Market Research Report

Paper Market Research Report: Results

Before beginning, this unit’s analysis of the results, identify a minimum of three hypotheses that your data will look to test. To begin the analysis of the results, you will enter the data you accumulated into a frequency table, contingency table, or another tabulation table of your choice. Use the appropriate statistical analysis tools, as described in Chapters 14 and 15 (regression analysis is required). Please note that the final discussion around proving/disproving the hypothesis and the conclusion/recommendations (implications) of the study will be added in the Unit VIII Assignment.

Your response for this unit should be a minimum of three pages, not including the title page, reference page, or appendix. References should include your textbook and a minimum of one additional credible reference. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations per APA guidelines

Levels of Scale Measurement
and Suggested Descriptive Statistics

The bottom part of Exhibit 14.1 displays example descriptive statistics for interval and ratio variables. In this case, the chart displays results of a question asking respondents how much they typically spend on a bottle of wine purchased in a store. The mean and standard deviation are displayed beside the chart as 11.7 and 4.5, respectively. Additionally, the frequency distribution is shown with a histogram. A histogram is a graphical way of showing the frequency distribution in which the height of a bar corresponds to the frequency of a category. Histograms are useful for any type of data, but with continuous variables (interval or ratio) the histogram is useful for providing a quick assessment of the distribution of the data. A normal distribution line is superimposed over the histogram providing an easy comparison to see if the data are skewed or multimodal.

Creating and Interpreting Tabulation

Tabulation refers to the orderly arrangement of data in a table or other summary format. When this tabulation process is done by hand the term tallying is used. Counting the different ways respondents answered a question and arranging them in a simple tabular form yields a frequency table. The actual number of responses to each category is a variable’s frequency distribution. A simple tabulation of this type is sometimes called a marginal tabulation.

Tabulation tells the researcher how frequently each response occurs. This starting point for analysis requires the researcher to count responses or observations for each category or code assigned to a variable. A frequency table showing where consumers generally purchase beer can be computed easily. The tabular results that correspond to the chart would appear as follows:

Histogram

A graphical way of showing a frequency distribution in which the height of a bar corresponds to the observed frequency of the category.

Tabulation

The orderly arrangement of data in a table or other sum­ mary format showing the number of responses to each response category; tallying.

Frequency table

A table showing the different ways respondents answered a question.

Response Frequency Percent Cumulative Percentage

Convenience store 225 45 55

Specialty 35 7 97

396 PART FIVE Basic Data Analytics

Who is your favorite movie star?

C OU!

The frequency column shows the tally result or the number of respon­ dents listing each store, respectively. The percent column shows the total per­ centage in each category. The cumulative percentage shows the percentage indicating either a particular category or any preceding category as their pre­ ferred place to purchase beer. From this chart, the mode indicates that the typical consumer buys beer at the convenience store since more people in­ dicate convenience store as the place where they usually buy beer than any other category.

Similarly, Americans’ responses to the simple question of”Who is your favorite movie star?” were recently tabulated. Overall, Tom Hanks, Denzel Washington,Jennifer Lawrence,Julia Roberts, and Sandra Bullock were the top five based on frequency of being mentioned as the favorite among 2,300 respondents. 2 However, the response varied by generation. For respondents aged 18-36 (Echo Boomers),Jennifer Lawrence was the favorite. For respon­ dents aged 37-48 (Gen X),Tom Hanks ranked first as was also the case for Boomers (49-67), while John Wayne led the pack among the Matures (68+ ). The idea that generation may influence choice of favorite movie star brings us to cross-tabulation.

on
Does your choice agree with others of your generation?

I I

Cross-tabulation

The appropriate technique for addressing research questions involving relationships among multiple less-than interval vari- ables; results in a combined frequency table displaying one variable in rows and another in

columns.

Contingency table

A data matrix that displays the frequency of some combina­ tion of possible responses to

multiple variables; cross-tabulation results.

Cross-Tabulation from Consumer Ethics Survey
A frequency distribution or tabulation can address many research questions. As long as a question deals with only one categorical variable, tabulation is the best approach to communicate the result. Although frequency counts, percentage distributions, and averages summarize considerable infor­ mation, simple tabulation may not yield the full value of the research when multiple variables are in­ volved. Cross-tabulation is generally a more appropriate technique for addressing research questions involving relationships among multiple less-than interval variables. A cross-tabulation is a combined frequency table. Cross-tabulation allows the inspection and comparison of differences among groups based on nominal or ordinal categories. One key to interpreting a cross-tabulation table is compar­ ing the observed table values with hypothetical values that would result from pure chance.

Exhibit 14.2 summarizes cross-tabulations from consumers’ responses to different ways of ob­ taining music in the United States.3 The study contrasts a questionable method of obtaining music online (downloading from an illegal file sharing site) by a basic demographic variable-genera­ tion.A sample of214 consumers of varying ages provides the data.When given a choice between obtaining a music file via the Internet, 166 of the 214 would choose to purchase it legally from a site like iTunes while 48 would obtain it free even if the download were illegal. The cross-tabu­ lation comes by looking at how generation membership (a less-than interval variable) influences choice of methods to obtain the music. Exhibit 14.2 breaks down the results and suggests that the echo boomer generation displays a preference toward obtaining the music illegally (35 of 53) while older generations tend toward purchasing the music rather than downloading illicitly.

Contingency Tables

Exhibit 14.3 shows an example of cross-tabulation results using contingency tables. A contingency table is a data matrix that displays the frequency of some combination of possible responses to

Generation Purchase Download Total

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Gen X 41 12 53 ®gi

Boomer 54 55 j

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Mature 53 0 53 u

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166 48 214 j

i4 Basic Data @ 397

(A) Cross-Tabulation of Question “Do you shop at Target?” by $ex of Respondent

Yes No Total

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(B) Percentage Cross-Tabulation of Question “Do you shop at Target?” by Sex of Respondent, Row Percentage

Total

Yes No (Base)

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(C) Percentage Cross-Tabulation of Question “Do you shop at Target?” by Sex of Respondent, Column Percentage

Yes No

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multiple variables. Two-way contingency tables, meaning they involve two less-than interval vari­ ables, are used most often. A three-way contingency table involves three less-than interval vari­ ables. Beyond three variables, contingency tables become difficult to analyze and explain easily.

Two variables are depicted in the contingency table shown in panel A:

1111 Row Variable: Biological Sex Male Female

1111 ColumnVariable:”Do you shop atTarget?YES or NO”

Several conclusions can be drawn initially by examining the row and column totals:

1. 225 men and 225 women responded as can be seen in the Total column.This means that alto­ gether 450 consumers responded.

2. Out of this 450 total consumers, 330 consumers indicated that “yes” they do shop at Target and 120 indicated “no,” they do not shop at Target. This can be observed in the column totals at the bottom of the table. These row and column totals often are called marginals because they appear in the table’s margins.

Researchers usually are more interested in the inner cells of a contingency table. The inner cells display conditional frequencies (combinations). Using these values, we can draw some more specific conclusions:

1. Out of330 consumers who shop atTarget, 150 are male and 180 are female.

2. Alternatively, out of the 120 respondents not shopping at Target, 75 are male and 45 are female.

This finding helps us know whether the two variables are related. If men and women equally patronize Target, we would expect that hypothetically, 165 of the 330 shoppers would be male and 165 would be female. Clearly, these hypothetical expectations (165 male/165 female) are not

Marginals

Row and column totals in a contingency table, which are shown in its margins.

398 ® PART FIVE Basic Data Analytics

observed. What is the implication? A relationship exists between respondent sex and shopping choice. Specifically, Target shoppers are more likely to be female than male. Notice that the same meaning could be drawn by analyzing non-Target shoppers.

A two-way contingency table like the one shown in part A is referred to as a 2 x 2 table because it has two rows (Men and Women) and two columns (Yes and No). Each variable has two levels. A two-way contingency table displaying two variables, one (the row variable) with three levels and the other with four levels, would be referred to as a J x 4 table. Any cross-tabulation table may be classified according to the number of rows by the number of columns (R by C).

Statistical base

The number of respondents or observations (in a row or column) used as a basis for computing percentages.

To the

,,;The more we study, the more we discover our ignorance.
··PERCY BYSSHE SHELLEY

Elaboration analysis

An analysis of the basic cross­ tabulation for each level of a variable not previously consid­ ered, such as subgroups of the

sample.

Moderator variable

A third variable that changes the nature of a relationship be­ tween the original independent and dependent variables.

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Cross-Tabulation of Marital
Percentage Cross-Tabulations

When data from a survey are cross-tabulated, percentages help the researcher understand the na­ ture of the relationship by making relative comparisons simpler. The total number of respondents or observations may be used as a statistical base for computing the percentage in each cell.When the objective of the research is to identify a relationship between answers to two questions (or two variables), one of the questions is commonly chosen to be the source of the base for determin­ ing percentages. For example, look at the data in parts A, B, and C of Exhibit 14.3. Compare part B with part C. Selecting either the row percentages or the column percentages will emphasize a particular comparison or distribution. The nature of the problem the researcher wishes to answer will determine which marginal total will serve as a base for computing percentages.

Elaboration and Refinement

The Oxford Universal Dictionary defines analysis as “the resolution of anything complex into its simplest elements.” Once a researcher has examined the basic relationship between two variables, he or she may wish to investigate this relationship under a variety of different conditions. Typically, a third variable is introduced into the analysis to elaborate and refine the researcher’s understand­ ing by specifying the conditions under which the relationship between the first two variables is strongest and weakest. In other words, a more elaborate analysis asks, “Will interpretation of the relationship be modified if other variubles are simultaneously considered?”

Elaboration analysis involves the basic cross.:.tabulation within various subgroups of the sam­ ple.The researcher breaks down the analysis for each level of another variable. If the researcher has cross-tabulated shopping preference by sex (see Exhibit 14.3) and wishes to investigate another variable (say, marital status), a more elaborate analysis may be conducted. Exhibit 14.4 breaks down the responses to the question “Do you shop at Target?” by sex and marital status. The data show women display the same preference whether married or single. However, married men are much more likely to shop at Target than are single men. The analysis suggests that the original conclusion about the relationship between sex and shopping behavior for women be retained. However, a relationship that was not discernible in the two-variable case is evident. Married men more frequently shop at Target than do single men.

The finding is consistent with an interaction effect.The combination of the two variables, sex and marital status, is associated with differences in the dependent variable. Interactions between variables examine moderating variables. A moderator variable is a third variable that changes the nature of a relationship between the original independent and dependent variables. Marital status is a moderator variable in this case. The interaction effect suggests that marriage changes the rela­ tionship between sex and shopping preference.

Married

Single

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Status, Sex, and Responses to
Men Women

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the Question “Do You Shop
at Target?”

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—– —– —- — ——— — —-f
Yes 55% 80% 86% 80% ‘-‘

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No

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45% 20% 14%

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CHAPTER 14 Basic Data Analysis 399

In other situations, the addition of a third variable to the analysis may lead us to reject the original conclusion about the relationship. When this occurs, the elaboration analysis suggests the relationship between the original variables is spurious.

How Many Cross-Tabulations?

Surveys may ask dozens of questions and hundreds of categorical variables can be stored in a data warehouse. Computer-assisted marketing researchers can “fish” for relationships by cross-tabulat­ ing every categorical variable with every other categorical variable. Thus, every possible response becomes a possible explanatory variable. A researcher addressing an exploratory research question may find some benefit in such a fishing expedition. Marketing analytics software exists that automatically searches through volumes of cross-tabulations looking for relationships.These results may provide some insight into the market segment structure for some product. Alternatively, the program may flag the cross-tabulations suggesting the strongest relationship. CHAID (chi-square automatic interaction detection) software exemplifies software that makes searches through large numbers of variables possible. Data-mining can be conducted with CHAID or similar techniques and may suggest useful relationships. A recent application paired promotion types against the type of product and suggests that coupons work best in getting consumers to come to your restaurant but television advertising works best in selling automobiles. 4 Although marketing analytics that mines data for information that may predict sales sounds complicated, cross-tabulation provides a basis for many of the search routines.

Outside of exploratory research, researchers should conduct cross-tabulations that address spe­ cific research questions or hypotheses. When hypotheses involve relationships among two cat­ egorical variables, cross-tabulations are the right tool for the job. However, as the number of categorical variables becomes greater, depicting the resuld in a table shown in a report or presen­ tation becomes difficult and complicated to interpret. Therefore, as the number of variables moves beyond three, analysts may not depict them in a report.

Data Transformation

Simple n aJ[1• ons

Data transformation (also called data conversion) is the process of changing the data format from the original form into a format more amenable to analytics appropriate for achieving the given research objectives. Researchers often recode the raw responses into modified or new variables. For example, many researchers believe that less response bias will result if interviewers ask respondents for their year of birth rather than their age.This presents no problem for the research analyst because a simple data transformation is possible.The raw data coded as birth year can easily be transformed to age by subtracting the birth year from the current year. In fact, some software automatically records dates in formats such as the amount of time since the arrival of January 1, 1900, or the arrival ofJanuary 1, 1980, considered the birth date of the PC age.The analyst may find it helpful to transform these into more user friendly formats.

In earlier chapters, we discussed recoding and creating summated scales. Reverse coding and the creation of composite scales represent common data transformations.

Collapsing or combining adjacent categories of a variable is a common form of data transfor­ mation used to reduce the number of categories. A Likert scale may sometimes be collapsed into a smaller number of categories. For instance, consider the following Likert item administered to a sample of state university seniors:

Data transformation

Process of changing the data from their original form to a format suitable for performing a data analysis addressing research objectives.

the Point

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