Frequency Distribution Cross Tabulation and Hypothesis Testing Marketing Research Help

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Commercial Battle of the Sexes

A comparison of television advertising in Australia, Mexico, and the United States focused on the analysis of sex roles in advertising. Results showed differences in the portrayal of the sexes in different countries. Australian advertisements revealed somewhat fewer, and Mexican advertisements slightly more, sex-role differences than U.S. advertisements. Cross-tabulation and chi-square analysis provided the following information for Mexico.

These results indicate that in Mexican commercials, women appeared in commercials for products used by women or by either sex but rarely in commercials for men’s products. Men appeared in commercials for

Product Advertised               Women           Men
Used by
Females                                  25.0                4.0
Males                                       6.8                 11.8
Either                                       68.2                84.2
x2 = 19.73.p ≤ 0.0001

products used by either sex, These differences were also found in the U.S. ads, although to a lesser extent, but were not found in Australian ads, Therefore, U.S. consumer products companies should not be advertising in Mexico in the same ways in which they advertise to the U.S,market. In the United States, the increasing population of Hispanic Americans has turned many advertisers attention to Spanish-language television advertising, Sex roles in the Hispanic culture show women as traditional homemakers, conservative, and dependent upon men for support, but many Hispanic families in the United States do not fit this traditionally held view. In 2009, more than half of Hispanic women worked outside the home, which almost matched the proportion of women in the Anglo population that worked outside the home in the United States.

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Twelve product categories were examined to compare catalog to store shopping, The null hypothesis that there is no significant difference in the overall amount of risk perceived when buying products by catalog compared to buying the same products in a retail store was rejected. The hypothesis was tested by computing 12 (one for each product) paired-observations test Mean scores for overall perceived risk for some of the products in both buying situations are presented in the following table with higher scores indicating greater risk.

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As can be seen, a significantly (p <0.01) higher overall amount of perceived risk was attached to products purchased by catalog as compared to those purchased from a retail store, Although this study reveals risk associated with catalog purchasing, terrorist threats, time shortage, and increased convenience have increased the amount of products that are purchased from catalogs as well as online.

These two examples show how basic data analysis can be useful in its own right, The cross-tabulation and chi-square analysis in the international television advertising example, and the paired 1 tests in the catalog shopping example, enabled us to draw specific conclusions from the data, Table 15.1 contains data for 30 respondents giving the sex (1 = male, 2 = female), familiarity with the Internet (1 = very)’ unfamiliar, 7 =very)’ familiar), Internet usage in hours per week, attitude toward the Internet and toward technology, both measured on a 7-point scale (1 = very)’ unfavorable, 7 = very)’ favorable), and whether the respondents have done shopping or banking on the Internet (1 = yes, 2 = no). For illustrative purposes, we consider only a small number of observations. In actual practice, frequencies, cross-tabulations and hypotheses tests are performed on much larger samples, such as that in the Dell running case and other cases with real data that are presented in this book, As a first step in the analysis, it is often useful to examine the frequency distributions of the relevant variables.

TABLE 15.1

TABLE 15.1

Frequency Distribution

Marketing researcbers often need to answer questions about a single variable. For example:

• How many users of the brand may be characterized as brand loyal?

• What percentage of the market consists of heavy users, medium users, light users, and nonusers?

• How many customers are very familiar with a new product offering? How many are familiar, somewhat familiar, and unfamiliar with the brand? What is the mean familiarity rating? Is there much variance in the extent to which customers are familiar with the new product?

• What is the income distribution of brand users? Is this distribution skewed toward low income brackets?

The answers to these kinds of questions can be determined by examining frequency distributions, In a frequency distribution, one-variable is considered at a time, The objective is 10 obtain a count of the number of responses associated with different values of the variable? The relative occurrence, or frequency, of different values of the variable is then expressed in percentages. A frequency distribution for a variable produces a table of frequency counts, percentages. and cumulative percentages for all the values associated with that variable.

TABLE  15.2

TABLE 15.2

Table 15.2 gives the frequency distribution of familiarity with the Internet. In the table, the first column contains the labels assigned to the different categories of the variable, and the second column indicates the codes assigned to each value. Note that a code of has been assigned to missing values, The third column gives the number of respondents checking each value. For example, three respondents checked value 5, indicating that they were somewhat familiar with the Internet. The fourth column displays the percentage of respondents checking each value. The next column shows percentages calculated by excluding the cases with missing values. If there were no missing values, columns 4 and 5 would be identical. The last column represents cumulative percentages after adjusting for missing cases. As can be seen, of the 30 respondents who participated in the survey, 10.0 percent checked value 5. If the one respondent with a missing value is excluded, this percentage changes to 103. The cumulative percentage corresponding to the value of 5 is 58.6. In other words, 58.6 percent of the respondents with valid responses indicated a familiarity value of 5 or less.

A frequency distribution helps determine the extent of item non-response (I respondent out of 30 in Table 15.1). It also indicates the extent of illegitimate responses. Values of 0 and 8 would be illegitimate responses, or errors. The cases with these values could be identified and corrective action taken. The presence of outliers or cases with extreme values can also be detected. In the case of a frequency distribution of household size, a few isolated families with household sizes of 9 or more might be considered outliers. A frequency distribution also indicates the shape of the empirical distribution of the variable. The frequency data may be used to construct a histogram, or a vertical bar chart, in which the values of the variable are portrayed along the X-axis and the absolute or relative frequencies of the values are placed along the Y-axis. Figure 15.1 is a histogram of the

FIGURE 15.1

FIGURE 15.1

frequency data in Table 15.1 From the histogram, one could examine whether the observed distribution is consistent with an expected or assumed distribution, such as the normal distribution.

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Basic Analysis Yields Olympic Results

For the 1996 Olympic games in Atlanta, more than 2 million unique visitors came to the games and more than II million tickets were sold. In Sydney, at the 2000 Olympic games, as well as in Athens at the 2004 Olympic games, 5 million tickets were sold. It is obvious that this is a potential target market that cannot be ignored. Researchers at the University of Colorado at Boulder decided to find what motivated the international and domestic travelers to come to the Olympic games. A survey was developed and administered to visitors to Olympic games via personal interviews. Three hundred twenty surveys were completed correctly and were used in the data analysis.

The results (see the following table) showed that the top three factors that motivated people to attend these games were a once-in-a-lifetime opportunity, availability of housing, and availability of tickets. The results of this study helped planners for the 2008 Olympic games in Beijing find what specific characteristics the city needed to improve. Fer instance, from this research, Beijing put funds into projects that added hotel rooms to the city. They also constructed state-of-the-art transportation and unique venues (Olympic parks, stadiums, tourist sites) so that visitors truly felt that they were getting a once-in-a-lifetime experience. As this survey continues to evolve over the years, the data received will become very valuable to the next host city. The planning for the 2012 Olympic games in London was also guided by these findings.

Motivational Factors That Influenced the Decision to Attend the Olympic Games

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Note that the numbers and percentages in the preceding example indicate the extent to which the various motivational factors attract individuals to the Olympic games, Because numbers are involved, a frequency distribution can be used to calculate descriptive or summary statistics.

Posted on December 1, 2015 in Frequency Distribution Cross Tabulation and Hypothesis Testing

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