Stats 2

Front 1

Systematic Between Groups Variance

Back 1

differences between the groups means as a result of the manipulated variable

Front 2

Nonsystematic, Within Groups Variance

Back 2

Average variation within each group on the DV resulting from additional variables not controlled or manipulated in a study

Front 3

T-Test Evaluate?

Back 3

Does the difference between the means of 2 groups exceed what would be expected from sampling error or chance

Front 4

Examples of T-Test

Back 4

Independent Samples

Dependent Samples

Front 5

Null Hypothesis

Back 5

All the group means are equal in the target population, any differences found in a sample is the result of chance fluctuations

Front 6

ANOVA Stats

Back 6

F= Ratio of between group variance to within group variance

(X,Y)= Degrees of freedom between groups and within groups respectively

P= Probably of some data if null hypothesis is true

Etq Squared (h2)= Effect size statistic similar to r squared

Front 7

Post-Hoc Test

Back 7

Follow-up comparison tests that are conducted following a significant F test to determine which group differences are statistically significant, while controlling for elevated type 1 error rates

Front 8

Post-Hoc test examples

Back 8

Tukey and Bonferroni

Front 9

Anova-Stat. sign. F Ratio? Yes

Back 9

Reject Null and conclude there is a relationship between IV and DV--conduct additional analyses to compare all the group means while controlling for Type 1

Front 10

Anova-Stat. sign. F ratio? No

Back 10

retain null and conclude insufficient data to rule out chance

Front 11

Axiom

Back 11

Clients behavior areinfluenced by multiple variables,not just a single independent variable!

Front 12

Interaction Effect

Back 12

(The sum is greater than the 2 halves)

-Effect of 1 IV on the DV depends on the level of a second IV

-Effect of 1 IV is not consistent across all groups of 2nd IV

Front 13

ANCOVA

Back 13

Evaluates whether the differences among group means are likely due to chance or sampling error, after controlling for the influence of a covareist

Front 14

Quasi-Experimental Designs

Back 14

Identify casual relationships among research variables when random assignment is not feasible for ethical or practical reaseons

Front 15

Quasi-Experimental Design procedure

Back 15

Non-equivalent, pre-existing groups are compared on a DV after being exposed to different levels of the IV

Front 16

Major Drawback of Quasi-experimental Design

Back 16

Lack of random assignment reduces the internal validity of the studies results (selectional biases)

Front 17

Temporal Precedence

Back 17

-Cause must precede effect

-Must be a functional relationship between A and B

-Alternative explanations for functional relationship between A and B must be ruled out

Front 18

Confounding variable hypothesis

Back 18

Another factor other than the IV was responsible for changes in the DV (Major threat to internal validity)

Front 19

Problems with between subjects experiments

Back 19

Require large number of participants so the groups within have adequate numbers of participants

May have low statistical power because of random error

Front 20

Within subjects design

Back 20

All participants are exposed to all levels of the IV, ideally in a random fashion

Front 21

Matching or dependent samples design

Back 21

Reduce error variance by intentionally matching participants confounding variables

Front 22

Experimental Designs-Between Groups

Back 22

Participants are separated into distinct groups and the average group scores are compared on a DV

Front 23

Experimental Designs-Within groups

Back 23

Every Participants experiences all levels of the IV: we compare the same people across all levels of IV

Front 24

Experimental Design-Matched Groups

Back 24

Matched pairs are identified and the members of these pairs are randomly assigned to different levels of the IV

Front 25

Basic Research

Back 25

Researchdirected toward theory development and understanding the human condition. The discovery of new knowledge is valuable inand of itself.

Front 26

Applied Research

Back 26

Researchdirected toward solving practical, real-world problems

Front 27

Outcome Research

Back 27

Evaluate the efficacy of a counseling model, set of interventions, or formal treatment program.

Front 28

Program Assessment and Evaluation

Back 28

Not doing research to benefit scientific community; rather for local stakeholders

Front 29

Program Assessment objective

Back 29

Identify the needs and priorities of a local population, organization, or school.

Front 30

Types of Program assessment and evalution

Back 30

Data-driven

Perceptions based

Front 31

Data-driven Program assessment and evaluation

Back 31

Needs are identified through an evaluation of (relatively) objective data; these data become the foundation for progammatic changes.

Front 32

Perceptions-base program assesment

Back 32

Needs are identified through soliciting the needs and priorities of various stakeholders

Front 33

Program Evaluation Objective

Back 33

Evaluate the effectiveness and benefits of a specific program for a specific group of individuals

Front 34

Steps to Program evaluation

Back 34

Needs assessment

Develop goals and objective

Design and implement Program

Data collection and Analysis

Communicate Results to Stakeholders

Use results and stakeholder evaluations to inform next steps

Front 35

Social Constructivism

Back 35

There is no single objective reality independent of an observer, reality is created in the mind of each individual and interpretations attached to his or her subjective experiences

Front 36

General Research Strategies of Qualitative Research

Back 36

Intensive interviewing

Participant Observation

Discovering themes, regularities, and categories in participants' personal stories/experiences

Reflective Writing and observation

Subjective interpretations of data

Documentation of the researcher's biases and perspectives

Front 37

Ethnographic research objective

Back 37

Describe or explain the collective experiences and behaviors of a specific cultural group in its natural setting

Front 38

Grounded theory objective

Back 38

Develop a theory to account for a particular phenomenon.

Front 39

Phenomenological Research objective

Back 39

Describe the subjective experiences of a group of individuals who have a particular phenomenon

Front 40

Multiple Regression Stats

Back 40

R= Correlation between actual criterion scores and predicted criterion scores (team scores)

R(Squared)= Proportion of variance in criterion variable explained by the combination of predictor variables

b or B= Unique contribution of a predictor variable to the prediction of the criterion variable (Individual Points)

Front 41

Basic Survey Research Objective

Back 41

Identify and describe attitudes, opinions, behaviors, or other characteristics of a group of respondents

Front 42

Basic survey research, basic strategy

Back 42

Collect data through administration of a questionnaire or interview to ideally a representative sample of a population

Front 43

Basic Survey Research examples

Back 43

Needs assessment

Epidemiology survey

Cross-sectional and longitudinal research

Front 44

Ex Post Facto/ Casual Comparative Objective

Back 44

Evaluate whether pre-existing groups (IV) differ on select research variables (DV)

Front 45

Ex Post Facto Strategy

Back 45

Measure characteristics of 2 or more groups on one or more research variables

Front 46

Ex Post Facto Challenges

Back 46

Groups should, ideally, differ only on IV (Grouping Factor)

Front 47

Ex Post facto IV

Back 47

Categorical Group Membership

Front 48

Ex post facto DV

Back 48

Outcome variable measure by researcher

Front 49

Basic Format of Correlational Research

Back 49

Every participant has numerical scores/ratings on at least 2 research variables--an IV and DV

Front 50

Correlational Design Objective

Back 50

Explain relationship between 2 or more research variables

Make predictions about how individuals will score on 2 variable from how they score on another variable

Front 51

Correlational Design Strategy

Back 51

Measure participants on 2 or more variable and use statistical procedures to describe relationship among variables

Front 52

Pearson Correlation Coefficient

Back 52

An index of the linear relationship between 2 continuous variables that indicates the extent to which changes in 1 variable correspond to changes in a 2nd variable

Front 53

Correlational Coefficient Range

Back 53

-1.00 to +1.00

Front 54

Coefficient of determination (r Squared)

Back 54

Percentage of variance shared between 2 variables

Front 55

Interpreting effect size (Small)

Back 55

.10

Front 56

Interpreting effect size (Medium)

Back 56

.30

Front 57

Interpreting effect size (Large)

Back 57

.50

Front 58

Moderator variable

Back 58

A 3rd variable that changes the strength or the direction of the relationship between X and Y

Front 59

Mediator Variable

Back 59

3rd variable that accounts for, or at least substantially reduces the relationship between X and Y. Essentially, it explains why the relationship between X and Y occur.

Front 60

Steps in linear regression

Back 60

Find regression line that best fits the data points shared by 2 variables, minimizing errors of prediction.

Slope intercept form: y= (X)b+a

Use the equation to predict future scores on Y variable

Front 61

Multiple Regression Purpose

Back 61

Predict scores on a criterion variable (Y) from scores on 2 or more predictor variables (x1, x2)

Front 62

Multiple Regression Benefit

Back 62

Results indicate how well the predictor variable (X), alone and in combination explain variation in the criterion variable (Y)