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Table of Contents

Executive Summary

Introduction

Methodology

Analysis

Conclusions

Summary

Appendix A

Appendix B

CONCLUSIONS

This section explains how the initial data was gathered from the sample group.

Basic Statistics Report

1.  Correlated (paired) t-tests comparing the Brand Index Scores (a sum of the 10 Likert question responses) for each of the three brands:

Based on Likert item responses, consumers rated Gillette the highest out of the three (mean score of 30.4), but only by a very small margin.  Adidas had the second-highest mean score with 29.8, and Sure was only a tenth behind with a BIS of 29.7. The paired t-tests are not significant, however, meaning that these results may not be the same in 85 out of 100 samples from the same population.

2.  For the brand Gillette, a between-groups t-test comparing the Brand Index Scores and Ad Index Scores (number of ‘favorable’ survey responses answered for the ad) of (1) the respondents whose constant-sum scale moved up pre-to-post exposure, and (2) those whose scale moved down:

Those in the “move up” group gave a higher Brand Index Score (32.3) for the Gillette brand and they also liked the Gillette ad better (7.0) than those in the “move down” group (BIS 31.2, AIS 5.1).  The Ad Index Score of both groups is significant, so those means will be the same in 85 out of every 100 samples drawn from the same population.  This significance will not necessarily be true for the Brand Index Score, however.

3.  For the Sure brand, a crosstab table of a Chi-square significance test that will show if there is a relationship between up, same, and down pre-to-post movers and being above or below the median Brand Index Score:

The relationship appears to be a negative or opposite one — a larger number of those whose pre-to-post score moved up or stayed the same had Brand Index Scores that were below the median (8 above/9 below for up-movers, 12 below/7 above for stayed-samers).  And a larger number who moved down after seeing the ad still had Brand Index Scores above the median (11 above, 9 below for down-movers).  This may suggest that the Sure ad is not effective enough; in other words, it cannot bring up the brand perception enough to get people over the median score.  This Chi-squared test is not significant, so it is uncertain if these same results will occur in 85 out of every 100 samples from the same population.

4.  Frequency chart showing how many respondents’ constant-sum scores moved up, stayed the same, or moved down after being exposed to the ads for all three brands:

The Adidas ad was the most effective one in the study, bringing up the change score of 28 respondents after exposure.  Conversely, the Sure ad was the least effective, only increasing the change score of 18 respondents.  The Sure ad also caused the most scores to move down (23).  This confirms the hypothesis that the Sure ad is not effective enough in changing attitude, as stated in point #3 above. Advertisers at Adidas might see these results and give themselves a pat on the back, while marketers at Sure might want to kick themselves and try to create a better ad next time.

5.  Comparison of Brand Index Scores for Gillette versus Brand Index Scores for Adidas to see which brand the respondents have ranked higher:

It was found that 35 out of 62 respondents (56.5%) had a higher Brand Index Score for Gillette, meaning that a majority of those who took the survey favored the Gillette brand over the Adidas brand.  Referring back to point #1 above will confirm this result, as the Gillette brand had the highest overall mean Brand Index score.

6.  Simple correlation coefficient between Gillette Brand Index Scores and Adidas Brand Index Scores:

A correlation coefficient of .43 was found, suggesting that the relationship between these two Brand Index Scores is only a moderate positive one.  This result is significant, meaning that the same correlation coefficient can be expected in 85 out of 100 samples drawn from the same population.

7.  Brand Index Score comparison (Gillette vs. Adidas, from problem #5 above) repeated, but with only a select number of cases included.  All respondents of age 35 and older are not considered in this chart:

Remaining consistent with the results from the initial comparison explained in #5 above, a majority of the younger respondents in the survey (those under age 35) have also given Gillette a higher Brand Index Score than Adidas (29 out of 42, 63.0%).  Advertisers at Gillette may see such results and be glad that their brand has a favorable perception among young people.

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Regression Analysis

1.  Sure:  The relationship between the change score (dependent variable) and the ten Likert items (independent variables) is not very strong at all.  Only 17.6 percent of the variance in Sure’s change score is explained by its 10 Likert items.  This means that respondents’ perception of the Sure brand was not greatly affected by the Sure advertisement they were shown.  This further confirms one of the hypotheses already stated, that the Sure ad is not effective in changing consumer attitude. The F-ratio is not significant so we are unable to project the results from Sure’s model onto the whole population.

2.  Gillette:  The relationship between Gillette’s variables is slightly stronger than the Sure brand in the first model, but it is still relatively weak overall. About 22.4 percent of the variance in Gillette’s change score is explained by its 10 Likert items.  The perception of the Gillette brand was not greatly affected by the Gillette ad seen in the survey.  Since its F-ratio is not significant, these multiple regression statistics may not be similar in 85 out of 100 samples taken from this same population.

Important Beta (β) items for each brand in the regression coefficient model:

For Sure, “consider buying,” “best brand,” “trusted brand,” and “recommend” are the largest factors that account for the variance in the change score.  The t-ratios indicate significance of the constant term and/or the unstandardized regression coefficients, telling which of these statistics can be accurately projected to the whole population.  In the Sure model, constant term, “consider buying,” “men only,” “best brand,” and “trusted brand” are the figures that should end up nearly the same in 85 out of 100 samples taken from this same population.

For Gillette, the important Beta items are “fit lifestyle,” “trusted brand,” “not use,” and “recommend.” These impact the variance in the change score the most.  Significant items for Gillette are “fit lifestyle” and “trusted brand,” meaning the numbers from those two should come out very similar in 85 out of 100 samples taken from this population.  All other Likert items are insignificant and may not generate the same results in further samples.

The important Beta items for Adidas are “good brand,” “prefer brand,” “consider buying,” “fit lifestyle,” “best brand,” and “not use.”  These items affect the variance in the change score the most.  For Adidas, constant term, “consider buying,” and “not use” are significant, so the results from those three would likely be almost the same in 85 out of 100 samples taken from the same population.  The rest of the Likert items are not significant and cannot be accurately projected to the whole population.

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Discriminant Analysis

Sure brand of deodorant, two groups selected: (1) respondents whose change score moved up on pre-to-post advertisement exposure and (2) respondents whose change score moved down; ndependent variables are the ten Likert items from the Sure brand’s survey section.

Most of the mean scores for the 10 Likert items are very similar between these two groups.  This suggests that the points are not very far apart on a scatterplot and therefore they will be sitting fairly close to the discriminant function line that separates the groups.

The group centroids (0.7 for up-movers, -0.5 for down-movers) indicate the mean function score of each group, or the average location of all the points.  The distance between these two means is 1.2, which tells us that there is a moderate level of separation between the two groups.  Wilks’ Lambda is not significant in this case, so we cannot be certain that the same values will be generated for the group centroids in 85 out of 100 samples from this population.

Important coefficients for the Sure discriminant function are “good brand,” “best brand,” and “only brand." These are the largest factors (Likert items) that account for the variance in respondents’ change scores.

In the classification accuracy matrix, 11 of the up-movers and 17 of the down-movers were correctly categorized, for an overall accuracy of 68.3 percent. This means that we will have a 68.3 percent chance of accurately placing a respondent in the correct group (up- or down-mover) based on their discriminant function scores. The t-ratio for this classification matrix is significant, so we can expect a very similar accuracy to occur in at least 85 out of 100 samples from this population.

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ANOVA / MANOVA

Two-way factorial ANOVA on the Gillette brand of deodorant, two independent factors are (1) respondents’ change score from pre-to-post advertisement exposure and (2) whether or not respondents always buy the same brand of deodorant, dependent variable is the Likert item statement, “Gillette is a good brand.”

The groups of respondents who do not always buy the same brand of deodorant have a higher mean score for the “good brand” Likert item, meaning that those consumers who are not loyal to a particular brand feel slightly more favorable to the Gillette brand based on the advertisement they saw.

The F-ratios for both the “Change Score” and the “Change Score by Same Brand” analyses are significant.  Since the F-ratio for the multiplied Interaction ANOVA is significant, we can apply this significance to all groups.  Therefore, we can expect all the Group Means from the ANOVA to be very similar in 85 out of 100 samples taken from this same population.

A multiple analysis of variance (MANOVA) which utilizes all the same inputs as the ANOVA test, except it includes all 10 Likert items as dependent variables:

In the Gillette MANOVA, it was found that respondents who do not buy a specific brand of deodorant all the time (they are not loyal to a particular brand) had a more favorable response to Gillette after advertising than did those who are loyal to a brand. In other words, those who are not brand loyal are more responsive to advertising. None of the F-ratios are significant at the 85 percent confidence level, however, so none of these Group Means can be accurately projected to the whole population.  The statistics may vary if more samples are taken.

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Factor Analysis

Three factor analyses, one for each brand of men’s deodorant:  the variables used in the analyses are the 10 Likert items for each brand.

For the Sure brand, 2 factors have Eigenvalues greater than 1.0.  These 2 factors combine to explain 62.0% of the variance in the original ten Likert items (38.0% of the variance is unexplained).  For Gillette, 3 strong factors account for 71.0% of the variance (29.0% is unexplained).  The Adidas analysis also has 3 significant factors that explain 63.5% of the variance (36.5% is unexplained variance).

[ Communalitiy and Factor Loading, Attitude Scores ]

For the Sure brand, the original Factor Matrix statistics are used to generate the attitude score because there were not any ambiguous or problem rows.  There is no need to use the Varimax Rotated Matrix numbers because they do not improve the results. A total of 9 Likert items loaded with the "good" item. The mean Attitude Score generated for Sure based on these 9 variables is 3.0.

The original Factor Matrix statistics will also be used to generate the attitude score for the Gillette brand because there were a fewer number of ambiguous or problem rows (only 2).  The Varimax Rotated Matrix columns do not improve the results; in fact, they give worse results (3 ambiguous rows). Gillette also had 9 Likert items load in the same factor as the "good" item. The mean Attitude Score was 3.0.

For the Adidas brand, the Varimax Rotated Matrix statistics were used to generate the attitude score because they have a fewer number of ambiguous or problem rows (only 3) compared to the original Factor Matrix figures (6 problem rows).  In this case, the Varimax Rotation does improve the results. Adidas had 4 Likert items load in the same factor as the "good" one. The mean Attitude Score was 3.2

The factor analysis results state that respondents have the most favorable attitude toward the Adidas brand. This contradicts the Brand Index Score results found earlier, in which Gillette had the highest score.

The paired t-tests indicate that in 85 or more samples out of every 100 samples drawn from this same population, it is expected that the differences between brand attitude scores for Sure and Adidas and for Gillette and Adidas would be similar to what they are in this sample.  The difference between brand attitude scores for Sure and Gillette is not significant and cannot be projected to the whole population.

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Cluster Analysis

Clusters were derived from survey responses to ten Likert item questions for the Gillette brand. Clusters were then validated by comparing them to a demographic variable by use of a Chi-squared significance test and a crosstab table.

Based on the mean scores between the two clusters, Cluster 1 had a more positive response to the Gillette brand while Cluster 2 had a more negative response to it. 

Significance test: For all ten Likert items, the F-ratios indicate that the cluster means and standard deviations are significant, telling us that all means and standard deviations from the Gillette two-means cluster analysis should be very similar in at least 85 out of 100 samples taken from this same population.

Clusters 1 and 2 were compared to a demographic variable, “Do you always purchase the same brand of deodorant?” in which respondents could answer either Yes or No.

It was found that about the same number of people who are brand loyal had positive and negative responses to the Gillette ad (22 in Cluster 1, 22 in Cluster 2). Of respondents who are not brand loyal, two-thirds of them had a more favorable response to the ad (12 in Cluster 1, 6 in Cluster 2). This confirms the hypothesis stated earlier that advertising has more effect on consumers who are not brand loyal.

The Chi-squared value is significant for the crosstab table, so we can project these statistics onto the whole population:  In 85 out of 100 samples taken from the same population as this one, about the same number of respondents would fall into Clusters 1 and 2 based on their brand loyalty.

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