SPSS overview
Compare means:
One sample t-test:
- Test value is a hypothetical mean
- Test variable is continuous: [score of something]
Compare the sample mean to a hypothetical population mean (μ)μ))
Make sure that the sample mean is a good fit for the hypothetical population
Assumptions:
- Normal distribution
H0 = The population mean is equal to [the hypothetical mean].
SPSS:
Analyze > Compare Means > One-Sample T Test
- Assign test variable
- Test value = [insert hypothetical mean]
Rapportage:
A one sample t-test showed that the average age was significantly lower than 45 (μ)M=32,5,
SD=.93),
t(μ)582)=84.72, p<.001, d=-1.66, 95% CI[6.18, 6.33].
Effect size:
Cohen's d = Mean Difference/Standard Deviation
Dependent / paired t-test:
Test variable 1 is continuous: [score of something] - Test variable 2 is continuous: [score of
something]
, Compare the means of two scales belonging to the same respondent to check whether the
population means are significantly different.
Compare mean differences within groups in the population.
Assumptions:
- Normal distribution
- N>100
H0 = The difference between both scales is equal to 0 in the population.
You are comparing scores within units such as a person, a family or a company
SPSS:
Analyze > Compare Means > Paired-Samples T Test
Assign variables 1 and 2: in anti-chronological order --> first the post, then the pre variables
Rapportage:
A dependent t-test revealed that
(μ)hygiene scores on day one) (μ)M = 1.65, SD = 0.64) were significantly higher than
(μ)hygiene scores on day three) (μ)M = 0.98, SD = 0.71) of visiting a festival,
t(μ)122) = 10.59, p < .001, d = 0.95, 95% CI [0.55, 0.80].
Effect size:
Cohen's d = Mean Difference/Standard Deviation
Independent sample t-test:
- Grouping variable is categorical (μ)X): [man/woman] or [control/experimental]
- Test variable is continuous (μ)Y): [score of something]
Compare the means of two groups (μ)determined by a grouping variable) to check if the
population means are significantly different.
Compare means:
One sample t-test:
- Test value is a hypothetical mean
- Test variable is continuous: [score of something]
Compare the sample mean to a hypothetical population mean (μ)μ))
Make sure that the sample mean is a good fit for the hypothetical population
Assumptions:
- Normal distribution
H0 = The population mean is equal to [the hypothetical mean].
SPSS:
Analyze > Compare Means > One-Sample T Test
- Assign test variable
- Test value = [insert hypothetical mean]
Rapportage:
A one sample t-test showed that the average age was significantly lower than 45 (μ)M=32,5,
SD=.93),
t(μ)582)=84.72, p<.001, d=-1.66, 95% CI[6.18, 6.33].
Effect size:
Cohen's d = Mean Difference/Standard Deviation
Dependent / paired t-test:
Test variable 1 is continuous: [score of something] - Test variable 2 is continuous: [score of
something]
, Compare the means of two scales belonging to the same respondent to check whether the
population means are significantly different.
Compare mean differences within groups in the population.
Assumptions:
- Normal distribution
- N>100
H0 = The difference between both scales is equal to 0 in the population.
You are comparing scores within units such as a person, a family or a company
SPSS:
Analyze > Compare Means > Paired-Samples T Test
Assign variables 1 and 2: in anti-chronological order --> first the post, then the pre variables
Rapportage:
A dependent t-test revealed that
(μ)hygiene scores on day one) (μ)M = 1.65, SD = 0.64) were significantly higher than
(μ)hygiene scores on day three) (μ)M = 0.98, SD = 0.71) of visiting a festival,
t(μ)122) = 10.59, p < .001, d = 0.95, 95% CI [0.55, 0.80].
Effect size:
Cohen's d = Mean Difference/Standard Deviation
Independent sample t-test:
- Grouping variable is categorical (μ)X): [man/woman] or [control/experimental]
- Test variable is continuous (μ)Y): [score of something]
Compare the means of two groups (μ)determined by a grouping variable) to check if the
population means are significantly different.
