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Instructions1.docx
PPnotes.pptx
Instructions1.docx
Please respond to this topic using the attached PowerPoint for notes. This is a discussion post, not a formal paper. 100% NO PLAGIARISM or AI!!!!! This should only be maybe 1 or 2 paragraphs at the most.
List the primary strengths of the experimental research design. In particular, what makes it the best method for revealing cause and effect relationships? Lastly, if it is the best method, why isn’t it used more frequently in the social sciences? In other words, what are some issues that may prevent us from using this design when conducting research?
PPnotes.pptx
The Practice of Social Research
Chapter 8 – Experiments
Chapter Outline
Topics Appropriate to Experiments
The Classical Experiment
Selecting Subjects
Variations on Experimental Design
An Illustration of Experimentation
Alternative Experimental Settings
Strengths and Weaknesses of the Experimental Method
Ethics and Experiments
Quick Quiz
LEGEND: common notation
IV – independent variable
DV – dependent variable
E – experimenter
S - subject
EG – experimental group
CG – control group
ES – experimental stimulus
P – placebo
T1: Time 1
T2: Time 2
T3: Time 3
Experiments
Experiments involve:
Taking action
Observing consequences of that action
Topics Appropriate to Experiments
In particular, best suited for
Explanatory research
E.g., Hypothesis testing
Evaluation research
Particularly, well-positioned for projects involving limited & well-defined concepts & propositions.
The Classical Experiment
Central Features
Variables, time order, measures, and groups
Major Components
IV & DV
Pre-testing & Post-testing
Experimental & Control Groups
The Classical Experiment
Independent & Dependent Variables
Independent – takes form of dichotomous stimulus (it is either present or absent), cause
i.e., it “varies”
Dependent – effect
Outcome measure of presence or absence of DV.
Examples: physical conditions, social behavior, attitudes, feelings, or beliefs
The Classical Experiment
Pre-testing – the measurement of a DV along subjects
prior to testing.
Time 2: implementation of IV
For experimental group only.
Post-testing – measurement of a DV among subjects after exposure to IV.
Differences btw measurements on DV at T1 and T3 can be attributed to influence of IV.
The Classical Experiment
Experimental Group – a group of subjects to whom an experimental stimulus is administered.
Control Group – a group of subjects to whom no experimental stimulus is administered.
Should resemble experimental group.
If we do detect a difference, we want to ensure it is due to the IV, and not a difference btw the two groups!
10
11
12
Hawthorne Effect
Pointed to necessity of control groups
IV: improved working conditions (better lighting)
DV: improvement in worker morale/productivity
Results: workers were responding to attention of researchers, not the improved working conditions.
Placebo
Because of Hawthorne effect, we don’t want subjects to know if they are receiving ES (i.e., IV)
Control groups are exposed to “dummy” IV just so it appears as if everyone is treated the same
Medical research uses this: S doesn’t know what they are taking
Ensures that changes in DV actually result from IV and are not psychologically based.
The Classical Experiment
E may be more likely to “observe” improvements among those who receive the drug (IV)
The Double-Blind Experiment – an experimental design in which neither S nor E know which is the EG and which is CG.
Ostensibly eliminates the possibility of interviewer bias.
i.e. witnessing a change that isn’t there
Selecting Subjects
Must decide the target population – group in which results are extrapolated
Next, decide how to select S from the target pop.
Cardinal rule – EG and CG must be as similar as possible!!!
Randomization purposes towards this goal.
Role of college students
Generalizability?
Selecting Subjects
Probability Sampling
You need at least 100, and this is hard to attain
Randomization – technique for assigning experimental subjects to EG and CG.
Matching –procedure whereby pairs of subjects are matched on basis of their similarities on one or more variables, and one member of the pair is assigned to EG, and the other to the CG – similar to quota sampling
Ideally, produces EG & CG that are statistically =.
Selecting subjects (cont’d)
David Farrington: “randomization insures that the average unit in the EG is approx. equivalent to the avg. unit in the CG before treatment is applied.
“All other things are equal”
19
Variations on Experimental Design
Quasi-Experimental Research Designs
One-shot case study – a single group of subjects is measured on DV following an ES.
One-group pre-test post-test design – a pre-test is added for the EG but lacks a CG.
Static-group comparison – includes EG and CG, but no pre-test.
21
Variations on Experimental Design
Validity Issues in Experimental Research
Internal Validity – possibility that conclusions drawn from experimental results may not accurately reflect what happened in the experiment itself.
Sources: history, maturation, testing, instrumentation, statistical regression, selection bias, experimental mortality, causal time order, diffusion or imitation of treatments, compensation, compensatory rivalry, demoralization
External Validity – the possibility that conclusions drawn from experimental results may not be generalizable to the “real” world
Internal validity threats
History – external events may occur during experiment that influences R.
Maturation – people are constantly evolving.
Testing – R may be learning how to test rather than the material – e.g., comprehensive exams/assessments.
Instrumentation – changes in measurement process.
Statistical regression – extreme scores regress to mean.
Selection biases – way in which subjects are chosen (random assignment).
Experimental mortality – S may drop out of experiment.
Threats to Internal Validity
8. Causal time order: Ambiguity about order of stimulus and Dependent Variable – which caused which?
9. Diffusion/Imitation of treatments: Experimental group may pass on elements to Control group when communicating
10. Compensatory treatment: Cgroup is deprived of something considered to be of value
24
24
Internal Validity Threats
11. Compensatory Rivalry: Control group deprived of the stimulus may try to compensate by working harder
12. Demoralization: Feelings of deprivation among control group result in subjects giving up
25
25
Alternative Experimental Settings
Web-Based Experiments
“Natural” Experiments
Strengths of Experimental Method
Isolation of experimental variable’s impact over time.
Replication
Experimental design ensures:
Cause precedes effect via taking posttest
Empirical correlation exists via comparing pretest to posttest.
No spurious 3rd variable influencing correlation via posttest comparison between EG & CG, via randomization.
Weaknesses of Experimental Method
ETHICS!!!
Artificiality of laboratory settings
Aforementioned issues of internal & external validity.
Generalizability and threats to validity
Potential threats to internal validity are only some of the complications faced by experimenters; they also have the problem of generalizing from experimental findings to the real world
Two dimensions of generalizability:
Construct Validity
External Validity
29
29
Threats to Construct Validity
Concerned with generalizing from experiment to actual causal processes in the real world
Link construct and measures to theory
Clearly indicate what constructs are represented by what measures
Decide how much treatment is required to produce change in Dependent Variable
30
30
Threats to External Validity
Significant for experiments conducted under carefully controlled conditions rather than more natural conditions
Reduces internal validity threats
John Eck (2002): "diabolical dilemma."
Suggestion:
explanatory studies internal validity
applied studies external validity
31
31
Threats to Statistical Conclusion Validity
Becomes an issue when findings are based on small samples
More cases allows you to reliably detect small differences; less cases result in detection of only large differences
Finding cause-and-effect relationships through experiments depends on two related factors:
Number of Subjects
Magnitude of posttest differences between the experimental and control groups
32
32
Quasi-Experimental Designs
When randomization isn’t possible for legal or ethical reasons
Renders them subject to Internal Validity threats
Quasi = “to a certain degree”
Two categories:
nonequivalent-groups designs
time series designs
33
33
Nonequivalent-Groups Designs
When we cannot randomize, we cannot assume equivalency; hence the name
We take steps to make groups as comparable as possible
Match subjects in Experimental and Control groups using important variables likely related to Dependent Variable under study
Aggregate matching – comparable average characteristics
34
34
Cohort Designs
Cohort – Group of subjects who enter or leave an institution at the same time
Ex: A class of police officers who graduate from a training academy at the same time, All persons who were sentenced to probation in May
Necessary to ensure that two cohorts being examined against one another are actually comparable
35
35
Time-Series Designs
Longitudinal Studies
Examine a series of observations over time
Interrupted – Observations compared before and after some intervention (used in cause-and-effect studies)
Instrumentation threat to internal validity is likely because changes in measurements may occur over a long period of time
Often use measures produced by CJ organizations
36
36
Variable-Oriented Research and scientific realism
A large number of variables are studied for a small number of cases or subjects
Case-oriented research: Many cases are examined to understand a small number of variables (Boston Gun Project)
Variable-oriented research: A large number of variables are studied for a small number of cases or subjects
Case Study Design: Centered on an in-depth examination of one or a few cases on many dimensions
37
37
Experimental Illustration in CJ: Program Evaluation
Evaluation of two prison programs run at Limestone Correctional Facility.
“IF” Project -- https://vimeo.com/162596857
Inside/Out Prison Exchange Program
IF Project Program Evaluation
Program Evaluation included Experimental design
Survey items tapping into static and dynamic risk factors for recidivism.
STATIC
adverse childhood experiences -- 10-item scale tapping into divorce, child abuse, etc.
DYNAMIC
Hopefulness -- 2-item scale
Loneliness --20-item scale
Anxiety 20-item scale
Emotional expression 9-item scale
Pre and post-test levels where examined among E group.
Inside/Out Program Evaluation
Experimental design for program evaluation
Compared inside (inmate) students with outside (college) students on academic self-efficacy
Hypotheses:
outside students will exhibit higher PRE-TEST levels of academic efficacy
Inside students will exhibit statistically significant INCREASE in self-efficacy from PRE-TEST to POST-TEST.
POST-TEST levels of academic efficacy btw Inside and Outside students will be insignificant.
Results:
It Appears that the program has no discernible impact on OUTSIDE students.
While there was positive movement on both the academic efficacy scale and the last item (conflict resolution with peer) from pre-test to post-test, these items were both (a) relatively modest, and (b) insignificant.
The results, while still failing to attain statistical significance, are a little more mixed for the INSIDE students.
While mean academic efficacy did technically drop from pre to post-test, this decline was both modest and failed to attain statistical significance.
Conversely, the mean improvement on conflict resolution with peers did improve noticeably from pre (4.00) to post-test (4.36).
Results (cont’d)
While OUTSIDE students exhibited statistically significant higher mean levels on both the academic efficacy scale and the conflict resolution item……
The statistically significant higher mean levels for OUTSIDE students on the conflict resolution item were reduced to insignificance in the post-test.
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Instructions1.docx
Please respond to this topic using the attached PowerPoint for notes. This is a discussion post, not a formal paper. 100% NO PLAGIARISM or AI!!!!! This should only be maybe 1 or 2 paragraphs at the most.
List the primary strengths of the experimental research design. In particular, what makes it the best method for revealing cause and effect relationships? Lastly, if it is the best method, why isn’t it used more frequently in the social sciences? In other words, what are some issues that may prevent us from using this design when conducting research?
PPnotes.pptx
The Practice of Social Research
Chapter 8 – Experiments
Chapter Outline
Topics Appropriate to Experiments
The Classical Experiment
Selecting Subjects
Variations on Experimental Design
An Illustration of Experimentation
Alternative Experimental Settings
Strengths and Weaknesses of the Experimental Method
Ethics and Experiments
Quick Quiz
LEGEND: common notation
IV – independent variable
DV – dependent variable
E – experimenter
S - subject
EG – experimental group
CG – control group
ES – experimental stimulus
P – placebo
T1: Time 1
T2: Time 2
T3: Time 3
Experiments
Experiments involve:
Taking action
Observing consequences of that action
Topics Appropriate to Experiments
In particular, best suited for
Explanatory research
E.g., Hypothesis testing
Evaluation research
Particularly, well-positioned for projects involving limited & well-defined concepts & propositions.
The Classical Experiment
Central Features
Variables, time order, measures, and groups
Major Components
IV & DV
Pre-testing & Post-testing
Experimental & Control Groups
The Classical Experiment
Independent & Dependent Variables
Independent – takes form of dichotomous stimulus (it is either present or absent), cause
i.e., it “varies”
Dependent – effect
Outcome measure of presence or absence of DV.
Examples: physical conditions, social behavior, attitudes, feelings, or beliefs
The Classical Experiment
Pre-testing – the measurement of a DV along subjects
prior to testing.
Time 2: implementation of IV
For experimental group only.
Post-testing – measurement of a DV among subjects after exposure to IV.
Differences btw measurements on DV at T1 and T3 can be attributed to influence of IV.
The Classical Experiment
Experimental Group – a group of subjects to whom an experimental stimulus is administered.
Control Group – a group of subjects to whom no experimental stimulus is administered.
Should resemble experimental group.
If we do detect a difference, we want to ensure it is due to the IV, and not a difference btw the two groups!
10
11
12
Hawthorne Effect
Pointed to necessity of control groups
IV: improved working conditions (better lighting)
DV: improvement in worker morale/productivity
Results: workers were responding to attention of researchers, not the improved working conditions.
Placebo
Because of Hawthorne effect, we don’t want subjects to know if they are receiving ES (i.e., IV)
Control groups are exposed to “dummy” IV just so it appears as if everyone is treated the same
Medical research uses this: S doesn’t know what they are taking
Ensures that changes in DV actually result from IV and are not psychologically based.
The Classical Experiment
E may be more likely to “observe” improvements among those who receive the drug (IV)
The Double-Blind Experiment – an experimental design in which neither S nor E know which is the EG and which is CG.
Ostensibly eliminates the possibility of interviewer bias.
i.e. witnessing a change that isn’t there
Selecting Subjects
Must decide the target population – group in which results are extrapolated
Next, decide how to select S from the target pop.
Cardinal rule – EG and CG must be as similar as possible!!!
Randomization purposes towards this goal.
Role of college students
Generalizability?
Selecting Subjects
Probability Sampling
You need at least 100, and this is hard to attain
Randomization – technique for assigning experimental subjects to EG and CG.
Matching –procedure whereby pairs of subjects are matched on basis of their similarities on one or more variables, and one member of the pair is assigned to EG, and the other to the CG – similar to quota sampling
Ideally, produces EG & CG that are statistically =.
Selecting subjects (cont’d)
David Farrington: “randomization insures that the average unit in the EG is approx. equivalent to the avg. unit in the CG before treatment is applied.
“All other things are equal”
19
Variations on Experimental Design
Quasi-Experimental Research Designs
One-shot case study – a single group of subjects is measured on DV following an ES.
One-group pre-test post-test design – a pre-test is added for the EG but lacks a CG.
Static-group comparison – includes EG and CG, but no pre-test.
21
Variations on Experimental Design
Validity Issues in Experimental Research
Internal Validity – possibility that conclusions drawn from experimental results may not accurately reflect what happened in the experiment itself.
Sources: history, maturation, testing, instrumentation, statistical regression, selection bias, experimental mortality, causal time order, diffusion or imitation of treatments, compensation, compensatory rivalry, demoralization
External Validity – the possibility that conclusions drawn from experimental results may not be generalizable to the “real” world
Internal validity threats
History – external events may occur during experiment that influences R.
Maturation – people are constantly evolving.
Testing – R may be learning how to test rather than the material – e.g., comprehensive exams/assessments.
Instrumentation – changes in measurement process.
Statistical regression – extreme scores regress to mean.
Selection biases – way in which subjects are chosen (random assignment).
Experimental mortality – S may drop out of experiment.
Threats to Internal Validity
8. Causal time order: Ambiguity about order of stimulus and Dependent Variable – which caused which?
9. Diffusion/Imitation of treatments: Experimental group may pass on elements to Control group when communicating
10. Compensatory treatment: Cgroup is deprived of something considered to be of value
24
24
Internal Validity Threats
11. Compensatory Rivalry: Control group deprived of the stimulus may try to compensate by working harder
12. Demoralization: Feelings of deprivation among control group result in subjects giving up
25
25
Alternative Experimental Settings
Web-Based Experiments
“Natural” Experiments
Strengths of Experimental Method
Isolation of experimental variable’s impact over time.
Replication
Experimental design ensures:
Cause precedes effect via taking posttest
Empirical correlation exists via comparing pretest to posttest.
No spurious 3rd variable influencing correlation via posttest comparison between EG & CG, via randomization.
Weaknesses of Experimental Method
ETHICS!!!
Artificiality of laboratory settings
Aforementioned issues of internal & external validity.
Generalizability and threats to validity
Potential threats to internal validity are only some of the complications faced by experimenters; they also have the problem of generalizing from experimental findings to the real world
Two dimensions of generalizability:
Construct Validity
External Validity
29
29
Threats to Construct Validity
Concerned with generalizing from experiment to actual causal processes in the real world
Link construct and measures to theory
Clearly indicate what constructs are represented by what measures
Decide how much treatment is required to produce change in Dependent Variable
30
30
Threats to External Validity
Significant for experiments conducted under carefully controlled conditions rather than more natural conditions
Reduces internal validity threats
John Eck (2002): "diabolical dilemma."
Suggestion:
explanatory studies internal validity
applied studies external validity
31
31
Threats to Statistical Conclusion Validity
Becomes an issue when findings are based on small samples
More cases allows you to reliably detect small differences; less cases result in detection of only large differences
Finding cause-and-effect relationships through experiments depends on two related factors:
Number of Subjects
Magnitude of posttest differences between the experimental and control groups
32
32
Quasi-Experimental Designs
When randomization isn’t possible for legal or ethical reasons
Renders them subject to Internal Validity threats
Quasi = “to a certain degree”
Two categories:
nonequivalent-groups designs
time series designs
33
33
Nonequivalent-Groups Designs
When we cannot randomize, we cannot assume equivalency; hence the name
We take steps to make groups as comparable as possible
Match subjects in Experimental and Control groups using important variables likely related to Dependent Variable under study
Aggregate matching – comparable average characteristics
34
34
Cohort Designs
Cohort – Group of subjects who enter or leave an institution at the same time
Ex: A class of police officers who graduate from a training academy at the same time, All persons who were sentenced to probation in May
Necessary to ensure that two cohorts being examined against one another are actually comparable
35
35
Time-Series Designs
Longitudinal Studies
Examine a series of observations over time
Interrupted – Observations compared before and after some intervention (used in cause-and-effect studies)
Instrumentation threat to internal validity is likely because changes in measurements may occur over a long period of time
Often use measures produced by CJ organizations
36
36
Variable-Oriented Research and scientific realism
A large number of variables are studied for a small number of cases or subjects
Case-oriented research: Many cases are examined to understand a small number of variables (Boston Gun Project)
Variable-oriented research: A large number of variables are studied for a small number of cases or subjects
Case Study Design: Centered on an in-depth examination of one or a few cases on many dimensions
37
37
Experimental Illustration in CJ: Program Evaluation
Evaluation of two prison programs run at Limestone Correctional Facility.
“IF” Project -- https://vimeo.com/162596857
Inside/Out Prison Exchange Program
IF Project Program Evaluation
Program Evaluation included Experimental design
Survey items tapping into static and dynamic risk factors for recidivism.
STATIC
adverse childhood experiences -- 10-item scale tapping into divorce, child abuse, etc.
DYNAMIC
Hopefulness -- 2-item scale
Loneliness --20-item scale
Anxiety 20-item scale
Emotional expression 9-item scale
Pre and post-test levels where examined among E group.
Inside/Out Program Evaluation
Experimental design for program evaluation
Compared inside (inmate) students with outside (college) students on academic self-efficacy
Hypotheses:
outside students will exhibit higher PRE-TEST levels of academic efficacy
Inside students will exhibit statistically significant INCREASE in self-efficacy from PRE-TEST to POST-TEST.
POST-TEST levels of academic efficacy btw Inside and Outside students will be insignificant.
Results:
It Appears that the program has no discernible impact on OUTSIDE students.
While there was positive movement on both the academic efficacy scale and the last item (conflict resolution with peer) from pre-test to post-test, these items were both (a) relatively modest, and (b) insignificant.
The results, while still failing to attain statistical significance, are a little more mixed for the INSIDE students.
While mean academic efficacy did technically drop from pre to post-test, this decline was both modest and failed to attain statistical significance.
Conversely, the mean improvement on conflict resolution with peers did improve noticeably from pre (4.00) to post-test (4.36).
Results (cont’d)
While OUTSIDE students exhibited statistically significant higher mean levels on both the academic efficacy scale and the conflict resolution item……
The statistically significant higher mean levels for OUTSIDE students on the conflict resolution item were reduced to insignificance in the post-test.
image4.jpeg
image5.jpeg
image6.jpeg
image7.jpeg
image8.jpeg
image9.emf
image10.emf
image11.emf
image12.emf
image13.emf
image14.emf
image15.emf
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