Fluent Essays

Conceptual Draft of Chapter 2 (Literature Review) Instructions Title: Restorative Justice in Prisons This chapter of the paper is the longest and usually the most difficult for students. Only two prescribed sections are included in chapter 2 as this presents the student’s research into what has been written about the topic. Chapter 2 will begin with an introduction and the student’s strategies to finding literature. The student must give a percentage ratio of his/her sources since 85% of the references in any dissertation must come from peer-reviewed research and 15% may come from other sources. After those two sections, the literature review is written. This literature review must be at least 30 pages. At the end of this section, you should have at least twenty (20) additional references though in this class (for a total of at least 50 pages, including chapter 1). A summary paragraph should be included at the end, with a transition to chapter 3. The Bible context must be included Due: by 11:59 a.m on Friday, September 17, 2021. NO LATE WORK! References King James Bible. (1970). The Holy Bible. Camden, New Jersey. Thomas Nelson, Inc. Maxfield, M. G., & Babbie, E. R. (2018). Research methods for criminal justice and criminology (8th ed.). Boston, MA: Cengage Learning. Criteria Ratings Points Literature Review Quality 33 to >29.0 pts Advanced Excellent details and examples are provided. The following three requirements are met. • Literature review is comprehensive and contains new information not introduced in chapter 1. • The literature review relates to the problem statement and research questions. • Literature review is written as a paper and NOT written in annotated bibliography format. 29 to >22.0 pts Proficient • The requirements are met in a proficient manner. • Some of the information is incomplete or unclear. • The author provides some details and examples. OR • Two of the requirements are met. 22 to >0.0 pts Developing • The requirements are poorly developed. • Most of the information is incomplete. • The author does not provide any details or examples. OR • One of the requirements is met. 0 pts Not Present 33 pts Discussion of the Literature 32 to >28.0 pts Advanced Excellent details and examples are provided. The following three requirements are met: • The discussion of the various literature is included. • There is information on how the literature affects or defines the problem statement. • Author’s ideas are synthesized within the literature review. 28 to >21.0 pts Proficient • The requirements are met in a proficient manner. • Some of the information is incomplete or unclear. • Incomplete thoughts are used. The author provides some details and examples. OR • Only two of the requirements are met. 21 to >0.0 pts Developing • The requirements are poorly developed. • Most of the information is incomplete. • The author does not provide any details or examples. OR • Only one of the requirements is met. 0 pts Not Present 32 pts Relationships 20 to >17.0 pts Advanced Excellent details and examples are provided. The following three requirements are met: • Described studies related to the chosen methodology that are consistent with the scope of the study. • Described ways researchers have approached the problem. • Justified from the literature the rationale for selection of student’s variables or concepts. 17 to >13.0 pts Proficient • The requirements are met in a proficient manner. • Some of the information is incomplete or unclear. • The author provides some details and examples. OR • Two of the requirements are met. 13 to >0.0 pts Developing • The requirements are poorly developed. • Most of the information is incomplete. • The author does not provide any details or examples. OR • One of the requirements is met. 0 pts Not Present 20 pts Research Paper: Conceptual Draft Chapter 2 Grading Rubric | CJUS740_B03_202140 Criteria Ratings Points Biblical Worldview 20 to >17.0 pts Advanced • One section (paragraph) should be on the Biblical Worldview of the issue. The three requirements are met. • The biblical worldview is included. • The paper includes at least one relevant Bible verse in the literature review. • The context of the Bible verse is relevant. 17 to >13.0 pts Proficient • The requirements are met in a proficient manner. • Some of the information is incomplete or unclear. • The author provides some details and examples. OR • Two of the requirements are met. 13 to >0.0 pts Developing • The requirements are poorly developed. • Most of the information is incomplete. • The author does not provide any details or examples. OR • One of the requirements is met. 0 pts Not Present 20 pts Grammar & Spelling 22 to >19.0 pts Advanced Correct spelling and grammar are used throughout the literature review. There are 0–2 errors in grammar or spelling that distract the reader from the content. 19 to >14.0 pts Proficient There are 3–5 errors in grammar or spelling that distract the reader from the content. 14 to >0.0 pts Developing There are more than 6 errors in grammar or spelling that distract the reader from the content (one point lost for each mistake over 7). 0 pts Not Present 22 pts APA & Page Length 23 to >20.0 pts Advanced The minimum page count of 30 pages is met or exceeded. This applies to only the literature review, NOT including the other chapters, cover page and/or references page(s). There are 0–1 minor errors in current APA format in the required items: title page, running head, abstract page, font type and size, line spacing, heading, citations, and references. 20 to >15.0 pts Proficient Page count is between 27 and 29 pages. There are 2–3 minor errors in current APA format in the required items. 15 to >0.0 pts Developing Page count is between 1 and 26 pages. There are more than 3 errors in current APA format in the required items. 0 pts Not Present 23 pts Total Points: 150 Research Paper: Conceptual Draft Chapter 2 Grading Rubric | CJUS740_B03_202140 Conceptual Draft of Chapter 2 (Literature Review) Instructions Title: Restorative Justice in Prisons This chapter of the paper is the longest and usually the most difficult for students. Only two prescribed sections are included in chapter 2 as this presents the student’s research into what has been written about the topic. Chapter 2 will begin with an introduction and the student’s strategies to finding literature. The student must give a percentage ratio of his/her sources since 85% of the references in any dissertation must come from peer-reviewed research and 15% may come from other sources. After those two sections, the literature review is written. This literature review must be at least 30 pages. At the end of this section, you should have at least twenty (20) additional references though in this class (for a total of at least 50 pages, including chapter 1). A summary paragraph should be included at the end, with a transition to chapter 3. The Bible context must be included Due: by 11:59 a.m on Friday, September 17, 2021. NO LATE WORK! References King James Bible. (1970). The Holy Bible. Camden, New Jersey. Thomas Nelson, Inc. Maxfield, M. G., & Babbie, E. R. (2018). Research methods for criminal justice and criminology (8th ed.). Boston, MA: Cengage Learning. Chapter 8: Sampling 1 © 2018 Cengage Learning. All Rights Reserved. Learning Objectives • Understand how the logic of probability sampling makes it possible to represent large populations with small subsets of those populations • Recognize that the chief criterion of a sample’s quality is the degree to which it is representative of the population from which it was selected • Summarize the chief principle of probability sampling: every member of the population has a known, nonzero probability of being selected into the sample • Describe how probability sampling methods make it possible to select samples that will be quite representative • Understand how our ability to estimate population parameters with sample statistics is rooted in the sampling distribution and probability theory • Recognize how simple random sampling is logically the most fundamental technique in probability sampling 2 © 2018 Cengage Learning. All Rights Reserved. Learning Objectives, cont. • Recognize how simple random sampling is logically the most fundamental technique in probability sampling • Distinguish the variety of probability sampling designs that can be used and combined to suit different populations and research purposes: systematic sampling, stratified sampling (proportionate and disproportionate), and multistage cluster sampling • Understand the basic features of the National Crime Victimization Survey and the British Crime Survey, two national crime surveys based on multistage cluster samples • Recognize how nonprobability sampling methods are less statistically representative than probability sampling methods, and be able to offer appropriate examples for nonprobability sampling applications • Distinguish the variety of nonprobability sampling types, including purposive sampling, quota sampling, and snowball sampling. Describe examples of each 3 © 2018 Cengage Learning. All Rights Reserved. Introduction • Sampling: The process of selecting observations • Often not possible to collect information from all persons or other units you wish to study • Often not necessary to collect data from everyone out there • Allows researcher to make a small subset of observations and then generalize to the rest of the population 4 © 2018 Cengage Learning. All Rights Reserved. The Logic of Probability Sampling • Enables us to generalize findings from observing cases to a larger unobserved population • Representative: Each member of the population has a known and equal chance of being selected into the sample • Since we are not completely homogeneous, our sample must reflect—and be representative of—the variations that exist among us 5 © 2018 Cengage Learning. All Rights Reserved. Conscious and Unconscious Sampling Bias • What is the proportion of FAU students who have been to an FAU football game? • Be conscious of bias: When sample is not fully representative of the larger population from which it was selected • Equal Probability of Selection Method (EPSEM) – A sample is representative if its aggregate characteristics closely match the population’s aggregate characteristics; basis of probability sampling 6 © 2018 Cengage Learning. All Rights Reserved. Sampling Distribution • Sample Element: Who or what are we studying (student) • Population: Whole group (college freshmen) • Population Parameter: The value for a given variable in a population • Sample Statistic: The summary description of a given variable in the sample; we use sample statistics to make estimates or inferences of population parameters 7 © 2018 Cengage Learning. All Rights Reserved. Sampling Distribution, cont. • Purpose of sampling: To select a set of elements from a population in such a way that descriptions of those elements (sample statistics) accurately portray the parameters of the total population from which the elements are selected – The key to this process is random selection • Sampling Distribution: The range of sample statistics we will obtain if we select many samples 8 © 2018 Cengage Learning. All Rights Reserved. Sampling Distribution, slide 3 • Sampling Frame: list of elements in our population • By increasing the number of samples selected and interviewed, increase the range of estimates provided by the sampling operation 9 © 2018 Cengage Learning. All Rights Reserved. Estimating Sampling Error • If many independent random samples are selected from a population, then the sample statistics provided by those samples will be distributed around population parameter in a known way • Probability theory gives us a formula for estimating how closely the sample statistics are clustered around the true value – Standard Error: A measure of sampling error • Tells us how sample statistics will be dispersed or clustered around a population parameter 10 © 2018 Cengage Learning. All Rights Reserved. Confidence Levels and Intervals • Two key components of sampling error • We express the accuracy of our sample statistics in terms of a level of confidence that the statistics fall within a specified interval from the parameter • The logic of confidence levels and confidence intervals also provides the basis for determining the appropriate sample size for a study 11 © 2018 Cengage Learning. All Rights Reserved. Discussion Question 1 What if someone told you that they were 100% confident in an interpretation of their survey results? How might you reply? 12 © 2018 Cengage Learning. All Rights Reserved. Sampling Distribution Summary • Random selection permits the researcher to link findings from a sample to the body of probability theory so as to estimate the accuracy of those findings • All statements of accuracy in sampling must specify both a confidence level and a confidence interval • The researcher must report that he or she is x percent confident that the population parameter is between two specific values 13 © 2018 Cengage Learning. All Rights Reserved. Populations & Sampling Frames • Different types of probability sampling designs can be used alone or in combination for different research purposes • Key feature of all probability sampling designs: the relationship between populations and sampling frames – Sampling frame: The quasi-list of elements from which a probability sample is selected 14 © 2018 Cengage Learning. All Rights Reserved. Simple Random Sampling • Each element in a sampling frame is assigned a number, choices are then made through random number generation as to which elements will be included in your sample – Forms the basis of probability theory and the statistical tools we use to estimate population parameters, standard error, and confidence intervals 15 © 2018 Cengage Learning. All Rights Reserved. Systematic Sampling • Systematic Sampling: Elements in the total list are chosen (systematically) for inclusion in the sample – List of 10,000 elements, we want a sample of 1,000, select every tenth element – Choose first element randomly – Danger: “Periodicity" A periodic arrangement of elements in the list can make systematic sampling unwise 16 © 2018 Cengage Learning. All Rights Reserved. Stratified Sampling • Stratified sampling: Ensures that appropriate numbers are drawn from homogeneous subsets of that population – Method for obtaining a greater degree of representativeness—decreasing the probable sampling error • Disproportionate stratified sampling: Way of obtaining a sufficient number of rare cases by selecting a disproportionate number – To purposively produce samples that are not representative of a population on some variable 17 © 2018 Cengage Learning. All Rights Reserved. Multistage Cluster Sampling • Compile a stratified group (cluster), sample it, then subsample that set... • May be used when it is either impossible or impractical to compile an exhaustive list of the elements that compose the target population (Ex.: All law enforcement officers in the US) • Involves the repetition of two basic steps: – Listing – Sampling 18 © 2018 Cengage Learning. All Rights Reserved. National Crime Victimization Survey • Seeks to represent the nationwide population of persons 12+ living in households (≈ 42K units, 74K occupants in 2004) • First defined are primary sampling units (PSUs) – Largest are automatically included, smaller ones are stratified by size, population density, reported crimes, and other variables into about 150 strata • Census enumeration districts are selected (CED) – Clusters of four housing units from each CED are selected 19 © 2018 Cengage Learning. All Rights Reserved. British Crime Survey • First stage: 289 Parliamentary constituencies, stratified by geographic area and population density • Two sample points were selected, which were divided into four segments with equal #’s of delivery addresses – One of these four segments was selected at random, then disproportionate sampling was conducted to obtain a greater number of inner-city respondents – Household residents aged 16+ were listed, and one was randomly selected by interviewers (n=37,213 in 2004) 20 © 2018 Cengage Learning. All Rights Reserved. Discussion Question 2 What if you could administer a project such as the NCVS or the British Crime Survey? Which would you choose? 21 © 2018 Cengage Learning. All Rights Reserved. Nonprobability Sampling • There are situations when it is impossible to select a probability sample • Nonprobability sampling can be used • Nonprobability sample is sampling in which the probability that an element will be included in the sample is not known • Cannot generalize to larger population 22 © 2018 Cengage Learning. All Rights Reserved. Nonprobability Sampling, cont. • Purposive sampling: Selecting a sample on the basis of your judgment and the purpose of the study • Quota sampling: Units are selected so that total sample has the same distribution of characteristics as are assumed to exist in the population being studied • Reliance on available subjects • Snowball sampling: You interview some individuals, and then ask them to identify others who will participate in the study, who ask others, etc., etc. 23 © 2018 Cengage Learning. All Rights Reserved. Discussion Question 3 What if someone asked you to explain the strengths and weaknesses of snowball sampling? How would you respond? 24 Chapter 7: Experimental and Quasi- Experimental Designs 1 © 2018 Cengage Learning. All Rights Reserved. Learning Objectives • Recognize that experiments are well suited for the controlled testing of causal processes and for some evaluation studies • Describe how the classical experiment tests the effect of an experimental stimulus on some dependent variable through the pretesting and posttesting of experimental and control groups • Understand that a group of experimental subjects need not be representative of some larger population but that experimental and control groups must be similar to each other • Describe how random assignment is the best way to achieve comparability in the experimental and control groups • Describe how the classical experiment with random assignment of subjects guards against most of the threats to internal invalidity 2 © 2018 Cengage Learning. All Rights Reserved. Learning Objectives, cont. • Understand that the controlled conditions under which experiments take place may restrict our ability to generalize results to real-world constructs or to other settings • Recognize how the classical experiment may be modified by changing the number of experimental and control groups, the number and types of experimental stimuli, and the number of pretest or posttest measurements • Know the reasons that quasi-experiments are conducted when it is not possible or desirable to use an experimental design, and be able to describe different categories of quasi-experiments • Understand the differences between case-oriented and variable-oriented research • Be able to describe how experiments and quasi-experiments can be customized by using design building blocks to suit particular research purposes 3 © 2018 Cengage Learning. All Rights Reserved. Introduction • Experimentation is an approach to research best suited for explanation and evaluation • An experiment is “a process of observation, to be carried out in a situation expressly brought about for that purpose” • Experiments involve: – Taking action – Observing the consequences of that action • Especially suited for hypothesis testing 4 © 2018 Cengage Learning. All Rights Reserved. The Classical Experiment • Variables, time order, measures, and groups are the central features of the classical experiment • Involves three major pairs of components: – Independent and dependent variables – Pretesting and posttesting – Experimental and control groups 5 © 2018 Cengage Learning. All Rights Reserved. Independent Variables • The Independent Variable takes the form of a dichotomous stimulus that is either present or absent • It varies (i.e., is independent) in our experimental process • “The Cause” 6 © 2018 Cengage Learning. All Rights Reserved. Dependent Variables • The outcome, the effect we expect to see • Depends on the Independent Variable • Might be physical conditions, social behavior, attitudes, feelings, or beliefs • “The Effect” 7 © 2018 Cengage Learning. All Rights Reserved. Pretesting and Posttesting • Subjects are initially measured in terms of the Dependent Variable prior to association with the Independent Variable (pretested) • Then, they are exposed to the Independent Variable • Then, they are remeasured in terms of the Dependent Variable (posttested) • Differences noted between the measurements on the Dependent Variable are attributed to influence of the Independent Variable 8 © 2018 Cengage Learning. All Rights Reserved. Discussion Question 1 What if you took part in a social science experiment? What assurances would you expect from the administrators of the experiment, if any? 9 © 2018 Cengage Learning. All Rights Reserved. Experimental and Control Groups • Experimental group: Exposed to whatever treatment, policy, or initiative we are testing • Control group: Very similar to experimental group, except that they are NOT exposed • If we see a difference, we want to make sure it is due to the Independent Variable, and not due to a difference between the two groups 10 © 2018 Cengage Learning. All Rights Reserved. Hawthorne Effect • Pointed to the necessity of control groups • Independent Variable: improved working conditions (better lighting) • Dependent Variable: improvement in employee satisfaction and productivity • Workers were responding more to the attention than to the improved working conditions 11 © 2018 Cengage Learning. All Rights Reserved. Placebo • We often don’t want people to know if they are receiving treatment or not • We expose our control group to a “dummy” Independent Variable just so we are treating everyone the same • Medical research: Participants don’t know what they are taking • Ensures that changes in Dependent Variable actually result from Independent Variable and are not psychologically based 12 © 2018 Cengage Learning. All Rights Reserved. Double-Blind Experiment • Experimenters may be more likely to “observe” improvements among those who received drug • In a Double-Blind experiment, neither the subjects nor the experimenters know which is the experimental group and which is the control group – Broward County Florida and Portland, Oregon domestic violence policing units study: “keeping safe” strategies 13 © 2018 Cengage Learning. All Rights Reserved. Discussion Question 2 Would you ever participate in a double- blind experiment? Why or why not? 14 © 2018 Cengage Learning. All Rights Reserved. Selecting Subjects • First, must decide on target population, the group to which the results of your experiment will apply • Second, must decide how to select particular members from that group for your experiment • Cardinal rule: ensure that Experimental and Control groups are as similar as possible • Randomization aims to achieve this 15 © 2018 Cengage Learning. All Rights Reserved. Random Assignment • “Randomization” • Central feature of the classical experiment – Produces experimental and control groups that are statistically equivalent • Farrington and associates: – “Randomization insures that the average unit in the treatment group is approximately equivalent to the average unit in another group before the treatment is applied” • “All Other Things Are Equal” 16 © 2018 Cengage Learning. All Rights Reserved. Discussion Question 3 How difficult is it to randomize an experiment? Is it costly? Can any researcher do it? 17 © 2018 Cengage Learning. All Rights Reserved. Experiments and Causal Inference • Experiments potentially control for many threats to the validity of causal inference • Experimental design ensures: – Cause precedes effect via taking posttest – Empirical correlation exists via comparing pretest to posttest – No spurious third variable influencing correlation via posttest comparison between experimental and control groups, and via randomization 18 © 2018 Cengage Learning. All Rights Reserved. Threats to Internal Validity • Conclusions drawn from experimental results may not reflect what went on in experiment • History: External events may occur during the course of the experiment • Maturation: People constantly are growing • Testing: The process of testing and retesting 19 © 2018 Cengage Learning. All Rights Reserved. Threats to Internal Validity, cont. • Instrumentation: Changes in the measurement process • Statistical regression: Extreme scores regress to the mean • Selection biases: The way in which subjects are chosen (use random assignment) • Experimental mortality: Subjects may drop out prior to completion of experiment 20 © 2018 Cengage Learning. All Rights Reserved. Threats to Internal Validity, slide 3 • Causal time order: Ambiguity about order of stimulus and Dependent Variable— which caused which? • Diffusion/Imitation of treatments: Experimental group may pass on elements to Control group when communicating • Compensatory treatment: Control group is deprived of something considered to be of value 21 © 2018 Cengage Learning. All Rights Reserved. Threats to Internal Validity, slide 4 • Compensatory Rivalry: Control group deprived of the stimulus may try to compensate by working harder • Demoralization: Feelings of deprivation among control group result in subjects giving up 22 © 2018 Cengage Learning. All Rights Reserved. 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 23 © 2018 Cengage Learning. All Rights Reserved. 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 24 © 2018 Cengage Learning. All Rights Reserved. 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 25 © 2018 Cengage Learning. All Rights Reserved. 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 26 © 2018 Cengage Learning. All Rights Reserved. Variations in the Classical Experimental Design • Four basic building blocks present in experimental designs: – The number of experimental and control groups – The number and variation of experimental stimuli – The number of pretest and posttest measurements – The procedures used to select subjects and assign them to groups • Variations on the classical experiment can be produced by manipulating the building blocks of experiments 27 © 2018 Cengage Learning. All Rights Reserved. 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 28 © 2018 Cengage Learning. All Rights Reserved. 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 29 © 2018 Cengage Learning. All Rights Reserved. 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 30 © 2018 Cengage Learning. All Rights Reserved. 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 31 © 2018 Cengage Learning. All Rights Reserved. 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 (e.g., 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 32