Stats Kahoot
Mean square is most related to which of the following
Standard deviation
Mean
Variance
A square curmudgeon
An F close to 1.00
Is statistically significant
Is associated with high probability
Lead to rejection of the null
Both one and two
In a one-factor repeated measures ANOVA, into how many sources is the variability partitioned
2
4
3
Equal to the value of K
How about a 2x2 factorial ANOVA
2
3
4
1
Which of the following is an inaccurate pairing
A significant F with significant post hoc results
A significant F with only one significant post hoc results
A significant F without significant post hoc results
Tik tok and millennials
As a theoretical distribution, F is
Symmetrical
Skewed to the left
Skewed to the right
Rectangular
A design had two factors: social class (lower, middle, upper) and sex (male,female) What the RxC
2x4
3x2
3x4
2x2
ANOVA is a
Post hoc test
Null hypothesis test
A priori test
Both a NHST and a priori test
In factorial designs, a measure of variations amongst treatments, Independent of other factors is
An interaction effect
A main effect
An error
A subjects test
Upon graphing cell means for a factorial ANOVA, all the lines are parallel. This suggests
A significant interaction
Am error is graphing
An interaction that is not significant
A significant main effect
I measure cats and dogs’ ability to two different mazes I should use
A one way ANOVA
A factorial ANOVA
A repeated measures ANOVA
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I measure mean voter turnout in five different states. I should use
A one way ANOVA
A repeated measures ANOVA
A factorial ANOVA
None of these
I measure a cohort of students across seven different points of time, I should use
A repeated measures ANOVA
A factorial ANOVA
A one way ANOVA
A paired sample t test
To claim that the effect of one factor on a DV is conditioned by another factor is a
Main effect
Interaction effect
Post hoc
Effect size
The ANOVA assumes
Equal Variances in the populations measured
Random selection/assignment
Normal distribution of scores in the populations measured
All of these
The philosophy that a process can be better understood if essential parts are express as number is
Number analysis
Rationalism
Quantification
Numberism
A bivariate distribution
Has two possible correlations
Has two possible regression lines
Has one regression line
Is a set of two independent samples
If I had scores on a depression scale, I could do a correlation with
Biological sex
Social class
Race
Anxiety scale
A correlation is best for
Linear relationships
Curvilinear relationships
Categorical sta
Categorical IV continuous DV
“The more I study, the higher my grades” characterizes
A positive relationship
A negative relationship
A weak relationship
No relationship
Which of the following is not a possible correlation coefficient
0.00
-.99
1.70
.50
The coefficient “a” in regression is
The slope
The place the line crosses the y-axis
The values of y with x=o
Both two and three
Which of the following is a condition for causation
Temporal ordering
Ruling out confounding factors
A relationship between two variables
All of these
The “b” coefficient in regression is
The slope of the regression line
The y-axis
The rate of change in the Y variable per every one unit change in x
Both one and three
The line of best fit
Has the least amount of error
Is constructed from a regression equation
Always has the mean of x and y
All of these
Which method allows for making predictions
Regression
Test of independence
Correlation
Goodness of fit test
Which test has the null hypothesis: the prediction fits the observed data
Chi square goodness of fit test
Chi square test of independence
One sample t test
Paired samples t test
If I reject a null hypothesis when it is actually true, I am making the
Type I error
Type II error
No idea
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A test statistic that is greeted than the critical value means
I should retain the null hypothesis
Could be making a Type I error
I should reject the null hypothesis
Both two and three
Chi square has in common with ANOVA and t test techniques
Population scores are assumed to be normally distributed
A null hypothesis is tested
Mean scores are being evaluated
All of these
Which of the following is not true of chi square
Its nature is additive
The population are assumed to be normally distributed
It can be direct that two variables are related to each other
It deals with frequencies
Contingency tables go with chi square
Test of independence
Test of effect size
Test of goodness of fit
Test with df > 1
Which describes the null hypothesis for a goodness of fit test
That two variables are associated
That the predictions fit the data
That the two variables are independent
That predictions do not fit the data
Theoretical/expected frequencies in tests of independence are obtained
By assuming the categories of events are independent
By assuming the categories of events are related in some way
From predictions based on theory
In the same way as in tests of goodness of fit
Odds ratio are most similar to
Null hypothesis testing
Standard deviation
Inferential statistics
Effect size index
When an odds ratio equals 1, it means
Event A is more likely, relative to event B
Event A is equally likely, relative to event B
Event A is less likely, relative to event B
None of these
Degrees of freedom for a test of independence is
K - 1
(R-1)(C-1)
N - 1
R - C
In a test of independence there is a variable gender (male, female, nonbinary) and edu (undergrad, grad) level. What is RxC
2x2
3x1
3x2
2x4
Set alpha to .05 As df increases, what happens to the chi square critical values
It increases
It decrease
It matches the population mean
It doesn't change