PSYC 210 Quiz 5- hypothesis test typically used with one or more nominal (and sometimes
ordinal) independent variables (with at least three groups overall) and a scale dependent
Questions with CORRECT Answers (Grade A+)
Question 1:
Analysis of Variance (ANOVA) F statistic between-groups variance within-groups variance grand
mean r squared
Answer:
hypothesis test typically used with one or more nominal (and sometimes ordinal) independent
variables (with at least three groups overall) and a scale dependent variable = the larger the
between groups variability and the smaller the within groups variability, the larger the F statistic
value, and the more likely you are to reject the null hypothesis ratio of two measures of variance:
between-groups variance, which indicates differences among sample means, and within-groups
variance, which is essentially an average of the sample variances = significance is determined when
the between group variance is much larger than the within groups variance, producing a large value
= if the between groups variance and within groups variance are practically the same, with a value
coming out to around one, then the means are not really different from one another an estimate of
the population variance, based on the differences among the means an estimate of the population
variance, based on the differences within each of the three (or more) sample distributions the mean
of every score in a study, regardless of which sample the score came from proportion of variance in
the dependent variable that is accounted for by the independent variable = formula
- r squared = SS(between)/SS(total) = conventions
- small => 0.01
- medium => 0.06
- large => 0.14
, Question 2:
One Way ANOVA Hypothesis significant omnibus difference problem two methods for testing
particular means A Priori Comparisons Post Hoc Tests All Possible Comparisons Factorial ANOVA
What does a Factorial ANOVA explore?
Answer:
-Null hypothesis: MS between and MS within are just error variance fail to reject — no group
differences, any differences are due to chance and that all groups come from same underlying
population -Null hypothesis is false: MS between = group differences + error MS within = error at
least one of these means come from a different population — group matters (p was less than .05 —
instructional type matters, scores on stats test differ as a function of the group you are in — SINGLE
OMNIBUS EFFECT) if there is or are significant differences between groups relative to what we'd
expect to see by chance alone, a significant F value is great but it just tells us at the very least that
one mean is not like the other (maybe 1, 2, or all means come from different populations) -simply
know that null hypothesis is not viable -Planned contrasts (a priori): planned ahead of time, based
on a theory or question of interest (we think that the best way to teach is a combination — so we
set up a test that tests just that — is the combination more effective than the 2 means) -Post hoc
tests: dont have a specific hypothesis or want to keep our options open— after the fact to see if any
of the means differ from any of the others -If we wanted to test whether workbook was different
than the control, we would... carry out a planned contrast to get an F statistic with DF to get this:
Thus, we fail to reject — we do not have enough evidence to argue that 5.2 is different than 4.0
because the critical F does not surpass the critical value with 1 and 16 degrees of freedom on the
table -do not have enough evidence to suggest that students who just use textbook puts students in
a different population than just simple lecture. -when want to leave no stone unturned -Fisher's LSD
Test: least significant difference test — criticized for being too liberal, likely to find significant
differences that might not actually be there -Tukey Test: honestly significant difference test, in
many papers today (gold standard) -Scheffe Test: more conservative -Bonferroni Correction: MOST
conservative, probably will never find anything — not really a post hoc test, but really just raises
ordinal) independent variables (with at least three groups overall) and a scale dependent
Questions with CORRECT Answers (Grade A+)
Question 1:
Analysis of Variance (ANOVA) F statistic between-groups variance within-groups variance grand
mean r squared
Answer:
hypothesis test typically used with one or more nominal (and sometimes ordinal) independent
variables (with at least three groups overall) and a scale dependent variable = the larger the
between groups variability and the smaller the within groups variability, the larger the F statistic
value, and the more likely you are to reject the null hypothesis ratio of two measures of variance:
between-groups variance, which indicates differences among sample means, and within-groups
variance, which is essentially an average of the sample variances = significance is determined when
the between group variance is much larger than the within groups variance, producing a large value
= if the between groups variance and within groups variance are practically the same, with a value
coming out to around one, then the means are not really different from one another an estimate of
the population variance, based on the differences among the means an estimate of the population
variance, based on the differences within each of the three (or more) sample distributions the mean
of every score in a study, regardless of which sample the score came from proportion of variance in
the dependent variable that is accounted for by the independent variable = formula
- r squared = SS(between)/SS(total) = conventions
- small => 0.01
- medium => 0.06
- large => 0.14
, Question 2:
One Way ANOVA Hypothesis significant omnibus difference problem two methods for testing
particular means A Priori Comparisons Post Hoc Tests All Possible Comparisons Factorial ANOVA
What does a Factorial ANOVA explore?
Answer:
-Null hypothesis: MS between and MS within are just error variance fail to reject — no group
differences, any differences are due to chance and that all groups come from same underlying
population -Null hypothesis is false: MS between = group differences + error MS within = error at
least one of these means come from a different population — group matters (p was less than .05 —
instructional type matters, scores on stats test differ as a function of the group you are in — SINGLE
OMNIBUS EFFECT) if there is or are significant differences between groups relative to what we'd
expect to see by chance alone, a significant F value is great but it just tells us at the very least that
one mean is not like the other (maybe 1, 2, or all means come from different populations) -simply
know that null hypothesis is not viable -Planned contrasts (a priori): planned ahead of time, based
on a theory or question of interest (we think that the best way to teach is a combination — so we
set up a test that tests just that — is the combination more effective than the 2 means) -Post hoc
tests: dont have a specific hypothesis or want to keep our options open— after the fact to see if any
of the means differ from any of the others -If we wanted to test whether workbook was different
than the control, we would... carry out a planned contrast to get an F statistic with DF to get this:
Thus, we fail to reject — we do not have enough evidence to argue that 5.2 is different than 4.0
because the critical F does not surpass the critical value with 1 and 16 degrees of freedom on the
table -do not have enough evidence to suggest that students who just use textbook puts students in
a different population than just simple lecture. -when want to leave no stone unturned -Fisher's LSD
Test: least significant difference test — criticized for being too liberal, likely to find significant
differences that might not actually be there -Tukey Test: honestly significant difference test, in
many papers today (gold standard) -Scheffe Test: more conservative -Bonferroni Correction: MOST
conservative, probably will never find anything — not really a post hoc test, but really just raises
