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-testIf 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-testUsing 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 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-testWe 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-squareWe 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 = 49.240, p = .002), thus providing support for H4.

CorrelationIf 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.

CorrelationWe 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 notesAim 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).