Effect of Male and Female Supervisors on Employees Rate of Risk Occurrence

ABSTRACT

The purpose of this study was to investigate the rate of occurrence of risk for employees under male and female supervisors in three manufacturing locations. Using a t-test for independent samples, it was found that the rate of injury and risk for employees under male and female employees in three manufacturing locations was the same at t (49) = -.609, p > .05. However, the test also indicates that safety climate for employees under male and female employees in three manufacturing locations was different at t (49) = -2.052, p < .05.

It was concluded that the rate of occurrence of risk for employees under male and female supervisors in three manufacturing locations was the same at a 95% level of precision.

INTRODUCTION

The primary safety and health hazards for the construction worker are: falls, being struck by/against (falling object, machinery), caught in/between (trench cave-ins, between vehicle and object), electrocution, musculoskeletal disorders (lifting, awkward postures, repetitive motion, hand-tool vibration, flying/falling objects), and exposure to a variety of chronic health hazards (noise, silica, asbestos, manmade fibers, lead and other metals, solvents, hazardous wastes, heat, and extreme cold).

One of the major concerns in the manufacturing sector is employee safety. Male supervision has been dominant in the manufacturing sector for decades. Globally, gender based work place supervision has been changing with women acquiring supervision roles at a steady rate at just over 50% for the past 25 years. In the USA, women comprise 27% of the manufacturing workforce2 and almost half (47%) of the labor force with the annual growth of women in the labor force projected at approximately 0.7% during the next decade (Quinlan, et. al, 2001).

Research is constantly providing new methods of increasing safety at the work place. One of the major areas under study is the inclusion of the female gender in the supervisory roles. To make it possible for the realization of these methods, this paper strives to study the occurrence of risk to employees under male and female in different manufacturing locations empirically.

Statement of the problem

The purpose of this study was to investigate the rate of occurrence of risk for employees under male and female supervisors in three manufacturing locations.

Statement of Hypothesis

Work place safety was studied in 3 manufacturing locations and the research question of the study was used to formulate the study hypothesis. The null hypothesis stated that the rate of occurrence of risks is the same for the employees under female and male supervisors in the three manufacturing locations while the alternative hypothesis stated that the rate of risks occurrence is different for the employees under female and male supervisors in the three manufacturing locations. The hypothesis was tested at a 95% confidence interval.

METHOD

Participants for the study were selected through a stratified sampling technique in three manufacturing locations in the Unites States i.e. Boston, Phoenix and Results. A random sample of both male and female supervisors was picked in the 3 locations then the employees assigned to each of the selected supervisors were picked for the study using random numbers.

A measuring instrument was used to allocate indexes to each of the variables under study. The test was designed to measure the employees injury rate, safety climate and per safe beh to scale through an index. Employees risk was also measured as different variable using ordinal scale. Content validity was maintained in obtaining base values and calculation of the indices assigned to the variables in the study.

The design used in this study was an experimental design. The selection of this design was influenced by its capability to provide control for sources of invalidity and also its capability of achieving randomization in sampling. Pre testing was conducted to assess the viability of conducting the study.

The procedure used in this study involved selecting male and female supervisors randomly and assigning employees to each of the supervisors. Interviews were conducted in the field and data was recorded in questionnaires. Components of the questionnaire captured different attributes of the following variables; site, supervisor gender, hours worked per safe beh, injury rate, safety climate and the operation conducted. Additional secondary data regarding a supervisor’s total number of employees and their total number of hours worked was also recorded. The manufacturing locations were allocated equal number of supervisors with the aim of ensuring equality of variance for the different locations and also achieving normality of the observations.

Data was entered into Ms excel and then transferred to SPSS for analysis.

RESULTS

Data was loaded into the analysis software for and the output explained below was obtained. An independent samples t-test was carried out for 3 of the variables i.e. injury rate, safety climate and risk. Supervisor gender was used as the grouping variable in conducting the study where the effects of the manufacturing location were ignored. A t test for independent samples was used since the male and female supervised groups were randomly picked and the data collected was interval. Examination of means was also conducted to study variations as shown from the tables in the appendix.

For the injury rate, it is concluded that the injury rate is the same for the employees under female and male supervisors in the three manufacturing locations at 95% level of precision. The results indicate that the injury rate for employees under male supervisors (M = 1.353, SD =12.98) was not different from injury rate for employees under female supervisors (M = 1.663, SD = 20.816), t (49) = -.609, p> .05 i.e. 0.532. The injury rate is the same for employees under male and female supervisors in the 3 locations at 95% level of precision where 95% confidence interval for the mean difference between the male and female cases was -13.0148 to 6.8087189 as shown in tables 1 and 2 in the appendix.

For the safety climate, it was concluded that the safety climate is the different for the employees under female and male supervisors in the three manufacturing locations at 95% level of precision. The results indicated that the safety climate for employees under male supervisors (M = 4.391, SD =0.8626) was different from safety climate for employees under female supervisors (M = 4.968, SD = 1.112), t (49) = -2.052, p< .05 i.e. 0.045. The safety climate is the different for employees under male and female supervisors in the 3 locations at 95% level of precision where 95% confidence interval for the mean difference between the male and female cases was -1.14324 to – 0.012037 as shown in tables 3 and 4 in the appendix section.

For the rate of risk, it was concluded that risk is the same for the employees under female and male supervisors in the three manufacturing locations at 95% level of precision. The results indicated that the risk for employees under male supervisors (M = 4.79, SD =2.0) was not different from risk for employees under female supervisors (M = 4.41, SD = 2.043), t (49) = .677, p> .05 i.e. 0.501. The risk is the same for employees under male and female supervisors in the 3 locations at 95% level of precision where 95% confidence interval for the mean difference between the male and female cases was -.756 to 1.525 as shown in tables 5 and 6 from the appendix section.

DISCUSSION

The results of this study support the original hypothesis that the rate of occurrence of risk was the same for employees under male and female supervisors in the 3 manufacturing locations. However, the results cannot be generalized for all the variables under study since risk portrayed different results.

REFERENCES

Cooper, D. R., & Schindler, P. S. (2011). Business Research Methods (11th ed.). New York:       

McGraw-Hill/Irwin.

Creswell, J. W. (2003). Qualitative, quantitative, and mixed methods approaches (2nd ed.).         

Thousand Oaks, CA: Sage.

Ghauri, et al. (2005). Research Methods in Business Studies: a Practical Guide.

Kumar, R. (2009). Research Methodology: A step-by-step Guide for Beginners. Greater Kalash:  Sage Publications.

Quinlan M, Mayhew C, Bohle P. The global expansion of precarious employment, work  disorganization, and consequences for occupational health: a review of recent research.  Int J Health Serv. 2001; 31 (2):335-414.

APPENDICES

Group Statistics
	SupervisorGender	N	Mean	Std. Deviation	Std. Error Mean
InjuryRate	1	24	1.353291E1	12.9881295	2.6511908
2	27	1.663595E1	20.8160156	4.0060441

Table 1

Injury rate	Levene’s Test for Equality of Variances
F	Sig	t	df	Sig.(2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Equal var. assumed   Equal var. not assumed	1.662	.203	-.609   -.646	49   44.182	.532   .522	-3.10304   -3.10304	4.93226   4.80387	Lower	Upper
-13.0148   -12.7834	6.8087189   6.5774012

Table 2

Group Statistics
	SupervisorGender	N	Mean	Std. Deviation	Std. Error Mean
SafetyClimate	1	24	4.391250E0	.8626466	.1760870
2	27	4.968889E0	1.1129183	.2141812

Table 3

Safety climate	Levene’s Test for Equality of Variances
F	Sig	t	df	Sig.(2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Equal var. assumed   Equal var. not assumed	1.489	.228	-2.052   -2.083	49   48.15	.045   .043	-.577638   -.577638	.281453   .277272	Lower	Upper
-1.14324   -1.13508	-.012037   -.020191

Table 4

Group Statistics
	SupervisorGender	N	Mean	Std. Deviation	Std. Error Mean
Risk	1	24	4.79	2.000	.408
2	27	4.41	2.043	.393

Table 5

Risk	Levene’s Test for Equality of Variances
F	Sig	t	df	Sig.(2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Equal var. assumed   Equal var. not assumed	.052	.820	.677   .678	49   48.52	.501   .501	.384   .384	.567   .567	Lower	Upper
-.756   -.755	1.525   1.523

Table 6