Question 1

If there is some doubt about the lasting quality of an outcome, it is wise to :

Accepted Answer

A)  Do a statistical test 
B)  Examine variability in the data 
C)  Continue monitoring 
D)  Terminate intervention 
A)  Do a statistical test 
B)  Examine variability in the data 
C)  Continue monitoring 
D)  Terminate intervention C

Question 2

When comparing actual client changes with conceptually-based, predicted changes, you are examining

Accepted Answer

A)  Practical significance 
B)  Clinical significance 
C)  Both a and b 
D)  Neither a nor b 
A)  Practical significance 
B)  Clinical significance 
C)  Both a and b 
D)  Neither a nor b D

Question 3

None of the statistics available for analysis of time-series data should be used if the data are autocorrelated.

Accepted Answer

A) True 
 B)False  False

Question 4

If you measured how many hours you worked with a client in relationship to what effects this had on changes in the client's behavior, this would be an example of effectiveness.

Accepted Answer

A) True 
 B)False

Question 5

When you attain statistical significance without clear practical and theoretical significance, it would be wise to:

Accepted Answer

A)  Terminate 
B)  Maintain the present intervention 
C)  Do a withdrawal 
D)  None of the above 
A)  Terminate 
B)  Maintain the present intervention 
C)  Do a withdrawal 
D)  None of the above

Question 6

If you measure how many hours you worked with a client, in the context of the large caseload you had to maintain at the same time, this would be an example of efficiency.

Accepted Answer

A) True 
 B)False

Question 7

You can always tell if your data are autocorrelated by visual inspection.

Accepted Answer

A) True 
 B)False

Question 8

The term "analysis" is used in the text to refer only to the statistical significance of the data.

Accepted Answer

A) True 
 B)False

Question 9

If you see a notation that some outcome had a

Accepted Answer

A) True 
 B)False

Question 10

If your data are auto-correlated, you could:

Accepted Answer

A)  Not use statistics 
B)  Use statistics, but be aware of possible problems due to autocorrelation. 
C)  Transform the data 
D)  All of the above 
A)  Not use statistics 
B)  Use statistics, but be aware of possible problems due to autocorrelation. 
C)  Transform the data 
D)  All of the above

Question 11

The first differences transformation plots the means between two data points over all baseline data.

Accepted Answer

A) True 
 B)False

Question 12

One way of evaluating goal achievement is to use statistical significance as the criterion.

Accepted Answer

A) True 
 B)False

Question 13

The statement, "The change was statistically significant at the .05 level" means that there are only 5 out of 100 chances that the treatment did not cause the change.

Accepted Answer

A) True 
 B)False

Question 14

If you see a discontinuity between the baseline and intervention data, this means your data are free of autocorrelation.

Accepted Answer

A) True 
 B)False

Question 15

If you were able to show statistical significance and theoretical significance, but no practical significance in a given evaluated case situation, should you consider terminating the intervention for this target?

Accepted Answer

A) True 
 B)False

Question 16

First differences transformation is best used when:

Accepted Answer

A)  There appears to be a linear direction (trend) in the data 
B)  The data are wildly fluctuating 
C)  The data are not autocorrelated 
D)  None of the above 
A)  There appears to be a linear direction (trend) in the data 
B)  The data are wildly fluctuating 
C)  The data are not autocorrelated 
D)  None of the above

Question 17

If the change in problematic behavior is statistically significant, you know your intervention produced the change.

Accepted Answer

A) True 
 B)False

Question 18

An autocorrelation of .2 will inflate the t-test by 122%.

Accepted Answer

A) True 
 B)False

Question 19

Effectiveness refers to the work that goes into a service program.

Accepted Answer

A) True 
 B)False

Exam 19: Basic Principles of Analysis

If there is some doubt about the lasting quality of an outcome, it is wise to :

When comparing actual client changes with conceptually-based, predicted changes, you are examining

None of the statistics available for analysis of time-series data should be used if the data are autocorrelated.

If you measured how many hours you worked with a client in relationship to what effects this had on changes in the client's behavior, this would be an example of effectiveness.

When you attain statistical significance without clear practical and theoretical significance, it would be wise to:

If you measure how many hours you worked with a client, in the context of the large caseload you had to maintain at the same time, this would be an example of efficiency.

You can always tell if your data are autocorrelated by visual inspection.

The term "analysis" is used in the text to refer only to the statistical significance of the data.

If you see a notation that some outcome had a

If your data are auto-correlated, you could:

The first differences transformation plots the means between two data points over all baseline data.

One way of evaluating goal achievement is to use statistical significance as the criterion.

The statement, "The change was statistically significant at the .05 level" means that there are only 5 out of 100 chances that the treatment did not cause the change.

If you see a discontinuity between the baseline and intervention data, this means your data are free of autocorrelation.

If you were able to show statistical significance and theoretical significance, but no practical significance in a given evaluated case situation, should you consider terminating the intervention for this target?

First differences transformation is best used when:

If the change in problematic behavior is statistically significant, you know your intervention produced the change.

An autocorrelation of .2 will inflate the t-test by 122%.

Effectiveness refers to the work that goes into a service program.

Integrating Evaluation and Practice: Introduction to

Basic Principles of Conceptualization and Measurement

Specifying Problems and Goals: Targets of Intervention

Developing a Measurement and Recording Plan

Behavioral Observation

Individualized Rating Scales

Standardized Scales

Logs

Reactivity and Non-Reactive Measures

Selecting a Measure

Basic Principles of Single-System Designs

Baselining: Collecting Information Before Intervention

From the Case Study to the Basic Single-System Design: A-B

The Experimental Single-System Designs: A-B-A, A-B-A-B, B-A-B

Multiple Designs for Single Systems: Baselines, Targets, Crossovers, and Series

Changing Intensity Designs and Successive Intervention Designs

Designs for Comparing Interventions

Selecting a Design

Visual Analysis of Single-System Design Data

Descriptive Statistics

Tests of Statistical Significance for Single-System Designs

Selecting a Procedure for Analyzing Data

Not for Practitioners Alone: Evaluation for Clients, Administrators, Educators, and Students

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