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#1
Identify and discuss the key tenets of quantitative research methods used in research in criminal justice:
What do you see as the most critical aspects of quantitative research methods?
How do these key tenets of quantitative research assist us when evaluating criminal justice data?
How do these key tenets contribute to successfully interpreting published research findings?
How would you apply these tenets to answer a criminal justice research question?
#2
Creswell identifies six steps critical to the construction of a quantitative research proposal (I have included the 6 steps from the textbook below). Discuss the six steps of the critical aspects of data analysis and interpretation identified by Creswell:
What are the six steps and why are they essential?
How do these steps assist us when evaluating criminal justice data?
How do these steps contribute to successfully interpreting published research findings?
How would you apply these steps to answer a criminal justice research question?
Step 1. Report information about the number of members of the sample who did and did not return the survey. A table with numbers and percentages describing respondents and nonrespondents is a useful tool to present this information.
Step 2. Discuss the method by which response bias will be determined. Response bias is the effect of nonresponses on survey estimates (Fowler, 2009). Bias means that if nonrespondents had responded, their responses would have substantially changed the overall results. Mention the procedures used to check for response bias, such as wave analysis or a respondent/nonrespondent analysis. In wave analysis, the researcher examines returns on select items week by week to determine if average responses change (Leslie, 1972). Based on the assumption that those who return surveys in the final weeks of the response period are nearly all nonrespondents, if the responses begin to change, a potential exists for response bias. An alternative check for response bias is to contact a few nonrespondents by phone and determine if their responses differ substantially from respondents. This constitutes a respondent-nonrespondent check for response bias.
Step 3. Discuss a plan to provide a descriptive analysis of data for all independent and dependent variables in the study. This analysis should indicate the means, standard deviations, and range of scores for these variables. In some quantitative projects, the analysis stops here with descriptive analysis, especially if the number of participants is too small for more advanced, inferential analysis.
Step 4. Assuming that you proceed beyond descriptive approaches, if the proposal contains an instrument with scales or a plan to develop scales (combining items into scales), identify the statistical procedure (i.e., factor analysis) for accomplishing this. Also mention reliability checks for the internal consistency of the scales (i.e., the Cronbach alpha statistic).
Step 5. Identify the statistics and the statistical computer program for testing the major inferential research questions or hypotheses in the proposed study. The inferential questions or hypotheses relate variables or compare groups in terms of variables so that inferences can be drawn from the sample to a population. Provide a rationale for the choice of statistical test and mention the assumptions associated with the statistic. As shown in Table 8.3, base this choice on the nature of the research question (e.g., relating variables or comparing groups as the most popular), the number of independent and dependent variables, and the number of variables controlled (e.g., see Rudestam & Newton, 2007). Further, consider whether the variables will be measured on an instrument as a continuous score (e.g., age from 18 to 36) or as a categorical score (e.g., women = 1, men = 2). Finally, consider whether the scores from the sample might be normally distributed in a bell-shaped curve if plotted out on a graph or non-normally distributed. There are additional ways to determine if the scores are normally distributed (see Creswell, 2012). These factors, in combination, enable a researcher to determine what statistical test will be suited for answering the research question or hypothesis. In Table 8.3, I show how the factors, in combination, lead to the selection of a number of common statistical tests. For further types of statistical tests, readers are referred to statistics methods books, such as Gravetter and Wallnau (2009).
Step 6. A final step in the data analysis is to present the results in tables or figures and interpret the results from the statistical test. An interpretation in quantitative research means that the researcher draws conclusions from the results for the research questions, hypotheses, and the larger meaning of the results. This interpretation involves several steps
