UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Personality trait and cognitive ability correlates of unsafe behaviours LeRoy, Zehra Pirani 2005

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
831-ubc_2005-0521.pdf [ 3.32MB ]
Metadata
JSON: 831-1.0092122.json
JSON-LD: 831-1.0092122-ld.json
RDF/XML (Pretty): 831-1.0092122-rdf.xml
RDF/JSON: 831-1.0092122-rdf.json
Turtle: 831-1.0092122-turtle.txt
N-Triples: 831-1.0092122-rdf-ntriples.txt
Original Record: 831-1.0092122-source.json
Full Text
831-1.0092122-fulltext.txt
Citation
831-1.0092122.ris

Full Text

PERSONALITY TRAIT A N D COGNITIVE ABILITY CORRELATES OF UNSAFE BEHAVIOURS by ZEHRA PIRANI LEROY B. Sc., The University of Victoria, 1998 B. A. Hons., The University of British Columbia, 2003 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE F A C U L T Y OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA August 2005 © Zehra Pirani LeRoy, 2005 11 Abstract Unsafe behaviours were presumed to be a primary precursor to accident involvement, related to personality, attention and memory. In Study 1, 633 undergraduates completed a personality inventory and a hazardous-behaviours questionnaire. A trait-based scale was empirically developed to assess safety-oriented tendencies. The scale is suitable for applied use, and draws on traits related to the Big Five, risk-taking, counterproductivity, and impulsivity. In Study 2, 140 undergraduates completed the Study 1 measures and a battery of cognitive ability tests assessing attention and memory. Two common-factors—Cognitive Errors and Performance Speed—were correlated with the Study-1 Safety-Orientation scale, but not with unsafe behaviours. Individual-differences variables may have a more complex role in the safety system than previously thought, and could be used to improve various Human Resources interventions to reduce accidents in the workplace, such as through selection, placement, training, and job redesign. Recommendations for future research are discussed. Il l Table of Contents Abstract ii List of Tables vi List of Figures viii Acknowledgements ix Introduction 1 Structure of Thesis 2 Conceptualizing Unsafe Behaviours 2 Unsafe Behaviours Defined 3 Conceptual Model 3 The Role of Unsafe Behaviours in the Safety-Systems Approach 4 Individual Difference Contributors to Unsafe Behaviours 4 Personality Traits and Unsafe Behaviours 5 Cognitive Abilities and Unsafe Behaviours 6 Methodological Issues Associated with Previous Research 8 Current Directions 9 Research Obj ectives 10 Study 1 11 Method 11 Participants 11 Operationalization of Unsafe Behaviours Criterion 11 Assessment of Safety-oriented Tendencies by means of a Personality Inventory. 12 Procedures 12 Statistical Analyses 13 iv Results and Discussion 16 Study 2 20 Method 21 Participants 21 Operationalization of Unsafe Behaviours Criterion 21 Assessment of Safety-oriented tendencies by means of a personality inventory ..21 Relating Unsafe Behaviours to Accident Involvement Differential 22 Relating Unsafe Behaviours to Cognitive Failures 22 Cognitive Ability Measures used in Sample 1 22 Cognitive Ability Measures used in Sample 2 23 Procedures 26 Statistical Analyses 27 Results and Discussion 29 General Discussion and Conclusions 33 Contributions to Accident Research 33 Individual-Differences Variables and the Safety System 34 Limitations and Future Research 35 References 37 Appendix A. Questions from Criterion Hazardous-Behaviours Scale 64 Appendix B. Normative data and reliability estimates for other BIODATA-250 scales 65 Appendix C. Questions from Accident-Related Events Scale 66 Appendix D. Additional Details for Study 2 Measures 67 Cognitive Failures Questionnaire (CFQ) 67 Work Skills Assessment (WSA), Part 4 67 V Comprehensive Ability Battery - Perceptual Speed and Accuracy (CAB-P) 68 Comprehensive Ability Battery - Memory Span (CAB-Ms) 68 Comprehensive Ability Battery - Associative Memory (CAB-Ma) 68 Colour-Word Stroop Test 69 Trail Making Tests (TMT) 69 Cancel H Test (CHT) 70 Digit Symbol Test (DST) 70 Digit Span Backward Test (DSB) 70 Test of Variables of Attention (TOVA) 71 vi List of Tables Table 1. Oblique primary-factor matrix for the 16 items from the Criterion Hazardous-Behaviour Scale 45 Table 2. Factor scales derived from the commom-factor analysis of the 16-item Criterion Hazardous-Behaviour Scale, including the weighted by gender internal consistency (a) reliability estimate for each subscale 46 Table 3. The 51 BIODATA-250 items selected for the BIODATA-250 Safety-Orientation scale 47 Table 4. Oblique primary-factor matrix for the 51 items from the Safety-Orientation scale, with R's indicating reverse-scored items 50 Table 5. Primary-factor intercorrelation matrix for the 51-item Safety-Orientation scale 53 Table 6. Factor scales derived from the commom-factor analysis of the 51-item Safety-Orientation scale, including the weighted by gender internal consistency (a) reliability estimate for each subscale, and R's indicating reverse-scored items 54 Table 7. Correlations between the Safety-Orientation scale and factor scales, and the other BIODATA-250 scales 57 Table 8. Criterion-related validity estimates for Safety-Orientation scale and subscales, and other BIODATA-250 scales 58 Table 9. Oblique primary-factor matrix for the 13 cognitive ability variables from Sample 2 59 Table 10. Factor scales derived from the commom-factor analysis of the cognitive ability variables from Sample 2, including the internal consistency (a) reliability estimate for each subscale 60 Table 11. Correlations between the cognitive ability subscales and variables, and the Criterion Hazardous-Behaviour Scale and subscales, and the Accident-Related Events Scale 61 V l l Table 12. Correlations between the Safety-Orientation scale and subscales, and the cognitive ability subscales 62 V l l l List of Figures Figure 1. Conceptual model for this thesis 63 ix Acknowledgements There are many people that I would like to thank who have supported, guided, and assisted me throughout the course of my Master's degree. I would like to acknowledge all the participants who have graciously given their time for my research. I would also like to thank my research assistants —Yasmin Ahamed, Rosemarie Ong, Vil i ja Petrauskas, Gwen Montgomery, Shirley Sarkodee-Adoo, and Joan Ewasiw— who have been both professional and diligent in maintaining high standards while collecting and entering data. I am grateful to Peter Graf for providing me with some of the cognitive ability measures that were included in the research, and to Carrie Cuttler for sharing her knowledge of neuropsychological assessment measures and prospective memory, and for coaching me on how to administer the cognitive ability tests for the second study. I also want to thank Ekin Blackwell, Darcy Hallett, and Kevin Williams, from the departmental stats consulting office, who helped me with the factor analyses. I want to thank the friends and colleagues who have followed my progress and tribulations, and helped keep a smile on my face. My family has always supported my desire to study; I am thankful for their continuing support and understanding during my time as a student. My husband Sean has been my biggest supporter; I am thankful for his patience and tenderness, and for the light he brings into my life everyday. I am grateful to the Elizabeth Young Lacey Foundation, the Social Sciences and Humanities Research Council of Canada, and the Workers' Compensation Board of British Columbia, and the T.O.V.A. Research Foundation for supporting my research and providing me with funding throughout the course of my Masters program. I would like to thank my committee members, Todd Handy, Sandra Robinson, and Linda Scratchley, for their time and expertise, and careful consideration of my thesis. And last, I would X like to thank my supervisor, Ralph Hakstian, for his time in helping me sort through my ideas and statistical analyses, for his continuing belief in me, and for his understanding of my trials this past year. Through his mentorship and expertise I have become a better researcher and writer, and am thankful for all that he has taught me. 1 Introduction Personnel selection practices have evolved in the last decade in response to increasing empirical evidence that personality traits and cognitive abilities successfully predict job performance (Barrick & Mount, 1991; Schmidt & Hunter, 1998). Notably, the prediction of workplace counterproductive behaviours has lead to increasing demands for integrity testing before hiring decisions are made (Hakstian, Farrell, & Tweed, 2002; Ones, Viswesvaran, & Schmidt, 1993). Sackett and DeVore (2001) defined occupational counterproductivity using three types of behaviours: deviant (theft and substance use at work), absentee (including tardiness), and unsafe (accidents and injuries). The overall purpose of this thesis is to explain variability in unsafe behaviours with personality traits and cognitive abilities, specifically attentional and memory abilities. If individuals who frequently engage in unsafe behaviours can be identified, organizations can use this information to reduce workplace accidents through Human Resource interventions such as selection, placement, training, and job design (Hale & Glendon, 1987; Jones & Wuebker, 1988; Lawton & Parker, 1998). Each year in Canada there are over 700 fatal occupational injuries and over 400,000 non-fatal occupational injuries requiring time off (Stewart, 2002). These statistics and the associated costs to society underscore the need to continue to research ways to reduce workplace accidents. Although the focus of this thesis is on predicting unsafe behaviours from an individual-differences approach, this does not suggest that workers are to blame for accidents or that other contributing factors are less important. The focus on individual differences is meant to add to the existing body of knowledge in accident research. 2 Structure of Thesis This thesis is the outcome of two empirical studies. Study 1 presents a trait-based scale to predict unsafe behaviours, and examines its properties in relation to unsafe behaviours. Study 2 explores the cognitive-ability correlates of unsafe behaviours. The general discussion and conclusions section provides an overall discussion as well as recommendations for future research. The remaining sections of the introduction review the theoretical underpinnings of this thesis, beginning with a brief review of the literature on unsafe behaviours and a discussion of how individual-differences variables relate to unsafe behaviours and differential accident involvement. This is followed by an examination of the methodological problems associated with past research, an overview of current research directions, and a summary of the objectives of this research. It should be noted at the outset that unsafe behaviours are also considered an individual-differences variable. To avoid confusion, the term "individual-differences variables" in this thesis will refer only to the enduring, measurable traits of individuals that relate to unsafe behaviours (e.g. personality traits, cognitive abilities). Conceptualizing Unsafe Behaviours Occupational accidents and injuries usually have multiple causes and contributing factors. Incidents may involve equipment, risk exposure, the actions of other workers, management decisions, leadership, and the overall safety climate. For this reason, accidents and accident involvement have not been predicted effectively. Several researchers have proposed a shift towards predicting the precursors of accidents, such as worker behaviour, because these are more easily studied than accidents (Bradley, 1997; Hale & Glendon, 1987; Lawton & Parker, 1998). Unsafe Behaviours Defined For the purpose of this thesis, unsafe behaviours are described as actions related to risk-taking, absentmindedness and carelessness, and include both intentional acts (e.g. conscious risk-taking) and unintentional acts (e.g. automatic behaviours). Safe behaviours include wearing safety equipment, following safety rules, and having a positive attitude towards safety (Neal & Griffin, 2004). Unsafe and safe behaviours can be understood to be either independent or on opposite ends of a continuum. If they are considered independent, an individual could engage in both types of behaviour simultaneously. If they are considered on a continuum, an individual who engages more in one of the behaviours would engage less in the other. As in Bradley (1997), the latter approach was adopted for this thesis because it is unlikely for individuals to engage in safe and unsafe behaviours simultaneously unless the environment imposes a strong safety structure (e.g. enforcement of safety-equipment rules), and for safety behaviours to generalize to the home once the safety structure has been removed (Lund & Hovden, 2003). Unsafe behaviours increase an individual's chance of being involved in an accident-related event (Bradley, 1997), a likelihood that McKenna (1983) referred to as differential accident involvement. The presumed positive relationship between unsafe behaviours and differential accident involvement is based on the idea that certain actions—such as taking risks, not following rules, and being careless—make an individual more likely to be in a situation that leads to an accident-related event (Hale & Glendon, 1987; Hofmann & Stetzer, 1996). Conceptual Model The conceptual model for this thesis appears in Figure 1. In this model, unsafe behaviours are neither necessary nor sufficient for accident-involvement to occur, whereas 4 organizational factors are. Unsafe behaviours are a mediating factor between individual-differences variables and the accident-involvement outcome. Organizational variables are also moderators of the relationship between individual-differences variables and unsafe behaviours. The Role of Unsafe Behaviours in the Safety-Systems Approach The safety-systems approach encompasses the entire organizational system and stresses the safety climate, attitudes, management initiatives, and the elimination of physical hazards (Petersen, 1996; Stewart, 2002). Various models of accident causation have been proposed within this approach, some of which have given prominence to the role of unsafe behaviours (Bradley, 1997; Hale & Glendon, 1987; Lund & Aaro, 2004). Individual-differences variables do not exist in isolation when influencing behaviours, but interact with the other components of the system. Most of the safety-systems research has focused on the role of organizational variables (Lawton & Parker, 1998). Hansen (1989) was one of the first to present an empirical accident-causation model that included individual-difference variables while controlling for risk exposure. In this study, social maladjustment and distractibility were found to be significant predictors of accidents. Studies of the relationship between everyday cognitive failures and accidents found that both conscientiousness (Wallace & Vodanovich, 2003b) and level of stress (Reason, 1990) each acted as moderator variables of this relationship. Individual Difference Contributors to Unsafe Behaviours Individual-differences variables have been studied more extensively outside the safety-system approach. As the focus of this thesis is on exploring the relationship between unsafe behaviours, personality traits, and variables of attention and memory, only theories and research related to these constructs are reviewed, starting with personality and then proceeding through the cognitive abilities. 5 Personality Traits and Unsafe Behaviours Behaviours related to accident involvement may include acting without planning, seeking thrilling activities, taking short-cuts, not taking responsibility for actions, working carelessly, or not following rules (Hale & Glendon, 1987). The propensity to engage in one or more of these behaviours on a regular basis can be characterized as a behavioural tendency. Personality traits are the characteristics that differentiate individuals according to relatively stable thought patterns and behavioural tendencies. The behaviours listed above describe aspects related to the following personality traits—impulsivity, risk-taking, locus of control, and conscientiousness. The dominant theory in accident research related to personality traits has been the accident-proneness model, which suggested that individuals are predisposed to having an unequal accident liability. Multiple accident-involved individuals were assumed to have inherited a stable personality trait, or set of traits, thus predisposing them to experience more accidents, regardless of other external variables, including risk exposure (Hale & Hale, 1972; McKenna, 1983). This model, based on Greenwood and Woods research (1919, reproduced in Haddon, Suchman, & Klein, 1964), was discredited by McKenna, and replaced by the safety-systems approach, which avoids blaming the victim and provides incentives for organizations to eliminate workplace hazards (Hale & Glendon, 1987; Hansen, 1988; Lawton & Parker, 1998). Even though the accident-proneness model is no longer at the forefront of accident research, researchers have continued to study the characteristics of accident-involved employees (e.g. Cellar, Nelson, Yorke, & Bauer, 2001; Forcier, Walters, Brasher, & Jones, 2001). These and other studies contribute to our understanding of how personality traits relate to accident-involvement, including from a safety-systems perspective. Personality correlates of accident involvement. Research on the personality differences between people who experience more versus fewer accidents has produced inconsistent results. Much of this research has used the five-factor model (Costa & McCrae, 1992), a conceptualization of personality that is currently the most widely used in research, and includes the Big Five traits: neuroticism, extraversion, openness, conscientiousness, and agreeableness. Within the five-factor model, conscientiousness stands out as a trait negatively correlated with accident involvement (Arthur & Doverspike, 2001; Arthur & Graziano, 1996; Cellar et a l , 2001) and unsafe work behaviours (Wallace & Vodanovich, 2003b). Other Big Five traits— neuroticism, extraversion, and agreeableness—have been positively correlated with accident involvement (Cellar, Nelson, & Yorke, 2000; Hansen, 1988; Lawton & Parker, 1998; Sutherland & Cooper, 1991). In a meta-analytic study of accidents using the Big Five personality measures, Salgado (2002) found that none of the traits was related to workplace accident involvement. Other traits positively correlated with accident involvement include external locus of control, aggression, sensation seeking, risk taking, social maladjustment (Hansen, 1988; Lawton & Parker, 1998; Salminen, Klen, & Ojanen, 1999; Thiffault & Bergeron, 2003), Type A personality (Magnavita, Narda, Sani, Carbone, De Lorenzo, & Sacco, 1997; Sutherland & Cooper, 1991), and negative affectivity (Iverson & Erwin, 1997). Additional traits found only among high-risk drivers include low altruism, and either very low or very high anxiety (Iverson & Rundmo, 2002; Ulleberg, 2002). Cognitive Abilities and Unsafe Behaviours Unsafe behaviours also have a strong cognitive component. Employees may have to: focus on tasks; evaluate risks and their abilities; make decisions; be vigilant for abnormalities and hazards; balance speed with accuracy, inhibit dominant responses when a different action is required (Hale & Glendon, 1987); remember past actions; and remember to complete tasks at a future time (Dornheim, 2000). These behaviours become more complex and challenging when stress, fatigue, and the social context play a role (Reason, 1990). In Reason's (1990) theory of human error, cognitive errors are understood as failures in different components of the cognitive system. Cognitive failures have been found to predict (using the self-report Cognitive Failures Questionnaire, CFQ; Broadbent, Cooper, FitzGerald, & Parkes, 1982) safety-related behaviours and accidents at work (Larson & Merritt, 1991; Wallace & Vodanovich, 2003a; Wallace & Vodanovich, 2003b). Reason divided errors into slips and lapses, considered failures in execution and memory, and mistakes, which are considered failures in judgement and decision-making. Slips and lapses describe functions related to attention, memory, and information processing (Bradley, 1997), of which attention and memory will be explored further. Mistakes describe functions related to executive functioning, which are beyond the scope of this thesis. Past research on variables of attention and memory (when available) are reviewed in relation to accident involvement. Attentional ability correlates of accident involvement. Attention is the primary cognitive resource required to monitor internal processes and to avoid making errors that can lead to accidents (Hale & Glendon, 1987; Reason, 1990). The Sohlberg and Mateer clinical model of attention is a common typology used to operationalize this complex function (Kerns, 1996). The model includes: focused and selective attention (often used interchangeably; Ponsford, 2000), the ability to focus on a stimulus while ignoring an irrelevant one; sustained attention (also known as vigilance or continuous attention; Warm, 1984), the ability to maintain focus over time; alternating attention, which involves shifting focus between stimuli; and divided attention, the ability to respond to multiple stimuli or making multiple responses. Selective attention, operationalized with auditory selective and dichotic listening tasks, was the most promising predictor of motor vehicle accidents in the Arthur et al. (1991) meta-analytical study. Lawton and Parker (1998) described mixed results with measures of visual attention, and Edkins and Pollock (1997) found a negative relationship between sustained attention variables and railway accidents. Measures of attention have also been compared with the CFQ. Cognitive failures were negatively correlated with sustained attention performance (Manly, Robertson, Galloway, & Hawkins, 1999; Robertson, Manly, Andrade, Baddeley, & Yiend, 1997; Wallace, Kass, & Stanny, 2001), but were found to have no relationship with selective attention or dichotic listening variables (Martin, 1983). Memory-related abUities and accident involvement. Both retrospective and prospective memory failures are presumed to have a significant impact on accident involvement (Dornheim, 2000). Failures to retrieve previously stored information are related to retrospective memory, whereas failures to remember to complete a task at a future time are related to prospective memory (Graf & Uttl, 2001). Methodological Issues Associated with Previous Research Most of the reviewed research has been inconsistent in predicting accidents because the accidents are infrequent (McKenna, 1983; Salgado, 2002), have multiple causes (Hale & Glendon, 1987), have been conceptualized and measured differently, or the studies have not controlled for variables that could moderate the effects of individual-differences (Lawton & Parker, 1998). Measurement of personality. The difficulty in finding stable relationships between personality traits, accident-involvement and unsafe behaviours has in part been attributed to a lack of special-purpose inventories designed to predict accidents or safety-related tendencies. An exception is the Employee Safety Inventory (ESI) by Pearson Performance Solutions (Forcier et a l , 2001; Jones & Wuebker, 1988), which is a measure of safety consciousness. However, a common problem with special-purpose inventories is that they are usually administered as a supplement to other tests, which can lead to motivational distortion (Hakstian et al., 2002). To avoid this issue, Hakstian et al. derived a trait-based scale from the California Psychological Inventory (CPI; Gough & Bradley, 1996) on the basis of item-criterion correlations with a self-report counterproductive behaviours questionnaire. This method produced a reliable and valid scale that could predict the behaviours of interest. Measurement of cognitive abilities. Another difficulty in identifying stable relationships between cognitive abilities, accident involvement and unsafe behaviours derives from the fact that the bulk of research has focused on motor vehicle accidents, rather than a wider range of jobs and behaviours. Furthermore, researchers have had mixed results determining the extent to which cognitive ability test results can be generalized to actual behaviours (Sbordone, 2001; Sbordone & Guilmette, 1999), and developing tests that can isolate and assess the cognitive ability of interest (Lawton & Parker, 1998). Current Directions In order to move beyond the difficulties in predicting differential accident involvement, a shift to predicting one of the central precursors to accidents, unsafe behaviours, is needed. Little research has explored the relationship between personality traits, attention and memory variables, and unsafe behaviours. Understanding these relationships is a necessary first step to learning their role in the safety system, as they should be comprehended before the varying functions of organizational variables are considered. Personality traits should be evaluated with an unobtrusive measure suitable for applied use. Attention and memory variables should be appraised with accessible, reliable and established tests of those abilities. This thesis focuses on everyday unsafe behaviours relevant to a wide variety of individuals and tasks. This was done in order to account for the wide variability in workplace safety behaviours, to control for the presence of a safety structure at work, and to be able to generalize the results to individuals without prior experience in high risk-exposure occupations. The participants for both studies were university undergraduate students. Although this raises 10 the issue of generalization to the workplace, this sample was preferred at this early stage of research because responses are less likely to be subject to motivational distortion (Hakstian et al., 2002), and because the participation time required would have posed logistical difficulties with industrial samples. Research Objectives The overall objective of this research was to explain a person's engagement in unsafe behaviours using individual-differences variables related to personality traits, and attention and memory. This knowledge was expected to contribute to the existing accident research literature and further theoretical understanding of the underlying constructs involved. A broadened understanding of these relationships can be used to improve job design, personnel selection, job placement and training decisions, with the intent of reducing injuries and accidents in the workplace. A specific research objective was to derive a personality trait-based predictor scale that predicts unsafe behaviours, and that has suitable psychometric properties for applied industrial use. It was hypothesized that the predictor scale would correlate negatively with conscientiousness, internal locus of control, and impulse control, and positively with risk-taking, extraversion, neuroticism, and agreeableness. A second research objective related to the relationship between cognitive ability variables and unsafe behaviours. Unsafe behaviours and differential accident involvement were hypothesized to correlate positively with sustained, selective/focused, and alternating attention variables (high scores indicating poor attentional abilities), and to correlate negatively with memory variables (high scores indicating good memory abilities). 11 Study 1 The purpose of this study was to develop a trait-based scale to predict unsafe behaviours by measuring safety-oriented tendencies using the BIODATA-250 personality inventory (Hakstian, 2002). The intent was to produce a scale that had internal consistency, test-retest reliability, and cross-validity estimates adequate for use in organizations. Other objectives for this study were to correlate the scale with other psychological constructs related to unsafe behaviours and to contribute to the existing literature on the personality correlates of unsafe behaviours. Method Participants Data were collected from 646 undergraduate students at the University of British Columbia (UBC) from September 2002 to December 2004. Thirteen cases were removed because participants had too much difficulty completing the measures in the time allotted. Of the remaining 633 (129 males, 504 females), 408 participated in a two-session design in which 16 did not return for the second session. There were a total of 617 participants with both predictor and criterion data (128 males and 489 females), and 390 participants with test-retest predictor data (78 males and 312 females). In this sample, the median age was 20 years, 56% spoke English as a second language, and the self-defined ethnic profile included 54% East Asians, 31% Canadians / Western Europeans / Americans, 6% South Asians, and 9% from other ethnicities. Operationalization of Unsafe Behaviours Criterion Unsafe behaviours were measured using the Criterion Hazardous-Behaviour Scale (Hakstian & Woolley, 1995), which is the second part of the criterion scale used in the article by Hakstian et al. (2002). Items are reproduced in Appendix A. Participants responded to 12 16 questions on the frequency of their engagement in unsafe behaviours over the last five years, targeting the constructs of absentmindedness, carelessness, and forgetfulness. Responses were made on a 6-point scale ranging from "Absolutely never did this or had it happen" to "Did this or had it happen frequently (more than 4 times)". Scores result from the simple summation of all 16 items; a higher score indicated more frequent engagement in unsafe behaviours. Assessment of Safety-oriented Tendencies by means of a Personality Inventory The Biographical Information about Occupationally Descriptive Attitudes, Traits, and Abilities inventory (BIODATA-250; Hakstian, 2002) is a personality inventory that consists of 250 statements to which participants respond on a 4-point scale ranging from "strongly disagree" to "strongly agree". The BIODATA-250 inventory yields scores on 31 trait scales (A. R. Hakstian, personal communication, February 5, 2003). Procedures The research sessions were administered by the author and five undergraduate psychology students following a strict protocol. Experimental sessions were administered to groups of 1 to 20 persons in one of three rooms in the UBC Psychology Department. At the start of the session, participants completed a demographics form. The first 227 students participated in one 1.5-hour session where they completed the Criterion Hazardous-Behaviour Scale, two perceptual speed and accuracy tests (see Study 2), and the BIODATA-250. The other 406 students participated in two 1-hour sessions separated by a two-week interval. Session 1 involved one of three procedures — (1) a forward digit span and an associative memory test (see Study 2), (2) the Cognitive Failures Questionnaire (see Study 2), or (3) no additional measure— followed by the BIODATA-250. Session 2 consisted of the Criterion Hazardous-Behaviour Scale followed by the BIODATA-250. 13 Statistical Analyses Data preparation. Missing item data on the Criterion Hazardous-Behaviour Scale were not replaced. Missing data on the BIODATA-250 were replaced with a neutral value, and any multiple responses were averaged. The 16 cases of Session-2 missing data constituted less than 3% of the data set, and were therefore not a concern (Cohen, Cohen, West, & Aiken, 2003). When appropriate, the following analyses used item-level data and scale scores in zero-centred form to prevent distortion from gender effects. Psychometric properties of the Criterion Hazardous-Behaviour Scale. Descriptive statistics and internal consistency estimates using Cronbach's alpha were computed by gender for the criterion scale. Exploratory factor analysis of the Criterion Hazardous-Behaviour Scale. The 16 items on the criterion scale were factor analyzed to enhance understanding of the underlying psychological constructs of everyday unsafe behaviours. Preparatory analyses indicated that the 16 items manifested significant gender mean differences (Hotelling's T2 procedure: F(16, 598) = 4.34, p < .001), and that the separate-gender covariance matrices were significantly different (Bartlett-Box test of homogeneity of dispersion: F(136, 171,204.5) = 130, p < .05). These results indicated that caution should be applied before pooling the data at the covariance matrix level, and that separate-gender factor analyses should follow. In the separate-gender analysis, the standard eigen-decomposition of R, along with the application of the Scree test, maximum likelihood factor analysis, and transformation of obtained factors, suggested a two-factor solution for both groups. The separate-gender factor pattern matrices were judged similar enough to allow pooling of the data. These same procedures were applied to a pooled sample with equal numbers of males and females. The Scree test provided the best indication of which factor solution to choose, and again a two-factor solution was used 14 (Unweighted Least Squares—ULS—solutions followed by oblique transformation using direct oblimin). Subscale scores for these factors were obtained by simple summation of the items that loaded on similar factors in each of the separate-gender analyses conducted earlier. Development of the Safety-Orientation predictor scale. Each of the 250 items from the BIODATA-250 inventory was correlated with the Criterion Hazardous-Behaviour Scale total scores. To control for capitalization on chance, only the item-criterion correlations that were significant at or beyond the .001 level were considered further for scale development. Each of the selected items was also required to demonstrate a conceptually meaningful link with the criterion scale. Scale scores were obtained by simple summation of the selected items. High scores indicated a higher tendency towards safety orientation. Psychometric properties of the Safety-Orientation predictor scale. Descriptive statistics, internal consistency estimates using Cronbach's alpha, and test-retest reliability estimates were computed by gender for the Safety-Orientation predictor scale. Exploratory factor analysis of the Safety-Orientation predictor scale. The 51 items on the empirically derived Safety-Orientation predictor scale were factor analyzed to explore the individual-differences predictors of unsafe behaviours and the underlying constructs of this new predictor scale. Preparatory analyses indicated that the 51 items manifested significant gender mean differences (Hotelling's T2 procedure: F(5l, 581) = 3.12,/? < .001), and that the separate-gender covariance matrices were significantly different (Bartlett-Box test of homogeneity of dispersion: F(l,326, 153,090) = 1.12,p < .005). These results indicated that caution should be applied before pooling the data at the covariance matrix level, and that separate-gender factor analyses should follow. In the separate-gender analysis, the standard eigen-decomposition of R, along with the application of the Scree test, maximum likelihood factor analysis, and transformation of obtained 15 factors, suggested a six-factor solution for both groups. The separate-gender factor pattern matrices were judged similar enough to allow pooling of the data. These same procedures were applied to a pooled sample with equal numbers of males and females. The Scree test provided the best indication of which factor solution to choose, and again a six-factor solution was used (ULS solutions followed by oblique transformation using direct oblimin). Factor-scale scores for these factors were obtained by simple item summation (the terms subscales and factor scales are equivalent and are used interchangeably). Evidence of construct validity for the Safety-Orientation predictor scale. To further understand the underlying constructs of the Safety-Orientation predictor and factor scales, these scales were correlated with the following BIODATA-250 scales: the Big Five (Emotional Stability, Extraversion, Openness, Agreeableness, and Conscientiousness), Risk-taking, Impulse Control, and Internal Locus of Control. Evidence of criterion-related validity for the Safety-Orientation predictor scale. To establish the usefulness of the Safety-Orientation predictor scale in predicting unsafe behaviours, the predictor scale and subscales were correlated with the criterion scale and subscales. These criterion correlations were then compared with those involving the other BIODATA-250 scales. Double cross-validation of the Safety-Orientation predictor scale. The criterion-related validity of the predictor scale was inflated because of capitalization on chance. To evaluate what the criterion-related validity estimate of the predictor scale would be in a different sample, a double cross-validation procedure (see, e.g. Hakstian et al., 2002) was used to obtain an average estimate of the cross-validity. The sample of 633 participants was randomly divided into two equal subsamples, each with an equal proportion of males and females. Within each of the two development subsamples, the same scale development procedures were followed as above, but all items (regardless of their content) yielding a correlation significant at the .005 level were 16 included on each scale. This resulted in two similar but different empirical predictor scales, Scale 1 and Scale 2. Scale-criterion correlations were then computed for each development subsample, i.e. Scale 1 in Subsample 1 and Scale 2 in Subsample 2. The scales were then calculated in each cross-sample to obtain the cross-sample scale-criterion correlations for each scale, i.e. Scale 1 in Subsample 2 and Scale 2 in Subsample 1. The reduction in the scale-criterion correlations, and the cross-validity estimates were averaged for the two scales. Results and Discussion Criterion Hazardous-Behaviour Scale. As in previous research (Frone, 1998), males ( M = 47.24, SD = 11.95) were found to engage in significantly more unsafe behaviours than females ( M = 41.61, SD = 11.32; t (613) = 4.93, p < .001; A = .49), justifying the use of zero-centred scores in the analyses. The reasonably high internal consistency estimate of .77 (weighted by gender) suggested that items were tapping the same constructs. Exploratory factor analysis of the Criterion Hazardous-Behaviour Scale. The common-factor analysis of the Criterion Hazardous-Behaviour Scale items produced a clear oblique primary-factor pattern with two factors; this appears in Table 1. The two common-factors were named according to their content: (1) Forgetfulness, and (2) Injury-involvement. The subscales and the items contributing to these subscales are listed in Table 2, along with the internal consistency estimates. The construct of forgetfulness is conceptually related to conscientiousness. This factor arose from the analysis because 7 of the 16 items on the criterion scale included memory-related content. Unfortunately, there were too few of the other types of items—such as self-control and focus—to yield other conceptually meaningful factors. The other factor was named Injury-involvement, indicating that individuals do tend to be differentially involved in injury-related 17 events. The correlation (r = .36) between the two factors confirms that forgetfulness and injury-related behaviours are positively related. Psychometric properties of the empirically derived Safety-Orientation predictor scale. The final Safety-Orientation predictor scale included 51 BIODATA-250 items; these are included in Table 3. Females (M=128.26, SD = 14.12) were found to have significantly higher safety-orientation tendencies than males (M= 123.45, SD = 16.67; t (631) = -3.32,p < .001; A = .33). The high internal consistency estimate of .87 (weighted by gender) suggested that items were drawing on related constructs. The test-retest reliability estimate over a two-week interval was .93 (weighted by gender), demonstrating the stability of the scale. Both reliability estimates indicate that the predictor scale is suitable for use in an industrial setting (Guion, 1998). Exploratory factor analysis of the Safety-Orientation predictor scale. The common-factor analysis of the 51 items on the Safety-Orientation predictor scale produced a clear oblique primary-factor pattern with six factors. The obliquely transformed primary-factor matrix appears in Table 4, and the intercorrelations among the six common-factors appear in Table 5. The degree of association among the six factors ranged from .02 to .34, with a mean absolute correlation of .16. The subscales and the items that loaded most highly on each of the above factors are listed in Table 6, along with the internal consistency estimates. The six common factors were named according to their salient item content: 1. Risk-taking: tendency towards sensation-seeking and dangerous activities; 2. Absentmindedness: tendency towards not focusing and being careless; 3. Assertiveness: tendency towards being outspoken and dominant; 4. Gregariousness: tendency towards being extraverted and impulsive; 18 5. Planfulness/Orderliness: tendency towards planning, thinking things through and being organized; and 6. Counterproductivity: tendency towards not respecting rules and lacking integrity. The presence of the Counterproductive factor suggests that unsafe behaviours are indeed related to counterproductive behaviours. The construct validity and usefulness of the factor scales is discussed in the following sections. Evidence of construct validity for the Safety-Orientation predictor scale. The expected correlations between the personality constructs and safety-oriented tendencies were found. The correlations between the BIODATA-250 scales—Big Five, Impulse Control, Risk-taking, and Internal Locus of Control—and the Safety-Orientation predictor scale and subscales appear in Table 7. Normative data and reliability estimates for the other BIODATA-250 scales appear in Appendix B. In general, individuals who were more oriented towards safety tended to be more conscientious, better able to control their impulses, avoided taking risks, and were less extraverted and open to change. Since the above scales were all based on the BIODATA-250, the correlations presented are appreciably inflated (e.g. r's > .50) by common method variance. Although the scale names are similar and the scales include some of the same items, they are different and were developed through different means (details are included in Appendix B). Therefore all the correlations should be interpreted with caution. Superior construct validity evidence could be obtained by having students complete both the BIODATA-250 along with a more established personality inventory, such as the CPI or the Revised NEO Personality Inventory (NEO PI-R; Costa & McCrae, 1992). One unexpected result was a small significant positive correlation between the Risk-taking predictor-subscale and the Emotional Stability scale. As the direction expected was 19 negative, one explanation could be that the relationship is truly curvilinear, where individuals who are either very low or very high in emotional stability both engage in risk-taking behaviours (see Ulleberg, 2002). Although the relationship is not curvilinear in this restricted sample, such a relationship in a broader population may explain the change in direction of the correlation, and would be stronger than the linear correlational results obtained here and in past research. Assertiveness has not been previously studied in relation to unsafe behaviours. Its negative relationship with safety-oriented tendencies is likely acting through its association with high impulsivity or low impulse-control, and high risk-taking. Internal locus of control does not correlate highly with the six safety-orientation factor scales because the criterion scale did not include item content on perceptions of control. Evidence of criterion-related validity for the Safety-Orientation predictor scale. The criterion-related validity estimates of the Safety-Orientation predictor scale and subscales with the Criterion Hazardous-Behaviour Scale and subscales appear in Table 8. These validity estimates should be interpreted with caution as they are inflated because of capitalization on chance (cross-validity estimates are given and discussed below). The criterion-related validity of the overall Safety-Orientation predictor scale with the overall Criterion Hazardous-Behaviour Scale was r = - .48 (p < .001). The total predictor-scale score was the best predictor of both the total score on the criterion scale, as well as the Injury-involvement criterion-subscale. The Absentmindedness predictor-subscale outperformed the total predictor-scale score in predicting the Forgetfulness criterion-subscale. The most useful predictor subscales correlated with unsafe behaviours were Risk-taking, Planfulness/Orderliness and Counterproductivity; Assertiveness and Gregariousness were also significantly correlated with the criteria. These results reflect the salience of the impulsivity and conscientiousness traits in relation to unsafe behaviours. 20 Similar results were found with the other scales derived from the BIODATA-250: Extraversion, Conscientiousness, Risk-taking, and Impulse Control. The Safety-Orientation predictor scale and subscales were more highly correlated with the criteria than any of the other BIODATA-250 scales. However, this was expected given that the scale was developed using item-criterion correlations with the Criterion Hazardous-Behaviour Scale. Double cross-validation of the Safety-Orientation predictor scale. The double cross-validation procedure provided an estimate of what the criterion-related validity would be if the scale were applied in a different sample. The same-sample scale-criterion correlations were .51 and .54 for Scales 1 and 2, respectively. These estimates are higher than the .48 obtained for the Safety-Orientation predictor scale in the total sample, because of capitalization on chance and the fact that no items were excluded based on content. When Scale 1 and Scale 2 were computed in their cross-samples, the cross-sample criterion-correlations decreased to .47 (Scale 1 in Subsample 2) and .42 (Scale 2 in Subsample 1). The cross-validities are lower bound values of what we would expect the criterion-related validity to be in a different sample. The average cross-validity reduction was .08, and the average estimate of cross-validity was .44. These estimates suggest that if the Safety-Orientation predictor scale were applied to a different sample (but drawn from the same population), we would expect the criterion-related validity estimate to equal or exceed .44. Study 2 The aim of this second study was to evaluate the relationship between everyday unsafe behaviours, and attention and memory-related cognitive ability variables. Furthering knowledge on the relationship between cognitive-domain variables and unsafe behaviours contributes to accident research and presents new approaches for minimizing accident-related events. A 21 secondary objective was to explore the relationship between cognitive abilities and personality traits to further understand the role of individual-difference variables in unsafe behaviours. Method Participants Sample 1: This sample consisted of 408 UBC undergraduate students from Study 1, 88 males and 320 females. The characteristics of this sample were similar to those of the larger sample (n = 633) used in Study 1. See Study 1 for further details. Sample 2: Data were collected on 140 UBC undergraduate students from February to April 2005. Two cases were excluded from the analyses because of too many disruptions during the study, and another two were removed because the student never returned the criterion scale. The sample of 136 included 28 males and 108 females, 50% of which spoke English as a second language. The self-defined ethnic profile included 46% East Asians, 39% Canadians / Western Europeans / Americans, 5% South Asians, and 10% from other ethnic groups. Operationalization of Unsafe Behaviours Criterion Unsafe behaviours were measured using the Criterion Hazardous-Behaviour Scale, as well as the Forgetfulness and Injury-involvement subscales derived in Study 1 (see Study 1 for details). Scale scores were the simple sum of all relevant items. High scores indicated more frequent engagement in unsafe behaviours. Assessment of Safety-oriented tendencies by means of a personality inventory Safety-orientated tendencies were measured using the Study-1 Safety-Orientation predictor scale and six subscales: Risk-taking, Absentmindedness, Assertiveness, Gregariousness, Planfulness/Orderliness, and Counterproductivity. (The whole BIODATA-250 inventory was given; see Study 1 for more details). Scale scores were the simple sum of all relevant items, with high scores indicating a higher display of the given personality trait. 22 Relating Unsafe Behaviours to Accident Involvement Differential The Accident-Related Events Scale was developed by the author (items appear in Appendix C). Study participants responded to seven questions on the frequency of major and minor near-accident and accident-related events in the past two years. Responses were made on a 6-point scale ranging from "Absolutely never did this or had it happen" to "Did this or had it happen frequently (more than 4 times)". Scale scores were the simple sum of all 7 items; high scores indicated a higher frequency in accident involvement. Relating Unsafe Behaviours to Cognitive Failures The Cognitive Failures Questionnaire (CFQ; Broadbent et al., 1982) was included to establish a link between unsafe behaviours and past research that focused on cognitive failures. The CFQ consists of 25 questions on the frequency of cognitive mishaps that occur every day. Responses were made on a 5-point scale ranging from "never" to "very often". Additional details about the CFQ appear in Appendix D. CFQ scores were the simple sum of all 25 items; high scores indicated a higher frequency of cognitive failures. Cognitive Ability Measures used in Sample 1 The following cognitive ability measures were selected on the basis of their internal consistency and test-retest reliability estimates, content validity, and accessibility for use in research. The exception was Part 4 of the Work Skills Assessment, which was a new test undergoing development. Work Skills Assessment (WSA), Part 4 (Hakstian, 2004). This test measures perceptual speed and accuracy, direct visual shifting, as well as focused and sustained attention. Participants matched 54 words or letter-digit sequences to one of five options following a key printed at the top of the page. Additional details about this measure appear in Appendix D. The 23 number of correctly completed trials during the prescribed time was recorded; higher values indicated better accuracy and faster performance. Comprehensive Ability Battery - Perceptual Speed and Accuracy (CAB-P; Hakstian & Cattell, 1975). The CAB-P measures perceptual speed and accuracy, as well as focused and sustained attention. Participants discriminated between 72 pairs of 8-letter and 8-digit sequences, noting whether they were the same or different. Additional details about this measure appear in Appendix D. The number correctly completed during the prescribed time was recorded; higher values indicated better accuracy and faster performance. Comprehensive Ability Battery - Memory Span (CAB-Ms; Hakstian & Cattell, 1975). The CAB-Ms measures working memory and attentional capacity (Lezak, 1995). An audiotape presented ten series of digits, increasing from five to ten digits in length. Participants recalled the series by writing down their responses after a tone. Additional details about this measure appear in Appendix D. The number of digits correctly recalled was recorded; higher values indicated better attention and memory. Comprehensive Ability Battery - Associative Memory (CAB-Ma; Hakstian & Cattell, 1975). The CAB-Ma measures associative memory and focused attention. Participants were given a list of 14 symbol-number pairs to memorize, and then matched the symbol with the correct number from a list of five options. Both parts were completed under timed conditions. Additional details about this measure appear in Appendix D. The number of correct responses was recorded; higher values indicated better attention and memory. Cognitive Ability Measures used in Sample 2 The cognitive ability measures of attention were selected on the basis of their test-retest reliabilities, their established ability to measure various types of attention, and their accessibility for research use. Given that cognitive tasks generally draw upon several cognitive abilities or 24 types of attention, most of the tests included assess more than one type of attention (see Lezak, 1995; Spreen & Strauss, 1998). Colour-WordStroop Test (Stroop, 1935). The Stroop test measures cognitive flexibility, inhibition of dominant response, and selective/focused attention (Lezak, 1995). The Graf, Uttl, and Tuoko (1995) version was used. Participants read a list of colour-words printed in black ink (Part 1), a list of coloured X X X s (Part 2), and a list of colour-words in incongruent colours (Part 3). Additional details about this measure appear in Appendix D. Reading time, and the number of corrected and uncorrected errors made on each list were recorded. Only the interference score (Part-2 time subtracted from Part-3 time, Spreen & Strauss, 1998), and Part-3 uncorrected errors were included as variables. A high score on both indicated lower attention, more errors, and greater difficulty in inhibiting a dominant response. Trail Making Tests (TMT; Reitan, 1992). The TMT measures performance speed, alternating and selective attention, cognitive flexibility, and visual search (Lezak, 1995). In Part A of the test, participants drew a line joining consecutively numbered circles (1 to 25) randomly organized on a page. In Part B, participants drew a line alternating between randomly ordered numbered and lettered circles (i.e. 1-A-2-B... 13-L). Additional details about this measure appear in Appendix D. The time taken and the number of errors for each part were recorded. Only the difference score (Part-A time subtracted from Part-B time, Spreen & Strauss, 1998) and Part-B errors were included as variables. A high score on both indicated poor attention and greater difficulty in cognitive flexibility. Cancel H Test (CHT; Graf, 2000). A modified version of the standardized letter cancellation test by Diller, Ben-Yishay and Gerstman (1974) was used. Letter cancellation tests measure perceptual speed and accuracy, sustained and selective attention, as well as shifts between activation and inhibition of quick responses (Lezak, 1995; Ponsford, 2000). 25 Participants scanned 12 rows of capital letters and crossed out all the H's (randomly interspersed 10 times in each row). Additional details about this measure appear in Appendix D. Completion time, and the number of errors and omissions were recorded. Only completion time and number of omissions were included as variables; a low completion time indicated faster performance, and more numerous omissions indicated poor attention. Digit Symbol Test (DST; Wechsler, 1981). The DST measures sustained and selective attention, direct visual shifting, response speed, and psychomotor performance (Lezak, 1995). Following a key printed at the top of the page, participants drew symbols into empty boxes below numbers. Additional details about this measure appear in Appendix D. The numbers of correct and incorrect substitutions made during a prescribed time were recorded. Only the number of correct substitutions was included as a variable; a higher score indicated faster performance and better visual shifting. Digit Span Backward Test (DSB; Wechsler, 1981). The DSB test measures working memory, attentional capacity and focused attention (Lezak, 1995; Spreen & Strauss, 1998). The experimenter read up to 14 series of digits that increased in length from two to eight digits. The participant was asked to verbally recall each series in reverse order. Additional details about this measure appear in Appendix D. The number of series correctly recalled was recorded; a higher score indicated better attention and working memory. Test of Variables of Attention (TOVA; Greenberg, 1999). The T O V A is a 22-minute computerized test that measures selective and sustained attention, as well as impulsivity. Using a microswitch, participants responded to a target stimulus while ignoring a non-target stimulus. Additional details about this measure appear in Appendix D. The percentage of omissions (a measure of inattention), commissions (a measure of impulsivity or disinhibition), reaction time, and reaction time variability were recorded (Leark, Duypuy, Greenberg, Corman, & Kindschi, 26 1999). Only the percentage of omissions and commissions from test-halves one (infrequent condition) and two (frequent condition) were included as variables. Higher scores on the omission percentages indicated attentional errors, while the commission percentages indicated cognitive errors made due to impulsive behaviour. Record date-of-completion task (ProM-Date). A modification of the Dobbs and Rule (1987) behavioural remember-the-date prospective-memory task was used. Participants were asked to write down the date they completed the take-home questionnaires (see Procedures, below), and were assigned a score on a 3-point scale based on whether they remembered the task with or without the aid of various retrieval cues. Additional details about this measure appear in Appendix D. A higher PRoM-Date score indicated better prospective memory. Personal information form. This form was given to participants to control for factors that could potentially influence the interpretation of results. Participants were asked whether they were taking any medication, had suffered from an injury, or were suffering from an illness or psychological disorder that could have affected their performance during the session. Procedures Sample 1 procedures. A l l participants completed a short demographics form at the start of the session. The first 227 participants completed one 1.5-hour session involving the Criterion Hazardous-Behaviour Scale, WSA-Part 4, CAB-P, and BIODATA-250 inventory. The other 182 students participated in two 1-hour sessions separated by a two-week interval. Session 1 included either: (a) the CAB-Ms, CAB-Ma and BIODATA-250 (n = 96); or (b) the CFQ and BIODATA-250 (n = 85). Session 2 always consisted of the Criterion Hazardous-Behaviour Scale followed by the BIODATA-250. Additional details are included as part of Study 1. Sample 2 procedures. Participants completed the same demographics form at the start of the 1-hour session, and then completed the Stroop test, TMT, CHT, DST and DSB in random 27 order, followed by the TOVA and personal information form. Students were then given the Criterion Hazardous-Behaviour Scale, Accident-Related Events Scale, and BIODATA-250 to complete at home, and were verbally reminded to write down the date they completed each questionnaire. Those who failed to write down the dates were cued for recall when they returned the questionnaires. Statistical Analyses Sample 1 data preparation. See Study 1 for details regarding the Criterion Hazardous-Behaviours Scale and BIODATA-250. Item-level data that were missing (for various procedural and non-systematic reasons) on cognitive ability measures were not replaced. Univariate outlier detection revealed one outlier beyond ±3 SD that was replaced with the nearest non-outlying value. When appropriate, the analyses used variable and scale-level data in zero-centred form to prevent distortion due to gender effects. Sample 2 data preparation. See Study 1 for details regarding the Criterion Hazardous-Behaviours Scale and BIODATA-250. The same procedures used for the Criterion Hazardous-Behaviour Scale in Study 1, were also used for the Accident-Related Events Scale for this sample. Item-level data that were missing on cognitive-ability measures were not replaced. Eighteen cases were excluded to prevent confounding based on responses in the personal information questionnaire (n = 14), or for other experimental reasons (n = 4). Univariate outlier detection on the 13 cognitive ability variables revealed 18 outliers beyond ±3 SD that were replaced with the nearest non-outlying value. A multivariate outlier analysis revealed three further outliers that were excluded (Mahalanobis distance statistic: % (13) = 22.36, and p < .05). Analyses for Sample 2 were based on a sample of 119 (23 males and 96 females), 59 of which had complete data (12 males and 47 females). When appropriate, the analyses that follow used variable and scale-level data in zero-centred form to prevent distortion due to gender effects. 28 Establishing a relationship between unsafe behaviours and differential accident involvement. The Criterion Hazardous-Behaviour Scale and subscales were correlated with the Accident-Related Events Scale to evaluate the relationship between unsafe behaviours and accident-involvement likelihood. Relationship between Reason's model of human error and unsafe behaviours. The total scores on the CFQ and the Criterion Hazardous-Behaviours Scale and subscales were correlated to establish a relationship between cognitive failures and unsafe behaviours. Exploratory factor analysis of the cognitive ability variables from Sample 2. The 13 cognitive ability variables from Study 2 were factor analyzed in anticipation that the types of attention common to the measures would be revealed as factors. Preparatory analyses prior to the factor analysis indicated that the 13 variables yielded a non-significant F-ratio (Hotelling's T2) for gender differences. There were too few males to compute the Bartlett-Box test of homogeneity of dispersion matrices, but since the Hotelling's T2 was non-significant, it was judged safe to pool the data and proceed using pairwise deletion to maximize the sample size. The standard eigen-decomposition of R, along with the application of the Scree test, maximum likelihood factor analysis, and transformation of obtained factors, suggested that a two-factor solution should be used (ULS solutions followed by oblique transformation using direct oblimin). Subscale scores for these factors were obtained by simple summation of the variables in z-score form. Cognitive ability correlates of unsafe behaviours and differential accident involvement. The cognitive-ability factor scales (Sample 2) and variables (Sample 1) were correlated with the Criterion Hazardous-Behaviour Scale and subscales (Samples 1 and 2), and the Accident-Related Events Scale (Sample 2) to explore the relationship between cognitive abilities and the given criteria. 29 The relationship between individual-differences precursors of unsafe behaviours. The Safety-Orientation predictor scale and subscales were correlated with the cognitive ability subscales to explore the relationship between these individual-differences precursors of unsafe behaviours. Results and Discussion Establishing a relationship between unsafe behaviours and differential accident involvement. The Accident-Related Events Scale was positively correlated with the Criterion Hazardous-Behaviour Scale (r = .51,p < .001), the Forgetfulness criterion-subscale (r = .37, p < .001), and the Injury-involvement criterion-subscale (r = .43, p < .001). Each of the correlations with the total criterion scale and Injury-involvement criterion-subscale are inflated because they share similar accident-related item content and the same response format. The correlation with the Forgetfulness criterion-subscale is less inflated (method variance is still operating), and thus more meaningful. These correlations are not a test of the model described in the introduction, but do suggest that a positive relationship exists between engaging in unsafe behaviours and a person's level of accident-involvement. Relationship between Reason's model of human error and unsafe behaviours. The CFQ was positively correlated with the Criterion Hazardous-Behaviours Scale (r = .39, p < .001) and the Forgetfulness criterion-subscale (r = .38,p < .001), but not with the Injury-involvement criterion-subscale. These correlations indicate that, even when accounting for method variance (both the CFQ and criterion scale used self-report formats and focused on everyday behaviours), there is indeed a relationship between cognitive failures and unsafe behaviours, particularly forgetfulness. According to Reason's model of human error (Reason, 1990), lapses in memory are a fundamental type of human error and have a strong potential to be associated with accident-related events. This is supported by the Forgetfulness criterion-subscale correlation with the 30 Accident-Related Events Scale; the Forgetfulness subscale could therefore be considered a measure of cognitive errors related to memory failure. Exploratory factor analysis of the cognitive ability variables from Sample 2. The common-factor analysis of the 13 cognitive-ability variables produced a clear oblique primary-factor pattern with two factors; this appears in Table 9. The factors were named according to their content: (1) Cognitive Errors, and (2) Performance Speed. The subscales and the items that loaded most highly on each of the above factors are listed in Table 10, along with the internal consistency estimates. Scale scores were computed by simple summation of the salient items. As the common factors above did not reflect various types of attention, the hypotheses regarding the relationship between unsafe behaviours and focused/selective, continuous, and alternating attention could not be tested. The most likely reasons for this outcome are that the aspects of attention the cognitive ability measures were intended to draw upon were not salient when placed together, or that these aspects were not accessed by the study participants. However, the factors that were obtained are meaningful and relevant to unsafe behaviours, and therefore warrant further exploration. The Cognitive Errors subscale is composed of variables tabulating different kinds of errors related to cognitive flexibility, omissions, commissions, and difficulty in inhibiting dominant responses. This subscale suggests a more global construct of cognitive errors that should be positively correlated with the CFQ, and unsafe behaviours. This subscale would also be a substantive improvement over the CFQ because it is based on objective performance measures of cognitive ability. The Performance Speed subscale includes two items related to speed of performance and is therefore conceptually clear. This subscale should also be positively correlated with unsafe behaviours, on the assumption that the monitoring of internal 31 processes is reduced when attentional resources are centred on one task, making other cognitive processes vulnerable to failures. Cognitive-ability subscale correlates of unsafe behaviours and differential accident involvement. None of the cognitive ability subscales was meaningfully correlated with the Criterion Hazardous-Behaviour Scale and subscales, or with the Accident-Related Events Scale. These correlations appear in Table 11. A significant negative correlation was noted between the Cognitive Errors subscale and Injury-involvement criterion-subscale (r = —.24,p < .05). This relationship is counterintuitive, though it could be explained by gender differences (this relationship was only found among females) in the responses to the three injury-related items. However, the correlation is not likely to be a true representation of the relationship, because the Cognitive Errors subscale and the Accident-Related Events Scale—a longer and better measure of accident involvement (a = .79, weighted by gender)—were uncorrected. According to past research on cognitive failures and accident involvement (Wallace, Kass, & Stanny, 2002; Wallace & Vodanovich, 2003a), there should be a relationship between the Cognitive Error subscale and the unsafe-behaviour criteria. The failure to find such a relationship can be attributed the gap between the broad everyday behaviours measured by the Criterion Hazardous-Behaviour Scale, and the narrowly focused cognitive ability variables. Another contributing factor is the range restriction operating on all the variables, reduced variability decreases the likelihood of obtaining a significant correlation. The absence of a correlation between the unsafe-behaviour criteria and the Performance Speed subscale could be attributed to the role of risk-taking. Partial correlations—holding the Risk-taking predictor-subscale constant—resulted in a positive correlation between the Performance Speed subscale and both the total criterion (r = .23,p < .05) and the Forgetfulness criterion-subscale (r = .32, p < .01). This suggests that performance speed was correlated with 32 the part of unsafe behaviours and forgetfulness that are not correlated with risk-taking tendencies. Cognitive-ability variable correlates of unsafe behaviours and differential accident involvement. The Criterion Hazardous-Behaviours Scale and the Accident-Related Events Scale were uncorrelated with the cognitive ability variables from Sample 1 and the ProM-Date task from Sample 2. These results appear in the lower portion of Table 11. No relationships were found because the cognitive ability variables were too narrow in scope to correlate independently with the broader criteria. A study design that combines similar measures into a more global variable (as in the Sample 2 procedures) would have a better chance of bridging the criterion and cognitive ability measures. The lack of significant correlations with the cognitive ability variables and subscales may also stem from issues related to the ecological validity of the measures. It is not known to what extent the measures of cognitive ability relate to actual unsafe behaviours, and little research has explored the extent to which laboratory conditions can be generalized to a work or home environment (Sbordone, 2001; Sbordone & Guilmette, 1999). If the measures used in this study do not generalize to actual behaviours, then it is also difficult to build evidence of a relationship between the cognitive ability subscales and measures of unsafe behaviours. The relationship among individual-difference precursors of unsafe behaviours. The correlations between the Safety-Orientation predictor scale and subscales, and the cognitive ability subscales appear in Table 12. The Cognitive Errors subscale was negatively correlated with the Safety-Orientation predictor scale (r = -.30,p < .005), and positively correlated with the Absentmindedness predictor-subscale (r = .38,p < .001), therefore individuals who make more cognitive errors are less likely to be safety oriented and more likely to be absentminded. Non-significant correlations also suggest that individuals who make more cognitive errors tend to be 33 greater risk-takers, more counterproductive, and less planning-oriented. These correlations are in line with past research (Wallace & Vodanovich, 2003b), and support further exploration of the relationship between risk-taking and cognitive errors. Furthermore, these relationships are not inflated by method variance, and thus are a better representation of how the constructs relate to one another than the relationships between the predictor scale and subscales, and the criteria. The Performance Speed subscale was positively correlated with the Assertiveness predictor-subscale (r = A6,p < .05). The correlations with the Performance Speed subscale describe individuals who tend to work more quickly as more assertive and counterproductive. As performance speed was not correlated with risk-taking, the part of assertiveness more relevant to performance speed may be impulsivity. The significant correlations obtained for both cognitive ability subscales indicate that the traits and tendencies assessed by the predictor and factor scales more closely match the range of tasks assessed by the cognitive ability measures than the unsafe behaviour criteria. General Discussion and Conclusions Contributions to Accident Research The Safety-Orientation predictor scale is most likely the first scale developed from a personality inventory that is specifically designed to assess safety-oriented tendencies. This scale draws on most of the constructs previously related to unsafe behaviours and accident involvement, and has six subscales—Risk-taking, Absentmindedness, Assertiveness, Gregariousness, Planfulness/Orderliness, and Counterproductivity. Assertiveness has not been previously studied in accident research and should be explored further. The predictor scale has suitable psychometric properties for use in an industrial setting. Future research should use a 34 workplace sample to compare this scale with safety behaviours and accident involvement at work, while controlling for exposure to risk. The common-factor analysis of the cognitive ability variables from Study 2 did not reveal factors related to types of attention; therefore, the hypotheses related to attention remain untested. The common-factor analysis did however produce two meaningful subscales: Cognitive Errors (or errors in attention in a very broad sense) and Performance Speed. Neither of these subscales has been previously studied in relation to unsafe behaviours. The subscales were not correlated with the criteria, but they were correlated with the trait-based Safety-Orientation predictor and factor scales. These results suggest that further research should explore the interrelationships among the individual-differences variables related to unsafe behaviours. These results also support the use of cognitive ability tests in accident research to study unsafe behaviours. Individual-Differences Variables and the Safety System In the safety-system approach, individual-differences variables are understood to be related to unsafe behaviours, and these relationships are moderated by organizational variables. The present results suggest that there are more complex relationships between personality traits, cognitive abilities, and unsafe behaviours than previously understood that could be investigated by exploring the partial relationships between variables. New findings, such as the one found between performance speed and unsafe behaviours while controlling for risk-taking, could account for past inconsistencies in accident research. Results with the Safety-Orientation predictor scale are encouraging but will require additional testing with industrial samples to obtain more realistic cross-validity estimates and utility analyses. Following this research, the scale could be used by organizations to reduce accidents through improved hiring decisions and job placement, helping to ensure a better fit 35 between the individual and position within the company. Continued research with attention and memory variables, and other cognitive abilities—such as executive functioning—could eventually contribute to accident prevention through the above interventions, and by improving job design and safety training programmes. Limitations and Future Research There were three major limitations across the two studies in this thesis. The first relates to the operationalization of unsafe behaviours. The Criterion Hazardous-Behaviour Scale was effective at tapping relevant everyday unsafe behaviours, but the range of items was restricted, limited to absentmindedness, carelessness, and injury involvement. A longer scale—including items such as following rules, taking short-cuts and risks, and past experience with safety-related situations—would shed more light on the underlying psychological constructs of unsafe behaviours, and would more closely relate these behaviours to those in the workplace. The unsafe behaviours assessed could also be more specific, resulting in a smaller gap between them and the cognitive abilities, and thus a larger overlap in the variance with the cognitive-ability variables. Risk exposure was not controlled for in this study and could confound the results, particularly with respect to accident-related events. Although the criterion scale focuses on everyday unsafe behaviours, there are some items—such as those related to driving—that individuals are not equally exposed to. Future research with everyday unsafe behaviours should control for exposure to risk, as well as ensure that all the questions are both relevant to and similarly interpreted by all the participants. An undergraduate student sample was suitable for this stage of the research, but does limit the extent to which the results can be generalized to other populations. Since the majority of university students are unlikely to work in a high-risk environment, future research should be 36 based on an industrial sample from relevant occupations, or from students in a trade school. The age of the sample was not a disadvantage, because it remains important to understand the characteristics of young workers entering the workplace. However, the gender composition was dominated by females; future research might focus on males, which are usually considered a higher-risk group (Frone, 1998), or a more gender-balanced group. 37 References Arthur, W. J. , Barrett, G. V., & Alexander, R. A. (1991). Prediction of vehicular accident involvement: A meta-analysis. Human Performance, 4, 89-105. Arthur, W. J., & Doverspike, D. (2001). Predicting motor vehicle crash involvement from a personality measure and a driving knowledge test. Journal of Prevention and Intervention in the Community, 22, 35-42. Arthur, W. J. , & Graziano, W. G. (1996). The five-factor model, conscientiousness, and driving accident involvement. Journal of Personality, 64, 593-618. Barrick, M. B., & Mount, M. K. (1991). The Big Five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44, 1-26. Bradley, G. (1997). Safe people and safe places: Psychological contributions to industrial accident prevention. Journal of Applied Social Behaviour, 3, 1-14. Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21, 1-16. Cellar, D. F., Nelson, Z. C , & Yorke, C. M. (2000). The five-factor model and driving behavior: Personality and involvement in vehicular accidents. Psychological Reports, 86, 454-456. Cellar, D. F., Nelson, Z. C , Yorke, C. M., & Bauer, C. (2001). The five-factor model and safety in the workplace: Investigating the relationships between personality and accident involvement. Journal of Prevention and Intervention in the Community, 22, 43-52. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.).'Mahwah, New Jersey: Lawrence Erlbaum Associates. Costa, P. T. J., & McCrae, R. R. (1992). The revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Odessa, FL: 38 Psychological Assessment Resources. Diller, L., Ben-Yishay, Y., & Gerstman, L. J. (1974). Studies in cognition and rehabilitation in hemiplegia (Rehabilitation Monograph No. 50). New York: New York University Medical Center Institute of Rehabilitation Medicine. Dobbs, A. R., & Rule, B. G. (1987). Prospective memory and self-reports of memory abilities in older adults. Canadian Journal of Psychology, 41, 209-222. Dornheim, M. A. (2000). Crew distractions emerge as new safety focus; to reduce accidents, researchers are targeting human vulnerability to preoccupation and distraction. Aviation Week & Space Technology, 153, 58-60. Edkins, G. D., & Pollock, C. M. (1997). The influence of sustained attention on railway accidents. Accident Analysis & Prevention, 29, 533-539. Forcier, B. H., Walters, A. E., Brasher, E. E., & Jones, J. W. (2001). Creating a safer working environment through psychological assessment: A review of a measure of safety consciousness. Journal of Prevention and Intervention in the Community, 22, 53-65. Frone, M. R. (1998). Predictors of work injuries among employed adolescents. Journal of Applied Psychology, 83, 565-576. Gough, H. G., & Bradley, P. (1996). California Psychological Inventory manual (3rd ed.). Palo Alto, CA: Consulting Psychologists Press. Graf, P. (2000). Cancel H Test (CHT). Unpublished test, University of British Columbia, Vancouver. Graf, P., & Uttl, B. (2001). Prospective memory: A new focus for research. Consciousness and Cognition, 10, 437-450. Graf, P., Uttl, B., & Tuokko, H. (1995). Color- and picture- word Stroop tests: Performance changes in old age. Journal of Clinical and Experimental Neuropsychology, 17, 390-415. Greenberg, L. M. (1999). Test of Variables of Attention (TOVA). Los Alamitos, CA : Universal Attention Disorders. Greenwood, M, & Woods, H. M. (1919) A report on the incidence of industrial accidents upon individuals with special reference to multiple accidents. Reproduced in W. Haddon, E. A. Suchman, & D. Klein (Eds.), Accident research (1964). New York: Harper & Row. Guion, R. M. (1998). Assessment, measurement, and prediction for personnel decisions. Mahwah, New Jersey: Lawrence Erlbaum Associates. Hakstian, A. R. (2002). Biographical Information about Occupationally Descriptive Attitudes, Traits, and Abilities (BIODATA-250). Unpublished inventory, University of British Columbia, Vancouver. Hakstian, A. R. (2004). Work Skills Assessment (WSA), Part 4. Unpublished test, University of British Columbia, Vancouver. Hakstian, A. R., & Cattell, R. B. (1975). The Comprehensive Ability Battery (CAB). Champaign, IL: Institute for Personality and Ability Testing. Hakstian, A. R., Farrell, S., & Tweed, R. G. (2002). The assessment of counterproductive tendencies by means of the California Psychological Inventory. International Journal of Selection and Assessment, 10, 58-86. Hakstian, A. R., & Woolley, R. M. (1995). Criterion Hazardous-Behaviour Scale. Unpublished scale, University of British Columbia, Vancouver. Hale, A. R., & Glendon, A. I. (1987). Individual behaviour in the control of danger (Vol. 2). Amsterdam: Elsevier Science. Hale, A. R., & Hale, M. (1972). A review of the industrial accident research literature. London: Her Majesty's Stationary Office. 40 Hansen, C. P. (1988). Personality characteristics of the accident involved employee. Journal of Business and Psychology, 2, 346-365. Hansen, C. P. (1989). A causal model of the relationship among accidents, biodata, personality, and cognitive factors. Journal of Applied Psychology, 74, 81-90. Hofmann, D. A., & Stetzer, A. (1996). A cross-level investigation of factors influencing unsafe behaviours and accidents. Personnel Psychology, 49, 307-339. Iverson, H., & Rundmo, T. (2002). Personality, risky driving and accident involvement among Norwegian drivers. Personality and Individual Differences, 33, 1251-1263. Iverson, R. D., & Erwin, P. J. (1997). Predicting occupational injury: The role of affectivity. Journal of Occupational and Organizational Psychology, 70, 113-128. Jones, J. W., & Wuebker, L. J. (1988). Accident prevention through personnel selection. Journal of Business and Psychology, 3, 187-198. Kerns, K. A. (1996). Walking and chewing gum: The impact of attentional capacity on everyday activities. In R. J. Sbordone & C. J. Long (Eds.), Ecological validity of neuropsychological testing (pp. 147-169). Delray Beach, FL: GR Press/St. Lucie Press. Larson, G. E., & Merritt, C. R. (1991). Can accidents be predicted? An empirical test of the Cognitive Failures Questionnaire. Applied Psychology: An International Review, 40, 37-45. Lawton, R., & Parker, D. (1998). Individual differences in accident liability: A review and integrative approach. Human Factors, 40, 655-671. Leark, R. A., Duypuy, T. R., Greenberg, L. M., Corman, C. L., & Kindschi, C. (1999). Test of Variables of Attention (TOVA) professional manual. Los Alamitos, CA: Universal Attention Disorder. 41 Lezak, M. D. (1995). Neuropsychological assessment (3rd ed.). New York: Oxford University Press. Lund, J., & Aaro, L. E. (2004). Accident Prevention. Presentation of a model placing emphasis on human, structural and cultural factors. Safety Science, 42, 271-324. Lund, J., & Hovden, J. (2003). The influence of safety at work on safety at home and during leisure time. Safety Science, 41, 739-757'. Magnavita, N., Narda, R., Sani, L., Carbone, A., De Lorenzo, G., & Sacco, A. (1997). Type A behaviour pattern and traffic accidents. British Journal of Medical Psychology, 70, 103-107. Manly, T., Robertson, I. H., Galloway, M., & Hawkins, K. (1999). The absent mind: Further investigations of sustained attention to response. Neuropsychologia, 37, 661-670. Martin, M. (1983). Cognitive failure: Everyday and laboratory performance. Bulletin of the Psychonomic Society, 21, 97-100. McKenna, F. P. (1983). Accident proneness: A conceptual analysis. Accident Analysis & Prevention, 15, 65-71. Neal, A., & Griffin, M. A. (2004). Safety climate and safety at work. In J. Barling & M. R. Frone (Eds.), The psychology of workplace safety (pp. 15-34). Washington, DC: American Psychological Association. Ones, D. S., Viswesvaran, C , & Schmidt, F. L. (1993). Comprehensive meta-analysis of integrity test validities: Findings and implications for personnel selection and theories of job performance. Journal of Applied Psychology Monograph, 78, 679-703. Petersen, D. (1996). Analyzing safety system effectiveness (3rd ed.). New York: Van Nostrand Reinhold. 42 Ponsford, J. L. (2000). Attention. In G. Groth-Marnat (Ed.), Neurological assessment in clinical practice: A guide to test interpretation and integration (pp. 355-400). New York: John Wiley & Sons. Reason, J. (1990). Human error. New York: Cambridge University Press. Reitan, R. M. (1992). Trail making test: Manual of administration and scoring. Tuscon, AZ: Reitan Neuropsychology Laboratory. Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). 'Oops!' Performance correlated of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35, 747-758. Sackett, P. R., & DeVore, C. J. (2001). Counterproductive behaviors at work. In N. Anderson, D. S. Ones, H. Sinangil & C. Viswesvaran (Eds.), Handbook of industrial,work, and organizational psychology (Vol. 1). London: Sage. Salgado, J. F. (2002). The Big Five personality dimensions and counterproductive behaviors. International Journal of Selection and Assessment, 10, 117-125. Salminen, S., Klen, T., & Ojanen, K. (1999). Risk taking and accident frequency among Finnish forestry workers. Safety Science, 33, 143-153. Sbordone, R. J. (2001). Limitations of neuropsychological testing to predict the cognitive and behavioral functioning of persons with brain injury in real-world settings. NeuroRehabilitation, 16, 199-201. Sbordone, R. J., & Guilmette, T. J. (1999). Ecological validity: Prediction of everyday and vocational functioning from neuropsychological test data. In J. J. Sweet (Ed.), Forensic neuropsychology: Fundamentals and practice (pp. 227-254). Lisse, Netherlands: Swets & Zeitlinger. 43 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, 262-274. Spreen, O., & Strauss, E. (1998). A compendium of neuropsychological tests: Administration, norms, and commentary (2nd ed.). New York: Oxford University Press. Stewart, J. M. (2002). Managing for world class safety. New York: John Wiley & Sons. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643-662. Sutherland, V. J., & Cooper, C. L. (1991). Personality, stress and accident involvement in the offshore oil and gas industry. Personality and Individual Differences, 12, 195-204. Thiffault, P., & Bergeron, J. (2003). Fatigue and individual differences in monotonous simulated driving. Personality and Individual Differences, 34, 159-176. Ulleberg, P. (2002). Personality subtypes of young drivers: Relationship to risk-taking preferences, accident involvement, and response to a traffic safety campaign. Transportation Research Part F, 4, 279-297'. Wallace, J. C , Kass, S. J., & Stanny, C. (2001). Predicting performance in 'go' situations: A new use for the Cognitive Failures Questionnaire? North American Journal of Psychology, 3, 481-490. Wallace, J. C , Kass, S. J., & Stanny, C. J. (2002). The Cognitive Failures Questionnaire revisited: Dimensions and correlates. The Journal of General Psychology, 129, 238-256. Wallace, J. C , & Vodanovich, S. J. (2003a). Can accidents and industrial mishaps be predicted? Further investigation into the relationship between cognitive failure and reports of accidents. Journal of Business and Psychology, 17, 503-514. 44 Wallace, J. C , & Vodanovich, S. J. (2003b). Workplace safety performance: Conscientiousness, cognitive failure, and their interaction. Journal of Occupational Health Psychology, 8, 316-327. Warm, J. S. (1984). An introduction to vigilance. In J. S. Warm (Ed.), Sustained attention in human performance (pp. 1-14). New York: John Wiley & Sons. Wechsler, D. (1981). Wechsler Adult Intelligence Scale-Revised manual. San Antonio: Psychological Corporation. 45 Table 1 Oblique primary-factor matrix for the 16 items from the Criterion Hazardous-Behaviour Scale Primary factor' Itemb 1 2 h2 1 .48 .07 .26 2 .46 .11 .26 3 .24 -.06 .05 4 .59 -.02 .34 5 .28 .09 .11 6 .58 -.08 .31 7 .49 -.16 .21 8 .39 .14 .21 9 .51 -.01 .26 10 -.07 .47 .21 11 .11 .43 .23 12 .50 .18 .35 13 .07 .71 .54 14 .57 .06 .35 15 .34 .10 .15 16 .34 .39 .36 Note. The total n is 254 (127 males; 127 females). Salient primary-pattern coefficients appear in boldface (all >. .25). aFactor 1 = Forgetfulness, Factor 2 = Injury Involvement. "'Numbers before items refer to those appearing in Appendix A. 46 Table 2 Factor scales derived from the commom-factor analysis of the 16-item Criterion Hazardous-Behaviour Scale, including the weighted by gender internal consistency (a) reliability estimate' for each subscale Itemb Statement Subscale 1: Forgetfulness (a = . 69) 2 Left an appliance (e.g., coffee pot or stove) on overnight or when you were out for the day. 4 Lost and/or misplaced important papers. 6 Lost your keys. 7 Forgot important events like family birthdays. 9 Lost your wallet or purse. 14 Forgot an appointment. Subscale 2: Injury-involvement (a = .49) 10 Suffered a broken bone in an accident. 11 Suffered a sprain (ankle, knee, wrist, finger, etc.) through accident, carelessness, or overexertion. 13 Missed one or more days of work or school because of injury or mishap of some kind. Note. Total n = 254 (127 males; 127 females). internal consistency reliability estimates were computed in the larger sample and are weighted averages across genders (127 males; 488 females). bNumbers before items refer to those appearing in Appendix A. Only the items that were also salient in each of the separate-gender common-factor analyses were included in the subscales, and appear in descending order of magnitude of primary-pattern coefficients (all > .25). 47 Table 3 The 51 BIODATA-250 items selected for the BIODATA-250 Safety-Orientation scale Item Statement 13 Other people consider me to be steady and reliable. 18 I always complete projects that I start. 19 People should never try to cheat on their income tax, even if they can get away with it. 24 I would rather read a book than go to a party. 30 I have had very few accidents in my life. 54 I have never "played sick" to get out of school or work. 55 I am more cautious than most people. 61 At work I am known as a calm person. 70 When driving I always try to stay within the speed limit. 78 I have never been interested in dangerous, risky activities. 86 Time management is one of my strengths. 138 I follow the rules and do not try to get around them. 145 I have suffered a broken bone in the past. 147 A l l existing laws should be strictly enforced. 162 I would rather spend an evening alone than with other people. 197 I never act impulsively. 201 I arrive at meetings 10 or 15 minutes early so that I can organize my thoughts first. 208 I find it awkward and embarrassing to take part in jokes at parties. 221 I usually think before I speak so that I don't say anything offensive. 234 Other people say that I am very organized. 248 I never do things spontaneously, or without stopping to think first. 48 Item Statement Reversed-Scored Items 5 Once in a while I like to bend the rules and do something that could get me into trouble. 6 In most group activities I take on the role of leader. 15 Most teenagers have shoplifted at one time or another. 46 Sky-diving appeals to me. 59 There's nothing wrong with failing to declare a few items when coming through customs, since most people do this. 63 Occasionally I have done something dangerous just for the thrill. 79 Often it's best to live for the present, instead of planning ahead. 92 It's all right for people to get drunk at parties occasionally as long as they're not driving. 102 Sometimes I use a little flattery to get others to do what I want. 105 I sometimes do absent-minded things like locking myself out or leaving the stove on while I'm away. 106 I was attracted to the rebel crowd when I was in school. 113 I speak up a lot at meetings. 116 Whether or not a work team reaches its objective is less important than how it operates. 130 I have occasionally mislaid or lost my wallet or purse. 135 I am a "big picture" person, and leave the details to others. 142 In groups that I've belonged to (clubs, teams, etc.), I have often been the one to suggest projects. 159 Every once in a while I like to go to wild parties. 160 I like to "just get on with things," instead of planning my activities carefully. 170 People have to follow too many rules in our society. 172 I like to take part in social gatherings. 49 Item Statement 177 Sometimes I find my thoughts wandering when I should be focused on something. 188 I often spontaneously say things that I later regret. 199 Victimless crimes—like petty theft from large companies that can afford it—should carry only light penalties. 200 I have done things in the past that others have told me were unsafe. 216 I avoid making detailed plans because I have difficulty carrying them out. 219 I like to take risks when I am working. 223 I am more forgetful than most people. 237 On occasion I have returned a slightly used item for a refund and pretended it was unused. 238 I am known as a talkative, outgoing person. 246 Those who know me would say that I take a lot of risks. Note. Items from "Biographical Information about Occupationally Descriptive Attitudes, Traits, and Abilities (BIODATA-250)," by A. R. Hakstian, 2002, Unpublished inventory, University of British Columbia, Vancouver. 50 Table 4 Oblique primary-factor matrix for the 51 items from the Safety-Orientation scale, with R's indicating reverse-scored items Primary factor1 Itemb 1 2 3 4 5 6 h2 13 -.15 -.16 -.02 .30 .26 -.24 .30 18 -.03 -.15 .11 .00 .48 -.11 .34 19 .06 -.06 .11 -.02 .00 -.59 .37 24 .12 -.03 .03 -.56 -.10 -.29 .43 30 -.26 -.05 -.10 .09 -.16 .04 .09 54 .04 -.11 -.13 .01 .09 -.31 .15 55 -.27 .07 -.02 -.21 .30 -.04 .28 61 .12 -.23 -.39 .09 .11 .00 .22 70 -.11 .01 .02 -.18 .02 -.32 .20 78 -.72 .07 -.02 -.03 .02 -.15 .62 86 -.01 -.22 -.04 .02 .44 .09 .29 138 -.18 .04 -.13 .09 .20 -.54 .48 147 -.13 .24 -.12 .07 .24 -.31 .23 162 .05 -.04 -.07 -.54 .08 .01 .30 197 -.23 .13 -.06 -.32 .30 .07 .29 201 .23 -.04 .00 -.09 .50 -.10 .29 208 -.09 .07 -.16 -.37 .09 .00 .22 221 -.06 -.22 -.08 -.19 .19 -.12 .23 234 -.09 -.13 .05 .11 .58 -.10 .45 248 -.27 .01 .00 -.36 .36 .00 .43 51 Primary factor' Itemb 1 2 3 4 5 6 h2 5 R -.35 -.05 -.04 -.08 .08 -.28 .35 6 R .09 -.01 -.67 -.25 -.21 -.13 .58 15 R -.14 -.06 .07 -.13 -.01 -.26 .16 46 R -.59 .01 .04 -.05 .10 -.04 .40 59 R .05 .23 .06 .02 -.03 -.65 .39 63 R -.64 -.01 .09 -.18 -.06 -.22 .61 79 R -.21 -.15 .11 -.20 .20 -.04 .21 92 R -.05 .00 -.02 -.25 -.06 -.40 .28 102 R -.11 -.16 -.08 -.12 -.16 -.16 .13 105 R -.09 -.55 -.13 -.03 .11 .03 .39 106 R -.31 .00 -.10 -.09 .00 -.28 .30 113 R -.04 .07 -.74 -.09 -.08 .03 .60 116 R -.16 -.06 -.09 .06 .09 .02 .06 130 R -.01 -.34 -.20 -.16 .17 .01 .26 135 R -.13 -.15 -.06 .02 .05 -.18 .13 142 R -.04 .15 -.60 -.02 -.01 -.01 .39 145 R -.18 .18 -.10 .03 -.06 .06 .07 159 R -.19 -.06 -.11 -.48 -.03 -.28 .53 160 R -.36 -.21 .06 -.14 .32 -.01 .41 170 R -.27 -.10 .07 .09 -.04 -.21 .16 172 R -.09 .00 -.12 -.64 -.04 .05 .48 177 R .06 -.44 .16 .03 .05 .02 .22 188 R -.07 -.57 .00 -.07 -.09 -.03 .34 52 Primary factor3 Itemb 1 2 3 4 5 6 h2 199 R -.06 -.05 -.05 .04 .08 -.39 .21 200 R -.47 -.18 .11 -.23 -.13 -.11 .39 216 R -.19 -.45 .02 .23 .32 -.02 .49 219 R -.54 .03 -.12 -.03 .11 -.03 .39 223 R -.08 -.44 -.07 .18 .13 .01 .29 237 R -.02 -.29 .04 -.03 -.18 -.38 .27 238 R -.11 -.06 -.51 -.40 -.08 .10 .52 246 R -.61 -.07 -.01 -.12 .08 -.05 .49 Note. The total n is 258 (129 males and 129 females). Salient primary-pattern coefficients appear in boldface (all > .25). aFactor 1 = Risk-taking, Factor 2 = Absentmindedness, Factor 3 = Assertiveness, Factor 4 = Gregariousness, Factor 5 = Planfulness/Orderliness, Factor 6 = Counterproductivity. bItem numbers refer to those appearing in Table 3. 53 Table 5 Primary-factor inter correlation matrix for the 51-item Safety-Orientation scale Primary factor' Primary factor 1 2 3 4 5 6 1 2 .15 — 3 .24 .02 — 4 .21 .02 .16 — 5 -.18 -.25 -.06 -.05 — 6 .34 .23 .02 .23 -.17 Note. The total n is 258 (129 males; 129 females). aPrimary factors: (1) Risk-taking, (2) Absentmindedness, (3) Assertiveness, (4) Gregariousness, (5) Planfulness/Orderliness, (6) Counterproductivity. 54 Table 6 Factor scales derived from the commom-factor analysis of the 51-item Safety-Orientation scale, including the weighted by gender internal consistency (a) reliability estimate" for each subscale, and R's indicating reverse-scored items Item1 Statement Subscale 1: Risk-Taking (a = .81) 78 R I have never been interested in dangerous, risky activities. 63 Occasionally I have done something dangerous just for the thrill. 246 Those who know me would say that I take a lot of risks. 46 Sky-diving appeals to me. 219 I like to take risks when I am working. 200 I have done things in the past that others have told me were unsafe. 160 I like to "just get on with things," instead of planning my activities carefully. 5 Once in a while I like to bend the rules and do something that could get me into trouble. 106 I was attracted to the rebel crowd when I was in school. 248 R I never do things spontaneously, or without stopping to think first. 55 R I am more cautious than most people. 30 R I have had very few accidents in my life. Subscale 2: Absentmindedness (a = .65) 188 I often spontaneously say things that I later regret. 105 I sometimes do absent-minded things like locking myself out or leaving the stove on while I'm away. 216 I avoid making detailed plans because I have difficulty carrying them out. 223 I am more forgetful than most people. 177 Sometimes I find my thoughts wandering when I should be focused on 55 Itemb Statement something. 130 I have occasionally mislaid or lost my wallet or purse. Subscale 3: Assertiveness (a = .73) 113 I speak up a lot at meetings. 6 In most group activities I take on the role of leader. 142 In groups that I've belonged to (clubs, teams, etc.), I have often been the one to suggest projects. 238 I am known as a talkative, outgoing person. 61 R At work I am known as a calm person. Subscale 4: Gregariousness (a = .73) 172 I like to take part in social gatherings. 24 R I would rather read a book than go to a party. 162 R I would rather spend an evening alone than with other people. 159 Every once in a while I like to go to wild parties. 238 I am known as a talkative, outgoing person. 208 R I find it awkward and embarrassing to take part in jokes at parties. 248 R I never do things spontaneously, or without stopping to think first. 197 R I never act impulsively. 13 R Other people consider me to be steady and reliable. Subscale 5: PlanfulnessIOrderliness (a = .72) 234 Other people say that I am very organized. 201 I arrive at meetings 10 or 15 minutes early so that I can organize my thoughts first. 18 I always complete projects that I start. 86 Time management is one of my strengths. 56 Itemb Statement 248 I never do things spontaneously, or without stopping to think first. 160 R I like to "just get on with things," instead of planning my activities carefully. 216 R I avoid making detailed plans because I have difficulty carrying them out. 55 I am more cautious than most people. 197 I never act impulsively. Subscale 6: Counterproductivity (a = . 66) 59 There's nothing wrong with failing to declare a few items when coming through customs, since most people do this. 19 R People should never try to cheat on their income tax, even if they can get away with it. 138 R I follow the rules and do not try to get around them. 92 It's all right for people to get drunk at parties occasionally as long as they're not driving. 199 Victimless crimes—like petty theft from large companies that can afford it— should carry only light penalties. 237 On occasion I have returned a slightly used item for a refund and pretended it was unused. 70 R When driving I always try to stay within the speed limit. 54 R I have never "played sick" to get out of school or work. 147 R A l l existing laws should be strictly enforced. Note. Total n = 258 (129 males; 129 females). internal consistency reliability estimates were computed in the larger sample and are weighted averages across genders (129 males; 504 females). bItem numbers refer to those appearing in Table 3. Salient primary-pattern coefficients were included in the subscales, and appear in descending order of magnitude of primary-pattern coefficients (all > .25). Table 7 Correlations between the Safety-Orientation scale and factor scales, and the other BIODATA-250 scales Safety-Orientation scale and factor scales BIODATA-250 scales Total Risk-taking Absentminded -ness Assertive -ness Gregarious -ness Planfulness/ Orderliness Counter-productivity Emotional Stability -.04 .15** - .36** 29** 23** .06 -.03 Extraversion - .54** .45** -.05 70** .81** - .20** .24** Openness - .47** .51** .04 37** .43** _ 32** .26** Agreeableness .04 .04 -.14** .19** .15** .03 - .18** Conscientiousness .45** _ 29** _ 4Q** 13** - .18** 73** _ 33** Impulse Control 47** - .36** - .36** _ 27** _ 33** 29** _ 23** Risk-taking _ 73** g9** .16** 26** 49** _ 37** .46** Internal Locus of Control .05 .00 - .21** .15** .12* 15** -.06 Note. Total n = 633 (129 males; 504 females. A l l the scales listed here are also BIODATA-250 scales, though each was developed using different methods (see Appendix B for details). The overlap with BIODATA-250 scales means that the correlations are inflated and should be interpreted with caution. */?<.005. < .001. 58 Table 8 Criterion-related validity estimates for Safety-Orientation scale and subscales, and other BIODATA-250 scales Criterion Hazardous-Behaviour Scale and subscales1 BIODATA-250 scales0 Total score Forgetfulness Injury-involvement Safety-Orientation scale and subscales0 Total score -.48* -.38* -.26* Risk-taking .33* .21* .22* Absentmindedness .45* .47* .12* Assertiveness .22* .20* .16* Gregariousness .28* .19* .17* Planfulness/Orderliness -.28* -.27* -.06 Counterproductivity .31* .24* .18* Big Five scales Emotional Stability -.10 - .11* -.01 Extraversion .19* .12* .18* Openness .12* .10 .06 Agreeableness -.05 -.03 .05 Conscientiousness -.18* -.16* .01 Impulse Control -.19* -.13* -.08 Risk-taking .25* .16* .16* Internal Locus of Control -.05 -.03 .03 Note. Total n = 615 (129 males; 504 females). a As described in the text, criterion-related validity estimates are inflated because of capitalization on chance. °A11 scales are BIODATA-250 scales and share common items, but were developed using different methods (see Appendix B). *p<.001 level. 59 Table 9 Oblique primary-factor matrix for the 13 cognitive ability variables from Sample 2 Primary factor3 Cognitive ability variables 1 2 h2 Number of errors on TMT-Part B .26 -.02 .07 Number completed on DSST -.15 .81 .71 Total correct on DBST -.19 .09 .05 Time to complete CHT -.11 -.41 .17 Number of omissions on CHT .61 .09 .37 Number of uncorrected errors on Stroop-Part 3 -.19 .07 .05 Percentage of omissions in TOVA-Half 1 .23 .04 .05 Percentage of omissions in TOVA-Half 2 .75 .19 .57 Percentage of commissions in TOVA-Half 1 .05 - .39 .16 Percentage of commissions in TOVA-Half 2 .38 -.14 .18 TMT difference score .12 -.23 .08 Stroop interference score .35 -.10 .14 ProM date task -.17 -.14 .04 Note. The total n is 119 (23 males and 96 females). Salient primary-pattern coefficients appear in boldface (all > .25). aFactor 1 = Cognitive Errors, Factor 2 = Performance Speed. 60 Table 10 Factor scales derived from the commom-factor analysis of the cognitive ability variables from Sample 2, including the internal consistency (a) reliability estimate for each subscale Subscale 1: Cognitive Errors (a = .55)a 1 Number of errors on TMT-Part B 2 Number of omissions on CHT 3 Percentage of omissions in TOVA-Half 2 4 Percentage of commissions in TOVA-Half 2 5 Stroop interference score Subscale 2: Performance Speed (a = .43)b 1 Number completed on DSST (reverse-scored) 2 Time to complete CHT Note. Total n = 119 (23 males; 96 females). Salient primary-pattern coefficients included in the subscales are listed in descending order of magnitude (all > .25). internal consistency reliability estimate based on n = 66 (scores in zero-centred form). bInternal consistency reliability estimate based on n = 89 (scores in zero-centred form). 61 Table 11 Correlations between the cognitive ability subscales and variables, and the Criterion Hazardous-Behaviour Scale and subscales, and the Accident-Related Events Scale Criterion Hazardous-Behaviour Scale and subscales Total Injury-score Forgetfulness involvement Accident-Related Events Scale Cognitive ability subscales Cognitive Errors .03 (n = 65) Performance Speed .00 (n= 115) Individual cognitive ability tests ProM-Date (n = 80) -.14 CAB-P (n = 227) -.03 CA-1(« = 219) -.04 CAB-Ms (n = 87) .14 C A M - M a (n = 91) .07 .05 .08 -.13 -.10 -.06 .09 .08 -.24s1 .00 -.02 .03 -.07 .17 .03 .06 .03 -.09 *p<.05. 62 Table 12 Correlations between the Safety-Orientation scale and subscales, and the cognitive ability subscales Cognitive ability subscales Safety-Orientation scale and subscales Cognitive Errors in = 65) Performance Speed (n= 115) Total score -.01 Risk-taking .12 -.08 Absentmindedness -.10 Assertiveness .07 .16* Gregariousness -.02 .04 Planfulness/Orderliness -.16 .09 Counterproductivity .14 .14 *p<.05. **/?<.005. ***/?<.001. 63 Figure 1. Conceptual model for this thesis Note. Unsafe behaviours are a mediating factor between individual-differences variables and the accident-involvement outcome. Organizational variables moderate the relationship between individual-differences variables and unsafe behaviours, or directly cause an accident. 64 Appendix A Questions from Criterion Hazardous-Behaviours Scale Item Statement 1. Forgot to lock the house (and close windows) when leaving for the day. 2. Left an appliance (e.g., coffee pot or stove) on overnight or when you were out for the day. 3. Fell asleep while at work. 4. Lost and/or misplaced important papers. 5. Were involved in a traffic accident. 6. Lost your keys. 7. Forgot important events like family birthdays. 8. Lost your temper and broke something. 9. Lost your wallet or purse. 10. Suffered a broken bone in an accident. 11. Suffered a sprain (ankle, knee, wrist, finger, etc.) through accident, carelessness, or overexertion. 12. Dropped and broke an expensive article (china, glass object, camera, radio, etc.). 13. Missed one or more days of work or school because of injury or mishap of some kind. 14. Forgot an appointment. 15. Were late for work or school. 16. Suffered an accident because you became distracted. Note. From "Criterion Hazardous-Behaviours Scale," by A. R. Hakstian and R. M. Wooley, 1995, Unpublished scale, University of British Columbia, Vancouver. Appendix B Normative data and reliability estimates for other BIODATA-250 scales Males Females BIODATA-250 scales No. Items M SD a n r„ a n„ b M SD a n rtt3 n„ b Big Five scales Emotional Stability 20 50.80 6.31 .77 437 .89 76 48.02 6.30 .79 1251 .86 307 Extraversion 15 41.15 5.98 .82 437 .90 77 40.37 6.15 .83 1253 .92 307 Openness 15 40.04 4.61 .74 439 .85 78 38.78 4.48 .74 1252 .86 307 Agreeableness 14 37.83 4.34 .69 438 .80 77 39.02 4.12 .69 1247 .79 304 Conscientiousness 39 96.30 10.18 .83 436 .90 75 97.58 9.66 .82 1246 .87 300 Other scales Risk-taking 7 17.61 3.28 .67 439 .89 77 16.26 3.37 .71 1252 .87 307 Impulse Control 4 9.67 3.67 .55 439 .77 78 10.27 1.84 .51 1254 .31 312 Internal Locus of Control 6 16.21 2.38 .51 439 .76 78 15.93 2.58 .65 1254 .78 311 Note. The above norms are based on university students. The Big Five and Internal Locus of Control scales are conceptually-based scales, whereas the Risk-taking and Impulse Control scales are empirical factor scales (from a common-factor analysis of the entire inventory). aTest-retest reliability estimates over a two-week interval. bSample size used for test-retest reliability estimates. 66 Appendix C Questions from Accident-Related Events Scale Item Statement 1. Injured yourself either at home, work, school, or play, but you did not have to take time off from your regular activities. 2. Injured yourself either at home, work, school, or play, but you did have to take time off from your regular activities. 3. Came close to injuring yourself either at home, work, school, or play. 4. Lost and/or misplaced important papers. 5. Came close to damaging property or an expensive object at home, work, school or play. 6. Unintentionally injured someone else at home, work, school or play. 7. Came close to unintentionally injuring someone else either at home, work, school or play. 67 Appendix D Additional Details for Study 2 Measures Cognitive Failures Questionnaire (CFQ; Broadbent et al., 1982) The CFQ consists of 25 questions on the frequency of everyday cognitive mishaps that occurred in the previous six months. Responses were made on a 5-point scale ranging from "never" to "very often". Representative items include "Do you find you forget why you went from one part of the house to the other?" and "Do you fail to notice signposts on the road?". Work Skills Assessment (WSA), Part 4 (Hakstian, 2004). Participants matched 54 words and letter-digit sequences to one of five options, following a key printed at the top of the page. Participants had 7 minutes to complete this task as quickly and as accurately as they could and wrote responses directly on the answer sheet in the square brackets. The 54 trials were organized in sections; an example follows: Facts: Details: Region: NW239W81 County: 19782229 Census Tract: 17895297 Block: NW92SBW4 Grid: NW9SSW84 Lot: 17982927 What is the: 1. Census Tract a. 17985297 b. 17895927 c. 17895792 d. 17895297 e. 17892297 . 2. Grid a. NW2SSW84 b. NW9SSW84 c. NW9SWS84 d. NW9SS8W4 e. NW9SSW48 3. County a. 17895297 b. 19872229 c. 19782229 d.19782259 e. 19728229 4. Region a. NW239N81 b. NW293W81 c. NW329W81 d. NW392W81 e. NW239W81 5. Lot a. 17982927 b. 17989227 c. 17928927 d. 17982297 e. 17982972 6. Block a. NW92BSW4 b. NW92SWB4 c. NW92SSW4 d. NW92SBW4 e. NW92BWS4 68 Comprehensive Ability Battery - Perceptual Speed and Accuracy (CAB-P; Hakstian & Cattell, 1975). Participants discriminated between 72 pairs of 8-letter and 8-digit sequences, noting whether they were the same or different on a separate answer sheet. Participants had 4.5 minutes to complete this task as quickly and as accurately as they could; examples follow: 1. TRSPUVGY TRSUPVGY 2. 10295364 10295364 3. lmjarpb lmjarpb Comprehensive Ability Battery - Memory Span (CAB-Ms; Hakstian & Cattell, 1975). An audiotape presented ten series of digits that gradually increased from five to ten digits in length; there were two series of each length. Recall for each series was made by filling in the number slots on an answer sheet and then writing the numbers down, after a tone. Comprehensive Ability Battery - Associative Memory (CAB-Ma; Hakstian & Cattell, 1975). Participants were given a list of 14 symbol-number pairs to memorize for 3.5 minutes. A l l symbols were abstract and all numbers included 2 digits. Participants then had 2.5 minutes to match the symbols with the correct numbers from lists of five number options. An example is included below: To memorize: Figure Number To recall: Figure Number 1. ^ a. 31 b. 39 c. 48 d. 82 e. 94 69 Colour-WordStroop Test (Graf et all995). As quickly and as accurately as they could, participants read a list of colour-words printed in black ink (Part 1), a list of coloured X X X s (Part 2), and a list of colour-words in incongruent colours (Part 3). Examples similar to the stoop test appear below: Part 1 Part 2 Part 3 (words read) (colours of X X X ' s read) (colour of words read) blue x x x x (printed in blue ink) 91*6611 (printed in blue ink) red XXX (printed in red ink) yellow (printed in red ink) green XXXXX (printed in green ink) red (printed in green ink) Trail Making Tests (TMT; Reitan, 1992). In Part A of the test, participants drew a line joining consecutively numbered circles (1 to 25) randomly organized on a page. In Part B, participants drew a line alternating between randomly ordered numbered and lettered circles (i.e. 1-A-2-B... 13-L). Participants completed both parts as quickly and as accurately as they could. If the participant made an error but did not self-correct, the experimenter stopped them by saying, "Stop, you made a mistake, continue from here," while pointing to the last correct circle the participant had reached. The time was not stopped when such errors were noted. Examples similar to Part A and Part B appear below: Part A PartB © © © © © Begin © End ® © © © Begin d © 70 Cancel H Test (CHT; Graf, 2000). Participants scanned 12 rows that each included 20 capital letters and 10 randomly interspersed capital H's. Participants had to cross out all the H's, scanning from left to right, as quickly and as accurately as they could. An example similar to the CHT appears below: H C F D H G F H C B H I H H D C H D B H A C B F H E D H A D Digit Symbol Test (DST; Wechsler, 1981). Following a key printed at the top of the page, participants drew symbols into empty boxes below numbers. The key included 9 single digit-symbol pairings. Each of the 6 rows included 14 randomly ordered numbers. Participants performed this task proceeding from left to right, as quickly and as accurately as they could during a 60 second interval. An example similar to the DST appears below: U 7 X 8 A 8 7 8 4 Digit Span Backward Test (DSB; Wechsler, 1981). The experimenter read at an even pace, up to 14 series of digits that gradually increased in length from 2 to 8 digits; there were 2 series of each length. The participant was asked to verbally recall each series in reverse order. The test ended when the participant failed to correctly recall, in reverse order, 2 consecutive series of the same length. 71 Test of Variables of Attention (TOVA; Greenberg, 1999). The T O V A is 21.6-minute computerized test of inattention and impulsivity commonly used with individuals suffering from Attention Deficit Hyperactivity Disorder (ADHD). Participants were seated in front of a computer screen in a dimly lit room and used a standardized microswitch to respond to a target stimulus while ignoring a non-target stimulus; these appear below. The first half of the test was an infrequent target condition (ratio of 1:3.5; target stimulus presented 22.5% of the time), whereas the second half was a frequent target condition (ratio 3.5:1; target stimulus presented 77.5% of the time). Each stimulus was presented for 100 milliseconds every 2 seconds. The percentage of omission errors (a measure of inattention) and commission errors (a measure of impulsivity or disinhibition) were recorded along with reaction time and reaction time variability for quarter, half, and total time of the test (Learketal, 1999). Target Stimulus - Non-target Stimulus • i. <> .•. * -flM||H| Record date-of-completion task (ProM-Date). A modification of the Dobbs and Rule (1987) behavioural remember-the-date prospective-memory task was used. Participants were asked to write down the date they completed the take-home questionnaires (see Procedures, below), and were assigned a score based on whether they remembered the task with or without 72 the aid of retrieval cues. The scores were on a 3-point scale: (3) had remembered to write the date, (2) remembered to write the date on cue that they were supposed to do something else before returning the questionnaire, (1) remembered to write the date on cue that they were supposed to write something down on the questionnaires, and (0) failed to remember that they were supposed to write the date on the questionnaires regardless of retrieval cues. 

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.831.1-0092122/manifest

Comment

Related Items