Unit 7 Discuss - II

Student II

 AM.

RE: Unit VII

There are several statistical techniques to be used when determining the differences between groups.  Some options are:

Bivariate tests of differences

Tests involving only two variables, often treating one like a dependent variable and the other as independent.

T-Test

Difference tests used when comparing one variable with two categories with a continuous independent variable.  For instance, this test would be applicable when testing how many times a month people go see a movie in Texas versus New York.

Paired Samples T-Test

A test for comparing scores “when two interval variables are drawn from related populations”, or the same respondent answers two related questions (Babin, B., & Zikmund, W., 2016, p.425).  For instance, if the same sample group is questioned on how much they like watching movies on Netflix and how much they like going to see movies in theaters.

The Z-Test

Difference tests for comparing two proportions.  For instance, this testing technique would be used when testing a hypothesis to compare differences in proportions of two groups.

One-Way Analysis of Variance (ANOVA)

Difference technique used when more than two groups or populations are compared.  ANOVA is essentially a T-Test technique with more than two levels.

 

Determining the differences between groups is vital in the comparisons built from research studies.  Knowing what differences are between groups being compared in a study will help researchers better understand the phenomenon at hand and why the differences in the groups present different actions or behaviors when related to a variable.

References:

Babin, B. J., & Zikmund, W. G. (2016). Essentials of marketing research. Boston, MA, USA: Cengage Learning.

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Student II:

AM

RE: Unit VII Discussion Board Question

In the Opening Vignette of Chapter 14, a study on company complaints is described.  The results find that out of 162 complainers sampled, 43.6% were under 25 years of age, 35.3% were from ages 26-39,  66.7% are from ages 40-53, and 65.9% of the complainers were over 54 years of age.  The results show that in this company complaint sample, older people were most likely to complain. However, information about how many people are in each group represented in the sample could have an influence on this percentage.  162 complainers is not that significant of a sample to make a broad observation that “older people are more likely to complain”.  There is the possibility that there were more consumers over 40 years old that purchased from the company than were under 40, or vice versa.  Without all of this data, one cannot assume that older people are more likely to complain.  Using the data in this study, 40-53 year olds had the highest complaint percentage, and 54 years and over were close behind.  This is does not necessarily mean that the older consumers are better sources of marketing information, nor that they are chronic complainers.  The quality of the complaints would be useful in detecting the useful sources and weeding out those who are “chronic complainers”, which exist at every age.  The firm would not be making a wise decision by deciding that they would be better off without the higher complaints group.  The firm needs to research why out of 237 consumers, only 75 seemed happy with the service.  We all know some complaints are more severe than others, so diving into the specific complaints and correlating to age group would be beneficial for more accurate answering of these questions following the vignette.

References:

Babin, B. J., & Zikmund, W. G. (2016). Essentials of marketing research. Boston, MA,    USA: Cengage Learning.

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