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Use this book to answer the questions : Bachman, R.D., Schutt, R.K., and Plass, P.S. (2017). Fundamentals of Research in Criminology and Criminal Justice: With Selected Readings. Thousand Oaks, CA: Sage Publications, Inc. Chapter 5 – Sampling (pp. , powerpoint, and lectures) Define each of the following, and be able to identify them in examples. · Sample · Population · Elements (cases) · Sampling frame Explain the relationship between generalizability and sampling, and representativeness and sampling. Define and describe probability sampling methods. Define and describe each of the following types of probability sampling. Be able to determine when to use, as well as the advantages and disadvantages of both. · Simple random sampling · Systematic random sampling · Stratified random sampling · Proportionate stratified sampling · Disproportionate stratified sampling · Multistage cluster sampling Define and describe nonprobability sampling methods. Define and describe each of the following types of nonprobability sampling. Be able to determine when to use, as well as the advantages and disadvantages of both. · Availability sampling · Quota sampling · Purposive sampling · Snowball sampling Chapter 6 – Causation and Experiments (pp. 231 – 257, powerpoint, and lectures) Identify and describe the five (5) criteria that should be considered when exploring whether a causal connection exists. Also, know which three (3) are considered necessary and most important for identifying a causal effect. Define and describe true experiments . Identify and describe their three (3) core features. Be able to determine when to use, as well as the advantages and disadvantages of both. Define quasi-experimental designs . Also, define and describe two (2) common types of quasi-experimental designs – nonequivalent control groups and before-and-after designs (i.e., time series design). Be able to determine when to use, as well as the advantages and disadvantages of both. Define and describe the following threats to internal validity (concern conclusions about causality). Be able to identify in scenarios, and know which designs each are most commonly associated with. · Selection bias · Endogenous change · External events Define and describe the features of the following types of nonexperimental designs – cross-sectional, repeated cross-sectional, and longitudinal. Be able to determine when to use, as well as the advantages and disadvantages of each.

Paper For Above instruction

Understanding research methodology is crucial in the field of criminology and criminal justice, as it ensures that research findings are valid, reliable, and applicable to broader populations. This paper explores key concepts related to sampling, causation, experimental designs, and nonexperimental studies, grounded in the foundational texts by Bachman, Schutt, and Plass (2017). The discussion aims to delineate the definitions, advantages, disadvantages, and appropriate contexts for various research techniques, enhancing clarity for practitioners and students alike.

Sampling in Criminological Research

Sampling is the process by which researchers select a subset of individuals or cases from a larger population to observe and analyze. The 'population' refers to the entire group of interest—such as all convicted offenders in a country—while 'elements' or 'cases' are individual members or specific instances within this population. The 'sampling frame' is a practical list or database from which the sample is drawn, such as a criminal record registry. These components are fundamental because they influence the representativeness of the sample, which in turn affects the generalizability of findings. A sample that accurately reflects the population enables researchers to extend conclusions beyond the specific cases examined, ensuring that results are applicable at a broader level.

Probability sampling methods include techniques where each population unit has a known, non-zero chance of selection. Simple random sampling involves selecting cases entirely at random, giving each element an equal chance of inclusion. Its primary advantage is its simplicity and statistical robustness; however, it can be inefficient with large populations. Systematic random sampling orders the population list and then selects every kth element, offering efficiency but risking bias if the list has a systematic pattern. Stratified random sampling partitions the population into strata—such as age groups—and samples from each, enhancing representativeness. Proportionate stratified sampling reflects the proportions of each stratum in the overall population, while disproportionate sampling oversamples less common groups to ensure their adequate representation. Multistage cluster sampling involves multiple stages, starting with large clusters (like neighborhoods) and then sampling within those clusters, suitable for large geographical areas but potentially increasing sampling error.

Nonprobability sampling methods are used when probability sampling is impractical or unnecessary. Availability sampling involves selecting readily accessible cases, useful for exploratory studies but limited in generalizability. Quota sampling requires researchers to set quotas to match key demographics but lacks randomness. Purposive sampling involves selecting cases based on specific characteristics or criteria, ideal for targeted qualitative research but biased in generalizability. Snowball sampling relies on participants recruiting future subjects, effective for hard-to-reach populations such as drug users but susceptible to bias and limited in broader inference.

Establishing Causality in Research

The criteria for establishing causality include correlation, temporal ordering, non-spuriousness, theoretical plausibility, and consistency. However, correlation, temporal precedence, and eliminating confounding variables—non-spuriousness—are deemed the most critical. The ability to demonstrate that the cause precedes the effect, is correlated with it, and is not due to extraneous factors forms the backbone of causal inference.

Experimental and Quasi-Experimental Designs

True experiments involve randomly assigning subjects to experimental and control groups, allowing researchers to infer causal effects with high internal validity. Their core features include manipulation of the independent variable, randomization, and control over extraneous factors.

Quasi-experimental designs lack random assignment but still aim to infer causality. The two common types are nonequivalent control group designs, where preexisting groups are compared, and before-and-after (time series) designs, which assess changes over time in the absence of randomization. These approaches are useful when random assignment is impractical or unethical, but they are more vulnerable to internal validity threats such as selection bias and endogenous change.

Threats to Internal Validity

Selection bias occurs when differences between groups are inherent rather than due to treatment. Endogenous change refers to naturally occurring changes within subjects over time, complicating causal attributions. External events, or history effects, can influence outcomes independent of the treatment. Correctly identifying and mitigating these threats is essential, often through control groups or statistical controls, especially within nonrandomized designs.

Nonexperimental Designs

Cross-sectional studies examine data at a single point in time, providing snapshots of phenomena but limiting causal inference. Repeated cross-sectional studies sample different populations over time, tracking trends while maintaining independent samples. Longitudinal studies follow the same subjects over extended periods, offering insights into developmental or causal processes but are costly and time-consuming. The choice among these depends on research questions, resources, and the necessity of causal inference.

Survey Research and Qualitative Methods

Survey research involves the systematic collection of data through questionnaires or interviews, appealing for its efficiency and ability to reach large populations. Effective survey question construction is critical; open-ended questions allow detailed responses, useful in exploratory phases, while close-ended questions facilitate quantitative analysis. Clarity is paramount, avoiding vagueness, negatives, and double-barreled questions, and ensuring mutually exclusive and exhaustive response categories (Fink, 2013). Various survey modes—mail, group-administered, telephone, in-person, and electronic—offer flexibility but must be chosen carefully based on the target population and research needs. Ethical considerations include voluntary participation, confidentiality, informed consent, and minimizing harm.

Qualitative Research Methods

Qualitative methods focus on understanding complex social phenomena through detailed, contextual data. Field research, especially participant observation, allows researchers to immerse themselves in the environment they study. Complete observation involves observing without interaction, whereas participant observation involves active engagement. Covert participation raises ethical issues related to deception, while overt participation requires transparency. Intensive interviewing and focus groups collect rich, nuanced data, capturing personal and group perspectives. Building rapport and maintaining ethical standards—such as confidentiality and informed consent—are central to qualitative research, helping to ensure valid and ethical interpretation of data.

Conclusion

This overview underscores the importance of selecting appropriate research designs, sampling methods, and data collection techniques within criminology and criminal justice. Understanding the strengths and limitations of each approach facilitates more accurate and meaningful insights into crime and justice phenomena. Ethical considerations remain a priority across all methodologies, ensuring that research not only advances knowledge but also adheres to moral standards.

References

• Bachman, R. D., Schutt, R. K., & Plass, P. S. (2017). Fundamentals of research in criminology and criminal justice: With selected readings. Sage Publications.
• Fink, A. (2013). How to conduct surveys: A step-by-step guide. Sage Publications.
• Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications.
• Trochim, W. M., & Donnelly, P. (2006). The research methods knowledge base. Atomic Dog Publishing.
• Patton, M. Q. (2002). Qualitative research and evaluation methods. Sage Publications.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
• Maxwell, J. A. (2013). Qualitative research design: An interactive approach. Sage Publications.
• Rubin, H. J., & Rubin, I. S. (2012). Qualitative interviewing: The art of hearing data. Sage Publications.
• Leedy, P. D., & Ormrod, J. E. (2014). Practical research: Planning and design. Pearson.
• Yin, R. K. (2018). Case study research and applications: Design and methods. Sage Publications.