BUSINESS DATA

Collecting and Business data (statistics)



Collecting and Business data (statistics)

Introduction

In social researches, data is often gathered for qualitative variables by which a group of individuals are classified into two or more mutually exclusive categories. The proportions are a common way of expressing frequencies when the variable under study has two possible answers, such as presenting or not an event of interest (disease, death, healing, etc.). When the aim is to compare two or more groups of subjects with respect to a categorical variable, the results are often presented as two-way tables that are called contingency tables (Ott, 2008). Thus, the simplest situation of qualitative comparison between two variables is one in which both have only two possible response options (i.e., dichotomous variables) (Triebold, 2007).

The chi-square test determines whether two qualitative variables are associated or not (Alexeyev, 2000). If at the end of the study, no significant results appear, it can be concluded that the variables are not related; it can be said with a certain confidence level that both variables are independent of each other (Buglear, 2004).

This paper examines the relationship between variables for a study conducted on 200 respondents. A questionnaire was distributed which collected information about their gender, employment grade, distance of living place from work, utilization of social staff club, and preferred mode of traveling to work.

Null and alternate hypothesis based on above stated variables is listed below.

Null Hypothesis

H1: There is no relationship between the employment grading class and the living place distance from work.

H2: Mode of transportation selected for travel to work is independent of the gender differences.

Alternate Hypothesis

H1: Living place distance from work significantly differs with respect to change in the employment grade.

H2: Mode of transportation selected for travel to work is highly dependent on gender (i.e. different for males and females).

Statistical Testing

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