Reply to the following 4 posts in 100 words or more for each.

Regression Analysis

posted by

Rosie Cordova

, May 15, 2018, 11:19 PM

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Regression analysis is commonly used for forecasting or predicting. This statistical technique is used to determine how a dependent variable changes with respect to an independent variable. It addresses relationships between one dependent variable and independent variables (Ghauri & Gronhaub, 2005). An HR manager may use regression analysis to predict how many applications would need to be received for a specific position in order to find qualified candidates.

The term regression analysis makes reference to high scores or to low scores returning to the mean. However, the term regression analysis means to predict, “The more regression toward the mean, the less well one can predict or explain. By analyzing regression to the mean, it is possible to determine the degree to which information about some variables can be used to predict or explain others” (Vogt, 2007, p. 146).

Vogt, W. P. (2007). Quantitative research methods for professionals. Boston, MA: Allyn & Bacon.

Ghauri, P. & Gronhaug, K. (2005). Research methods in business studies: A practical guide (3rd ed.). Harlow, Essex, England: Prentice-Hall.

posted by

Rosie Cordova

, May 15, 2018, 11:17 PM

Statistics may be a bad word for some HR practitioners and a scary word for others. The reality is that the concept of statistics is a friendly and proper term. Statistics is an exact science. There are many difficult decisions that managers must make with respect to management of human resources, and statistical analysis may provide the best available information for decision-making.

Share with the class the one statistical term that has your highest dislike/fear/don’t understand/etc. Students, please feel free to provide positive and/or clarifying information about that statistical term that others shared.

Chp 11

posted by

ASHLEY TOTAH MANGUM

, May 15, 2018, 7:00 AM

Hi professor and class,

These sections discuss examples of ways in which to analyze data. In particular, factor analysis, data reduction, cluster analysis, structure and dimensions and multidimensional scaling are discussed. Factor analysis analyzes correlations between large set of variables with common “dimensions” or factors (Ghauri & Grønhaug, 2010, p. 189). When running factor analysis, burnout amongst certain professions like nursing and teachers, were conducted. Each factor was labeled by researchers to capture the burnout construct and interpret the data. As demonstrated, in factor analysis, only “common variance is accounted for” (Ghauri & Grønhaug, 2010, p. 191). Therefore, it is used to identify variables which could be a potential cause, and is thus analyzed to see which cause is either the most important, or the most leading cause of burnout.

Ghauri, P., & Grønhaug, K. (2010). Research methods in business studies: A practical guide (4th ed.). New York, NY: Prentice Hall.

Cluster analysis

posted by

DENNIS GUERRERO

, May 15, 2018, 2:33 PM

Hello Professor Cordova and Class,

Chapter 11 briefly dives into several methods of analyzing data. Another method I read into is Cluster Analysis. The purpose of a cluster analysis is to determine natural grouping of the units in each sample. Each group within the sample is called a cluster. The cluster analysis is sometimes confused with e a discriminate analysis but the important difference is that in a discriminant analysis, the groups and the group membership of the units are already known before the start of the analysis. In cluster analysis there are no groups or cluster already identified. In studies, researchers have used grouping or clustering units often because it is useful and can help the researcher generate hypothesis that can be tested later (Ghauri, 2010, Chapter 11). Let say an organization wanted to enhance implement a hr wellness program for their employees. In my organization, we receive a credit on our premiums if we complete wellness assessments and biometric screening. A cluster analysis can be used to find groups of employees who may be obsessed, drink heavy, have good or poor health. The company can use this data to develop a better wellness program.

References

Ghauri, P. (2010). Research Methods in Business Studies (4th ed.). Retrieved from The University of Phoenix eBook Collection database.