Comparing recall vs. recognition measures of accident under-reporting: A two-country examination

Accident Analysis and Prevention 106 (2017) 1–9

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Comparing recall vs. recognition measures of accident under-reporting: A two-country examination

MARK

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Tahira M. Probsta, , Laura Petittab, Claudio Barbaranellib a b

Washington State University Vancouver, USA Sapienza University of Rome, Italy

A R T I C L E I N F O

A B S T R A C T

Keywords: Accident under-reporting Measurement validation Surveillance

A growing body of research suggests that national injury surveillance data significantly underestimate the true number of non-fatal occupational injuries due to employee under-reporting of workplace accidents. Given the importance of accurately measuring such under-reporting, the purpose of the current research was to examine the psychometric properties of two different techniques used to operationalize accident under-reporting, one using a free recall methodology and the other a recognition-based approach. Moreover, in order to assess the cross-cultural generalizability of these under-reporting measures, we replicated our psychometric analyses in the United States (N = 440) and Italy (N = 592). Across both countries, the results suggest that both measures exhibited similar patterns of relationships with known antecedents, including job insecurity, production pressure, safety compliance, and safety reporting attitudes. However, the recall measures had more severe violations of normality and were less correlated with self-report workplace injuries. Considerations, implications, and recommendations for using these different types of accident measures are discussed.

1. Introduction Workers around the globe annually experience approximately 260 million occupational injuries and 350,000 fatalities due to work-related injuries (Hämäläinen et al., 2006). According to recent national surveillance data, nearly 3 million of those work-related injuries and illnesses occurred in the United States (BLS, 2015), with over half being serious enough to require time away from work, job transfer, or restricted duty. An additional 640,000 work-related injuries occurred in Italy according to the National Institute for Insurance against Accidents at Work (Instituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro; INAIL, 2016). Despite these sobering statistics, research indicates that national surveillance statistics such as these significantly underrepresent the true extent of work-related accidents and injuries due to under-reporting(e.g, Hämäläinen et al., 2006; Leigh et al., 2004; Lowery et al., 1998; Probst et al., 2008; Probst and Estrada, 2010; Rosenman et al., 2006). The purpose of the current study was to examine the psychometric properties of two different self-report techniques used to operationalize accident under-reporting, one using a free-recall methodology and the other a recognition-based approach. In addition, the validity of these two under-reporting measures was evaluated by examining their relationships with known correlates of accident under-reporting,

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Corresponding author. E-mail address: [email protected] (T.M. Probst).

http://dx.doi.org/10.1016/j.aap.2017.05.006 Received 22 February 2017; Received in revised form 10 April 2017; Accepted 9 May 2017 0001-4575/ © 2017 Elsevier Ltd. All rights reserved.

namely job insecurity, production pressure, safety compliance, safety reporting attitudes, and workplace injuries (Probst et al., 2013a; Probst and Graso, 2013; Probst et al., 2013b). Below we begin our review of the literature by discussing the nature and prevalence of under-reporting. Next, we examine the two commonly used self-report techniques to estimate such under-reporting and place these within the literature on cognitive biases. Finally, we develop a nomological network (Cronbach and Meehl, 1955) of expected relationships with under-reporting that can be used to examine the validity of these two approaches to the measurement of under-reporting. 1.1. Prevalence and definitions of under-reporting Undercounts of the true prevalence of workplace injuries can occur at two points within the chain of reporting: 1) when an injury occurs and the employee decides whether to report it to their employer, and 2) when a company is notified by an employee of an injury and decides whether to include that injury in their official injury log. Organizationallevel under-reporting involves the latter part of the chain and occurs when organizations do not properly report work-related injuries meeting the definition of a recordable incident to the appropriate regulatory authority. For example, in the United States a recordable incident is one

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(Cohen and Squire, 1980). A substantial body of literature from cognitive psychology and cognitive neuroscience suggests that the processes involved in recall versus recognition memory differ in fundamental ways. Specifically, whereas recall relies upon conscious recollection and retrieval (Jacoby et al., 1993), recognition can occur due to either familiarity with the material or cued recollection (Mandler, 1980). Thus, it is largely accepted that recall-based tasks are more cognitively difficult than recognition-based tasks, and that recognition performance is usually superior to recall performance (Haist et al., 1992). Despite the preponderance of evidence suggesting the superiority of recognition memory over recall, there are advantages and disadvantages with constructing a measure of accident underreporting using either approach. On the one hand, a free recall approach results in a measure that is simple to administer (e.g., asking participants to indicate, “How many accidents did you experience and report?”) and only involves asking a limited number of questions (e.g., how many accidents they experienced, reported, and failed to report). Moreover, such a measure could be used without modification across a wide variety of different industries since the questions are not industry- or context-specific. However, as noted above, research indicates that individuals generally perform worse on recall measures compared to recognition measures. Moreover, research also suggests that memory performance on recall tasks declines more with age than performance on recognition-based tasks (Craik and McDowd, 1987; Danckert and Craik, 2013). Similarly, research (Mickes et al., 2008) has shown that recall memory declines during pregnancy, whereas performance on recognition-based tasks does not. Thus, reliance on recall measures of underreporting may be less accurate among certain segments of the workforce. On the other hand, constructing a recognition-based measure of under-reporting requires identification of all precipitating events that could be classified as a safety incident potentially leading to an accident or injury. Such precipitating events might differ by industry or occupation. For example, inhalation of hazardous fumes might be a risk factor present in one job, but not be pertinent to another. Thus, constructing a measure containing a broad universe of potential precipitating events that are applicable to a variety of occupations is challenging and would necessitate a lengthier measure than the recall approach described above. A recognition-based approach would also still rely on free recall if the recognition measure intends to quantify the number of times each precipitating event occurred, rather than whether it occurred, further increasing the complexity of the measure in terms of scale length and cognitive load to complete. Given the differing advantages and disadvantages associated with these two different approaches to under-reporting, the current study sought to compare the psychometric properties and differential validity of two commonly used measures of underreporting – one that relies on a free recall approach and one that utilizes a recognition-based approach.

that: leads to a work-related fatality; results in loss of consciousness, days away from work, restricted work, or transfer to another job; or requires medical treatment beyond first aid (OSHA, 2016). Estimates suggest that a large proportion of all recordable-eligible injuries fail to be properly logged by organizations. In a seminal article on this topic, Rosenman et al. (2006) matched companies and employees who reported work-related injuries and illnesses to OSHA with information contained within four workers’ compensation databases in the state of Michigan, finding that 60–67% of all workplace injuries were not captured by the official OSHA logs. In an industry-specific study, by comparing medical claims data from 38 construction companies participating in an owner-controlled insurance program to the company’s official OSHA recordable rates, Probst et al. (2008) found that 78% of all recordable-eligible injuries went unreported. Thus, organizationallevel under-reporting is a serious issue and widely pervasive. Nonetheless, these studies demonstrate that estimating the prevalence of organizational-level under-reporting – while complex – is objectively possible by examining employee medical records or workers’ compensation databases and calculating the extent to which injuries meeting the definition of a recordable event are properly coded and reported by employers. The measurement of individual-level underreporting is far more difficult. Individual-level under-reporting occurs when employees fail to report work-related injuries, illnesses or accidents to their employer. Unfortunately, research (e.g., Probst and Estrada, 2010; Probst et al., 2013a; Probst and Graso, 2013) indicates that there too are significant discrepancies between the number of workplace accidents that are experienced by employees and the number that are actually reported to the employer with estimates from those studies suggesting 57–80% of all accidents experienced by employees go unreported to their company. Unfortunately, despite increasing recognition of the problem of under-reporting, there has been little empirical research on the optimal way to measure this phenomenon. Measuring accident under-reporting at the individual-level is challenging for a variety of reasons. First, the criteria for what constitutes a reportable event may vary from company to company (e.g., ranging from “report everything” policies including near misses and unsafe conditions to only reporting actual injuries). More problematic, there is no objective measure of what actually gets reported to the employer. As noted above, many recordable injuries that employees accurately report to their employer do not ever get entered into the official log of workplace injuries (Probst et al., 2008). Thus, there is no objective standard by which to compare employee selfreports of injuries or accidents. Similarly, there are few objective measures of the injuries or accidents that employees actually experience on the job (particularly if the employees do not seek medical assistance following the event). As a result of these challenges, researchers typically rely upon selfreport data from employees to estimate the numbers of reported, unreported, and total experienced accidents. For the purpose of this study, a workplace accident is defined as any unplanned and uncontrolled event that led to: injury to persons, damage to property/plant/equipment, or some other loss to the company. Thus, we are moving beyond a sole focus on injury under-reporting to also include other significant events that organizations would typically expect their employees to report (i.e., damage to property or equipment). The primary aim of the current study was to examine the validity of two different self-report techniques used to operationalize accident under-reporting, one using a free-recall methodology and the other a recognition-based approach. Below, we next review these measures in light of research on cognitive biases associated with memory using freerecall versus recognition-based prompts.

1.3. Validation constructs In order to compare the two measures, we examined the relationship between known correlates of under-reporting and each measure of under-reporting. First, and perhaps not surprisingly, research has found that accident under-reporting is related to higher incidence of workrelated injuries. For example, in two studies of under-reporting (using free recall measures), Probst et al. (2013b) found that greater accident under-reporting was significantly associated with more workplace injuries (r = 0.32 and 0.43, respectively). In other words, employees who experience more injuries at work are also more likely to underreport those injuries and other related safety incidents to their organization. Therefore, our first research question was: RQ #1: Do the observed correlations between workplace injuries

1.2. Recall versus recognition memory Asking employees to accurately report their prior experiences of workplace accidents requires tapping into declarative memory, i.e., memories for events that can be consciously recalled or recognized 2

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that face little risk of exposure to workplace hazards (e.g., accounting firms, university employees, banking sector, etc.). Therefore, we chose to focus on sectors where safety is a highly relevant issue. From a statistical standpoint, it was also important, given that the experience of accidents is a low base rate phenomenon. Including employees from low-risk sectors would result in highly skewed distributions due to the vast majority of employees in those industries experiencing no accidents, let alone reporting them. After providing participants with informed consent materials that explained the anonymous nature of the data collection and their rights as research participants, members of the research team administered the surveys containing the research measures to employees during their working hours. In order to ensure participants were comfortable responding to the questions, they were informed that only members of the research team would have access to the data.

and accident reporting significantly differ among the recall vs. recognition measures? In order to further compare the two measures, we also examined the relationship between known predictors of under-reporting and each measure of under-reporting. Specifically, prior research on the nomological net (Cronbach and Meehl, 1955) of variables related to underreporting has demonstrated that job insecurity, production pressure, safety reporting attitudes, and safety compliance are all significant predictors of employee accident under-reporting. In a cross-cultural study of employees in the United States (using a recall measure) and Italy (using both recall and recognition measures), Probst et al. (2013a) found that job insecurity was not only related to a higher likelihood of experiencing a workplace accident, but also that insecure employees reported proportionally fewer of those experienced accidents compared to secure employees. In a study of copper miners, Probst and Graso (2013) predicted that there would be less congruence between experienced and reported accidents when workplace production pressure is high compared to when production pressure is lower. In support of this, they found that employees who perceived higher levels of production pressure not only experienced more accidents (using a recognition-based measure), but also reported proportionally fewer of those accidents compared to employees who perceived lower levels of production pressure. Additionally, this study also examined the relationship between accident reporting attitudes and reporting behaviors. Interestingly, they found that employees who had more positive reporting attitudes experienced fewer accidents (r = −0.23); however, they reported more accidents (r = 0.21) relative to employees with negative reporting attitudes. Together, this pattern of correlations would suggest that accident under-reporting would be higher among individuals with negative reporting attitudes than individuals who have positive attitudes toward reporting. In their two studies measuring under-reporting (again both utilizing a free recall measure), Probst et al. (2013b) found that employees who scored higher on a measure of behavioral safety compliance also had significantly lower levels of accident under-reporting (r = −0.20 and −0.42, respectively). Given this established nomological network of antecedents of under-reporting, our second research question was: RQ #2: Is there differential variance explained in accident underreporting when relying on recall vs. recognition measures of underreporting? In order to answer these research questions, two separate studies were conducted. In the first study, data were collected from 440 employees in two different U.S. organizations to provide an initial investigation. In the second study, we attempted to replicate our findings in a different cultural context using data from 592 employees in 22 Italian organizations. Consistent findings across the two contexts would strengthen our confidence in the generalizability of the effects.

2.2. Measures Below is a description of measures used to provide data for the current analyses. Recall Measure of Accidents. Using a measure (Probst et al., 2013b; see Appendix) adapted from Hayes et al., 1998, employees were asked to indicate how many safety accidents they experienced and reported to appropriate company officials (i.e., How many accidents did you experience and report to your supervisor in the last 12 months?) and how many accidents they had experienced but were not reported to appropriate company officials (i.e., How many accidents did you experience but NOT report to your supervisor in the last 12 months?) over the past 12 months. In order to ensure consistent interpretation of the question, we provided the following definition for the term Accident: An unplanned and uncontrolled event that led to: injury to persons, damage to property/plant/equipment, or some other loss to the company. Using these data, we could compute the total number of experienced accidents for comparison to the number actually reported. If no underreporting is occurring, then the number reported should equal the number experienced. To the extent that these numbers diverge, greater accident underreporting can be said to occur (Probst et al., 2013a, 2013b). Although the workplace accident variables were selfreport in nature, previous studies do indicate that self-report measures of accidents and unsafe behaviors are related to independent observations of these variables (Lusk et al., 1995). Specifically, Lusk et al. compared self-ratings, supervisor-ratings, and observer ratings of employee use of hearing protection. They found that supervisor reports were highly divergent from both self- and observer-ratings, and that self- and observer-ratings were highly correlated (.89). As a result, they concluded that self-reports of safety-related behavior are appropriate and “may be the best choice when time and monetary resources restrict measurement to one indicator” (p. 51). Recognition Measure of Accidents. The recognition measure of experienced and reported accidents was developed by Probst and Graso (2013) based on the U.S. Bureau of Labor Statistics’ Occupational Injury and Illness Classification System (OIICS; Bureau of Labor Statistics, 2007). The OIICS provides a classification system used to code the precipitating events or exposures related to workplace illnesses and injuries. Probst and Graso (2013) developed a list of seventeen such exposures/events that were presented to employees who were asked to indicate (yes = 1/no = 0) if they had experienced each of the following events during the previous year, and if that exposure had resulted in either personal injury or property damage. The events included: slip; trip; fall; struck or stepped on; rubbed or abraded; hit by object; contact with hazardous materials; heat or cold exposure; caught in or between objects; motor vehicle incident; repetitive motion; inhaled hazardous substance; electrical current shock; collapsed under an object or a rock; improper lifting; accidentally hit by another worker; and exposure to excessive dust. Thus, each employee’s experienced events score could

2. Study 1 method: United States 2.1. Participants and procedure In order to answer our Research Questions, surveys were administered to 440 employees from two different organizations in the United States. Seventy-six percent of respondents were male, 22% female, with 1.4% leaving the item blank. The median age category of participants was 40–44 yrs. The average employee tenure was 5.45 years (SD = 6.03). Organizations were recruited from the following industry sectors that, according to the Bureau of Labor Statistics (2011), have above average risk of employee injuries: mining (N = 195) and transportation (N = 245). The rationale for selecting organizations in these sectors was both conceptual and statistical in nature. From a conceptual standpoint, it would not make sense to study safety among employees 3

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skewed, the recall measure was much more highly skewed (12.49) and exhibited much higher kurtosis (185.68) than the recognition measure (1.31 and 1.84, respectively). The mean number of reported accidents was 3.19 for the recall measure (SD = 36.29) and 0.53 for the recognition measure (SD = 1.08) with the recall measure again demonstrating more skew and kurtosis. Overall, recall measures of accidents reporting have more severe violations of normality in comparison to recognition ones. Table 2 presents descriptive statistics, scale reliabilities (for reflective scales) and zero-order correlations among the study variables. All measures were reliable with alpha coefficients ranging from 0.74 to 0.97. Recognition measures of experienced and reported accidents were significantly and positively related to workplace injuries (r = 0.52, p < 0.01; r = 0.28, p < 0.01). Conversely, the recall measure of experienced accidents was significantly and positively related to workplace injuries (r = 0.16, p < 0.01) whereas reported accidents was not (r = 0.07, p = 0.165). Hotelling-Williams tests of the equality of dependent correlations confirmed that the strength of these relationships was significantly larger for the recognition-based measures of reporting, t(437) = 6.63, p < 0.001, and t(437) = 3.22, p < 0.01, for experienced and reported accidents respectively. These findings provide an initial answer to Research Question #1 by suggesting that workplace injuries tend to be more strongly correlated with recognition-based measures of under-reporting compared to recall-based measures.

range from 0 to 17. By examining the number of experienced events, one can ascertain the overall level of exposure. Each employee was also asked if they reported (yes = 1/no = 0) each experienced event to appropriate company officials when it occurred. Thus, reported events scores could also range from 0 to 17. By comparing the levels of experienced and reported events, the extent of underreporting can be determined. If all experienced events are reported (regardless of how many occurred), there is no underreporting.1 Job insecurity. Nine items from the Job Security Index (JSI; Probst, 2003) were used to measure employee perceptions of job insecurity. Respondents indicated on a 3-point scale (yes, don’t know, no) the extent to which each adjective or phrase described the future of their job (i.e., “can depend on being here,” “stable,” “unknown”). Responses were scored such that higher numbers reflect more job insecurity. Using a scoring system recommended by Hanisch (1992), item responses were coded as follows: agreement with negatively worded items (i.e., “unknown”) was scored 3; agreement with positively worded items (i.e., “stable” and “can depend on being here”) was scored 0; and “don’t know” responses were scored 2. This was based on prior analyses suggesting that endorsement of the “don’t know” anchor is psychometrically closer to a negative response than a positive one (Hanisch, 1992). Production Pressure. Organizational production pressure (Probst and Graso, 2013) was measured using five Likert-scale items. Participants indicated their agreement to the following items using response options ranging from 1 (strongly disagree) to 7 (strongly agree): “The main focus of this organization is on production. Everything else is second”. Note: this measure was only collected in the mining sample. Attitudes towards Reporting. Reporting attitudes were measured using a 4-item scale (Probst et al., 2013). Using a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), respondents indicated the extent to which they had positive versus negative attitudes toward reporting accidents at work. A sample item was, “I am hesitant to report on-the-job accidents or injuries.” Responses were scored such that higher numbers reflect more positive reporting attitudes. Safety Compliance. A 4-item safety compliance scale (Neal et al., 2000) assessed the degree to which employees comply with safety rules and policies (e.g., “I use the correct safety procedures for carrying out my job”). Items were presented in Likert-type format with a scale ranging from strongly disagree (1) to strongly agree (7). Responses were scored such that higher numbers reflect higher safety compliance. Workplace Injuries. We used a 12-item self-report formative measure of workplace injuries (Probst et al., 2013b) experienced during past year (e.g., back injury, cut/puncture wound, broken bone, eye irritation). Workplace injuries were assessed by totaling the number of injuries workers indicated they had experienced as a result of their job, and could range from 0 to 12.

3.2. Predictors of recall and recognition measures of accidents

Table 1 presents descriptive statistics of recall and recognition measures of accidents. The mean number of experienced accidents was 6.86 for the recall measure (SD = 44.99) and 2.69 for the recognition measure (SD = 2.75). Although accident data by definition tend to be

As noted earlier, the extent of underreporting can be determined by examining discrepancies between the number of experienced accidents compared to the number reported. Thus, to provide an explicit withinperson comparison between these two variables, four repeated-measures analysis of variance models were constructed in which job insecurity, production pressure, safety reporting attitudes, and safety compliance respectively were modeled as continuous between-subjects independent variables, and the recall measures of experienced accidents and reported accidents were modeled as within-subjects variables.2 Similarly, four repeated-measures analysis of variance models were constructed in which job insecurity, production pressure, safety reporting attitudes, and safety compliance respectively were modeled as continuous between-subjects independent variables, and the recognition measures of experienced accidents and reported accidents were modeled as within-subjects variables. An observed interaction between the within-subjects variable and the between-subjects predictor would be indicative of the level of within-person discrepancy (i.e., underreporting) being significantly explained by that predictor. Moreover, by examining the variance in this underreporting explained by the predictor, we could explore Research Question #2 to determine which form of underreporting measure resulted in the greatest amount of variance explained. There was a significant interaction between the recall measure of accidents (reported vs. experienced) and safety reporting attitudes, F(1, 411) = 6.96, p < 0.01 (η2 = 0.02), and safety compliance, F(1, 410) = 39.44, p < 0.001 (η2 = 0.09). The expected interaction was not significant for perceptions of job insecurity, F(1, 411) = 2.62, p = 0.106 (η2 = 0.01), and only marginally significant for production

1 Unlike many psychological constructs and accompanying reflective measures, the OIICS measure of accident underreporting is a formative measure (Borsboom et al., 2004) comprised of a variety of indicators representing failure to report different kinds of workplace exposures that all lead to the overarching construct of the level of underreporting. As a result, empirically, whereas inter-item correlations should be positive and high in a reflective measure, this is not the case with formative measures, nor are traditional psychometric approaches to evaluating the measure appropriate (e.g., Cronbach’s alpha, factor analytic assessment of underlying dimensionality). In the current paper, this also applies to the formative scale of workplace injuries.

2 Separate models were constructed for each predictor for two reasons. First, the purpose of this research was not to examine which independent variables are the best predictors of accident underreporting, but rather to determine whether known predictors of underreporting explain more variance in recall vs. recognition-based measures of underreporting. Thus, our focus is on the dependent variables (comparing variance explained using recall vs. recognition measures), rather than a comparison of the independent variables. Second, because production pressure was only collected in the mining sample, modeling all of the predictors in a single analysis would have resulted in the loss of the remaining samples and a great deal of statistical power.

3. Study 1 results 3.1. Descriptive statistics and correlations

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Table 1 U.S. Descriptive Statistics.

Recognition – Experienced Accidents Recognition – Reported Accidents Recall – Experienced Accidents Recall – Reported Accidents

Table 3 Study 1: U.S. Predictors of Under-reporting. M

SD

Range

Skew

Kurtosis

Experienced

2.69 0.53 6.86 3.19

2.75 1.08 44.99 36.29

0–15 0–6 0–745 0–745

1.31 2.44 12.49 19.65

1.84 5.93 185.68 400.57

Recall Measures B 6.87 −1.004 1.80 1.50* 6.83 −3.18 6.22 −5.84**

1. Job Insecurity 2. Production Pressure

pressure, F(1, 188) = 3.35, p < 0.07 (η2 = 0.02). On the other hand, there was a significant interaction between the recognition measure of accidents (reported vs. experienced) and perceptions of job insecurity, F (1, 438) = 7.72, p < 0.01 (η2 = 0.02), production pressure, F(1, 193) = 10.76, p < 0.01 (η2 = 0.05), safety reporting attitudes, F(1, 438) = 62.38, p < 0.001 (η2 = 0.13), and safety compliance, F(1, 437) = 17.55, p < 0.001 (η2 = 0.04). In addition to the larger number of significant effects, the recognition measure also resulted in slightly larger effect sizes (.02–.13) than the recall measure (.01–.09), thus providing an initial answer to our Research Question #2. Namely, known predictors of accident under-reporting tend to be more consistently significant and explain more variance in such under-reporting when utilizing recognition-based measures compared to free recall measures. The form of these significant interactions was examined using the method for plotting interactions suggested by Aiken and West (1991). Specifically, we used the regression coefficients from the repeated measures analyses (see Table 3) to plot the predicted level of experienced and reported accidents at ± 1 SD for each predictor. The findings are shown on Fig. 1a–c for recall measures, and Fig. 1d–g for recognition measures. As can be seen, while significant accident underreporting occurred when measured using the recall method (i.e., total experienced accidents outnumbered reported accidents), this underreporting was significantly attenuated by positive reporting attitudes and safety compliance, and marginally exacerbated by increased production pressure. Similarly, significant levels of accident underreporting were observed when using the recognition method (i.e., total experienced accidents outnumbered reported accidents); moreover, this underreporting was significantly exacerbated by perceptions of job insecurity and high production pressure, yet was attenuated by positive reporting attitudes and safety compliance.

3. Reporting Attitudes 4. Safety Compliance

1. Job Insecurity 2. Production Pressure 3. Reporting Attitudes 4. Safety Compliance

Reported

SE 2.22 2.22 0.70 0.70 2.21 2.18 2.09 2.18

Recognition Measures B SE 2.70 0.13 0.39** 0.13 2.85 0.21 0.58** 0.21 2.70 0.12 −1.06** 0.12 2.70 0.13 −0.54** 0.13

B 3.27 −2.99 0.78 0.51 3.27 −0.01 3.27 0.41

SE 1.84 1.84 0.29 0.29 1.84 1.82 1.85 1.93

B 0.53 0.06 0.50 −0.07 0.53 −0.16** 0.53 −0.03

SE 0.05 0.05 0.08 0.08 0.05 0.05 0.05 0.05

Notes: Intercept and slope coefficients are reported on the first and second lines respectively for each predictor. *p < 0.05; **p < 0.01.

gical and practical reasons. From a methodological perspective, we wanted to verify that our results from Study 1 were not context dependent, i.e. to determine if they would generalize to a different cultural context. Moreover, with the growing economic crisis within Italy, concerns regarding job insecurity are becoming increasingly prevalent. Thus, there were practical reasons as well for the collection of Italian data. Surveys were administered to 592 employees from 22 different small to mid-sized organizations in Italy. Organizations were recruited from the following industry sectors that, according to INAIL (2011), have above average risk of employee injuries: health care (N = 2); manufacturing (N = 5); transportation (N = 4); energy (N = 4), construction (N = 5), and commerce (N = 2). Eighty-three percent of respondents were male, 15.5% female, with 0.2% leaving the item blank. The average employee age was 40.38 years (SD = 10.52). The average employee tenure was 12.1 years (SD = 9.67). Because exact population counts of employees were not obtained for all organizations, corresponding response rates cannot be computed. The research staff provided participants with informed consent materials that explained the anonymous nature of the data collection and their rights as research participants, and distributed the questionnaire in a sealed envelope in order to assure confidentiality. Furthermore, to facilitate accurate and honest responses, questionnaires were not distributed in the presence of organizational officials; employ-

4. Study 2 method: Italy 4.1. Participants and procedure Study 2 was conducted to attempt to replicate (and extend) our findings from Study 1 to a different cultural setting for both methodoloTable 2 U.S. Correlations.

1. Job Insecurity 2. Production Pressure 3. Reporting Attitudes 4. Safety Compliance 5. Workplace Injuries 6. Recognition – Exp. Accidents 7. Recognition – Reported Accidents 8. Recognition – Unreported Accidents 9. Recall – Exp. Accidents 10. Recall – Reported Accidents 11. Recall – Unreported Accidents

Mean

SD

1

2

3

4

5

6

7

8

9

10

11

1.42 2.71 5.15 6.23 2.66 2.69 0.53 2.16 6.86 3.19 3.60

1.07 1.27 1.26 0.88 2.80 2.75 1.08 2.56 44.99 36.29 24.96

(0.86) 0.28** −0.25** −0.15** −0.00 0.14** 0.05 0.13** −0.02 −0.07 0.08

(0.83) −0.62** −0.33** −0.37** −0.19** 0.06 0.23** −0.15* −0.15* 0.13

(0.74) 0.33** 0.40** 0.39** 0.15** −0.35** 0.07 0.00 −0.13**

(0.97) −0.27** −0.20** −0.03 −0.20** −0.13** 0.01 −0.30**

– 0.52** 0.28** 0.45** 0.16** 0.07 0.19**

– 0.36** 0.92** 0.15** 0.06 0.19**

– −0.03 0.07 0.00 0.12*

0.14** 0.06 0.16**

– 0.83** 0.56**

– 0.00

–

Notes: Exp. Accidents = Experienced Accidence. All measures have N = 440, except Production Pressure (N = 190) and Recognition – Experienced Accidents and Unreported Accidents (N = 413). *p < 0.05; **p < 0.01.

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Fig. 1. Predictors of reported vs. experienced accidents in the United States. Recall = recall-based measure; Recog = recognition-based measure.

loadings were all significant and ranged from 0.53 and 0.74, with the exception of one item which displayed a factor loading of 0.22. Because this was the only reverse worded item, we interpreted this finding as a method effect resulting from item phrasing and, therefore, retained all items from initial item pool. The AVE and CR values, respectively 0.35 and 0.77, were satisfactory. Overall, these results demonstrated the construct validity of the Italian version of both production pressure and attitudes towards reporting scales.

ees either completed the survey containing the research measures during working hours or had the option to complete it at home. 4.2. Measures The survey contained all the measures previously described in Study 1. Specifically, for the following scales we used the previously translated Italian versions: 1) recall measure of accidents, recognition measure of accidents, and job insecurity (Probst et al., 2013a); and 2) safety compliance (Barbaranelli et al., 2015). The production pressure (Probst and Graso, 2013), attitudes towards reporting (Probst et al., 2013b), and workplace injuries (Probst et al., 2013b) scales were translated into Italian from the English version using the standard translation-back-translation procedure recommended by Brislin (1980). The correspondence of the original and the back-translated items was then verified by the authors.

5.2. Descriptive statistics and correlations Table 4 presents descriptive statistics of the recall and recognition measures of accidents in Italy. The mean number of experienced accidents was 0.60 for the recall measure (SD = 1.76) and 1.50 for the recognition measure (SD = 2.49). Similar to Study 1, the recall measure was much more highly skewed (5.49) and exhibited much higher kurtosis (41.75) than the recognition measure (1.92 and 3.40, respectively). The mean number of reported accidents was 0.25 for the recall measure (SD = 0.78) and 0.19 for the recognition measure (SD = 0.63) with the recall measure again demonstrating more skew and kurtosis. This comports with the results from Study 1, showing recall measures of accidents reporting demonstrated more severe violations of normality in comparison to recognition ones. Table 5 presents descriptive statistics, scale reliabilities and zeroorder correlations among the study variables. All measures were

5. Study 2 results 5.1. Preliminary analyses Before conducting tests to answer our Research Questions, two preliminary confirmatory factor analyses (CFA) examined the factorial validity of the Italian version of latent construct scales (i.e., production pressure, attitudes towards reporting) respectively measured by five and six observed variables. The models were tested on the covariance matrix and using the Maximum Likelihood Robust estimation method (Muthén and Muthén, 1998–2012). Results from the CFA on production pressure showed good fit of the structure to the data: χ2 (5, N = 592) = 23.807, p < 0.01, RMSEA = 0.080 (.049; 0.113), CFI = 0.96, TLI = 0.93, with factor loadings all significant and ranging from 0.56 and 0.74. Moreover, the Average Variance Extracted (AVE) was 0.44 and Composite Reliability (CR) was 0.79, thus showing satisfactory values. Results from the CFA on attitudes towards reporting also showed a good fit to the data: χ2 (9, N = 592) = 21.129, p < 0.05, RMSEA = 0.048 (.021; 0.074), CFI = 0.97, TLI = 0.97. The factor

Table 4 Italy Descriptive Statistics.

Recognition – Experienced Accidents Recognition – Reported Accidents Recall – Experienced Accidents Recall – Reported Accidents N = 592.

6

M

SD

Range

Skew

Kurtosis

1.50 0.19 0.60 0.25

2.49 0.63 1.76 0.78

0–14 0–5 0–20 0–10

1.92 4.32 5.49 7.08

3.40 22.27 41.75 72.72

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Table 5 Italy Correlations.

1. Job Insecurity 2. Production Pressure 3. Reporting Attitudes 4. Safety Compliance 5. Workplace Injuries 6. Recognition – Exp. Accidents 7. Recognition – Reported Accidents 8. Recognition – Unreported Accidents 9. Recall – Exp. Accidents 10. Recall – Reported Accidents 11. Recall – Unreported Accidents

Mean

SD

1

2

3

4

5

6

7

8

9

10

11

1.42 3.52 4.66 5.20 1.75 1.50 0.19 1.30 0.60 0.25 0.35

0.84 1.33 1.12 1.26 3.83 2.49 0.63 2.29 1.76 0.78 1.24

(0.79) 0.12** −0.09* −0.07 0.10* 0.21** 0.10* 0.20** 0.12** 0.10* 0.10*

(.79) −0.60** −0.27** −0.13** −0.20** −0.10* 0.19** −0.14** −0.12** 0.13**

(0.73) −0.06 0.02 0.16** 0.04 −0.16** 0.11* 0.08 −0.11**

(0.91) −0.11** −0.16** −0.14** −0.14** −0.15** −0.17** −0.10*

– 0.30** 0.23** 0.26** 0.24** 0.23** 0.19**

– 0.43** 0.97** 0.54** 0.36** 0.54**

– 0.19** 0.31** 0.31** 0.24**

– 0.50** 0.31** 0.52**

– 0.79** 0.92**

– 0.50**

–

Notes: Exp. Accidents = Experienced Accidence. All measures have N = 592, except Safety Compliance and Injuries, where N = 591. *p < 0.05; **p < 0.01.

reliable with alpha coefficients ranging from.73 to 0.95. Recognition measures of experienced and reported accidents were significantly and positively related to workplace injuries (r = 0.30, p < 0.01; r = 0.23, p < 0.01), as were recall measures of experienced accidents (r = 0.24, p < 0.01) and reported accidents (r = 0.23, p < 0.01) Again, a Hotelling-Williams test of the equality of dependent correlations was conducted. Results indicate the strength of the relationship between injuries and experienced accidents was marginally larger for the recognition-based measure compared to the recall-based measure, t (589) = 1.60, p = 0.056. Thus, these findings partially replicate findings from Study 1 suggesting that workplace injuries tend to be more strongly correlated with recognition-based measures of under-reporting compared to recall-based measures.

Table 6 Study 2: Italy Predictors of Under-reporting. Experienced

1. Job Insecurity 2. Production Pressure 3. Reporting Attitudes 4. Safety Compliance

1. Job Insecurity

5.3. Predictors of recall and recognition measures of accidents

2. Production Pressure

In order to answer our Research Question #2 within Italy, the same repeated-measures analysis of variance models used in Study 1 were constructed in which job insecurity, production pressure, safety reporting attitudes, and safety compliance were modeled as continuous between-subjects independent variables and the within-subjects measure compared the number of experienced accidents to the number of reported accidents. As with Study 1, these analyses were conducted first for the free recall measure of experienced and reported accidents, and then again for the recognition measure of accidents. There was a significant interaction between the recall measure of accidents (reported vs. experienced) and job insecurity, F(1, 590) = 5.89, p < 0.05 (η2 = 0.01), production pressure, F(1, 590) = 10.48, p < 0.01 (η2 = 0.02), safety reporting attitudes, F(1, 590) = 6.79, p < 0.01 (η2 = 0.01), and safety compliance, F(1, 589) = 6.45, p < 0.05 (η2 =0.01). Similarly, there was a significant interaction between the recognition measure of accidents (reported vs. experienced) and perceptions of job insecurity, F(1, 590) = 25.51, p < 0.001 (η2 = 0.04), production pressure, F(1, 590) = 22.02, p < 0.001 (η2 = 0.04), safety reporting attitudes, F(1, 590) = 15.95, p < 0.001 (η2 = 0.03), and safety compliance, F(1, 589) = 11.10, p < 0.01 (η2 = 0.02). Again, using the recognition measure resulted in slightly larger effect sizes (.02–.04) than the recall measure (.01–.02), thus further corroborating results obtained in Study 1. In order to determine the form of the interactions, the Aiken and West (1991) method for plotting interactions described in Study 1 was once again followed; see Table 6 for the regression coefficients used to plot these figures. The figures demonstrate similar interaction patterns as found in Study 1. Specifically, for both recall (see Fig. 2a–d) and recognition (Fig. 2e–h) measures, there were significantly larger discrepancies between reported and actually experienced number of accidents among employees who perceived their job as insecure, reported high production pressure, and displayed poor reporting attitudes and low safety compliance.

3. Reporting Attitudes 4. Safety Compliance

Recall Measures B 0.60 0.20** 0.60 −0.19* 0.60 0.26** 0.60 −0.27**

Reported

SE 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

B 0.25 0.08* 0.25 −0.05 0.25 0.09** 0.25 −0.14**

SE 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Recognition Measures B SE 1.50 0.10 0.53** 0.10 1.50 0.10 0.50** 0.10 1.50 0.10 −0.40** 0.10 1.50 0.10 −0.41** 0.10

B 0.19 0.07* 0.19 0.07* 0.19 −0.03 0.20 −0.09**

SE 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Notes: Intercept and slope coefficients are reported on the first and second lines respectively for each predictor. *p < 0.05; **p < 0.01.

6. Discussion Although annual figures in the U.S. and Italy show meaningful numbers of work-related injuries, research suggests that these national surveillance statistics significantly underrepresent the true prevalence of occupational accidents and injuries due to under-reporting. Furthermore, while such undercounts can occur at both the organizational- (i.e., when a company decides whether to include a notified injury in their official injury log) and individual-level (i.e., when an employee decides whether to report an experienced injury to their employer), there is still little research on the optimal way to measure the extent to which employees fail to report a work-related injury they experience. Specifically, we note that the study of individual-level under-reporting relies on self-report data from employees to estimate discrepancies between the number of accidents that they experience and the number that are actually reported to their company. In our study, we focused on the extent to which employees’ accuracy in reporting their prior experiences of workplace accidents may depend upon free-recall vs. recognition-based techniques commonly used to operationalize accident under-reporting. Therefore, the purpose of the current research was to: a) examine the psychometric properties of two different methods used to operationalize accident under-reporting, one using a free recall methodology and the other a recognition-based approach; and b) assess the cross-cultural generalizability of these under-reporting measures by replicating our psychometric analyses in 7

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Fig. 2. Predictors of reported vs. experienced accidents in Italy. Recall = recall-based measure; Recog = recognition-based measure.

information processing during storage and retrieval of information in human memory. For example, a recency effect (Neil et al., 2007) may cause accidents that are more recent in time to be easiest to recall and report, whereas an accident more distant in time will be the least likely to be remembered. The ostrich effect may lead one to ignore an obvious negative (i.e., accident) situation. Social biases, as well as authority bias, may lead employees to report memories that are more consistent with the opinion or expectations of an authority figure (e.g., supervisor) rather than the actual experiences. Given these potential biases, a free recall approach that frames the period of experienced and reported accidents within one year preceding a survey is more likely conducive to distorted memories than a recognition prompted technique listing multiple job-specific accidents that might potentially have occurred. Secondly, we extend prior research on job insecurity, production pressure, safety reporting attitudes, and safety compliance as known correlates of accident under-reporting by demonstrating the significance of these antecedents when predicting under-reporting measured by both free-recall versus recognitions-based techniques. Thirdly, we demonstrated cross-country generalizability of our U.S.-based results by further testing our research questions in a large sample from 22 different organizations in Italy. From a practical perspective, the results of this study may assist organizations that wish to minimize the unwanted and costly effects of employee safety violations, as well as build a strong safety process that encourages accurate report of accidents. Organizations can send the message that safety is emphasized and rewarded by investing in the development of recognition-based tools for reporting accidents, specifically crafted for their own occupational setting and the related prototypical precipitating events or workplace injuries. In doing so, organizations may support their employees in accurately reporting experienced accidents. This may be particularly valuable for more vulnerable segments of the workforce such as aged workers and/or pregnant women, given earlier reviewed research indicating less accuracy with recall measures among these groups. Thus, organizations interested in preventing unwanted safety outcomes (e.g., days away from work, restricted duties, compensation claim costs) may invest in effective monitoring of injuries by developing

the United States and Italy. Specifically, we aimed to test whether the observed correlations between workplace injuries and accident reporting significantly differ among the recall vs. recognition measures, and whether there was differential variance explained by known predictors of employee accident under-reporting (i.e., job insecurity, production pressure, safety reporting attitudes, and safety compliance) when relying on recall vs. recognition measures. Our findings showed that in the U.S. and Italy both recall and recognition measures of accident under-reporting exhibited similar interaction patterns with job insecurity, production pressure, safety reporting attitudes, and safety compliance. As such, employees who perceived their job as insecure, reported high production pressure, and displayed poor reporting attitudes and low safety compliance displayed a significant and higher discrepancy between reported and actually experienced number of accidents (i.e., under-reporting). However, results from the two accident reporting techniques also showed that recall measures resulted in smaller effect sizes, had much more severe violations of normality, and were less correlated with self-report workplace injuries. Therefore, despite the ease of use and brevity of the recall measures, evidence from two different national contexts (i.e., U.S., Italy) seems to suggest that recognition-based measures of experienced and reported accidents may be psychometrically better ways of measuring work-related accidents and associated reporting behaviors. 6.1. Theoretical and practical implications The current study has several theoretical implications. Firstly, our research contributes to empirical investigation of the optimal way to validly measure accident under-reporting by comparing the psychometric properties of free-recall versus recognitions-based techniques. While the current literature acknowledges advantages and disadvantages of both types of under-reporting operationalization, this study is the first to include both measures in order to empirically contrast one with another. Accuracy of declarative memory can be affected by a wide variety of different biases. According to Hilbert (2012), at least eight seemingly unrelated biases can be produced and cause noisy 8

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more accurate surveillance tools. As such, a context-specific inventory measure of accident reporting may increase precision on recognitionbased tasks, thus assisting employees in accurately reporting to their employer the injuries they have experienced. 6.2. Strength, limitations and future directions The present findings clearly demonstrated that recognition-based measures of accident reporting have less severe violations of normality, are more correlated with workplace injuries, and are associated with slightly higher effect sizes when testing the nomological net of predictors related to under-reporting. Therefore, the use of recognition-based self-report measures may help employees’ memory in reporting their experience of injuries at work thus resulting in a more valid estimate of under-reporting. Furthermore, using a two-country study further strengthened the generalizability of these findings. However, while the set of data in the Italian context was drawn from numerous organizational samples representing a wide variety of industry sectors, they were nonetheless convenience samples (as were the U.S. ones). Hence, our findings might arguably be affected by selfselection biases in the kinds of organizations that agreed to participate. An additional aspect that should be addressed in future work is that all the study variables rely on self-reported data. Future research should attempt to further validate these measures and compare their psychometric properties by using independent sources of data (e.g., supervisors, coworkers). In doing so, a more rigorous test of the predictors’ effect on recall vs. recognition measures of under-reporting might be achieved by collecting multi-source data. Although no measure of accident reporting is likely to be 100% accurate, studying the relationship between other types of measures of under-reporting (e.g., archival records) and recall vs. recognition self-report measures of such phenomenon could further strengthen our present conclusions. Finally, while the current study tested the psychometric properties of recall vs. recognition measures of under-reporting with regard to job insecurity, production pressure, safety reporting attitudes, and safety compliance, additional variables related to under-reporting could be studied in the future. For example, safety climate and supervisor enforcement could contribute to extend the nomological net of variables related to under-reporting, and might fruitfully be included in future studies in order to further test the differential validity of the two different approaches (i.e., recall vs. recognition). Acknowledgements This study was partially funded by a research grant from the SHRM Foundation (Project No. 147). However, the interpretations, conclusions and recommendations are those of the authors and do not necessarily represent the views of the SHRM Foundation. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.aap.2017.05.006. References Aiken, L.S., West, S.G., 1991. Multiple Regression: Testing and Interpreting Interactions. Sage, Thousand Oaks. Barbaranelli, C., Petitta, L., Probst, T.M., 2015. Does safety climate predict safety performance in Italy and the USA? Cross-cultural validation of a theoretical model of safety climate. Accid. Anal. Prev. 77, 35–44. Borsboom, D., Mellenbergh, G.J., Van Heerden, 2004. The concept of validity. Psychol. Rev. 111, 1061–1071.

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