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“Recidivism Among Female Prisoners”: Data Coding & Measurement

Coding technique

The measures for this study are divided into two categories. The first category describes the attributes of the former female prisoners who are reasserted within the first three years after being released from state prison. The second category studies the factors that influence recidivism among female prisoners. In the first category, four standard measures of recidivism were used: rearrest, reconviction, resentence to prison and return to prison with or without a new sentence (Deschenes, Owen, and Crow, 2006, p.19). The method of coding used in this study is the “case-by-variable” matrix. The major presentation of data consists of the profile of the female and all prisoners that were released in 1994 from prisons in the 15 states used in the study. The data is presented in three columns: characteristic (variable), females, and all. The variables used include: race (White, Black and Other), ethnicity (Hispanic and Non-Hispanic), age at release (14-17, 18-24, 25-29, 30-34, 35-39, 40-44, and 45 or older), an offense for which inmate was serving a sentence (violent, property, drugs, public-order and other), sentence length in months (mean and median), time served before release in months (mean and median), percent of sentence served before release, prior arrest (mean and median), prior conviction (mean and median) and prior prison sentence.

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Measurement of variables and choice of measurement scales used in the study

The main variables used in the study are: race, ethnicity, age at release, an offense for which inmate was serving a sentence, sentence length in months, time served before release, percent of sentence served before release, a prior arrest, prior conviction, and prior prison sentence. Both the ordinal and nominal measurement scales were used in the study. The variables race, ethnicity, age at release, and offense for which inmate was serving a sentence, were measured using the nominal scale of measurement. This scale of measurement groups the data into various categories. The data for each case can only belong to one category and not more. The variable race in this study consists of three categories: white, black, and other. The variable ethnicity consists of two categories: Hispanic and non-Hispanic. The variable “age at release” consists of seven categories: 14-17, 18-24, 25-29, 30-34, 35-39, 40-44 and 45 or older. The variable “offense for which inmate was serving a sentence” consists of five categories: violent, property, drugs, public order, and others. The other variables – sentence length in months, time served before release, percent of sentence served before release, a prior arrest, prior conviction, and prior prison sentence – were measured using the ordinal scale of measurement (Deschenes, Owen, and Crow, 2006, p.21). The researchers only had to assign the appropriate number to each participant. For the nominal level data, the responses given by the participants were coded as numbers to facilitate the statistical analysis. For instance, for the race, white was assigned the number 1, black the number 2, and other the number 3. For the ordinal level data, the values associated with the numbers are specified for analysis.

Graphic presentations used in the study

Several graphic presentations have been used throughout the study and include tables, line graphs, bar graphs, pie charts, and column graphs. Tables are the most commonly used graphic presentations and total 21 in number. Table 1 shows the profile of females and all prisoners released in 1994 from prisons in 15 states as described above (Deschenes, Owen, and Crow, 2006, p.21). Table 2 shows the recidivism rates for female offenders. The female offenders are categorized into four groups: rearrested, reconvicted, returned to prison with a new prison sentence, and returned to prison with or without a new prison sentence. Table 3 indicates the length of the period (in months) from release in 1994 to the first incarceration for female inmates. Table 6 shows the recidivism rate of all prisoners by type of incarceration offense (Deschenes, Owen, and Crow, 2006, p.31). The majority of these tables are derived from table 1, which is the main table. Tables are derived commonly by separating the variables used in the study. Three line graphs have been used to present data. Figure 1 is a line graph that indicates the time to recidivism for female prisoners for specified time intervals. The Y-axis shows the percent of released prisoners (ranging from 0 to 100%). The X-axis shows the time after the release and ranges from 0 months to 3 years. Bar graphs have also been used, for instance, in figure 2 and figure 5. Figure 2 shows the percent of prisoners rearrested by incarceration type. On the Y-axis is the type of offense committed while on the X-axis is the percentage of those rearrested. Each bar is divided into two; the black bar representing the female prisoners and the white bar representing all prisoners. Figure 5 shows the offense category and yearly mean arrest rates before and after incarceration. The Y-axis shows the type of incarceration offense committed before and after incarceration. The X-axis shows the yearly arrest rates (Deschenes, Owen, and Crow, 2006, p.41). Each bar is divided into two; the blue bar represents before incarceration and the red bar represents after incarceration. Figure 3 is a pie chart that shows the rearrest categories for female offenders for the first new arrest following release. The categories include violence, property, drug, public order, other, and none. Each category is differentiated from the others using a distinct color and the percentage for each category is also indicated. Figure 4 is a column graph that shows the percentage of prisoners with any rearrest by type of offense (Deschenes, Owen, and Crow, 2006, p.39). The Y-axis shows the percentages which range from 0% to 50%. The X-axis shows the types of offense which include: violent, property, drug, public order, other and unknown. Each of the offense types is shaded differently and the data labels are indicated above each column.

Improvement of the coding and measurement scales used in the study

The measurement and coding scales used in the study could have been improved in different ways. First, the variable race could have included other significant categories such as Hispanic, Asian Americans, and Native Americans in place of the “other” category. This would make it easier for readers to know how other significant minority races are affected by recidivism. Second, the variable “age at release” could have been measured using the ratio scale of measurement instead of the nominal scale. The researchers should have let the participants indicate their exact age rather than categorizing them. This would have provided the researchers with exact information about the ages at which the highest and lowest rates of recidivism occur. Lastly, the variable “offense for which inmate was serving a sentence” should have been left as an open-ended question. There are many offenses that can be committed and therefore the researchers in this study limited the responses of the participants by listing the categories. The responses to the open-ended question could have been coded once all the responses had been identified after the data collection. This would have enabled the researchers to identify the perspectives of the participants without limiting them.

References

Deschenes, E.P., Owen, B., and Crow, J. (2006). Recidivism among female prisoners: Secondary analysis of the 1994 BJS recidivism data set, 2004-IJ-CX-0038.

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StudyCorgi. "“Recidivism Among Female Prisoners”: Data Coding & Measurement." March 12, 2022. https://studycorgi.com/recidivism-among-female-prisoners-data-coding-and-amp-measurement/.

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StudyCorgi. 2022. "“Recidivism Among Female Prisoners”: Data Coding & Measurement." March 12, 2022. https://studycorgi.com/recidivism-among-female-prisoners-data-coding-and-amp-measurement/.

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StudyCorgi. (2022) '“Recidivism Among Female Prisoners”: Data Coding & Measurement'. 12 March.

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