Statistics In Psychology

Front 1

3 things that make up a p-value

Back 1

Sample size, effect size, and distribution.

Front 2

P-value

Back 2

A statistical conclusion; displays the probability that your results are not random chance, NOT that there is an effect.

Front 3

Three essential parts of the scientific method for psychological statistics

Back 3

Design, measurement, and analysis.

Front 4

Of design, measurement and analysis, which is the most important?

Back 4

Design is the most important. A good analysis can't make up for a flawed design.

Front 5

Descriptive statistics

Back 5

Statistics that give you the basic shape of your data. Mean, median, mode, standard deviation, range.

Front 6

Importance of descriptive statistics

Back 6

Give you the basic 'shape' or 'feel' of the data. This is important because you need a basic understanding of what your data is like before you can know how to analyze it. But descriptive statistics lack context.

Front 7

Covariance

Back 7

A measure of how much two variables change together.

Front 8

Range of covariance

Back 8

Negative infinity to positive infinity.

Front 9

What can covariance tell you?

Back 9

Before it is standardized, covariance is only able to tell you directionality (whether the relationship is positive or negative).

Front 10

Correlation

Back 10

"r". Shows how two variables are related, but does not show the reason for or the nature of that relationship. r^2 is variance explained.

Front 11

Range of correlation

Back 11

-1 to +1

Front 12

Partial correlation

Back 12

The relationship between two variables, when accounting for another. Limited because it can only look at the relationship between 2 variables.

Front 13

Regression

Back 13

A type of statistical analysis built on correlations. Gives all the correlations between a set of predictors on one criterion.

Front 14

Limits of regression

Back 14

Regressions require continuous variables, and can't look at group differences.

Front 15

Point estimate

Back 15

Every analysis is based on a point estimate. This is your good variance over bad variance.

Front 16

Relationship between types of variance and p-value

Back 16

Your effect size is good variance, and your distribution is bad variance.

Front 17

Relationship between regression, residual and p-value in a regression analysis

Back 17

Regression = effect size
Residual = distribution

Front 18

How does your sample size (N) affect your F-statistic?

Back 18

N does not affect the F-statistic. It does, however, affect your critical F-value.

Front 19

Benefits of regression

Back 19

Can compare more variables than a correlation; can retain unstandardized units; can be used to predict scores.

Front 20

Polynomial regression

Back 20

The shape of your data is not always perfectly linear; an equation can be modeled using an nth polynomial to create a better fit.

Front 21

Continuous-by-continuous interactions

Back 21

When the relationship between X and Y is dependent on variable Z.

Front 22

Mediator

Back 22

Controls the relationship entirely

Front 23

Moderator

Back 23

Changes the relationship

Front 24

Regression F-value

Back 24

(Mean square regression)/(mean square residual) = F

Front 25

Validity vector

Back 25

Contained within the correlation matrix, this is the column beneath your 'Y'. Gives the relation between each individual predictor and your dependent variable.

Front 26

Correlation matrix

Back 26

The diagonally symmetric matrix of all your correlations.

Front 27

Model summary (regression)

Back 27

Shows our multiple correlation and r^2 mult.

Front 28

r^2 mult

Back 28

The variance in our dependent variable explained by the set of all our predictors.

Front 29

Coefficients table

Back 29

Shows the overall coefficients needed for the regression equation, in both standardized and unstandardized form.

Front 30

Shape of the line in polynomial regression

Back 30

Changes based on the values of X.

Front 31

Centering

Back 31

When dealing with higher-variable terms and interactions, the values of your variable must be centered by subtracting them from the mean (Xbar - X). This reduces non-essential multicollinearity.

Front 32

b0

Back 32

Our predicted value at the mean of x, or at x=0 when uncentered.

Front 33

Type I error

Back 33

Probability you're saying something is meaningful when it isn't.

Front 34

Type II error

Back 34

Probability you're saying something isn't meaningful when it is.

Front 35

Cronbach alpha

Back 35

A statistical measure of the internal consistency or reliability of a test.

Front 36

Covariance equation

Back 36

[(x-xbar)(y-ybar)]/(n-1)

Front 37

r

Back 37

Pearson correlation. Sxy/SxSy.

Front 38

r^2

Back 38

Percent overlap in a variable; percentage by which one variable accounts for another.