Eppp--Statistics And Research Design

Front 1

Formula for the standard error of the mean

Back 1

Standard Deviation
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square root of sample size

Front 2

To increase the standard error of the mean, what do you need to do to the sample size and standard deviation?

Back 2

Decrease the number of individuals in the sample size and increase the standard deviation.

Front 3

Define Selection

Back 3

Pre-existing subject differences that threaten the internal validity of a research study...these could account for differences between the groups on the dependent variable.

Front 4

What is Path Analysis

Back 4

A way to test assumptions about the causal relationships among multiple variables without actually manipulating variables and doing a true experiment.

Front 5

F Ratio

Back 5

MSB--mean square between (between group variance)
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MSW--within-group variance

Front 6

What is a factorial design?

Back 6

Several independent variables are assessed at the same time. It allows for the interaction of several variables to be assessed.

Front 7

What does LISAREL mean?

Back 7

Linear Structural Relationships. A structural equation modeling technique--desiged to assess the degree to which a causal model involviing multiple variables is supported by observed data.

Front 8

What does LISREL involve?

Back 8

Latent and observed variables. Unidirectional and bidirectional causal relationships.

Front 9

Regression to the mean

Back 9

The tendency of extreme values or characteristics to be less extreme.

Front 10

Pearson r

Back 10

The relationship between X and Y variables can be depicted on a scatterplot as a straight line.

Front 11

Name several threats to internal validity

Back 11

Maturation
Testing
Instrumentation
Selection
Statistical Regression

Front 12

Maturation

Back 12

Any internal change that occurs in the subjects while the experiement is in progress. I.e., getting hungry, tired, etc.

Front 13

History

Back 13

Any external event that affects scores or status on the dependent variable.

Front 14

Instrumentation

Back 14

A change in DV may be observed from pretest to post-test because the nature of the measuring instrument has changed. I.e., the scale broke.

Front 15

Rosenthal Effect
(Pygmalion Effect)

Back 15

Behavior of subjects changes as a result of experimenter expectancies, i.e., teacher expectations.

Front 16

Hawthorne Effect

Back 16

Tendency of subjects to behave differently due to the mere fact that they are participating in research, i.e, changing the work environment.

Front 17

Cross-Sequential Study

Back 17

Involves studying groups of subjects that are divided on the basis of age. Involves studying them for a period of time. Controls for cohort effects, less costly than longitudinal studies.

Front 18

Multiple Baseline Study

Back 18

A single-subject study that involves the sequential application of a treatment across different baselines. Does not entail withdrawal of treatment.

Front 19

One-group time-series

Back 19

Administering multiple pretests and post-tests to one group of subjects before and after a treatment is administered. Controls for maturation, testing, and regression.

Front 20

What is power?

Back 20

The probability of rejecting a false null hypothesis, or detecting a true effect of the independent variable.

Front 21

How would you increase power?

Back 21

Increasing reliability, or decreasing error variance reduceds the power that you will "miss" a true effect.

Front 22

Central Limit Theorem

Back 22

Predicts that as sample size increases, the shape of the sampling distribution of means approaches normality, regardless of the shape of the population distribution.

Front 23

Scheffe Test

Back 23

Post-hoc test that is considered to be the most conservative. Most likely to result in a type II error.

Front 24

Type II error

Back 24

Incorrect retention of the null hypothesis.

Front 25

Canonical Correlation

Back 25

Used to identify the relationship between multiple predictors and multiple criterion variables.

Front 26

Validity

Back 26

Looks at accuracy...does the measure what it purports to measure.

Front 27

Trend Analysis

Back 27

Quantitative independent variable, non-linear. Repeated measures design.

Front 28

Formula for Z-Score

Back 28

(X) Raw Score - (M) Mean
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Standard Deviation

Front 29

Percentile Ranks (PR)

Back 29

Flat or rectangular distribution; non-linear

Front 30

Type I Error

Back 30

Rejecting a true null hypothesis. Thinking you have something when you don't.

Front 31

Alpha Level

Back 31

The probability of making a type I error. Level of significance.

Front 32

Type II Error

Back 32

Retaining a false null hypothesis. Thinking you don't have something when you really do.

Front 33

Beta Level

Back 33

Probability of making a type II error.

Front 34

Power

Back 34

The probability of rejecting the null hypothesis when it is false...saying there is a difference when there really is a difference.

Front 35

Name four ways to increase power:

Back 35

1. Increase the sample size
2. Increase alpha
3. Use a one-tailed test
4. Increase the difference of the population means under study.

Front 36

Mann-Whitney U

Back 36

A nonparametric test that's equivalent to an independent samples t-test.

Front 37

Demand characteristics

Back 37

Cues in the experimental setting that allow subjects to deduce the experimental hypothesis.

Front 38

Quasi-experimental research

Back 38

Subjects are not randomly assigned to treatment groups. This threatens internal validity.

Front 39

Percentile ranks

Back 39

Always have a rectangular or flat, distribution. The same number of scores fall within any given range.

Front 40

Z-Scores

Back 40

Any distribution can be converted into z-scores. The z-score distribution will maintain the shape of the original distribution. The mean always = 0 and the SD = 1.

Front 41

Power

Back 41

The probability of rejecting a false null hypothesis. Can it detect a true difference between population means.

Front 42

Chi-Square

Back 42

Research that involves categorizing observations. Do obtained frequencies differ?