• Shuffle
    Toggle On
    Toggle Off
  • Alphabetize
    Toggle On
    Toggle Off
  • Front First
    Toggle On
    Toggle Off
  • Both Sides
    Toggle On
    Toggle Off
  • Read
    Toggle On
    Toggle Off
Reading...
Front

Card Range To Study

through

image

Play button

image

Play button

image

Progress

1/49

Click to flip

Use LEFT and RIGHT arrow keys to navigate between flashcards;

Use UP and DOWN arrow keys to flip the card;

H to show hint;

A reads text to speech;

49 Cards in this Set

  • Front
  • Back
sample
the people we collected our data from
population
larger group that we think our sample represents
outlier; what measure of central tendency do they most affect?
score that is far from most other scores; mean
z-score
a centered, standardized score
explained variance
When one or more predictor variables help us make a better guess at an outcome variable, we say that we have explained variance in the outcome variable
unexplained variance
The discrepancy between this guess and the actual outcome is the unexplained variance --- some of which might include systematic relationships we haven't thought of, and some of which is just random noise.
why is variance important with psychological research?
The more variance we can explain, the better we can claim to understand why people's responses differ, making it a useful goal for psychological research.
noise
random influences on a variable that are equally likely to occur in one direction as the other
bias
influences on a variable that are more likely to occur in one direction
Type I error
when the null hypothesis is actually True, but you conclude that it is False
how we explain variance, or the General Linear Model
1) single sets of groupings = t test, one way ANOVA
2) cross classified groupings = factorial ANOVA
3) continuous predictors = correlation, regression
power
probability of finding a difference that does exist
type II error
failing to reject the null hypothesis when you should have
2 branches of statistics? examples?
descriptive: central tendency, variability, correlation, regression, z-scores
inferential: z-tests, t-tests, ANOVA, factorials, HYPOTHESIS TESTING
score
value of a variable that applies to a particular observation
if two researchers make the same measurement but get very different values, we would say that the measure isn't _________.
reliable
manipulation check
making sure that whatever is supposed to affect the subjects' behavior actually affects the subjects' behavior. (an experimenter tries to affect participants' moods by watching sad movies. manipulation check would be useful)
nominal variable
no relationship between the categories, the variable categories are just names for things. example: race. also called categorical
ordinal variable
also called rank order. assigns numbers or ranks things in a particular order. example: shirt size S M L
equal interval variable
has to do with spectrums, dichotomies. an ordering that makes all separating intervals the same. example: scale of 1 to 10, date of birth (day month year)
ratio variable
says something about the presence or absence of something, there IS a zero point. example: height, age, temp in kelvin
variance (formula in words)
average squared deviations from the mean
Standard Error of the Mean, or Standard Error
the standard deviation of the distribution of sample means
what does the 95% confidence interval tell you?
the range of true values of the population mean that, when compared to your sample, would not lead to rejecting the null hypothesis
characteristics of the T Distribution
fatter tails than the Z distribution, more extreme scores because it estimates the population variance based on the sample scores. critical values on the t-distribution will always be greater than the z-distribution
one way ANOVA - the Within Groups variance
has the same value regardless of whether the null hypothesis is true
one way ANOVA - both the Between and Within Groups variances
they both include the effects of random variation, and they both require the assumption that the groups all have the same population variances
one way ANOVA - the Between Groups variance
estimate is done by treating sample means as scores taken from the distribution of sample means
alpha
probability of making a Type I error (probability of getting statistical significance if the null hypothesis is actually true)
beta
probability of making a Type II error (probability of not getting statistical significance if the research hypothesis is actually true)
Type I sums of squares (Two Way ANOVA)
sensitive to difference in cell group sizes - it focuses on weighted cell means. Marginal means reflect these differences
Type III sums of squares (Two Way ANOVA)
not sensitive to differences in cell group sizes - it focuses on unweighted cell means. relative cell sizes are ignored and the means tend to be roughly even.
Which test do you use to determine whether a correlation is significant?
t- test
when we ______ for another variable we are trying to see if our variable of interest still explains variance even when taking a secondary/nuisance variable into account
control
can you use correlation, regression, both or neither when: you want to test the relationship between a categorical IV and a categorical DV?
neither! this is a non-parametric test, the chi-squared test
can you use correlation, regression, both or neither when: you want to look at the strength of the relationship between 2 variables
Both correlation and regression can be used to look at the strength of the relationship between 2 variables
can you use correlation, regression, both or neither when: you want to compare multinested models to determine which best explain your DV
regression only
criterion variable
the variable that is predicted (usually Y)
when we ______ for another variable we are trying to see if our variable of interest still explains variance even when taking a secondary/nuisance variable into account
control
can you use correlation, regression, both or neither when: you want to test the relationship between a categorical IV and a categorical DV?
neither! this is a non-parametric test, the chi-squared test
can you use correlation, regression, both or neither when: you want to look at the strength of the relationship between 2 variables
Both correlation and regression can be used to look at the strength of the relationship between 2 variables
can you use correlation, regression, both or neither when: you want to compare multinested models to determine which best explain your DV
regression only
criterion variable
the variable that is predicted (usually Y)
predictor variable
variable that is used to predict scores of individuals on another variable (usually X)
general null and research hypotheses for the Chi-squared test of Independence and Goodness of Fit
There are 2 populations. Null= The two populations are the same. Research= The two populations are different.
Whats the difference between the Chi-squared tests for Independence and Goodness of Fit?
Independence involves 2 nominal variables, each with several categories. The Goodness of Fit test simply involves one nominal variable with several categories.
predictor variable
variable that is used to predict scores of individuals on another variable (usually X)
general null and research hypotheses for the Chi-squared test of Independence and Goodness of Fit
There are 2 populations. Null= The two populations are the same. Research= The two populations are different.
Whats the difference between the Chi-squared tests for Independence and Goodness of Fit?
Independence involves 2 nominal variables, each with several categories. The Goodness of Fit test simply involves one nominal variable with several categories.