12/4/2020 Anomie and Strain Theory Case Study Scoring Guide

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Anomie and Strain Theory Case Study Scoring Guide

Due Date: Unit 8 Percentage of Course Grade: 20%.

CRITERIA NON-PERFORMANCE BASIC PROFICIENT DISTINGUISHED

Examine elements of general strain theory (Agnew) in the film and their impact on characters' behavior.

20%

Does not examine elements of general strain theory (Agnew) in the film and their impact on characters' behavior.

Identifies elements of general strain theory (Agnew) in the film.

Examines elements of general strain theory (Agnew) in the film and their impact on characters' behavior.

Examines elements of general strain theory (Agnew) in the film and their impact on characters' behavior, supported by scholarly literature.

Examine elements of anomie theory (Merton) in the film and their impact on characters' behavior.

20%

Does not examine elements of anomie theory (Merton) in the film and their impact on characters' behavior.

Identifies elements of anomie theory (Merton) in the film.

Examines elements of anomie theory (Merton) in the film and their impact on characters' behavior.

Examines elements of anomie theory (Merton) in the film and their impact on characters' behavior, supported by scholarly literature.

Examine elements of institutional anomie theory (Messner/Rosenfeld) in the film and their impact on characters' behavior.

20%

Does not examine elements of institutional anomie theory (Messner/Rosenfeld) in the film and their impact on characters' behavior.

Identifies elements of institutional anomie theory (Messner/Rosenfeld) in the film.

Examines elements of institutional anomie theory (Messner/Rosenfeld) in the film and their impact on characters' behavior.

Examines elements of institutional anomie theory (Messner/Rosenfeld) in the film and their impact on characters' behavior supported by scholarly literature.

Evaluate the impact of anomie and strain theories on a selected group's behavior.

20%

Does not evaluate the impact of anomie and strain theories on a selected group's behavior.

Describes the impact of anomie and strain theories on a selected group's behavior.

Evaluates the impact of anomie and strain theories on a selected group's behavior.

Evaluates the impact of anomie and strain theories on a selected group's behavior supported by real world examples.

Integrate recent and relevant peer- reviewed research and literature to support analysis.

10%

Does not integrate recent and relevant peer-reviewed research and literature to support analysis.

Integrates research and literature that are not recent, peer- reviewed, or relevant to analysis.

Integrates recent and relevant peer- reviewed research and literature to support analysis.

Integrates recent and relevant peer-reviewed research and literature to support all aspects of analysis.

Write content clearly and logically, with correct use of grammar, punctuation, and mechanics.

5%

Does not write content clearly, logically, or with correct use of grammar, punctuation, and mechanics.

Writes with errors in clarity, logic, grammar, punctuation, or mechanics.

Writes content clearly and logically, with correct use of grammar, punctuation, and mechanics. There are very few errors in spelling, punctuation, and/or grammar.

Writes clearly and logically, with correct use spelling, grammar, punctuation, and mechanics; uses relevant evidence to support a central idea; and competently uses the correct terminology of criminal justice.

12/4/2020 Anomie and Strain Theory Case Study Scoring Guide

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CRITERIA NON-PERFORMANCE BASIC PROFICIENT DISTINGUISHED

Correctly format paper, citations, and references using APA style.

5%

Does not format paper, citations, and references using APA style.

Formats paper, citations, and references using APA style but with several errors.

Correctly formats paper, citations, and references using APA style. Citations contain a few errors.

Correctly formats paper, citations, and references using APA style. Citations are free from all errors.

Prof. Heiman thinks how much people are aware of their own biases affects the severity of a bias. This is Research Question 1 (Res. Q 1)

He also thinks that severity of bias affects how well people perform problem formulation (thinking of good problems to work on). This is Res. Q 2.

For Res. Q1, as bias-awareness increases, bias severity decreases. (RQ1-H1)

We’re predicting a negative relationship (that’s indicated by the (-) sign under Res. Q 1 in Fig. 1 for RQ1-H1. 

For Res. Q 1, consider the regression coefficient table calculated from some data his team gathered in Figure  2 (near the end of this document) 

We might also say that Awareness varies negatively with bias severity. 

For Res. Q2, as severity decreases, problem formulation performance increases. (RQ2-H1) The negatively predicted sign of the coefficient is indicated by the (-) sign under Res. Q 2 in Fig. 1 

Also, the data is international. There is one regression for a combined data set originating from managers in three countries (the US and China—a combined dataset) and a separate regression for managers from Finland.  

1. In running regressions, sometimes the control variables matter. Being in the Manufacturing sector is one such control variable (middle of Figure 2, Model 1).

For Model 1 of Figure 2, is the regression coefficient of the measure showing if a firm is in (or is not in) the manufacturing industry significant for either the pooled US/China dataset or the dataset from Finland? How do you interpret the results indicated?

Is being in the Manufacturing business a significant predictor of bias awareness?

This is another way of asking, “does the analysis give us any reason to think that the coefficients for this variable (in manufacturing sector) are NOT equal to 0?

If yes (and ONLY if yes,) what are the signs and magnitudes of the coefficients? What does this finding (or lack of a finding) mean?

Your response should be one paragraph of explanation that covers these questions. Hint: Your answer can be brief.

2. Also in Model 1 (Fig. 2),

Does Awareness significantly affect Bias Severity?

Is this true for both the US/China and Finland datasets?

If yes for at least one dataset (and ONLY if yes,) what are the signs, magnitudes, and significance levels of any significant coefficients? What does this finding (or lack of a finding) mean?

Your response should be one paragraph (or less) of explanation that covers these questions.

3. Model 2 uses a different DV to measure the same DV, Bias severity, but is otherwise quite similar to Model 1. A good way to see if a theory is supported is when you get statistically significant coefficients for more than way of measuring the outcome (DV). This is why Model 2 uses a different DV than Model 1. If both of our hypothesized relationships are supported by the analysis (coefficients are significant) this adds support to the argument that our hypothesis is true, and is highly desirable.

In Model 2, is awareness a significant predictor of bias severity? If yes, what are the coefficient magnitudes, signs and significance levels for each region (US/Finland and China)?

What does this finding mean? Again, explain this in one paragraph.

The next questions refer to Figure 3, which looks at RQ2-H1. It is also labeled Model 5. There are 4 models in the exploration of RQ1, Models 1-4. Above, we only asked you about Models 1 and 2, but now you know why Fig. 3 is also called Model 5.

In questions 1-3, we asked about the “meat” of the findings, the regression coefficients, but before we should really even look at the coefficients, we want to know something about whether the model itself is OK. Note also that Fig. 2 shows how we explored the extent to which Bias Severity affects Problem Formulation Performance. So, in this case, in Figure 3, Bias Severity is now the Independent Variable (IV) and not the DV (remember there are two stages shown in Fig. 1). The DV of interest is now Problem formulation performance.

4. ​Looking at the F-statistics for Model 5,

is do the analyses of US/China and Finland datasets show that the models predict outcomes better than a model consisting solely of a constant?

If, yes, what is the magnitude and significance level of Model 5’s F-tests (for both datasets, US/China and Finland)? Respond with 1 paragraph maximum.

5. ​Using the adjusted R-Square test,

what percentage of the variation in the data does Model 5 explain for the US/China dataset, and the Finland dataset? Are these good results? Why? Respond with 1 paragraph maximum.

6. ​If the model is bad (fails to predict outcomes better than a constant, and/or predicts very little of the variation in the data) we should not look at the regression coefficients. ​In this case, there would be no evidence that any of the coefficients have other than a zero value.

From questions 4 and 5, above, what can you conclude about the suitability of the data for continuing on and looking at the regression coefficients?

Is it worth bothering to even look at the coefficients for Model 5’s two analyses (one of the US/China dataset, and the other of the Finland dataset)?

7. ​For Model 5, there are several distinct IVs that test the hypothesis, RQ1-H1, as well as some controls.

Which of the bias variables significantly predict the outcome of degree of problem formulation performance for BOTH the US/China dataset and the Finland dataset? The bias variables in Fig. 3 are as follows:

Dominance Bias

Solution Jumping Bias

Information Distortion Bias

Perceptual Bias

For any significant predictors, give the name of the coefficient, as well as the magnitude, and sign of the coefficient. How do you interpret this finding? What does it mean?

8. ​Is the proposed framework from Fig. 1, above supported in both stages? Do you find the evidence convincing? Speculating, what would make the evidence more convincing?

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Extra Credit (Worth 15 points total)

9. (Extra Credit) Recall that a control variable is a measure that we think matters, but we don’t have a hypothesis about it. We don’t predict the sign of a control variable, but we understand it may have an effect on the outcome, so we include it in the regression so that we can say we have a “complete” model that takes into consideration factors that matter, but that we don’t care that much about. Sometimes control variables give us coefficients that are interesting and worth discussion. Going back to Model 1 in Figure

2, for the US/China dataset, we coded a control variable called CountryChinaUS, which =0 if the data is from the US, and =1 if the data is from China. Using the regression coefficient for CountryChinaUS, how do you interpret the meaning of the coefficient for CountryChinaUS? Does working in a firm from China or from the US significantly predict degree of Bias Severity? If so in what manner does it predict Bias Severity (is the relationship negative or positive)? Are there any implications for this finding? Is it a good thing that we took this variable into account, or could we have skipped it altogether in our analysis? Limit your response to two paragraphs. Shorter is better.

In this table, the RQ1-H1 is explored: The outcome of interest (what we are trying to predict) is Awareness (of biases). Use this table for questions 1-3 and the Extra Credit question.