (Mean Age = 44.8 years). All of the participants had been working with the organization for at least one year with an average of 16.6 years of experience. Participants had been working with their current supervisor for an average of 22.8 months. There were 137 males and 23 females, of which 129 were Caucasians and 23 belonged to a Minority group. Multi-tasking Simulation The computerized multi-tasking simulation used in this study was developed and validated based on information provided from four manufacturing facilities in four separate organizations (O'Connell et al., 2002a). The task involved a complex visual display of graphics and text. Two tasks were simultaneously displayed on the computer monitor, both requiring immediate attention from the participant (see Figure 1). Participants were given 6 minutes to complete the task with a one-minute practice period. The multi-tasking simulation had two goals: (a) to monitor the needles of graphicallygenerated gauges, keeping the needles in white areas and out of areas shaded red for all gauges; and (b) to evaluate pairs of 6 digit numbers. On the left side of the simulation screen, needles within the six gauges were in constant motion. If a needle moved near or into a shaded red area,

8 Polychronicity and Selection 8 the participant was required to click on a small button next to the gauge to change the direction of the needle. Participants monitored all six gauges and reversed needle directions as, necessary, to keep the needles in the white area. At the same time, participants compared two sets of 6 digit numbers on the right side of the simulation screen, determining whether the numbers were exactly the same or different from one another. Following participant responses, a new set of expressions appeared. The goal for this task was to make as many correct comparisons as possible. Measures Polychronic Preference. Polychronicity preference was measured with the individual version of the 10-item Inventory of Polychronic Values (IPV; Bluedorn et al., 1999). This measure assessed the extent to which individuals preferred to participate in polychronic behavior. Performance on Multi-tasking Simulation. The computerized multi-tasking simulation yielded two raw scores and one final multi-tasking score. One score for the total time that the participant allowed the gauges to remain in the shaded red zone (reverse scored, such that less time receives a higher score). The second score was the total number of correct comparisons that candidates were able to make minus the number of incorrect comparisons. These two scores were then standardized to yield a combined average score for performance on the multi-tasking simulation. As described in O Connell, et al. (2002a) this final combined variable is the more theoretically meaningful measure of polychronicity than either of the individual variables. Cognitive Ability. A 35-item computer-administered cognitive ability measure was used in this research. Items in the ability measure were written to reflect three broad domains, including analytical reasoning, numerical reasoning, and applied reasoning. These domains were

9 Polychronicity and Selection 9 considered relevant to the jobs of interest given the requirements of each job for problem solving, manipulation of quantitative information, and reliable performance of assembly and production tasks. Hence, verbal reasoning, or verbal skills more generally, were not considered particularly relevant for the jobs of interest in the present research. The 15 analytical reasoning items in the measure were very similar to the Raven s standard matrices (Raven, 1958) and required examinees to view a series of figures and determine the next figure in the series. Ten numerical reasoning items required examinees to view a series of single digit numbers to determine which number did not belong in the series. Ten applied reasoning items provided examinees with data and information about a work process and then asked questions about the process. The 35 items were scored dichotomously and summed to calculate a composite ability score. O Connell and Kato (2001) reported an internal consistency reliability of.81 based on a sample of 3,311 individuals. Meta-analysis by O Connell & Smith (1999) based on 7 manufacturing samples and 718 individuals reported a corrected correlation of.43 with supervisor s performance ratings. Task-related Job Performance. Supervisory ratings of task performance were collected for research purposes only. The performance rating form contained 8-items that related to taskspecific behavior. A similar version of this scale has previously appeared in the literature (O'Connell, McDaniel, Grubb, Hartman, & Lawrence, 2002b). Results Table 1 presents descriptive statistics and correlations between all of the variables of interest in the current study. Hypothesis 1 proposed that polychronic preference would be positively related to performance on the multi-tasking simulation. As shown in Table 1, there was a significant positive correlation between polychronic preference and polychronic

10 Polychronicity and Selection 10 performance (r =.19, p <.05). Therefore, Hypothesis 1 was supported as a positive relationship existed between polychronic preference and performance. Hypothesis 2 and Hypothesis 3 were tested with a series of hierarchical regression analyses using task performance as a dependent variable. In each regression, participant age, gender, and race were entered in step 1 as control variables. Hypothesis 2 proposed that actual performance on the multi-tasking simulation would predict variance in task-related job performance over and above an individual s polychronic preference. As shown in Table 2, after controlling for age, gender, and race, performance on the multi-tasking simulation accounted for significant incremental variance beyond one s polychronic preference in task-related job performance. Specifically, 6% of the variance in job-related task performance was explained by performance on the multi-tasking simulation (ß =.28, p <.01), whereas one s polychronic preference was not a significant predictor of variance in job-related task performance (ß =.01, p >.05). Therefore, Hypothesis 2 received strong support as polychronic performance accounted for variance in task performance ratings beyond the effect of polychronic preference. Hypothesis 3 proposed that performance on the multi-tasking simulation would account for variance in task-related job performance beyond that accounted for by cognitive ability. After controlling for age, gender, and race, performance on the multi-tasking simulation accounted for significant incremental variance beyond general cognitive ability in task-related job performance (see Table 3). In addition, performance on the multi-tasking simulation accounted for 6% of the variance in job-related task performance (ß =.28, p <.01) over and above cognitive ability (ß =.04, p >.05). Therefore, Hypothesis 3 was supported as polychronic performance accounted for variance in task performance ratings beyond the effect of cognitive ability.

11 Polychronicity and Selection 11 Discussion The competing demands of today s work environment require workers to increasingly engage in polychronic behaviors. Previous research has suggested that some individuals have a preference for polychronic work, which makes them better suited for jobs that place multiple demands on attention (Bluedorn et al., 1992; Slocombe & Bluedorn, 1999). However, the results of this study suggest that more than a preference for polychronic behavior is required to succeed in multi-tasking situations, a finding that has implications for personnel managers in organizations characterized by complex jobs. Support for our first hypothesis suggests that individuals who prefer polychronic activity do perform better on polychronic tasks. A positive significant relationship was found between an individual s polychronic preference and performance on the multi-tasking simulation. Our second and third hypotheses were designed to examine the validity of measuring polychronic ability as a predictor of task performance. The test of our second hypothesis found that polychronic ability, as assessed with a multi-tasking simulation, accounted for significant variance in task performance beyond the effect of a commonly used measure of polychronic preference (IPV; Bluedorn et al., 1999). Previous polychronic research has suggested the integration of the construct in selection batteries for jobs requiring multi-tasking behaviors (Kaufman et al., 1991a). Results for Hypothesis 2 suggest that a measure of polychronic ability would be more appropriate to use in selection than a measure of polychronic preference. Hypothesis 3 tested the efficacy of the polychronic ability measure to predict task performance beyond the most robust measure used in selection, cognitive ability (Hunter & Hunter, 1984). Again, we found support for the use of polychronic ability in selection, as it predicted task performance beyond the effect of cognitive ability.

12 Polychronicity and Selection 12 Our results indicate that organizations selecting individuals for jobs that require multitasking behavior would likely benefit from utilizing simulations that assess polychronic ability (the ability to multi-task) as a screening device. Although this study specifically focused on a sample of manufacturing employees, today s economic environment has increasingly required workers, across all business sectors, to simultaneously handle multiple job demands (Anderson, 2002). Organizations have put premiums on individual productivity and recent productivity statistics suggest that this emphasis has had a positive effect (McAuley, 2002). To continue these gains, organizations must add labor that can handle modern, polychronic work flow. Using computer-based assessments to pre-screen job applicants allows organizations a more sophisticated means of filtering candidates who do not fit with job requirements. The challenge presented to HR Departments is in the selection of screening measures that provide the most useful filter of applicants. This study suggests that computer-enabled simulations may indeed provide an effective filter, particularly for those jobs that require polychronic behavior. Limitations A strength of this study was that our sample came from an applied setting and our criterion measure was actual supervisor ratings of job performance. However, this study directly applies only to individuals in a manufacturing setting, since our sample consisted of employees from one manufacturing organization. The importance of the construct of polychronicity has been demonstrated in other environments, such as academic departments (Taylor, Locke, Lee, & Gist, 1984) and for air traffic controllers (Ackerman & Kanfer, 1993). Future investigation should examine the utility of measures of polychronic ability for predicting performance across jobs in other industries (e.g., retail, service, finance). Despite our use of the most commonly used measure of polychronic preference (IPV), a second limitation applies to our use of a relatively

13 Polychronicity and Selection 13 new measure of cognitive ability. Although this measure was developed and used in previous research (O'Connell & Smith 2000; O'Connell et al., 2002b), we did not use more commonly used measures of cognitive ability (e.g., Wonderlic). It is possible that using a different measure of cognitive ability would alter the results of Hypothesis 3. However, strong support for the validity of this particular cognitive ability measure exists (O'Connell & Smith, 2000) and this particular measure is currently used for selection purposes in over 70 manufacturing organizations. Additionally, the cognitive ability measure in this study significantly related to task performance, as predicted. Conclusion This study found support for the use of a measure of polychronic ability for selection purposes using a sample from a manufacturing organization. Scores on this measure were derived from a simulation of multi-tasking behavior and predicted performance beyond the effects of both polychronic preference and cognitive ability. In the context of the increasing demands of jobs on attentional resources, it seems that simulations assessing polychronic ability should be used to effectively screen job applicants. Future research should adapt simulations to examine the importance of polychronic ability across multiple job types for selection purposes. Successful use of polychronic ability measures should lead to the realization of savings in the acquisition of personnel, retention of workers in demanding jobs, and improved performance as workers abilities more closely fit job demands.

14 Polychronicity and Selection 14 References Ackerman, P. L., & Kanfer, R. (1993). Integrating laboratory and field study for improving selection: Development of a battery for predicting air traffic controller success. Journal of Applied Psychology, 78, Anderson, P. (2002). Study: Multitasking is counterproductive. CNN.com. Available: [2002, June 10]. Barrett, G. V., Polomsky, M. D., & McDaniel, M. A. (1999). Selection tests for firefighters: A comprehensive review and meta-analysis. Journal of Business and Psychology, 13(4), Benabou, C. (1999). Polychronicity and temporal dimensions or work in learning organizations. Journal of Managerial Psychology,14, Bluedorn, A. C. (2000). Polychronicity, change orientation, and organizational attractiveness. Paper presented at the 15th Annual Society for Industrial/Organizational Psychology Conference, New Orleans, LA. Bluedorn, A. C., Kalliath, T. J., Strube, M. J., & Martin, G. D. (1999). Polychronicity and the Inventory of Polychronic Values (IPV): The Development of an instrument to measure a fundamental dimension of organizational culture. Journal of Managerial Psychology, 14, Bluedorn, A. C., Kaufman, C. F., & Lane, P. M. (1992). How many things do you like to do at once? An introduction to monochronic and polychronic time. Academy of Management Executive, 6, Conte, J. M., Rizzuto, T. E., & Steiner, D. D. (1998). A construct-oriented analysis of individuallevel polychronicity. Journal of Managerial Psychology, 14,

15 Polychronicity and Selection 15 Gatewood, R. D., & Field, H. S. (1994). Human Resource Selection. (3rd ed.). Fort Worth, TX: The Dryden Press. Hall, E. T. (1983). The dance of life. New York, NY: Anchor Press. Hazeltine, E., Teague, D., & Ivry, R. B. (2002). Simultaneous dual-task performance reveals parallel response selection after practice. Journal of Experimental Psychology: Human Perception and Performance, 28(3), Hunter, J. E., & Hunter, R. F. (1984). Validity and utility of alternative predictors of job performance. Psychological Bulletin, 96(1), Kaufman, C. F., Lane, P. M., & Lindquist, J. D. (1991a). Exploring more than 24 hours a day: A preliminary investigation of polychronic time use. Journal of Consumer Research, 18, Kaufman, C. F., Lane, P. M., & Lindquist, J. D. (1991b). Time congruity in the organization: A proposed quality-of-life framework. Journal of Business and Psychology, 6, McAuley, J. (2002). Big picture: Productivity best since woodstock. DJ Newswire. Available: [2002, August 12]. O Connell, M.S., & Kato, M. (2001). Reliability analysis for all scales used in the Select Assessment TM for Manufacturing system based on a normative sample (Technical Report). Pittsburgh, PA: Select International, Inc. O'Connell, M. S., Lawrence, A. D., & Klinvex, K. C. (2002a). Development and validation of a computerized multi-tasking simulation. Paper presented at the 16th Annual Society for Industrial/Organizational Psychology Conference, Toronto, Canada. O'Connell, M. S., McDaniel, M. A., Grubb, W. L., Hartman, N. S., & Lawrence, A. D. (2002b). Incremental validity of situational judgment tests for task and contextual performance.

16 Polychronicity and Selection 16 Paper presented at the 17th Annual Society for Industrial/Organizational Psychology Conference, Toronto, Canada. O'Connell, M.S., & Smith, M.E. (2000). Meta-Analysis of the Select Assessment TM for Manufacturing System (Technical Report). Pittsburgh, PA: Select International, Inc. O'Connell, M. S., & Smith, M. E. (1999). Normative and validity results for entry-level assessment scales (Technical Report). Pittsburgh, PA: Select International, Inc. Organ, D. W. (1997). Organizational citizenship behavior: It's construct clean-up time. Human Performance, 10, Persing, D. L. (1999). Managing in polychronic times: Exploring individuals creativity and performance in intellectually intensive venues. Journal of Managerial Psychology, 14(5), Podsakoff, P. M., MacKenzie, S. B., Paine, J. B., & Bachrach, D. G. (2000). Organizational citizenship behaviors: A critical review of the theoretical and empirical literature and suggestions for future research. Journal of Management, 26, Raven, J. C. (1958). Standard progressive matrices. San Antonio, TX: Psychological Corporation. Rubinstein, J. S., Meyer, D. E., & Evans, J. E. (2002). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124,

17 Polychronicity and Selection 17 Schmidt, F. L., & Hunter, J. E. (1977). Development of a general solution to the problem of validity generalization. Journal of Applied Psychology, 62(5), Schumacher, E. H., Seymour, T. L., Glass, J. M., Fencsik, D. E., Lauber, E. J., Kieras, D. E., & Meyer, D. E. (2001). Virtually perfect time sharing in dual-task performance: Uncorking the central cognitive bottleneck. Psychological Science, 121(2), Slocombe, T. E. (1999). Applying the theory of reasoned action to the analysis of an individual's polychronicity. Journal of Managerial Psychology, 14(3/4), Slocombe, T. E., & Bluedorn, A. C. (1999). Organizational behavior implications of the congruence between preferred polychronicity and experienced work-unit polychronicity. Journal of Organizational Behavior, 20, Spector, P. E. (1988). Development of the work locus of control scale. Journal of Occupational & Organizational Psychology, 61, Taylor, M. S., Locke, E. A., Lee, C., & Gist, M. E. (1984). Type A behavior and faculty research productivity. Organizational Behavior and Human Decision Processes, 34,

18 Polychronicity and Selection 18 Table 1 Means, Standard Deviations, Internal Consistency Reliabilities, and Intercorrelations of Variables Examined Variable Mean SD Age Race.70 N/A Gender.70 N/A.26** Polychronic *.01 (.88) Preference 5. Polychronic **.27** * Performance 6. Cognitive Ability **.17*.23**.40** (.73) 7. Job-related Task **.22**.03.36**.23** (.93) Performance Note. Gender 0 = Female, 1 = Male; Race 0 = Minority, 1 = Non-Minority. Coefficient Alpha for scales in parenthesis. * p <.05 ** p <.01.

19 Polychronicity and Selection 19 Table 2 Hierarchical Regression Analysis: Task Performance on Polychronic Preference and Performance. Predictor B-weights on Task Performance Step 1 Age -.11 Gender.20* Race.25** R 2.12** Step 2 Polychronic Preference.01 R 2.01 R 2.12** Step 3 Polychronic Performance.28** R 2.06** R 2.18** Note. Gender 0 = Female, 1 = Male; Race 0 = Minority, 1 = Non-Minority; * p <.05. ** p <.01.

20 Polychronicity and Selection 20 Table 3 Hierarchical Regression Analysis: Task Performance on Cognitive Ability and Polychronic Performance. Predictor B-weight on Task Performance Step 1 Age -.14 Gender.24** Race.23** R 2.13** Step 2 Cognitive Ability.04 R 2.02 R 2.14** Step 3 Polychronic Performance.28** R 2.06** R 2.20** Note. Gender 0 = Female, 1 = Male; Race 0 = Minority, 1 = Non-Minority; * p <.05. ** p <.01.

21 Polychronicity and Selection 21 Figure 1. Example screen of the gauge simulation.