Statistical Data Analysis
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Statistical Data Analysis

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PPT example of some moderate statstical data analysis and representation.

PPT example of some moderate statstical data analysis and representation.

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  • Data from a cross sectional field study is used to determine if there is a relationship between a sense of employee empowerment and productivity. The notional data generated for Pearson correlational analysis supports a positive relationship between the two variables. Alternate methods of statistical analysis are also discussed herein. The relationship between the independent and dependent variables is supportive of findings from past studies.
  • Previous studies have supported a positive relationship between employee empowerment and productivity (Shrednick, Shutt, & Weiss, 1992; Foltz, 2010; Fabre, 2010; Poteet, 2005). Many of these studies even provide evidence for a causal relationship between the two. In addition, there are numerous studies addressing the moderating effect of workplace environmental factors (Laschinger & Wong, 1999; Boudrias et al., 2010; Carnicer, Sanchez, Perez, & Jimenez, 2005). Our aim however is to simply determine in an empirical manner if our own study can support previous findings and reject the null hypothesis in favor of a relationship between the two variables. We will be engaging in primarily quantitative analysis to test our hypothesis. If we are able to establish such a relationship through correlational analysis of our own simulated primary data, we will be contributing to the validity of the prevailing theories on this topic. We will add to the replicability factor of other similar studies and increase the confidence in the scientific nature of such research (Sekaran, 2009).
  • Using information gathered form employee interviews as well as existing literature, a 10-question survey is administered to 20 employee test subjects. For each survey, 5 of the questions aim to rate the employee’s sense of empowerment. The remaining 5 questions are completed by the employee’s direct supervisor too measure the relative productivity. This research instrumentation approach is optimal because it is minimally-intrusive and does not attempt to manipulate any of the variables involved in the study. Thus from the outset, our study will not aim to determine causality as it is intended to be as observational as possible in nature. Respondents supply answers to the survey questions using a 5-point Likert scale. The interval data we will obtain from the summated scores will be useful for or purposes of correlational analysis. Since the Likert scale is widely used in the academic research community, it should confer some validity to our scientific methodology (Sekaran, 2009). This Likert-survey instrumentation profile has been used successfully in past studies to measure subjective employee variables such as empowerment as well as to assess productivity (Ford, 1985; Jauch, Glueck, & Osborn, 1978; Williamson,2007).
  • Using the summated data from the administered survey, we are able to see the frequencies of each variable for our subjects. The mean occurs at 3.0 as expected for both variables, and the standard deviation is 1.3 for empowerment and 1.2 for productivity. All questions from our survey are assumed to have high interitem consistency. The Cronbach’s alpha and split-half reliability coefficient are other advanced measures of reliability that can be calculated through computer analysis (Sekaran, 2009). Using this data we can proceed to the next step of Pearson correlation analysis.[Simulated data. Generated from “Random Integer Generator”]
  • A Pearson correlation analysis allows us to see the strength and direction of a bivariate relationship (Sekaran, 2009) if it exists. From our data, a strong correlation is indicated with r=0.85 and its square, the coefficient of determination, at 0.72. The correlation is in the positive direction, which does support the prevailing theories that empowerment leads to productivity. However we can not infer a causal relationship from this data. It is still evidence that will support the rejection of our null hypothesis, but while we have determined a strong correlation, it must still be proven significant considering the size of the sample and the population from which it was drawn. To jump to a conclusion at this point could lead to a Type I error, where we end up rejecting a null hypothesis that is actually true (“Significance of”, 2001).[Calculations from Microsoft Excel 2010; “Statistics Calculators”]
  • A scatterplot is useful to visualize the data for our two variables of interest. Even before any statistical calculations are considered, an upward positive trend appears to be present. A simple regression analysis to plot a trend line with a least squares function makes this even more salient. The r2 coefficient calculated in the previous slide is related to the positive slope of the trend line. The coefficient of determination for any correlation ranges from 0 to 1, and our value of 0.72 is indicative of a strong correlation between the two variables. To be more exact, r2 defines the “proportion of variance in common” between empowerment and productivity (Rummel, 1976). Next we must determine if our results so far are statistically significant (p < 0.01).
  • With a calculated p-value of 2x10-6, we find that are the positive correlation observe in our sample is statistically significant. We are now able to reject the null hypothesis in favor of the alternative hypothesis. Our low p-level means that we have a high significance level very low probability of our observed relationship resulting purely from chance in our sample. The Pearson correlation has been proven as a useful tool to test hypotheses concerning organizational multivariate relationships in many other studies (Dwivedula& Bredillet, 2010; Bobko & Karren, 1979). Our bivariate correlational analysis has conclusively substantiated evidence for a positive relationship between employee empowerment and productivity in the context of this study.[Calculations from Microsoft Excel 2010; “Statistics Calculators”]
  • The Pearson correlational method was chosen for this analytical effort because it best fit the needs of the study. The requisite bivariate analysis to determine if the null hypothesis could be rejected called for a statistical comparison of the data obtained from the survey instrument. The parametric nature of our data also allowed for any relationship to be visualized in a scatterplot. A simple regression showed the positive upward trend between the observed measurements of empowerment and productivity.Other methods such as a chi-squared test could have been used to analyze our data, but we would have needed to treat our data as nonmetric. This may have been more appropriate if we classified our subjects into distinct categories such as empowered and not-empowered, or productive and not-productive. Since our data was of the interval type, this method was not chosen to test the hypothesis.A t-test was a potential option to test our observed results against the expected population standard if we had had access to or assumed the actual level of correlation between our variables among the population our sample was taken from. In most cases where this test is used to address a relationship between variables in a null hypothesis, this is assumed to be 0. This test might have been appropriate if we made an such an assumption. We did, however, make a similar assumption in calculating our p-value. The low p-level we attained was indicative of the low likelihood our observed correlation between variables was not typical of the population at large.The analysis of variance, or ANOVA, is a third method that can be used in similar statistical analyses. The ANOVA allows the comparison of two or more groups on a dependent variable (Sekaran, 2009). This may have been useful if we were investigating a broader range of variables, such as motivation level and competency level in addition to empowerment and productivity levels. Since our analysis was only bivariate in nature, employing this technique would not have added much value to our analytical efforts.
  • The Pearson correlation analysis resulted in a correlation coefficient of 0.85 which indicates that the null hypothesis should be rejected. With a p-value of 2x10-6, based on our sample size of 20, we can conclude that our observed correlation is statistically significant. These results allow us to reject the null hypothesis with confidence. Our alternative hypothesis that a sense of employee empowerment is related to productivity is consistent with findings of previous related studies, as referenced the purpose statement. However the limitations of the data in this research study prevent us from supporting any causal factors in the relationship.Since our findings are consistent with prevailing knowledge and theories on the nature of the relationship between the two variables of interest, it our recommendation that employers encourage their employees to engage in empowered behavior and implement policies that are conducive to employee empowerment. Even without causal determination, the context of this study shows conclusively that employees that have a sense of empowerment tend to have higher productivity levels. Our findings and use of standardized research instruments also add to the replicability and confidence levels to past studies and theoretical frameworks in the academic body of knowledge. The findings of this study may also serve as supporting evidence of the established relationship between the two variables in future studies that address causal factors or other related multivariate relationships.
  • Bazerman, M. H., & Moore, D. A. (2009). Judgment in managerial decision making (7th ed.). Hoboken, NJ: Wiley.Bobko, P., & Karren, R. (1979). The perception of Pearson product moment correlations from bivariate scatterplots. Personnel Psychology, 32(2), 313-325. Retrieved from EBSCOhost.Boudrias, J., Brunet, L., Morin, A. J. S., Savoie, A., Plunier, P., & Cacciatore, G. (2010). Empowering employees: The moderating role of perceived organisational climate and justice. Canadian Journal of Behavioural Science, 42(4), 201-211. Retrieved from PsycARTICLES.Carnicer, P. L., Sanchez, A. M., Perez, M. P., & Jimenez, M. J. V. (2005). Team empowerment: An empirical study in Spanish University R&D teams. International Journal of Human Resources Development and Management, 5(1), 69-84. Retrieved from ABI/INFORM Global. Correlation and Linear Regression (n.d.). Retrieved from http://richardbowles.tripod.com/maths/correlation/corr.htmDwivedula, R., & Bredillet, C. N. (2010). The relationship between organizational and professional commitment in the case of project workers: Implications for project management. Project Management Journal, 41(4), 79-88. Fabre, J. (2010). The importance of empowering front-line staff. Supervision, 71(12), 6-7. Retrieved from Associates Programs Source Plus.Foltz, J., & Wilson, C. (2010). Motivate and engage your employees. Feed & Grain, 49(5), 58-62. Retrieved from Associates Programs Source Plus.Ford Jr., D. L. (1985). Facets of work support and employee work outcomes: An exploratory analysis. Journal of Management, 11(3), 5. Retrieved from EBSCOhost.Jauch, L. R., Glueck, W. F., & Osborn, R. N. (1978). Organizational loyalty, professional commitment, and academic research productivity. Academy of Management Journal, 21(1), 84-92. Retrieved from EBSCOhost.Laschinger, H. K. S., & Wong, C. (1999). Staff nurse empowerment and collective accountability: Effect on perceived productivity and self-rated work effectiveness. Nursing Economic$, 17(6), 308. Retrieved from EBSCOhost.Poteet, M. L. (2005). Improving employee productivity. In Encyclopedia of health care management, Sage. Retrieved from Credo Reference.Random Integer Generator (2011). Retrieved from http://www.random.org/integers/Rummel, R. J. (1976). Understanding Correlation. Retrieved from http://www.hawaii.edu/powerkills/UC.HTM#*Sekaran, U., & Bougie, R. (2009). Research methods for business: A skill building approach (5th ed.). Hoboken, NJ: Wiley.Shrednick, H. R., Stutt, R. J., & Weiss, M. (1992). Empowerment: Key to IS world-class quality. MIS Quarterly, 16(4), 491-505. Retrieved from Business Source Premier.Significance of the Correlation Coefficient (2001). Retrieved from http://janda.org/c10/Lectures/topic06/L24-significanceR.htmSoper, D. (2011). Statistics Calculators. Retrieved from http://www.danielsoper.com/statcalc/calc44.aspxWilliamson, K. M. (2007). Home health care nurses' perceptions of empowerment. Journal of Community Health Nursing, 24(3), 133-153. Retrieved from Academic Search Premier.

Statistical Data Analysis Presentation Transcript

  • 1. MGT600 UNIT 4 IP DATA ANALYSIS AND HYPOTHESIS TESTING:EMPLOYEE EMPOWERMENT AND PRODUCTIVITY Eric Braatz AIU Dr. Maria Marin 1/27/2011
  • 2. ABSTRACTData from a cross sectional field study is used to determine ifthere is a relationship between a sense of employeeempowerment and productivity. The notional data generatedfor Pearson correlational analysis supports a positiverelationship between the two variables. Alternate methods ofstatistical analysis are also discussed herein. The relationshipbetween the independent and dependent variables issupportive of findings from past studies.
  • 3. PURPOSE OF STUDY Cross-sectional correlation study to determine the relationship between employee empowerment and productivity Null Hypothesis (H 0 )There is no relationship between a sense of employee empowerment and productivity. Alternative Hypothesis (H A ) There is a relationship between a sense of employee empowerment and productivity.
  • 4. RESEARCH INSTRUMENTATION Semi-structured interviews with employees and literature review were used to determine survey instrument type A 10-question, 5-Point Likert scale survey administered to 20 subjects  5 questions measuring sense of empowerment  section is employee self-reported  5 questions assessing productivity  section is completed by employee’s supervisor
  • 5. STUDY DATA PREPARATIONSubject Empowerment Productivity A B 4.9 3.0 4.7 2.7 Survey response data C 2.5 1.6 is transformed and D 3.1 4.0 E 2.4 3.0 summated into one F G 2.0 1.7 2.0 1.1 score for each H I 1.0 1.5 1.0 2.2 measured variable J 3.2 3.7  Empowerment K 1.0 1.8 L 2.8 3.1  Productivity M 4.2 4.5 N 5.0 4.1 Empowerment Productivity O 4.2 4.0 Range 1.0 - 5.0 P 5.0 5.0 Median 2.9 3.1 Q 3.7 2.6 R 2.3 3.3 Mean 3.0 3.0 S 2.1 2.5 1.37368421 Variance 1.677894737 T 4.4 3.1 1 St. Dev. 1.295335762 1.172042751
  • 6. ANALYSIS OF DATA The data shows a strong positive correlation between the variables of empowerment and productivity  Pearson correlation coefficient: r = 0.850043314  Coefficient of determination: r 2 = 0.722573636
  • 7. VISUALIZATION OF DATA Scatter Diagram & Regression Analysis 6.0 y = 0.7691x + 0.6926 5.0 R² = 0.7226 4.0Productivity 3.0 Subjects Linear (Subjects) 2.0 1.0 0.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 Empowerment
  • 8. HYPOTHESIS TESTING H 0 is rejected and a positive relationship between the two variables is supported by H A Strength of correlation is strong r 2 = 0.722573636 Significance of correlation is adequate p = 0.000002
  • 9. DISCUSSION OF ANALY TICAL METHODS Pearson Correlation deemed appropriate  Compares two parametric variables  Tests against null hypothesis Allows relationship to be visualizedChi-square test Used for nonparametric data t -test Single sample or paired  Used to compare sample to population standardANOVA  Tests significance of mean differences among >2 groups
  • 10. CONCLUSION The null hypothesis is rejected Data supports a positive relationship between  independent variable: employee empowerment and  dependent variable: productivity Conclusion supports findings of previous studies and adds validity to prevailing theories Recommendation to employers: Emphasis on empowerment policies & training for employees
  • 11. REFERENCESBazerman, M. H., & Moore, D. A. (2009). Judgment in managerial decision making (7th ed.). Hoboken, NJ: Wiley.Bobko, P., & Karren, R. (1979). The perception of Pearson product moment correlations from bivariate scatterplots. Personnel Psychology , 32(2), 313-325. Retrieved from EBSCOhost.Boudrias, J., Brunet, L., Morin, A. J. S., Savoie, A., Plunier, P., & Cacciatore, G. (2010). Empowering employees: The moderating role of perceived organisational climate and justice. Canadian Journal of Behavioural Science, 42 (4), 201-211. Retrieved from PsycARTICLES.Carnicer, P. L., Sanchez, A. M., Perez, M. P., & Jimenez, M. J. V. (2005). Team empowerment: An empirical study in Spanish University R&D teams. International Journal of Human Resources Development and Management , 5(1), 69-84. Retrieved from ABI/INFORM Global.Correlation and Linear Regression (n.d.). Retrieved from http://richardbowles.tripod.com/maths/correlation/corr.htmDwivedula, R., & Bredillet, C. N. (2010). The relationship between organizational and professional commitment in the case of project workers: Implications for project management. Project Management Journal , 41(4), 79-88.Fabre, J. (2010). The importance of empowering front -line staff. Supervision, 71(12), 6-7. Retrieved from Associates Programs Source Plus.Foltz, J., & Wilson, C. (2010). Motivate and engage your employees. Feed & Grain, 49(5), 58-62. Retrieved from Associates Programs Source Plus.Ford Jr., D. L. (1985). Facets of work support and employee work outcomes: An exploratory analysis. Journal of Management, 11(3), 5. Retrieved from EBSCOhost.Jauch, L. R., Glueck, W. F., & Osborn, R. N. (1978). Organizational loyalty, professional commitment, and academic research productivity. Academy of Management Journal , 21(1), 84-92. Retrieved from EBSCOhost.Laschinger, H. K. S., & Wong, C. (1999). Staff nurse empowerment and collective accountability: Effect on perceived productivity and self -rated work effectiveness. Nursing Economic$, 17 (6), 308. Retrieved from EBSCOhost.Poteet, M. L. (2005). Improving employee productivity. In Encyclopedia of health care management, Sage . Retrieved from Credo Reference.Random Integer Generator (2011). Retrieved from http://www.random.org/integers/Rummel, R. J. (1976). Understanding Correlation. Retrieved from