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401077 Introduction To Biostatistics For Transport Behaviors

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  • Course Code: 401077
  • University: Western Sydney University
  • Country: Australia

Questions:

1. Critically appraise of the statistical material in this paper against items 10, 12-17 of the STROBE checklist. Present your review

2. Using R Commander and your assigned data set, write a statistical report addressing the following research question:
In the population of 17-year-old residents of NSW, does the number of activities attended in the past month (activities) predict self-reported sedentary hours per week (sed) after correcting for gender (sex)?

 

Answer:

1. A critical evaluation of the article on “Transport behaviors among older teenagers from

semi-rural New Zealand” written by Ward et al. (2015) has been conducted in this answer. The article was published in the “Australian and New Zealand journal of Public Health”. According to the checklist provided by Strobe, for the purpose of making reports on the observational studies on clinical data, this journal article has been analyzed critically.

Item 10

In this article, the sample size determined by the researchers is not designed properly. 13 schools were requested to participate in the study, out of which 12 schools agreed to participate. Among the 12 secondary schools, 8 schools conducted in class study and 4 schools had at-home participants. The total number of students who participated in the study has been mentioned but the number of students from in class participants and the number of students from at home participants have not been mentioned. Moreover, the number of males and females have been classified but the number of males and females classified according to their mode of study has not been classified.

Item 12

In the article, a particular mode of transport used by the students has been represented in the form of the number of participants as well as percentage of the total number of participants. Moreover, transport frustration in the last month has been represented also as frequency and percentage. However, the representation of the sample size determined for the males and females preferring the modes of transport, licensing and transport frustration in the last month is ambiguous. Information about the missing data has not been discussed. Moreover, description of the Chi-Square test to analyze the data is not clear from the article.

Item 13

There is a mention that the total rate of response is 71.5%, but the total number of students in the 12 participating schools is not mentioned anywhere in the research paper. However, the survey conducted in-class and at home had a response rate of 77.2% and 65.6% respectively. However, there has been no mention of the total number of students in class and at home.

 

Item 14

I the article, there has been discussions about the demographic profile of the participants such as their gender, age, area where they live, ethnicity and their weekly income. However, no information about the missing data has been discussed in the article.

Item 15

Summary measures have been provided on the activities of the students in Table 3 of the article. The mean, standard deviation as well as the minimum or maximum hours of activity performed by the total number of students, the male students as well as the female students has been provided in the table.

Item 16

Here, analysis has been conducted with the help of a chi-square test, but only the significance of the tests has been provided. No discussion about the confidence intervals has been provided in the article.

Item 17

Analysis of thee subgroups has been conducted in this research, such as, association between gender and mode of transport has been conducted but no discussion about the sensitivity of the analysis has been provided in the paper.

2. Descriptive Analysis

This analysis is conducted on the 17-year residents of New South Wales. This research is mainly based on the prediction of the self-reported sedentary hours every week with the help of the number of activities attended by the residents in the last month after correcting for gender.

In order to conduct the above stated analysis, at first a descriptive summary of the variables in need has been conducted. It can be seen from the analysis that the number of female residents are 119 comprising of 43.51% of the population and male residents are 152 comprising 56.09% of the population. The bar graph in figure 1 shows the result graphically.

Table 1: Frequency distribution of Sex

Sex

Female

Male

Count

119

152

Percentage

43.51

56.09

Figure 1: Bar graph of sex

The summary of the other two related variables number of activity hours in the last month and sedentary hours has been provided in table 2. The average number of activities is 7.30 in the last month. Again, it can be seen that 50 percent of the participants has done more than 7 activities. This indicates that the number of activities last month is symmetric and can be seen clearly from figure 2. The average number of sedentary hours is 10.42 hours each week. Again, it can be seen that 50 percent of the participants has experienced more than 10.2 hours each week. This indicates that the number of sedentary hours each week is not symmetric and can be seen clearly from figure 3.

Table 2: Descriptive analysis of activities and sed

 

Activities

Sedentary Hours

Mean

7.30

10.42

Standard Deviation (SD)

2.37

3.39

Standard Error (SE)

0.14

0.20

Interquartile Range (IQR)

3.0

4.5

Coefficient of Variance

0.33

0.33

Skewness

0.04

0.60

Kurtosis

-0.28

0.21

Minimum

2

4

First Quartile

6.00

7.85

Median

7.0

10.2

Third Quartile

9.0

12.3

Maximum

14.0

22.32

Count

271

271

Regression and Inferential Analysis

The required regression models and inferential analysis are attached in the following tables. From the table of goodness of fit, it can be seen that the model can predict the results 59.34 percent correctly. It can also be seen from the significance value that the value is less than 0.05, the standard 95 percent level of significance. Thus, the developed model can be said to be significant. The required equation of prediction of sedentary hours can be given as follows: 

Table 3: Summary of Regression result

Table 3.1 Goodness of fit

Multiple R Square

0.5934

Adjusted R Square

0.5904

Table 3.2 Summary of Residuals

Minimum

IQ

Median

3Q

Max

-6.3848

-1.3276

-0.2619

1.1495

6.9853

Table 3.3 Significance of the regression

Residual Standard Error

F Statistics

Significance value

2.169

195.6

< 2.2e-16

Table 3.4 Summary of coefficient estimation

Coefficient

Estimates

Standard error

t value

Pr (> |t|)

Intercept

22.9648

0.6711

34.22

<2e-16***

activities

-1.4114

0.0714

-19.76

<2e-16***

Sex

-4.0043

0.03409

-11.75

<2e-16***

Significance codes: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’, 0.1 ‘ ‘ 1

 

Addressing the Research Question

The research question in this research has been whether the number of activities last month has been affecting the sedentary hours each week after correction of the gender. From the regression it has been observed that the regression model so obtained is significant. Thus, it can be said that after correction of gender, the sedentary hours are affected by the number of activities.

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