Quantitative Analysis of Workplace Bullying Data

Quantitative Analysis of Workplace Bullying Data Bullying behavior was typically experienced on the playground or within the school systems of school age children, but now this multi-causal phenomenon is being reported by adults in the workplace at an astronomical level. Exploratory research has insinuated that there is not just one clear definition of workplace bullying but a combination of definitions (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014). Workplace bullying transpires when an employee encounters a steadfast pattern of ill-treatment from others in the work environment that brings about harm (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014). This type of harassment can consist of such tactics as verbal, nonverbal, emotional, physical abuse and public disgrace (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014). This form of workplace hostility is, for the most part, difficult to validate because, unlike the classical forms of school bullying, workplace bullies often functio n within the traditional regulations and policies of their organization (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014). In examining three peer-reviewed articles (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014), the researchers used several different types of descriptive statistics to measure workplace bullying (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014). Therefore, it is important to describe descriptive statistics data and the different methods used that summarizes the sample and the measures (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014). In combination with charts and graphic analysis (Jackson, 2016), descriptive statistics (Jackson, 2016; Trochim, Donnelly Arora, 2015) formed the basis of virtually every quantitative analysis of data which were used by each researcher to describe workplace bullying (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014) as it affects ones physical health (de pression), on how role stressors can influence bullying behavior and the academic levels of the individuals being bullied (Chipps et al., 2013: McTernan et al., 2013; Reknes et al., 2014). First, is to define descriptive statistics (Jackson, 2016; Trochim, Donnelly Arora, 2015). Descriptive statistics is the term given to the analysis of data that helps describe (Jackson, 2016; Trochim, Donnelly Arora, 2015), illustrate or summarize data in a study, for example, such as patterns of large numbers of data (Jackson, 2016; Trochim, Donnelly Arora, 2015). Descriptive statistics (Jackson, 2016; Trochim, Donnelly Arora, 2015) do not, however, allow one to make conclusions beyond the data that is analyzed or reach conclusions regarding any hypotheses that might have made (Jackson, 2016; Trochim, Donnelly Arora, 2015). They are simply a way to describe ones data (Jackson, 2016; Trochim, Donnelly Arora, 2015). For example, Chipps et al., (2013) study illustrates the incidence of workplace bullying among preoperative RNs, surgical technologists, and unlicensed preoperative personnel in two academic medical centers (Chipps et al., 2013). The study sought to determine whether the demographic variables of gender, ethnicity, hospital location, years of experience on the unit, years in the profession, and job title predict the experience of workplace bullying (Chipps et al., 2013). In addition, to ascertain whether a relationship exists between workplace bullying and emotional exhaustion (Chipps et al., 2013); and whether bullying is associated with perceptions of patient safety in the operating room (Chipps et al., 2013). The cross-sectional analysis included one hundred and sixty-seven preoperative nurses, surgical technologists, and unlicensed preoperative personnel (Chipps et al., 2013). The IBM SPSS Statistics version 20 for Windows (IBM, New York, NY ) was used to analyze the data. Descriptive statistics was calculated for each hospital in two different demographic areas (Chipps et al., 2013). In Table (1), the participants in Hospital A had a response rate of forty percent, and Hospital B had a response rate of twenty-three percent. Table 1. Comparison of Demographic Variables Of the total study sample of professionals, forty-five percent of participants were registered nurses (SD=14.3), fifty-three percent were nonRN surgical technologists (SD=15.9) and two percent (SD=1.2) were another unlicensed personnel who reported to nursing services in Table 2 (Chipps et al., 2013). Table 2. Comparison of Job Title/Profession The sample size in Figure 1, the sample size was seventy-four percent predominately white, twenty percent black and sixty percent identified self in the other category (Chipps et al., 2013). The demographics differences were significant between Hospital A and Hospital B (Chipps et al., 2013). Figure1. Sample size In Table 2 illustrated a frequency of bullying acts in in order of frequency on at least a monthly basis (Chipps et al., 2013), for example, his or her opinions ignored at twenty-eight percent (Chipps et al., 2013); twenty-seven percent reported being shouted at by peers (Chipps et al., 2013).; twenty-six percent reported experiencing information purposely withheld which hindered his or her work performance (Chipps et al., 2013), twenty-five percent humiliated in front of others and twenty-five percent experiencing rumors or gossip spread about him or her (Chipps et al., 2013). Table 1. Frequency of Bullying Acts The next study examined several hypotheses as it relates to role stressors within an individuals work environment and modern day occurrences of self-reported workplace bullying. Reknes et al., (2014) research revealed that role stressors are associated with workplace bullying after conducting a study with twenty Norwegian businesses (Reknes et al., 2014). During 2004 to 2009, a longitudinal study was conducted in the private and public sectors to focus on the characteristics of the work environment (Reknes et al., 2014). It was to ascertain whether role stressors, at baseline, can foretell new incidents of workplace bullying in the near future (Reknes et al., 2014). A sum of two thousand, eight hundred and thirty-five Norwegian employees joined the baseline and follow-up, with an interim of two years within the measurements (Reknes et al., 2014). The average ages of the participants were forty-five years of age, and sixty-four percent were women (Reknes et al., 2014). A sum of one hundred and six participants reported to be bullied in Figure 1, whereas, two thousand and three hundred and eighty-five participated said to not being bullied (Reknes et al., 2014). Figure 1. Respondent to the 1st and the 2nd surveys (N=2,835) Even though drop out analyzes were conducted using independent sample t-test on role conflict (N=245) and role ambiguity (N=158 [(Reknes et al., 2014).], the survey revealed no significant differences in the scores for role conflict for those who only participated in completing the 1st survey (Reknes et al., 2014), compared to those who participated in both measurement points (Jackson, 2016; Trochim, Donnelly Arora, 2015). However, those who participated in the 1st and the 2nd surveys (Reknes et al., 2014), illustrated a mean of 1.68 (Reknes et al., 2014) for role ambiguity and a mean of 2.47 (Reknes et al., 2014) for role conflict. Three hundred and forty-four (Reknes et al., 2014) self-reports of bullying behavior were missing from the baseline survey (Jackson, 2016; Trochim, Donnelly Arora, 2015). In additions, Figure 2 illustrated the following distribution of the respondents in the 1st and 2nd surveys (N=2835 [(Reknes et al., 2014)], in a table format showing an age frequency distribution with five categories of age ranged defined (Reknes et al., 2014). Figure 2. A frequency distribution of Age in table format The respondents in the 1st and the 2nd survey regarding the baseline characteristics of the respondents (Reknes et al., 2014), illustrated that individuals within forty to forty-nine of age (33.5%) is more likely to complete the survey than respondents (Reknes et al., 2014), under the age of thirty (5.5%). Whereas, self-reported bullying (Reknes et al., 2014), illustrated that role conflict nearly duplicate the likelihood of becoming a new target of bullying at T2 (odd ratio of 1.92). Consequently, the results showed no significant difference in the scores for role conflict for those who only participated in the first measurement (Reknes et al., 2014), compared to those who participated at both measurement points (Jackson, 2016; Trochim, Donnelly Arora, 2015). The research supported the data that role ambiguity and role conflict, individually, added to consequent different reports of workplace bullying. The statistical analysis was administered using IBM SPSS Statistics version 20 for Windows ( IBM, New York, NY ). Lastly, McTernan et. al., 2013, conducted a longitudinal survey design over a twelve months’ time frame. This investigation was to examine how job stressors and depression can influence productivity loss (McTernan et. al., 2013). First, to investigate this underlying assumption was to begin with data preparation on how to carry out data analysis (Jackson, 2016; Trochim, Donnelly Arora, 2015). Data preparation involved acquiring or collecting the data (Jackson, 2016; Trochim, Donnelly Arora, 2015); reviewing the data for accuracy (Jackson, 2016; Trochim, Donnelly Arora, 2015); inserting the data into the computer; modifying the data, and generating and documenting a database structure that integrates the various measures (Jackson, 2016; Trochim, Donnelly Arora, 2015). Secondly, is to utilize a codebook that represents each variable in the data and where and how it can be accessed (Jackson, 2016; Trochim, Donnelly Arora, 2015). For example, indicating the variable descripti on, organizing the variable format (number and data), identifying the respondent or group, and identifying the variable location (Jackson, 2016; Trochim, Donnelly Arora, 2015). Initially, the data was collected in 2009 and repeated in 2010 by an Australia workplace barometer (McTernan et. al., 2013). The data was gathered from two Australian states, Western Australia, and New South Wales (McTernan et. al., 2013). The sample size was increased, in order to weigh the gain in the control versus the time and cost of having more participants or gathering more data (Jackson, 2016; Trochim, Donnelly Arora, 2015). In this review, twenty thousand Austrian homes (McTernan et. al., 2013) phone numbers were called, yet the final sample consisted of one thousand three hundred twenty-six participants from north of South Wales and one thousand four hundred and sixty-four from Western Australia (McTernan et. al., 2013). The experiment consisted of one thousand three hundred and ninety females age d between eighteen and seventy-seven and between eighteen and eighty-five; the total male participants were one thousand three hundred and ninety –six (McTernan et. al., 2013). Time 2 data was composed of participants from Time 1 who agreed to take a follow-up questionnaire at least one year later in 2010 (McTernan et. al., 2013). Of the primary participants at Time 1, there were two thousand and seventy-four (McTernan et. al., 2013) who participated in the survey, whereas, with Time 2 consisted of nine hundred and twenty-seven females between nineteen and seventy-eight and one thousand and one hundred and forty-seven males between nineteen and eighty-two (McTernan et. al., 2013). The measure utilized the Patient Health Questionnaire 9 depression measure for a nine-item scale based on several criteria’s of depressive disorders in the Diagnostic Statistical Manual DSM-IV (McTernan et. al., 2013). Table 1 depicts the individual yearly productively loss cost (McTernan et. al., 2013) estimations as it relates to depression (N=2074). The relationship was significant as it related to the unstandardized parameters (McTernan et. al., 2013) by comparing the annual sickness absence (hours) with productivity loss using descriptive statistics (McTernan et. al., 2013). The annual sickness absence illustrated 138.4 hours loss (SD=48.24) due to the severity level of the workers’ depression (McTernan et. al., 2013), as compared to, 28% productivity loss (SD=3.5). Table 2 illustrated the estimated odd ratios for job strain and workplace bullying on depression as well as the odd ratios and population attributable risk for job strain, bullying and job strain without bullying (McTernan et. al., 2013). The researchers used the population attributable risk as a method to estimate the proportion of a disease burden that could theoretically be eliminated by the removal of a causal factor (McTernan et. al., 2013). The findings confirmed that the underlying assumption of the link between job stressors and productively loss via depression (McTernan et. al., 2013). The prevalence of exposure to job strain was 22.5% (McTernan et. al., 2013) compared to bullying exposure 5.9% (McTernan et. al., 2013). The population attributable failed to yield any significant difference when job strain and bullying was used independently, yet, when combined, the results showed the annual depression cost which contributed to productivity loss (McTernan et. al., 2013). Table 2. Population Attributable Risk References Chipps, E., Stelmaschuk, S., Albert, N., Bernhard, L., Holloman, C. (2014). Workplace bullying in the OR: Results of a descriptive study, AORN Journal, 98(5):479-493. IBM Corp. Released 2011. IBM SPSS Statistics for Windows, Version 20.0. New York, NY: IBM Corp. Jackson, S. (2015). Research methods and statistics: A critical thinking approach. (5th ed.) United States of American: Boston, MA. McTerman, W., Dollard, M., LaMontagne, A. (2013). Depression in the workplace: An economic cost analysis of depression-related productivity loss attributable to job strain and bullying, Work Stress, 27(4):321-338. Reknes, I., Einarsen, S., Knardahl, S., Lau, B. (2014). The prospective relationship between role stressors and new cases of self-reported workplace bullying. Scandinavian Journal of Psychology, 55:45-52. Trochim, T., Donnelly, J., Arora, A. (2015). Research methods: The essential knowledge base. United States of America: Boston, MA.