Eppp Statistics And Research Best

Front 1

6 Steps for conducting an experimental research study

Back 1

1. Develop a hypothesis
2. Choose a research design
3. Select a sample
4. Conduct the study
5. Analyze data
6. Report results

Front 2

Protocol Analysis

Back 2

A type of content analysis when experimenters ask S to "think outloud."

Front 3

Interval Recording

Back 3

Method for behavior sampling during discrete intervals (i.e., is it occring now). Good for sampling complex beh. with no clear cut beginning or end such as laughing, talking, or playing.

Front 4

Event Sampling

Back 4

Recording each time the event occurs. Good for beh. that infrequently happen.

Front 5

Sequential Analysis

Back 5

Coding behavioral sequences rather than isolated beh. events when studying complex social behaviors.

Front 6

Situational Analysis

Back 6

Alt to beh sampling and used when the goal is to observe beh in multiple settings.

Front 7

Nonexperimental Research

Back 7

Conducted to collect data on variables.

Front 8

Experimental Research

Back 8

Conducted to test hypotheses about the relationship between variables. (True exp or Quasi exp)

Front 9

Variables

Back 9

Characteristics or behaviors that researchers can vary.

Front 10

Random Assignment

Back 10

"Randomization" helps ensure that any observed diff between groups is due to the IV. Random assignment of S to control or experimental group.

Front 11

Quasi-Exp Research

Back 11

Must use intact pre-existing groups or a single treatment group. No random ass b/c your just using one group.

Front 12

Random Sampling

Back 12

Every member of the pop has an equal chance of being included. Reduces biased sampling.

Front 13

Stratified Random Sampling

Back 13

Dividing the pop into the "strata" (e.g., SES, ed., gender, age, ract)and then using random sampling.

Front 14

Cluster Sampling

Back 14

Selecting pre-existing units/clusters/groups of ind. Used when it's not poss to id an entire pop.

Front 15

Random Assignment

Back 15

Allows an investigator to be more certain that the DV was caused by the IV.

Front 16

Random Selection

Back 16

Enables the investigator to generalize findings from the pop to the sample.

Front 17

Extraneous (confounding) variable

Back 17

Source of systematic error that effects the DV, but is irrelevant to the research.

Front 18

Techniques to control confounding variables:

Back 18

1. Random Assignment
2. Holding the Ext Var Constant
3. Matching S's on the Ext Var
4. Building the Ext Var into the study ("Blocking")
5. Stasticial Control (ANCOVA)

Front 19

Random Error

Back 19

Experimental research attempts to minimize fluctuations in S's, conditions, and measuring instruments.

Front 20

Internal Validity

Back 20

Successfully determining if there is a casual relationship between IV and DV.

Front 21

8 Threats to Internal Validity

Back 21

1. Maturation - S's change
2. History - External events
3. Testing - Learning tests
4. Instrumentation - Var in testing specificity (Ex: raters accuracy)
5. Stastical Regression - When extreme groups are studied
6. Selection - Really assignent problem. Systematic diff b/t groups at beginning of study.
7. Attrition - drop outs
8. Interactions w/ Selection - Int w/ history. One selected group is diff on a var. Nonequivalent groups.

Front 22

External Validity

Back 22

Being able to generalize findings to other settings.

Front 23

Threats to External Validity

Back 23

1. Int b/t Testing and Treatment - Pretest sensitization
2. Int b/t Selection and Treatment - Pre S variables such as motivation of volunteers.
3. Reactivity - Responding in a way b/c they're being observed.
4. Mult Treat Int - When mult IV's effects DV. Needs balancing.

Front 24

Between-Group (S's) Designs

Back 24

The effects of diff levels of an IV are assessed by admin each level to a diff group of S's and then comparing the status on the DV.

Front 25

Factoral Design

Back 25

When a B-G study includes 2 or more IV and gives more thorough infor about the rel and a main effect.

Front 26

Main Effect

Back 26

Effect of 1 IV on the DV, disregarding the effects of all other IV. When the marginal means show differences.

Hint 26

Self-control example

Front 27

Interaction

Back 27

When the effects of an IV differ at different levels of another IV (crossing lines). Requires 2 IV's.

Hint 27

Self-control example

Front 28

Within-S's Designs (Repeated Measures)

Back 28

All levels of the IV are administered sequentially to all S's. Can include only 2 levels of a IV or can be expanded to include 3 or more levels of a single IV or two or more IV's.

Front 29

Single-group time series deisgn

Back 29

Type of W/in-S's design. Assess one group sequentially before and after treatment. Threatened by history.

Front 30

Another type of W/in S's

Back 30

Two or more levels of the IV are applied sequentially to each S and the DV is measured. Ex: Low dose to group and then high dose measuring both times with BPRS. Carryover effects can be problematic, but solved with counterbalancing.

Front 31

Autocorrelation

Back 31

Disadvantage of time series W/in S's designs due to test-retest effects and increased Type I error.

Observations obtained close together in time from the same subjects tend to be highly correlated. This violates the independence of observations assumption made by statistical tests.

Front 32

Mixed Designs

Back 32

Combines B/t S's and W/in S's methods.

Front 33

Single-S Designs

Back 33

Each single S design includes at least one baseline phase and one treatment phase. And the DV is measured repeatedly at regular intervals throughout the baseline and treatment phases.

Front 34

AB Design

Back 34

Baseline and Treatment

Front 35

Reversal Designs (ABA, ABAB, Etc.)

Back 35

Withdrwal designs b/c treatment is taken away. Provides more data to support inferences if treatment works twice. Can be unethical.

Front 36

Multiple Baseline Design

Back 36

Sequentially applying a treatment condition to diff beh in diff settings to see if it changes DV. Really an AB in diff settings. Used when withdrawing treatmet is unethical.

Front 37

Descriptive Statistics

Back 37

Describe and summarize the date collected on a variable or the relationship between variables.

Front 38

Inferential Statistics

Back 38

Answer the question: can the data be generalized to the gen. pop.?

Front 39

Continuous Variable

Back 39

Infinite # of values. Ex: Time

Front 40

Discrete Variable

Back 40

Finite # of values.

Front 41

Nominal Scale

Back 41

Divides variables into unordered categories. Ex: Male of Female, Eye color, DSM diagnosis, Religion, Political affiliation. Weakness: Only frequencies can be obtained.

Front 42

Ordinal Scale

Back 42

Places information into "order." Ex: Ranks and Likert-scales. Weakness: Does not tell how much difference b/t scores.

Front 43

Interval Scale

Back 43

Order and equal intervals b/t successive data pts. Ex: Standard scores on IQ and Temp. No absolute 0.

Front 44

Ratio Scale

Back 44

Order, equal intervals, and an absolute 0. 0 is the complete absence of the characteristic. Ex: # of calories, # of correct items on a test, & reaction time in sec.

Front 45

Kurtosis

Back 45

Height or flatness of a distribution.

Front 46

Leptokurtic

Back 46

"peaked" distribution.

Front 47

Platykurtic

Back 47

Flat distribution

Front 48

Mesokurtic

Back 48

A normal curve

Front 49

Skewed distribution

Back 49

More than half of the observations fall on one side.

Front 50

Positively Skewed

Back 50

Most scores are low (negateve end) and the positive tail is extended.

Front 51

Negatively Skewed

Back 51

Most scores are high and the negative tail is extended.

Front 52

Mode

Back 52

Most frequent score in a set of data.

Front 53

Median

Back 53

The score that divides the data.

Front 54

Mean

Back 54

Average

Front 55

Scales of measurement

Back 55

Nominal-Mode
Ordinal-Mode or Median
Interval-Mode, Median, or Mean
Ratio-Mode, Median, or Mean

Front 56

Median is used when...

Back 56

the distribution is skewed, b/c the mean is sensitive to all scores (i.e., pull from outliers).

Front 57

Normal distribution stats

Back 57

68% = 1 SD
95% = 2 SD
99% = 3 SD

Front 58

Range

Back 58

Simplest measure of variability which is calculated by sub the lowest score from the highest score in the distribution.

Front 59

SD

Back 59

Square root of the variance.

Front 60

When constants are added or subtracted...

Back 60

the measures of central tendency stay the same.

Front 61

When scores are multiplied or divided...

Back 61

the measures of central tendency all change.

Front 62

Inferential Statistics

Back 62

Tells if the obtained sample values can be generalized to the pop w/ confidence.

Front 63

Population Paramaters
mu
sigma
sigma squared

Back 63

Sample Statistics
M or X
S or SD
S^2 or V

Front 64

Sampling Distribution

Back 64

Allows a researcher to determine the probability that a sample having a particular mean or other value could have been drawn from a pop with a known parameter.

Front 65

Sampling Distribution of the Mean

Back 65

Taking several means and finding a normal curve.

Front 66

Central Limit Theorem (CLT)

Back 66

1. Regardless of the shape of the distribution, as the sample size increases, the sampling distribution approaches a normal distribution.
2. The M of the sampling dist is equal to the pop M.
3. The SD of the sampling dist is = sigma/sq root of N (SEM).

Front 67

The larger the pop SD and the ________ the sample size, the ______ the SEM.

Back 67

smaller, larger

Front 68

The smaller the pop SD and the ______ the sample size, the ________ the SE M.

Back 68

larger, smaller

Front 69

Rejection Region

Back 69

Region of unlikely values or your H was right rather than the null.

Front 70

Retention Region

Back 70

Region of likely values or your H was wrong...keep the Ho.

Front 71

Type I Error (experiment-wise error rate)

Back 71

False positive. When you reject a true null. Directly related to the size of alpha. As alpha increases, your probability of making a Type I error increases.

Front 72

Type II Error

Back 72

False negative. When you retain a false null. The probability of making a Type II error is = Beta. When Beta is low, the sample is small, and when the IV is not sufficient, then a Type II error is more likely.

Front 73

Statistical Power

Back 73

When a statistical test enables an experimenter to reject a false null.

Front 74

Ways to Increase Power

Back 74

1. Increase Alpha
2. Increase Sample
3. Max IV
4. Min Error
5. One tailed-test
6. Parametric test

Front 75

Parametric Test

Back 75

Are used to evaluate hyp about pop means, variances, or other parameters.

Interval or Ratio scale.

Assumptions: 1)Normal dist. & 2)Homoscedasticity (normal variances). Ex: T-test, ANOVA, ANCOVA, MANOVA.

Front 76

Nonparametric Test

Back 76

Used to analyze data collected on variables on a nominal or ordinal scale or when the assumptions of a Parametric test are not met.

Front 77

Degrees of Freedom

Back 77

N-1

Front 78

Chi-Square Test (Singel or 1 var & Multiple or 2+ var)

Back 78

Used to analyize the frequency of observations in each category of a NOMINAL VARIABLE. Frequency cannot be less than 5 and obs must be independent.

Front 79

Single-Sample x^2 Test (Goodess-of-fit)

Back 79

1 var (NOM)
df = c-1

Front 80

Multiple-Sample x^2 Test
(chi-square test for contingency tables)

Back 80

2+ var (NOM)
df = (c-1)(r-1)

Front 81

Mann-Whitney U Test

Back 81

One IV: 2 Ind groups
One DV: Rank order data (ORD)
Stasitc: U
ALT: T-test for ind samples

Front 82

Wilcoxon Matched-Pairs Signed-Ranks Test

Back 82

One IV: 2 corr groups
One DV: Rank order data (ORD)
Stistic: T
ALT: T-test for corr samples

Front 83

Kruskal-Wallis Test

Back 83

One IV: 2 or more ind groups
One DV: Rank order data (ORD)
Stistic: H
ALT: one-way ANOVA

Front 84

T-test for a Single Sample

Back 84

One IV: Single group
One DV: Int or ratio
Statistic: T
df = n-1

Front 85

T-test for Ind Samples

Back 85

One IV: 2 Ind groups
One DV: Int or ratio
Statistic: T
df = n-2

Front 86

T-test for Correlated Samples

Back 86

One IV: 2 corr groups
One DV: Int or ratio
Stistic: T
df = n-1

Front 87

ANOVA

Back 87

Uses to compare 2 or more means and helps control for Type I error.

Front 88

One-Way ANOVA

Back 88

One IV: 2 or more ind groups
One DV: Int or Ratio
Stastic: F
df = (c-1)(n-c), where C=levels in IV and N=# of sub

Front 89

F-ratio

Back 89

Mean sq within/mean sq between

(treatment+error)/error

When null is true, MSB & MSW are similar.

When null is false, MSB is larger than MSW.

Front 90

Factorial (Two-Way when 2 IV's) ANOVA

Back 90

2+ IV's: Indep groups
One DV: Int or ratio
Stistic: F

Front 91

Randomized Block Factorial ANOVA

Back 91

When "blocking" is used to control extraneous variables. Treats the ext var as a IV which reduces w/in group variability and increases power.

Front 92

ANCOVA

Back 92

Combines ANOVA with regression and seperates ext var in the DV.

Front 93

Repeated Measures ANOVA

Back 93

When using W/in subj designs when diff levels of the IV are admin sequentially.

Front 94

Mixed (Split-Plot) ANOVA

Back 94

For mixed designs and one IV is B/t groups and one IV is W/in groups.

Front 95

Trend Analysis

Back 95

Type of analysis of variance used to assess linear and nonlinear trends when the IV is quantitative.

Front 96

Multivariate ANOVA (MANOVA)

Back 96

1+ IV's and 2+ DV's.

Front 97

Bivariate Correlation

Back 97

Used to summarize the degree of association b/t two variables. Ex: Scatterplot or Correlation coefficient

Front 98

Point Biserial Corr

Back 98

True dicotomy such as sex (m/f) and Int or Ratio variable.

Front 99

Biserial Corr

Back 99

Artificial dicotomy such as climate comfort (fav/unfav) and Int or Ratior variable.

Front 100

Eta (Used to assess nonlinear relationships)

Back 100

Int/Rat and Int/Rat

Front 101

Correlation assumptions

Back 101

1. Linearity
2. Unrestricted Range
3. Homoscendasticity (range of x scores is similar to the range of y scores)

Front 102

Shared variability

Back 102

Squared corr coef which represents degree of association.

Front 103

Regression Analysis

Back 103

Prediction of x and y.

Front 104

Multiple Regression

Back 104

Multivariate technique for 2+ continuous or discrete predictors.

Front 105

Multicollinearity

Back 105

High corr b/t predictors

Front 106

Types of multiple regression

Back 106

1. Simultaneous (simple)
2. Stepwise (step-up and step-down)

Front 107

Cross Validation

Back 107

Trying out a multiple correlation and multiple regression equation on another sample causing the corr coef to "shrink" and decrease the predictive value of the regression equation.

Front 108

Canonical Correlation

Back 108

Extension of multiple regression that is used when 2+ continous predictors are to be ued to predict status on 2+ continuous criteria.

Front 109

Discriminant Function Analysis

Back 109

Appropriate technique when 2+ cont predictors will be used to predict or estimae a person's status on a single discrete (nominal) criterion.

Front 110

Logistic Regression

Back 110

Same as the discriminant analysis, but assumes a non linear relationship.

Front 111

Path analysis

Back 111

Extension of multiple regression and translates theory about casual relationships into a path diagram.

Front 112

LISREL (linear structural relations analysis)

Back 112

Used when a casual model includes recursive (one-way) and non-recursive (two-way paths).

Front 113

Reliability Coefficient

Back 113

In most cases, you would square the correlation coefficient to obtain the answer to this question. However, the reliability coefficient is an exception to this rule: it is never squared. Instead, it is interpreted directly. This means that the value of the reliability coefficient itself indicates the proportion of variance in a test that reflects true variance.