MARKETING RESEARCH PART 17 pot

Testing for Differences Between Two Groups or Among More than Two Groups Why Differences are Important • Market segmentation holds that within a market, there are different types of consumers who have different requirements, and these differences can be the bases of marketing strategies Ch 17 Why Differences are Important • Some differences are obvious – differences between teens’ and baby boomers’ music preferences • Other differences are not so obvious and marketers who “discover” these subtle differences may take advantage of huge gains in the marketplace Ch 17 Why Differences are Important Market Segmentation • Differences must be statistically significant – Statistical significance of differences: the differences in the sample(s) may be assumed to exist in the population(s) from which the random samples are drawn Ch 17 Why Differences are Important Market Segmentation • Differences must be meaningful – Meaningful difference: one that the marketing manager can potentially use as a basis for marketing decisions Ch 17 Why Differences are Important Market Segmentation • Differences should be stable – Stable difference: one that will be in place for the foreseeable future • Differences must be actionable – Actionable difference: the marketer can focus various marketing strategies and tactics, such as advertising, on the market segments to accentuate the Ch 17 differences between segments Small Sample Sizes: The Use of a t Test or a z Test • Most of the equations in this chapter will lead to the computation of a z value • There are certain circumstances in which the z test is not appropriate • The t-test should be used when the sample size is 30 or less • The t-test is defined as the statistical inference test to be used with small sample sizes (n is less than or equal to Ch 17 30) Determining Statistical Significance: The P value • Statistical tests generate some critical value usually identified by some letter; i.e., z, t or F • Associated with the value will be a p value which stands for probability of supporting the null hypothesis (no difference or no association) • If the probability of supporting the null hypothesis is low, say 0.05 or less, Ch 17 we have significance! Determining Statistical Significance: The P value • P values are often identified in SPSS with abbreviations such as “Sig.” or “Prob.” • P values range from to 1.0 • See MRI 17.1 on page 491 Ch 17 Some Example P Values and Their Meaning • First, we MUST determine the amount of sampling error we are willing to accept and still say the results are significant Convention is 5% (0.05), and this is known as the “alpha error.” Ch 17 10 Ch 17 28 Ch 17 29 An Example • Is there a difference between the mean for “prefer simple décor” vs the mean for “prefer elegant décor”? – Both “prefer simple décor” and “prefer elegant décor” are intervally scaled so it is proper to calculate a mean for each question Ch 17 30 An Example – Second, since the same members of the sample answered both questions, the two groups generating the means to these questions are not independent, they are paired – Under these conditions, it is appropriate to use SPSS: Ch 17 • ANALYZE, COMPARE MEANS, PAIRED SAMPLES T-TEST (See p 503.) 31 Ch 17 32 Ch 17 33 Online Surveys and Databases: A “Significance” Challenge to Marketing Researchers • Sample size has a great deal to with statistical significance • Sample size n appears in statistical formulas dealing with differences, confidence intervals, hypothesis tests, etc • Online surveys allow data collection from large sample sizes, so most tests may be found to be significant • The difference should be meaningful Ch 17 34 as well Testing for Significant Differences Among More than Two Groups • ANOVA – Analysis of variance (ANOVA): used when comparing the means of three or more groups – ANOVA will “flag” when at least one pair of means has a statistically significant difference, but it does not tell which pair Ch 17 35 Testing for Significant Differences Among More than Two Groups – When the F values “Sig.” is less than or equal to 0.05, ANOVA is telling you that “at least one pair of means is significantly different.” – To determine which pair(s) are different, you must rerun the test and select a POST HOC test (Duncan) Ch 17 36 Testing for Significant Differences Among More than Two Groups – Assume that we wish to know if the mean score on “likelihood of patronizing an upscale restaurant” differs across sections of newspaper read most – ANALYZE, COMPARE MEANS, ONE-WAY ANOVA – “Likely” goes in Dependent list; “section of newspaper” goes in factor Ch 17 37 Testing for Significant Differences Among More than Two Groups – Output shows Sig Is 0.000 meaning at least one pair of means is different – Now rerun the ANOVA but select Duncan under the POST HOC button Ch 17 38 Ch 17 39 Ch 17 40 In Summary: Test of Differences Among More than Two Groups • The basic logic – ANOVA (Analysis of Variance) – Test all pairs of averages simultaneously Ch 17 41 In Summary: Test of Differences Among More than Two Groups – If no pair is different at the 95% level of confidence, stop the analysis and say all pairs are “Equal.” – If at least one pair is different at the 95% level of confidence, make a table to show what pairs are “Equal” or “Unequal” by running post hoc test Ch 17 42 ... appropriate to use SPSS: Ch 17 • ANALYZE, COMPARE MEANS, PAIRED SAMPLES T-TEST (See p 503.) 31 Ch 17 32 Ch 17 33 Online Surveys and Databases: A “Significance” Challenge to Marketing Researchers • Sample... drawn Ch 17 Why Differences are Important Market Segmentation • Differences must be meaningful – Meaningful difference: one that the marketing manager can potentially use as a basis for marketing. .. button Ch 17 38 Ch 17 39 Ch 17 40 In Summary: Test of Differences Among More than Two Groups • The basic logic – ANOVA (Analysis of Variance) – Test all pairs of averages simultaneously Ch 17 41