Deciphering Results For each survey item you are analyzing, choose one of the following: Independent samples t-test Paired samples t-test One sample t-test Crosstab with chi-square Correlation Averages and percentages are interesting, but they are not enough on their own.

Independent samples t-test If you are testing for differences between groups, run an independent samples t- test. EXAMPLE: H1: Commuting students will have a stronger preference for off-campus restaurants than residential students.

Independent samples t-test We found that commuting students have a stronger preference for off-campus restaurants (mean = 5.50) then did residential students (mean = 3.67). The results of an independent samples t-test revealed that this difference was significant (t = 4.201, p =.000), thus providing support for H1.

Paired samples t-test If you are testing for differences between variables, run a paired samples t-test. EXAMPLE: H2: Food shoppers will place more importance on price than on food quality

Paired samples t-test Using a constant sum scale, we found that price (mean = 41.21) was more important to shoppers than food quality (mean = 27.66). The results of a paired samples t- test revealed that this difference was significant (t = 6.451, p =.000), thus providing support for H2.

One-sample t-test One-sample t-test If you are testing for differences between your average and some value (i.e. testing to see if responses are higher than a neutral point), run a one-sample t-test and pick the neutral point in the scale as your test value. EXAMPLE: H3: The establishment of a casino will lead to a perceived increase in traffic.

One-sample t-test We compared the average of all responses to the neutral point on our scale (4) to see if there was widespread agreement that a new casino would increase traffic. We found that respondents generally agreed that traffic would increase with a new casino (mean = 5.11, t = 2.755, p =.021), thus providing support for H3.

Crosstab with chi-square If you are looking for associations between two variables, run a crosstab with chi-square. EXAMPLE: H4: The frequency of listening to traditional radio is associated with the respondent’s age.

Crosstab with chi-square We found that 94.1% of older respondents listen to traditional radio at least once a week while only 44.4% of younger respondents listen at least once a week. Our chi-square test reveals that age is related to the frequency of listening to traditional radio (chi-square = , p =.002), thus providing support for H4.

Correlation If you are looking for correlations between two variables, run a correlation. EXAMPLE: H5: A customer’s price sensitivity is negatively correlated with their preference for high quality products.

Correlation We have found a negative correlation between a customer’s price sensitivity and their preference for high quality products (-.567). This correlation was significant as the p-value was below.05. Given these results, we have found support for H5.

Final notes Aim for at least two survey items per hypothesis (to increase reliability). ◦ If the results are not consistent, you can say that you have partial support. Each survey item can only be used once. It is okay if your results do not support your hypotheses! That is still a finding. ◦ Don’t change hypotheses. Aim to have 3-6 graphs in your paper (bar charts or pie charts).