Range of Movement in Sports-Related Injuries - Assignment Example

Running head: STATISTICAL REPORT Off Campus Assignment 2: Final Statistical and Research Report Name: University: Course: Tutor: Date: Assignment 2: Part I Of the total of 98 prisoners in this study, 22.4 % (22) were female. Mean age of the sample was 36.41 (with a standard deviation of 7.56). About half (52) of the prisoners were in medium-level security, about a quarter (28) in high-level security, and just under a fifth (18) in low-level security. 48% (47) were victims of violence within the prison. Overall, 30 (30.6 %) of the 98 subjects were determined to be clinically depressed. At the bivariate level of analysis, the prevalence of depression was significantly higher for males (38.2 %) than females (4.5 %) (Fisher’s exact test, p = 0.002). The prevalence of depression was lower (16.7 %) in those detained in low-security environments compared to medium with the highest prevalence of depression (36.5 %), or high security (28.6 %) environments. However, this difference did not attain statistical significance (Likelihood ratio 22 = 2.7, p = 0.255). Prevalence of depression was significantly high (Fisher’s exact test, p = 0.001) for prisoners who had experienced violence during their detention. 96.1 percent of those not experiencing violence were determined to be clinically depressed compared with 40.4% of prisoners who had experienced violence (Fisher’s exact test, p < 0.001). Relative to females, the odds of depression in males was 0.1078 times higher. Relative to those with no experience of violence, the odds of depression was over 0.0962-fold higher among those who had experienced violence in the prison. Although the association was statistically significant, the confidence interval was wide, indicating imprecision in this estimate. Female prisoners are almost one and a half times likely to be clinically depressed compared to male prisoners. In other words, female prisoners are more depressed than male prisoners. For each additional year of age, the level of depression is likely to increase by 1.05 times (Table 1). Assignment 2: Part II Range of Movement in Sports-related Injuries Background It has been suggested by previous research findings that surgery is an effective treatment for sports-related injury. This study aimed to explore the improvement of range of movement of arm after surgery at one month and 12 months, and examine the relationships between movement of arm at baseline, and gender, severity of injury, or dominant side. Methods Range of arm movement (degree), age (years), gender (male=1 or female=0), dominant side (dominant or not), and severity of injury, was collected from 96 patients from hospital outpatients clinics in four Brisbane hospitals. Question 1 To identify whether there is an overall improvement in the range of movement over time, a One Way ANOVA test was conducted. This was the most appropriate analytical test since the dependent variable (range of movement) is a categorical variable hence ANOVA would enable easy comparison of means for the three ranges of movement and the fact that ANOVA is a parametric test. In addition, the variability of range of movement is assumed to be homogenous as confirmed by a non-significant Levene’s test, F(2, 285) = .610, p>.05. Using ANOVA would enable post-hoc tests to be conducted to clearly identify which groups differ (Field, 2009). In this case, it is necessary to identify at what time (baseline, 1 month post-surgery and 12 months post-surgery) the range of movement differs. The mean range of arm movement during baseline is 44.33 degrees with a standard deviation of 16.15. During 1 month post-surgery, the mean range of arm movement is 75.75 degrees with a standard deviation of 16.51 (Table 2). Table 2 also shows that the mean range of movement at 12 months post-surgery is 1.23E2 with a standard deviation of 16.52. Table 3 affirms that there is homogeneity of variances in arm movement between the three time periods since the Levene’s statistic is not significant (p =.544 and this is greater than .05). From the ANOVA table (Table 4), the F ratio is significant, F(2, 285) = 556.846, p.05. This indicates that range of arm movement at baseline did not depend on the patient’s age. Table 8 shows the value of R square as .009 and this can be converted to a percentage to become 0.9 percent. This implies that 0.9 percent variance in arm movement is explained by the patient’s age considering other variables to be constant. This is an indication that age does not affect the range of arm movement significantly at baseline. The regression equation derived from Table 9 is y = .356x + 36.505. From this equation, the y-intercept value (constant) is 36.505 and this is a positive value implying that the intercept occurs above the x-axis. Table 9 also displays the unstandardised regression coefficient between range of arm movement at baseline and the patient’s age as B= .356. From this output, it is evident that as age changes by 1 unit, the range of arm movement changes by .356 units. The 95% CI for B ranges from -.409 to 1.120 and for the intercept ranges from 18.444 to 54.566 indicating that from this sample, the intercept of the population is 95 percent likely to be within the above ranges. Table 9 indicates that the standardized Beta is .096 implying that 0.096 degrees of arm movement at baseline is dependent on the patient’s age when all other factors are held constant. This is a very small variation in arm movement as a factor of patient’s age. Question 3 To determine whether severity of arm injury depended on gender and dominant side, a logistic regression was conducted. It is important to note that the severity of injury and dominant side are categorical variables thus logistic regression is a suitable analysis in this case. Logistic regression is the most ideal analysis where the outcome variable is categorical (Field, 2009) and in this case severity of injury is a categorical variable and the outcome is based on categorical predictor variables (gender and dominant side). In this case, there was need to determine which variable (between gender and dominant side) predicted injury (severe or not severe) In addition, the fact that there are two independent variables (gender and dominant side) affecting the dependent variable (severity of injury) calls for a logistic regression analysis as a viable way of determining the effect of the two independent variables on severity of injury. In 50 patients, arm injury was on the dominant whereas 50 patients had arm injury on the non-dominant side. There were 50 female patients and 46 male patients (Table 10). Of the 96 patients in this study, 83.3 percent (80) did not have severe arm injury with the rest, 13.7 percent (16) having severe arm injury (Table 11). A logistic regression analysis was conducted to determine whether severity of arm injury depended on the patient’s gender as well as the dominant side of injury. The influence of gender on the severity of arm injury at baseline is significant, B = 2.357, p .05). The odds ratio in this case is .938 implying that patients who had arm injury on the dominant side were .938 times more likely to have severe arm injury at baseline compared to patients whose injury was on the non-dominant side. The 95% confidence interval for this odds ratio range from .297 to 2.966. In summary, it is evident from the logistic regression that severity of injury at baseline depended on patient’s gender but not on the dominant side. Discussion Looking at the association between range of movement and sports-related injury, it is evident that range of arm movement improved significantly from baseline to 1 month after surgery and 12 months post-surgery. The significant improvement of range of movement over time confirms other research findings that surgery is an effective treatment for sports-related injury. It is evident that patient’s age causes a non–significant effect on the range of movement at baseline in sports-related arm injury. Severity of arm injury at baseline depends on gender of the patient with females experiencing having more severe injuries compared to male patients. On the contrary, the dominant side of injury is not a significant determinant of severity of injury at baseline. It can therefore be concluded that surgery is effective in treating sports-related arm injury and the severity of arm injury at baseline is a factor of patient’s gender with age causing no significant contribution on arm movement during baseline treatment period. Assignment 2: Part III Rationale Studies from literature review suggest that an increasing number of infants who were born prematurely would have learning and behavioural problems when they reach to the school age. This may be due to various reasons such as parental education, family income, and marital status. Objective To determine whether children who were prematurely born have a poorer motor and mental ability measure by motor and mental score of Development Quotients (DQ) compared to those children who were born full-term. Research Design –Case Control Study Design On consent mothers of both preterm and full-term infants completed a self- administered questionnaire to answer the questions in demographic information including education, family income and marital status. Development Quotients of Infants were assessed based on: Motor Score and Mental Scores. Sample Eligible subjects were infants and their mothers. Infants who were prematurely born at Mater mother’s hospital in Brisbane were recruited into the study (N =74). Inclusion required that preterm infants were aged 8 months; to be classified as having a birth weight less than 1200 grams and gestational age less than 32 weeks when they were born. Full-term born infants (N=74) who had birth weight of 2500 -4000 grams, gestational age 36-42 weeks and who were born in the same hospital were randomly selected. Preterm and full-term infants were matched in variables: age and gender. Data Collected Infants were preterm or not: (1 = preterm infant group; 2 = full-term infant group) DQ was measured by a motor and mental ability, with higher scores meaning higher DQ: Physical Score (70-130), Mental Score (70-130). Demographic variables were measured: Mother’s education, Family income, Mother’s marital status and Mother’s depression level. Research Questions: 1. Is premature born infants group different from the full-term infants group in mother’s education, income, marital status and depression level? 2. Are there any differences between premature born children and full-term infants in motor and mental scores? Research question 1: Is premature born infants group different from the full-term infants group in mother’s education, income, marital status and maternal depression level? H0: There is no difference between premature born infants group and full-term infants group in terms of mother’s education, income, marital status and depression level. H1: There is a difference between premature born infants group and full-term infants group in terms of mother’s education, income, marital status and depression level. A Chi-Square analysis was conducted to test the hypotheses since a Chi-Square test is used to test the independence of two variables. In this case, the two groups of infants (preterm and full-term infants) are assessed in terms of differences in mother’s marital status, education, income and depression levels. Most important is that Pearson Chi-Square is very useful in determining the existence of relationships between two categorical variables. It is notable that since it is impossible to calculate compare means for categorical variables (like marital status and depression level are categorical variables in our case); Chi-Square test is the best option since it compares frequencies in the various categories of data (Field, 2009). The Chi-Square test also produces the Likelihood Ratio output which is also useful for testing relationship between categorical variables. In addition, Chi-Square requires that the expected number for each cell to be at least five and this has been met by the data used in this case. Since Chi-Square provides cross-tabulations of each test variable against the infant groups, the test is the most appropriate for identifying any differences in the groups. The Chi-Square test was conducted at a 2-tailed significance level of p.05. Table 15 shows the 2*2 crosstabulation of infants groups and mother’s education level. The observed count of preterm infant mothers who have a lower than 10th grade level of education is 10 and an expected count of 8.0 compared to an observed count of 6 and an expected count of 8.0 in full-term infant mothers of the same education level. As such, preterm infant mothers with lower than 10th grade education level constitute 62.5% compared to 37.5% in full-term infant mothers. The observed count of mothers with an education level of 10-12th grade is 38 (60.3%) against 25 (39.7%) in the full-term infants group. For preterm infant mothers with an education level of Tafe and Diploma constitute an observed count of 14 (50%) against an observed count of 14 (50%) in the full-term infant mothers group. There was an observed frequency of 12 (29.3%) mothers in the pre-term infants group with a tertiary level of education against an observed frequency of 29 (70.7%) mothers in the full-term infants group. The Chi-Square value for this test was 10.731, three degrees of freedom and 2-sided p value of .013 (Table 16). The Likelihood ratio is 10.98, 3 df and 2-sided p value of .012 (less than .05). This indicates that there is a significant difference in education level among mothers of preterm infants and full-term infants. In essence, full-term infants’ group mothers have attained higher levels of education compared to preterm infants’ group mothers. A cross tabulation of infants groups and mothers’ annual family income is shown in Table 17. An observed count of 35 (81.4%) of mothers in the pre-term infants group had an annual income less than 30,000 compared to an observed count of 8 (18.6%) in the full-term infants group. For pre-term infants group mothers, there was an observed frequency of 30 (55.6%) of mothers who had an annual income of 30,000-49,999 compared to an observed frequency of 24 (44.4%) of mothers in the full-term infants group. There was an observed frequency of 9 (17.6%) preterm infants group mothers who had an annual income of 50,000 to 79,999 compared to 42 (82.4%) of full-term infants group mothers in the same income level. Table 18 indicates the Chi-Square value for this analysis as 38.97, 2 degrees of freedom and a 2-sided significance value of .001. The Likelihood ratio is 42.13, 2 df and 2-sided significance value of .001. This indicates that there exists a significant difference in income levels among preterm infant’s group mothers and full-term infant’s group mothers. Precisely, most preterm infants’ mothers have an annual income of less than 49,000 whereas most full-term infants’ mothers have an annual income greater than 49,000. Table 19 shows a cross tabulation of infant groups and mother’s depression levels. It is evident that 53 (86.9%) of preterm infant group mothers were depressed compared to 8 (13.1%) of full-term infant group mothers. In addition, there is an observed frequency of 21 (24.1%) of preterm infant group mothers who had no depression compared to 66 (75.9%) of full-term infant group mothers who were not depressed. From Table 20, it is evident that the Chi-Square value for this cross tabulation was 56.47, 1 df and a 2-sided significance value of .001. There was a significant difference in mother’s depression level between preterm infant and full-term infant mothers (Fisher’s Exact Test, p Read More