Midterm

Question 1

The General Linear Model Goal

Answer 1

try to account for as much variability in a dependent variable as possible
the Y variable must be continuous (interval or ratio scale)
can use one or multiple IV’s to account for the variance in the DV

Question 2

Categorical Variables

Answer 2

Binary Variable- 2 distinct categories
Nominal Variables- more than 2 distinct categories
Ordinal Variables- more than 2 distinct categories which go in a logical order

Question 3

Continuous Variables

Answer 3

entities get a distinct score on a scale
Interval Variable: equal intervals on the variable represent equal differences in the property being measured
Ratio Variable: same as interval, but the ratios of scores are important and 0 is meaningful

Question 4

Measuring Effect Size

Answer 4

R2 (Coefficient of Determination)
how much variance in the outcome variable is accounted for by the IV
We test whether the effect size is significant by the F and t statistics

Question 5

The Statistical Model

Answer 5

We want to try and theoretically reflect real world phenomena
we want to be 95% sure that our findings are due to our model (only 5% chance it happened by chance)

Question 6

Sum of Squares Total

Answer 6

The total variability between scores and mean

The sum of each score minus the mean squared

Question 7

Sums of Squares Error

Answer 7

Deviance between the model and each person’s predicted score

The sum of each score minus the predictor score squared

Question 8

Sums of Squares Model

Answer 8

deviance between the mean and the model

The sum of The mean subtract each person’s predicted score squared

Question 9

Mean Square

Answer 9

The average of the sum of squares

the SS divided by the associated degrees of freedom

Question 10

Degrees of Freedom

Answer 10

the wiggle room in the data set
because our mean must stay constant, all of our scores can be anything except our last score, which must bring us to our mean

Question 11

Central Limit Theorem

Answer 11

if there are 30 or more participants in a study, a normal distribution will begin to emerge

Question 12

Confidence Intervals

Answer 12

Describes the upper and lower bounds of a score

We want to be 95% sure the score will land in the confidence intervals

Question 13

Types of Hypotheses

Answer 13

Null Hypothesis- we assume there is no effect of the IV on the DV
Alternative Hypothesis- that there is an effect of the IV on the DV
We assume the null hypothesis until shown otherwise

Question 14

One and Two Tailed Error

Answer 14

One Tailed- probability only goes one way (.05 on either the positive or negative side)
Two Tailed- probability is taken on both sides, .025 on each side

Question 15

Type i error

Answer 15

when we believe there is a genuine effect and there is not

probability of this happening is measured at the alpha level (usually .05)

Question 16

Type ii error

Answer 16

when we believe there is no effect and there is an effect

the probability of this happening is measured at the beta level (usually .2)

Question 17

Confidence Intervals and Statistical Significance

Answer 17

if CI’s overlap, generally the findings are not significant

As sample size increases, CI’s decrease and we are more likely to find a significant result

Question 18

Misconceptions about the P Value

Answer 18

1. Significant result DOES NOT mean it is important
2. A Non-significant hypothesis DOES NOT mean there is no effect, only that it is not big enough to be found
3. A significant result DOES NOT mean the Ho is false

Question 19

Problems with NHST

Answer 19

All or nothing thinking (that significance is everything; instead, we can also look at effect size)
Reliant on sample size

Question 20

Wider Science Problems with NHST

Answer 20

Incentive structure- you are more likely to exceed in the research field if your findings are significant
Researcher Degrees of Freedom- a researcher’s decisions can change the P value and make it significant
P-Hacking (changing certain numbers or methods after the fact to make your P significant)
Harking- finding a significant result in your data you weren’t studying and then changing your hypothesis to match

Question 21

Avoiding the Wider Science Problems

Answer 21

Open science- movement to make the process, data, and outcomes of research freely available
Pre-registering research- receiving feedback and promises of publishing by preregistering with a journal; ensures less competition

Question 22

Effect Sizes

Answer 22

standardized measures o the size of an effect which can be compared across studies
not as reliant on sample size as p
Cohen’s d, Pearson’s r, and odds ratio are all examples

Question 23

r, Correlation Coefficient

Answer 23

a good measure when group size is the same
A positive correlation suggests that the values increase or decrease together
A negative correlation suggests that as one increases, the other decreases

Question 24

Effect Sizes of Pearson’s r

Answer 24

r=.1 (S)
r=.3 (M)
r=.5 (L)

Question 25

Advantages of Effect Size

Answer 25

encourages interpreting effects on a continuum and not the categorical sig. or non sig. Effect size is affected by sample size but not confounded by it Results in less incentive to mess wth the data

Question 26

Meta Analysis

Answer 26

computing effect sizes for a series of studies that investigated the same research question and looking at a weighted average of those effect sizes Helps us get closer to discovering the true effect size of the population

Question 27

Bayesian Estimation

Answer 27

we use previous research and knowledge to predict what will happen in our study then you compute with previous knowledge and your findings to improve future predictions

Question 28

Benefits of Bayesian Estimation

Answer 28

Evaluates the evidence for the Ho. Not confounded by sample size and stopping rules No way to p-hack because it is based on estimation and interpretation

Question 29

Assumptions of Parametric Tests

Answer 29

Additivity and Linearity Normality Homogeneity of Variance ( the variance is the same at each level of the variable) Independence of scores

Question 30

Outliers

Answer 30

data points which land outside our normal range of data and which increase error because they increase variance

Question 31

Additivity

Answer 31

the combined effect of all our variable should be greater than any on its own (thereby reducing error)

Question 32

Linearity

Answer 32

the outcome is linearly related to the predictors

Question 33

Normal Distribution

Answer 33

If the data is normal distributed, the mean and other parameters are accurate reflections of the data need to be more concerned with this in smaller samples

Question 34

Homoscedasticity/ Homogeneity of Variance

Answer 34

the assumption that your groups are fair to compare based on similar levels of variance We assess using Levene's Test (tests if variances are the same) Levene's error doesn't detect large errors in small samples and does detect small errors in large samples

Question 35

Independence

Answer 35

the assumption that each participant's error is unrelated to the error of the other participants

Question 36

Listwise Deletion

Answer 36

completely excludes a participant from all calculations if one piece of data is missing

Question 37

Pairwise Deletion

Answer 37

excludes a participant from a calculation only if they are missing data from one of the variables in question

Question 38

Bootstrapping

Answer 38

runs calculations on your data multiple times multiple ways to find the most accurate mean and confidence intervals accepted practice is 10 000 bootstraps

Question 39

Transforming Data

Answer 39

``` Log Transformation (log of the values; reduces positive skew) Square Root Transformation (square root of the values, reduces positive skew and stabilizes variance Reciprocal Transformation(1 divide by values reduces the impact of large scores) ```

Question 40

Cautions with Transformations

Answer 40

They change your data - change your scale - they can change what we are measuring - last case scenario

Question 41

Correlation

Answer 41

a standardized measure of the relationship between two continuous variables Can be positive or negative

Question 42

Covariance

Answer 42

a measure of the similarity of variance in 2 variables | how much scores vary from the mean on 2 variables

Question 43

Problems with Covariance

Answer 43

depends on the units of measure we must standardize it Correlation is standardized covariance

Question 44

Correlation Does Not Imply Causation

Answer 44

direction of causality cannot be inferred | there may be other confounding variables (3rd variable problem)

Question 45

Linear Regression

Answer 45

a method of predicting the value of one variable from another it is the hypothetical relationship between two variables related linearly In order to estimate the line of best fit, we use method of least squares

Question 46

F Statistic

Answer 46

Testing the fit if the model results in better prediction than using the mean, we expect the model to be significant influences by population size

Question 47

Hierarchical Model

Answer 47

Process of entering multiple predictors in steps; known predictors are entered first to become constants before unknown predictors are entered - you can see the unique predictive influence of a variable - Drawback: requires the researcher to know what they are doing

Question 48

Forced Entry

Answer 48

Process of entering multiple predictors; all variables are entered simultaneously

Question 49

Stepwise Entry

Answer 49

Process of entering multiple predictors; variables are entered in based on the amount of variance they can explain, highest first SPSS does this automatically Problem: reliance on mathematical criterion can mean that tiny math difference lead to huge interpretation errors

Question 50

Standardized Residuals

Answer 50

A way of identifying outliers based on SD, suggests that if a case falls outside of a SD of +/- 3 (above/below 99% of the population) it can be considered an outlier

Question 51

Influential Cases

Answer 51

Certain outliers can pull the mean so far that the outlier does not have the greatest deviance (ie it doesn't seem like the outlier) so deviance is not the best way to identify outliers

Question 52

Cook's Distance

Answer 52

measures how much influence any individual case has on the model as a whole allows for the identification of influential cases

Question 53

Common Sense Real- World Assumptions of Regression

Answer 53

1/ Outcome variable is continuous 2. Predictor variable is continuous or dichotomous 3. Predictors must not have 0 variance 4. Linearity 5. Independence

Question 54

Assumptions that Matter in Regression IN Order

Answer 54

1. Additivity and Linearity 2. Homoscedasticity 3. Independence 4. Normal distribution 5. No Multicollinearity between predictors

Question 55

Moderation

Answer 55

the combined effect of two variables on another (interaction effect) A moderator variable changes the strength or direction of the relationship between x and y We follow up moderation with a simple slopes test

Question 56

Centering Variables

Answer 56

the process of transforming a variable so that the deviation is centered around 0, which represents the mean Basically, take every score and subtract the mean

Question 57

Mediation

Answer 57

the situation when the relationship between a predictor and outcome variable can be explained by their relationship to a third variable

Question 58

Baron and Kenny, 1986

Answer 58

Mediation is tested through 3 regression models 1. Predicting the outcome from the predictor (c path) 2. Predicting the mediator from the predictor (a path) 3. Predicting the outcome from both the predictor and mediator (Indirect effect)

Question 59

Baron and Kenny; 4 Conditions That Suggest Mediation

Answer 59

1. Predictor must significantly predict the outcome (Sig. c path) 2. Predictor must significantly predict the mediator (sig. a path) 3. Mediator must significantly predict the outcome (sig. b path) 4. Predictor must predict outcome less strongly in model 3 than model 1 (c' must be lower than c)

Question 60

Sobel Test

Answer 60

alternate to estimate the indirect effect and its significance If the Sobel Test comes up as significant, then there is significant mediation

Question 61

Effect Sizes of Mediation

Answer 61

indirect effect = ab indirect effect, partially standardized = ab/S outcome indirect effect, standardized ab/Soutcome X Spredictor

Question 62

Dummy Variables

Answer 62

When we have categorical predictors with more than one category, we create dummy variables rather than coding as 1's and 2's You choose one variable that always is assigned 0 and for each dummy variable all the variables are 0 except 1, which is 1