Introduction
Research methodology provides the researcher with the necessary guideline in which to approach and perform the activities involved in the accomplishment of the study. Also, this section provides the researcher with principles for organizing, planning, designing, and conducting successful research. The methodology is the science and philosophy that supports all researches (Mohajan, 2017). The United Kingdom Concordat outlines the following as the responsibilities of a researcher:
Researchers have a responsibility to develop the capacity for independent, honest, and critical thought throughout the study.
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Researchers have a responsibility to express their research and share knowledge for the benefit of society.
Researchers have a responsibility to conduct themselves honestly and ethically throughout the study.
Researchers are ultimately responsible for personal and professional development in the field of study.
The Concordat to Support the Career Development of Researchers, commonly referred to as the Researcher Development Concordat, is an agreement between interested parties to improve the employment and support for researchers in higher education in the United Kingdom (U.K. Research Integrity Office, 2019). The research onion is significant in portraying the procedures that will be employed during the research process. The research onion refers to the combined stages that are essential in conducting any study in a reasonable sequence (Saunders, 2011). The research onion entails six stages that are considered significant in undertaking any research effectively. The six phases of the research onion include philosophies, approaches, strategies, time horizons, choices, and techniques.
Figure 1: Research onion (Saunders 2011)
Looking at each of the six sections, individually, will provide useful insight in developing this study. A research philosophy elaborates on the set of beliefs that surround the phenomena under observation. There exist three main philosophies, i.e., ontology, which is the study of reality, epistemology, which tries to find an acceptable universal truth, and lastly, axiology, which entails the effect of values and opinions on the collection and analysis of the research. The approach in research exists in two forms, deductive and inductive approach. A deductive approach develops the hypotheses based on an already existing theory, while an inductive approach relies on the formulation of a new theory rather than relying on a pre-existing one. Strategies describe the way the researcher plans to carry out the study. This is followed by choice, where the researcher has to choose the methodology, be it quantitative, qualitative, or both. Time Horizon describes the time necessary to complete the study. This is the stage where the study decides whether to implement a cross-sectional or longitudinal approach. Finally, we have techniques. This combines all the processes that are used to collect and analyze the data.
From the research onion, each outer layer affects the subsequent inner layer. This ensures that once a researcher adopts the research onion, the study will be undertaken chronologically, paying attention to the objectives and nature of the study. In this chapter, the research onion will be developed and discussed, more so on the research strategies, time horizons, and a bit of the technique.
Research Design and Rationale
As stated in the introduction, in research, two primary schools of thought exist based on the type and nature of data a researcher aims to fulfill. These are qualitative and quantitative research. When it comes to qualitative research, it is primarily used to gather information on opinions and motivations. It can also be used to dive deeper into a problem. Data used in this type of research is collected using unstructured or semi-structured techniques so as not to lead the respondent. Quantitative research is all about numbers, i.e., it is used to quantify a problem. In contrast to qualitative research, quantitative studies use measurable data to uncover patterns. A researcher is tasked with choosing the right study design that will ensure he or she meets the objectives of the study in an efficient manner.
Cokley and Awad (2013) note that quantitative research was initially used by some scholars to taint scientific progress in the field of psychology, which has resulted in the view that quantitative methods are limited when it comes to promoting justice for marginalized groups. The authors use clear historical examples, for instance, the Tuskegee Syphilis Study that led to several African Americans not being treated for the disease, which has led to this belief. Cokley and Awad (2013) were geared towards proving that quantitative research can be used in the field of social justice. In their conclusion, they note that quantitative research promotes social justice only when used correctly. This involves the detachment of the researcher to the subject matter and being objective.
Fassinger and Morrow (2013) compound this conclusion by suggesting that quantitative approaches in social justice can help a researcher provide large representative samples of cultural communities, reliably assert cause and effect relationships, as well as confirm a theoretical hypothesis and summarize numerical data to persuade leaders and policymakers to act on a given problem. Fassinger and Morrow (2013) introduce a new concept of mixed-method approaches that combine the benefits of both qualitative and quantitative approaches. They, however, note that this latter method is unfamiliar to most researchers despite its potential to offer flexibility and compensate for uni-paradigmatic limitations.
From the above discussions, the research design will assume a quantitative approach due to the nature of the data of interest. Also, a significant proportion of studies published on juvenile recidivism have been noted to adopt a quantitative approach (Letourneau & Armstrong, 2008; Cottle, Lee, & Heilbrun, 2001). Quantitative research entails a variety of methods, all geared towards the systematic assessment of social phenomena, using numerical data (Watson, 2015). With this in mind, juvenile arrest and recidivism rate falls under the blanket of social phenomena. Watson (2015) further goes ahead and suggests that quantitative research is primarily used to analyze data for trends and relationships. In this study, the two hypotheses under review aim to assess the relationship between the rate of arrest and recidivism rates among juveniles in Fulton County, Georgia. Therefore, a quantitative study will be useful in meeting the objectives of the study.
A one-time cross-sectional survey involving convicted juveniles in Fulton County, Georgia, will be used to meet the quantitative aspect of the study. Cross-sectional studies are relatively easy to carry out since they are only done once. The situation is different when it comes to longitudinal studies that are more complex and require to be conducted over several years. Aldridge and Levine (2001) note that in longitudinal studies, the main limitation is attrition, which is not present in a cross-sectional design; thus, data collected using the latter design can be generalized across a population. The study will also assume a comparative approach to determine any statistically significant difference in the rate of arrest and recidivism rates among juveniles within Fulton County, Georgia.
Population and Sample Size
Turner (2016) identifies the causes of juvenile recidivism as inefficiencies in the program, which are often associated with program implementation, inattention of the programs towards the risk factors of recidivism, and the actual type of the program implemented. All of these factors play an essential role in this study’s findings. The study population will consist of juveniles with a conviction record irrespective of their sex, who have been admitted into a juvenile program in Fulton County. Carter (2019) states that about 60% of all juvenile convicts are aged between 14 and 17 years old. In Fulton County, 16-year-old juveniles account for about 30% of total convictions. This is then followed by juveniles aged 15 years, accounting for an additional 16%. With all this data, the study population will be narrowed down to convicted juveniles aged between 14 years and 17 years.
The Fulton County Juvenile Court enlists numbers for all juvenile cases that were either put on probation or diverted between 2013 and 2017. The total juveniles put on probation were 5846, with 1162 being adjudicated, while those diverted were 6298, with 525 being adjudicated. Both of these numbers account for the total juvenile recidivism rates in Fulton County, Georgia. Based on Carter, the target population size for the study will be about 1012 juveniles, which accounts for about 60% of all cases. This number falls in line with the intended population size of juveniles aged between 14 and 17 years (Carter, 2019).
The sampling procedure will use a stratified probability sampling method, whereby, the population will first be segregated based on sex, i.e., male or female. From the two groups, samples will be obtained at a ratio of 3:2, with the male population being higher since males are more likely to be adjudicated (Carter, 2019). Purposive sampling will then be used to identify the sample from the population that meets the study’s requirements, i.e., aged between 14 and 17 years, convicted of a crime but enrolled in a juvenile program, for instance, probation. Data will be gathered at the Fulton County Juvenile Court with the help of the Court clerk after obtaining permission from the Georgia District Attorney’s Office.
Accurate sample size calculation is an important part of any research as it affects the results of the study significantly. Charan and Biswas (2013) note that the calculation of sample size varies depending on the study design, and no single formula can be used for all research designs. The authors segregate the formulas based on the nature of the research adopted. The authors note that in cross-sectional studies or surveys, sample size calculations for qualitative and quantitative variables are different. The authors further note the importance of standard deviations in the calculations of sample sizes in clinical trials. Based on the nature and objectives of this study, the sample size will be calculated using the Fischer’s formula recommended by Charan and Biswas (2013) below.
n =
Where:
n = the desired sample size
Z = the standard normal deviate at the required confidence level
p = the proportion in the target population estimated to have characteristic being measured
q = 1-p
d = the level of statistical significance set
The proportion in the target population estimated to have the characteristic being measured could not be identified. Mugenda and Mugenda (2003) recommend the adoption of a 50% estimate in such a scenario where the characteristic prevalence is unknown.
Therefore, n = (1.96)2 × (0.5) × (0.5)/ (0.05)2
n = 384 convicted juveniles
Of the 384 juveniles of interest, about 230 will represent the male population, and 154 will represent the female population, as based on gender as a factor affecting the occurrence of a crime.
Statistical Test
In any study, this is a vital part that either makes or breaks the study. This section will entail the analysis procedures that will be implemented in the assortment of the data collected. Nayak and Hazra (2011) acknowledge the use of analysis of variance (ANOVA) when comparing more than two sets of numerical data. Chi-Square Test is primarily used to compare categorical variables. In this study, the Chi-Square Test will be used as the first statistical test to contrast the relationship between juvenile recidivism rates and juvenile programs in place to prevent this from happening, such as probation.
The Chi-Square statistic is commonly used to evaluate tests of independence when using a bivariate table. The table presents the distributions of two categorical variables simultaneously, with the intersections appearing in the cells of the table. The test of independence assesses whether a relationship exists between the two variables by comparing the visible pattern of responses in the table to that which would be expected if the variables were genuinely independent of each other. One thing to note is that Chi-Squares are extremely sensitive to the sample size because of how they are calculated. For instance, a large sample size of around 500, almost any small difference will appear statistically significant. The study estimates a sample size of 384 people; thus, the Chi-Square Test can still be relied on to produce reliable results. Getting a p-value of less than 0.05 will show that there is a significant relationship between the two, after which the analysis of variance will be used to test for individual variables.
As initially stated, ANOVA is used when comparing more than two sets of numerical data. ANOVA exists in two forms, i.e., one-way ANOVA used to compare the difference between three or more groups of a single independent variable, and MANOVA used to test how one or more independent variables affect two or more dependent variables. MANOVA is an extension of ANOVA that extends an ANOVA analysis by taking into account several continuous dependent variables and joining them together into a composite variable. The MANOVA will then compare whether the composite variable differs by groups or levels when contrasted with the independent variable. MANOVA tests whether or not the independent variable directly explains a statistically significant amount of variance in the dependent variable. Due to the nature of the variables of interest in the study, MANOVA will be used to contrast between the two independent variables, i.e., juvenile arrest records and recidivism rates against dependent variables such as types of crimes being committed, the juvenile programs a convict is enrolled to among other significant factors. Similarly to the Chi-Square Test, getting a p-value of less than 0.05 indicates a statistical significance between the variables. In SPSS, the p-value is often annotated as the “Sig.” column in either a bivariate (Chi-Square Test) or multivariate (MANOVA) analysis table.
Nayak and Hazra (2011) suggest the use of linear regression to assess the association between variables. Linear regression in the analysis is used commonly as predictive analysis. Regression is primarily done to determine two things in research; does a set of predictor variables do a good job in predicting a dependent variable and which variables are significant predictors of the outcome variable. These estimates are used to explain the relationship between one dependent variable and one or more independent variables.
In linear regression interpretation, an inverse correlation between two variables will yield a negative coefficient. Also, all correlation coefficients range from 0 to 1, with 0 being the least correlated and 1 being a perfect correlation. Pearson’s correlation coefficient will be used to assess the degree at which each independent variable affects a given dependent variable. It is from all these analyses that an informed conclusion can be made, and generalized recommendations can also be forwarded to the juvenile programs. All analyses will be conducted using the Statistical Package for the Social Sciences software that will be installed on a computer.
Assurance of Validity
Validity is the extent to which a tool measures what it is supposed to measure. Validity in research entails a similar concept and affects the degree to which the results are accurate. In a quantitative study, validity is the extent to which any instrument measures what it is intended to measure. Validity in research entails two essential components: credibility and transferability. Credibility refers to whether the results are legitimate based on the sampling design and analyses carried out. To some extent, credibility also affects the replication of a study (Mohajan, 2017). In the case of transferability, it shows whether the results can be generalized to a similar group.
With all these cautions in mind, the study will meet its credibility since it has used a globally accepted sampling design. The Fulton County Juvenile Court has published the numbers estimated during the population determination, thus are credible and factual. Furthermore, based on the nature of the study, the independent variables will be interrelated with the covariables to acquire informed conclusions. This process will involve a series of procedures all dependent on the previous one, which is bound to make the data more and more legitimate. Additionally, the analysis procedures discussed are statistically accepted and globally used; hence, thresholds are well understood when making conclusions, for instance, the p-value in the ANOVA test.
The transferability of the study has been achieved by obtaining a close representation of the population by choosing a sampling technique that upholds this factor. The ratio of male to female has also been catered for to ensure as close as an accurate representation of the population. Also, a description of the age bracket of concern has been detailed in the population and sample size category. This ensures that the findings of this study can be generalized across a similarly aged bracket with similar characteristics. All these are considerations that have been made to ensure the study is valid.
Measurement of Validity
Cronbach’s coefficient alpha is a measure of internal consistency; that is, how closely a set of items are related as a group. It is used in research to test the consistency of the sample population. Cronbach’s alpha is considered to be a measure of scale reliability. A high value does not imply that the measure is unidimensional; thus, additional tests are required to prove that the scale in use is unidimensional. It is important to note that the Cronbach’s alpha is not a statistical test, but more of a coefficient of reliability. Basing on the nature of how the Cronbach’s coefficient alpha is calculated, an increase in the number of items under review will consequently lead to an increase in the alpha value.
The Kuder-Richardson formula is also another measure of reliability for a test with two variables, i.e., the answers to the test can either be right or wrong with no in-between. The Kuder-Richardson has two tests, that is, the Kuder-Richardson 20 and the Kuder-Richardson 21. The K-R 20 is used for items that have varying difficulty, while the K-R 21 is used for items with equal difficulty. In the Kuder-Richardson test, a score of above 0.5 is usually considered acceptable. In this case, Cronbach’s coefficient alpha, which is a general form of Kuder-Richardson (K-R) 20 formula, will be used. A value of 0.7 and above is considered acceptable, with 0.9 and above being excellent (Cronbach & Meehl, 1955). This will be done together with the analyses and reported in chapter four of this study.
Population and Population Size
Fulton County is located in Georgia, Atlanta, in the United States of America. Fulton County covers an estimated area of about 527 square miles, with a population of about 1748 per square mile. The United States Census Bureau (2019) estimates the population of Fulton County to be about 1.05 million people. Persons under 18 years of age account for about 22% of this figure. Generally, the female population is slightly higher in Fulton County, reporting up to 52% of the general population. When it comes to education, the United States Census Bureau reports that at least 90% of the people in Fulton County have a high school diploma or higher and 50% possess a Bachelor’s degree or higher. With such statistics, it will be interesting to learn about the recidivism rates in the County and how effective the juvenile programs are in curbing recidivism.
With all the above statistics forming the base of the population, the population size can be determined. The population of interest is juvenile convicts that have been enrolled in a program that curbs recidivism and are between the age of 14 and 17 years old. In this case, a program is one that has been endorsed by the government to mitigate recidivism rates among juveniles. Other non-endorsed programs will not be included, despite having similar objectives. For one to be included as a respondent in the study, he or she has to meet the above criteria. Based on the data reported by Fulton County Juvenile Court, an estimate of about 1012 juveniles meet the above interest criteria, and this is the population from which the sample will be drawn.
Summary and Transition
Saunders's research onion has commonly been used by researchers to conduct a study systematically. The research onion encompasses six main sections, which are philosophies, approaches, strategies, time horizons, choices, and techniques. All of these sections interrelate with each other such that the outer layer affects the subsequent inner layer. In this chapter of the study, much focus will be placed on choices and time horizons, with a few notable mentions to the last phase of the research onion, which is, techniques and procedures.
Looking at the choices, three methods exist. However, as noted in the discussion, most researchers are familiar with two methods, i.e., qualitative and quantitative methods. Quantitative and qualitative research designs exist for use, depending on the nature of the study. Initially, quantitative studies were not preferred when it came to the issue of social justice since, in the past, they were used to taint progress in scientific fields. Despite this, several studies have been published, legitimizing the use of quantitative study designs in the field of justice. Furthermore, a significant number of publications on recidivism and juvenile arrests have used a quantitative approach.
After careful consideration, paying attention to the objectives and nature of the study, a one-time cross-sectional study design will be used to obtain the quantitative data needed to perform the study. The study will be carried out in Fulton County, Georgia, which has an estimated population of about 1.05 million people living there. National publications indicate that Fulton County is significantly educated, with up to 90% of the population aged 25years and above holding high school diplomas, with 50% of the same population having a Bachelor’s degree or higher. From these statistics, it will be interesting to find out the recidivism rates and juvenile conviction patterns and how effective juvenile programs are in the County.
The population of interest is juvenile convicts enrolled in a program endorsed by the government to reduce recidivism rates. For one to be included as a respondent in this study, the inclusion mentioned above criteria has to be met. From the large population size of juvenile convicts in Fulton County, Georgia, a sample will be drawn using stratified probability sampling, where the population will be segregated based on sex, i.e., male and female. This will then be followed by purposive sampling to obtain a sample size composed of both genders at the ratio of 3:2 with males being the higher proportion. This is because, based on the literature review, the probability of males being adjudicated is higher than that of females; hence, this consideration had to be made to ensure the validity of the results.
Other than the descriptive analyses which are usually familiar to most people, inferential statistics using Statistical Package for the Social Sciences software will be conducted. The research plans to use Chi-Square and ANOVA tests to determine the significance of the relationship between the independent and dependent variables. These analyses will then be supplemented by the Pearson correlation coefficient, which will determine the nature and degree of correlation between the variables.
Once the study has been done, chapter 4 will report on the results and findings. The findings will be contrasted against similar studies that were done in this area to identify any discrepancies among the studies. It is from this chapter that informed conclusions can be made.
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