98 Table 12
Length of Current Sentence
Length of current sentence N %
3 to 6 months 7 9.6
7 to 11 months 3 4.1
1 to 2 years 9 12.3
2 to 3 years 24 32.9
4 to 5 years 4 5.5
More than 5 years 26 35.6
The length of the participants sentences seem to be skewed toward lengthier sentences as opposed to shorter sentences with 74% of participants (n=54) having been sentenced to incarceration for a period of two years and longer. Of the remaining 26%, participants with three to six month sentences constitute 9.6% (n=7), seven to eleven months constitute 4.1%
(n=3) and participants serving sentences of one to two years constitute the remaining 12.3%
(n=9). The length of time participants had been incarcerated for their offense/s at the time of assessment was also recorded and presented in Table 13.
Table 13
Time Currently Served in Corrections
Time currently spent in prison N %
Less than 6 months 26 35.6
6 to 12 months 17 23.4
12 to 18 months 5 6.8
19 to 24 months 3 4.1
More than 2 years 22 30.1
Lastly it was found that the length of time participants had spent in corrections clustered on either end of the scale. 35.6% of participants had spent less than six months in the correctional centre at the time of assessment whereas 30.1% had been there for more than two years. This was followed by 23.3% of participants who had been in the correctional centre for between six and 12 months and those who had been there for between 12 and 24 months who collectively consisted of 10.9% of the total sample (n=8).
99 5.7.1 Descriptive Statistics
Due to the relatively unique nature of the study within the South African context statistical evidence of a descriptive nature was required. Descriptive statistics allow for all the raw data to be summarised and organised into smaller, more simple groupings representative of the actual factors under study (Gravetter & Wallnau, 2007:6). Through obtaining frequencies, means and standard deviations the researcher is able to gain a better understanding of the nature of the sample as well as a preliminary overview of the magnitude of the similarities or differences between the individual participants as well as how much variability is present.
5.7.2 Inferential Statistics
To increase the understanding of the factors under study it becomes necessary to explore the relationship between these factors in a statistical manner. Inferential statistics allow the researcher to assess the data obtained from a study in terms of whether or not there is a statistically significant relationship or how well the obtained data fits a statistical model representing the predicted nature of the relationship. The probability of the obtained data fitting a statistical model that represents no effect or no relationship between the variables is obtained and as that probability increases or decreases the researcher may conclude with increasing confidence the nature of the relationship between the variables as either non- existent or existent respectively (Field, 2009:49). This will allow for generalisations to be made, not only within the sample population but possibly of the greater repeat offending population of which they are representative (Gravetter & Wallnau, 2007:7). The inferential tests used in this study refer to t-tests, analysis of variance (ANOVA) and correlation analysis.
5.7.2.1 T-Test
The t-test is essentially an assessment of mean scores between two groups or subgroups. A t- test can be used to compare the means between two different sample groups or two subgroups within a sample that consist of different participants. Assessment within a single group consisting of the same participants at different time intervals or under different conditions is also possible (Field, 2009:325). For this study, the mean self-esteem scores of different
100
subgroups within the sample will be compared in terms of different demographic, psychosocial and correctional variables. The resulting score will allow the researcher to determine whether or not the differences or similarities found between the two groups or sub- groups are statistically significant. This will allow for generalisations to be made in relation to the greater repeat offending population (Gravetter & Wallnau, 2007:276).
5.7.2.2 Analysis of Variance (ANOVA)
If however the means of more than two sample groups or in the case of this study, subgroups within the sample need to be compared it becomes necessary to utilise analysis of variance or ANOVA. ANOVA allows for the simultaneous analysis of the means of more than two variables using an F-statistic or F-ratio to test the null hypothesis which states that all group means are equal by comparing the amount of unsystematic variance in the variable to the amount of systematic variance. This allows for the Type 1 error rate (the probability of falsely rejecting the null hypothesis) or level of probability (usually set to 95%) to remain constant throughout the analysis (Field, 2009:348-349). In summary, if a significant p-value of less than .05 (5%) is found for the ANOVA the researcher can be at least 95% confident that there is a significant difference between the mean scores of two or more of the variables being tested. However, the data utilised for this type of analysis need to be parametric and therefore conform to a number of assumptions namely, normal distribution, homogeneity of variance, independence and interval data (Field, 2009:133). If any of these assumptions are not met, there are alternate, non-parametric statistical analyses available to ensure a more accurate statistical conclusion.
5.7.2.3 Correlations
The term correlation essentially describes the nature of the relationship between two factors.
Unlike the t-test, correlational analysis identifies the presence of a linear relationship, the strength of that relationship as well as the direction in which the relationship moves. The variable that communicates this information is known as the correlation coefficient. The correlation coefficient will always be a number between -1 and +1 therefore the closer it gets to -1 or +1 the stronger the relationship between the two variables. A 0 value would however indicate no relationship at all. The “-” and “+” signs indicate the direction of the relationship and therefore a perfect negative relationship would imply that as one variable increases the
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other variable decreases whereas a perfect positive relationship would indicate that both variables increase simultaneously (Field, 2009:170-172). When performing a correlational analysis the nature of the data collected would dictate what type of statistical test is most appropriate to use. As the majority of data collected for the current study was non-parametric a Spearman’s correlation was utilised in order to find a monotonic relationship which refers to the consistent directionality of the relationship between the variables (Gravetter &
Forzano, 2006:402).
5.7.2.4 Statistical Significance
The term statistical significance refers to the level of confidence with which a researcher can state that an effect or a relationship was present between variables during statistical analysis and that the observed effect was due to a true effect and not chance (Gravetter & Forzano, 2006:381). Conventionally an acceptable significance or alpha level is anything less than .05 (p < .05) indicating that the probability of making a false conclusion is restricted to 5%.
Setting the alpha level to .05 would mean that if a significant result is established the researcher can be 95% confident that the result was not due to chance and may be therefore reject the null hypothesis which states that there will be no effect (Gravetter & Wallnau, 2007:240). For the purpose of this study a significance level of .05 was selected.