STATISTICS PORTAGE CNSL 503 TEST PAPER QUESTIONS
AND ANSWERS GRADED A+
✔✔When do you look at post-hoc tests? - ✔✔ONLY IF overall F value is significant
✔✔What is a 2 way ANOVA? - ✔✔2 independent variables
✔✔What are the # of levels?
Ex:
You want to know how gait speed varies based on age and gender.
Define IV, DV, and levels. - ✔✔# of groups within each independent variable
DV: Gait speed
IV: Age and Gender
3 levels (groups) for age
2 levels (groups) for gender
✔✔You want to test for sex differences in gait velocity among 3 age groups used
earlier.
What is the "main effect"? - ✔✔Average effect for each independent variable.
Main effect for sex (combing all ages)
"What is the effect of sex on gait speed? Is there a difference in gait speed if you're
male versus female? Do men and women have different gaits speeds?"
Main effect for age (combining both genders)?
"Does gait speed depend on age?"
✔✔What is the interaction ? - ✔✔Is there an interaction BETWEEN the independent
variables?
Analyze all subgroups for significant differences
✔✔What do main effect and interaction effect have in common? - ✔✔Assessing effect
of the IV
✔✔You want to test for sex differences in gait velocity among 3 age groups used
earlier.
What is the interaction asking? - ✔✔Does the effect of gender (on gait speed) DEPEND
on how old you are?
Does the effect of age (on gait speed) DEPEND on sex?
, or: For gait speed, is there a different trend of increasing age for women versus men?
✔✔What does a SIGNIFICANT INTERACTION tell us? - ✔✔This tells us that any main
effects may be MISLEADING or MEANINGLESS
✔✔You want to test for sex differences in gait velocity among 3 age groups used
earlier.
What is the null hypothesis for each main effect?
What is the null for the interaction? - ✔✔One null for each main effect:
Age: μ1 = μ2 = μ3
Sex: μ males = μ females
One null for the interaction:
μ1 males = μ1 females = μ2 males = μ2 females = μ3 males = μ3 females
✔✔t test - ✔✔"ANOVA's younger sibling."
✔✔independent sample t test - ✔✔When making simple, straightforward comparisons
of the means of two independent variables with two levels, the independent samples t-
test is usually the statistic of choice. Example: Two independent samples of high school
seniors (60 boys; 60 girls) to see if there are gender differences on vocab test.
✔✔independent variable (IV) - ✔✔the factor being manipulated by the experimenter; the
thing we think affects other things; can be continuous, ordinal, or categorical, but always
categorical in this course; also called factors or effects; each IV has greater than or
equal to 2 levels (number depends on what was measured or reported); careful about
collapsing levels
✔✔dependent variable (DV; Y) - ✔✔the factor being measured (i.e., the result of
interest); the thing we think is affected by other things; always measured; can be
continuous, ordinal, or categorical, but only continuous in this course
✔✔paired sample t test - ✔✔usually based on groups of individuals who
experience both conditions of the variable of interest. For instance, one
study might examine the effects of Drug A versus Drug B on a single sample of
100 diabetics. Subjects in this sample would receive Drug A one week, and
Drug B the next; participants receive both drug/stimulus conditions
✔✔statistically significant - ✔✔Simply put, if you have significant result, it means that
your results likely did not happen by chance. If you don't have statistically significant
results, you throw your test data out (as it doesn't show anything!); in other words, you
can't reject the null hypothesis. In general, if your calculated F value in a test is larger
AND ANSWERS GRADED A+
✔✔When do you look at post-hoc tests? - ✔✔ONLY IF overall F value is significant
✔✔What is a 2 way ANOVA? - ✔✔2 independent variables
✔✔What are the # of levels?
Ex:
You want to know how gait speed varies based on age and gender.
Define IV, DV, and levels. - ✔✔# of groups within each independent variable
DV: Gait speed
IV: Age and Gender
3 levels (groups) for age
2 levels (groups) for gender
✔✔You want to test for sex differences in gait velocity among 3 age groups used
earlier.
What is the "main effect"? - ✔✔Average effect for each independent variable.
Main effect for sex (combing all ages)
"What is the effect of sex on gait speed? Is there a difference in gait speed if you're
male versus female? Do men and women have different gaits speeds?"
Main effect for age (combining both genders)?
"Does gait speed depend on age?"
✔✔What is the interaction ? - ✔✔Is there an interaction BETWEEN the independent
variables?
Analyze all subgroups for significant differences
✔✔What do main effect and interaction effect have in common? - ✔✔Assessing effect
of the IV
✔✔You want to test for sex differences in gait velocity among 3 age groups used
earlier.
What is the interaction asking? - ✔✔Does the effect of gender (on gait speed) DEPEND
on how old you are?
Does the effect of age (on gait speed) DEPEND on sex?
, or: For gait speed, is there a different trend of increasing age for women versus men?
✔✔What does a SIGNIFICANT INTERACTION tell us? - ✔✔This tells us that any main
effects may be MISLEADING or MEANINGLESS
✔✔You want to test for sex differences in gait velocity among 3 age groups used
earlier.
What is the null hypothesis for each main effect?
What is the null for the interaction? - ✔✔One null for each main effect:
Age: μ1 = μ2 = μ3
Sex: μ males = μ females
One null for the interaction:
μ1 males = μ1 females = μ2 males = μ2 females = μ3 males = μ3 females
✔✔t test - ✔✔"ANOVA's younger sibling."
✔✔independent sample t test - ✔✔When making simple, straightforward comparisons
of the means of two independent variables with two levels, the independent samples t-
test is usually the statistic of choice. Example: Two independent samples of high school
seniors (60 boys; 60 girls) to see if there are gender differences on vocab test.
✔✔independent variable (IV) - ✔✔the factor being manipulated by the experimenter; the
thing we think affects other things; can be continuous, ordinal, or categorical, but always
categorical in this course; also called factors or effects; each IV has greater than or
equal to 2 levels (number depends on what was measured or reported); careful about
collapsing levels
✔✔dependent variable (DV; Y) - ✔✔the factor being measured (i.e., the result of
interest); the thing we think is affected by other things; always measured; can be
continuous, ordinal, or categorical, but only continuous in this course
✔✔paired sample t test - ✔✔usually based on groups of individuals who
experience both conditions of the variable of interest. For instance, one
study might examine the effects of Drug A versus Drug B on a single sample of
100 diabetics. Subjects in this sample would receive Drug A one week, and
Drug B the next; participants receive both drug/stimulus conditions
✔✔statistically significant - ✔✔Simply put, if you have significant result, it means that
your results likely did not happen by chance. If you don't have statistically significant
results, you throw your test data out (as it doesn't show anything!); in other words, you
can't reject the null hypothesis. In general, if your calculated F value in a test is larger
