Target Population

The article has been constructed to address the following research problem.

“To address the negative consequences of social networks among high school students, teachers are considering other after-school sports activities. Before going any further, they want to gather information about how much time students spend now on optional activities and how much time they spend on social networks.”

The intermediate school was considered an important part of their day, so children have the ability to play, have a chat, do homework, exercise, take music and other enrichment classes and relax. In this article, the scholar has revised two topics related to school activities. First, the characteristics of a child, a family, and work related to differences in school activities of young people. Second, does participation in these events relate to differences in child’s adaptation with regard to the later date in different activities, and his/her time devoted to various activities including social media interaction. These issues were addressed in a sample of 194 American children examined from fifth, sixth, and seventh grade in a longitudinal study.

1. Target Population

The target population was selected from an intermediate school in the United States. The school was selected randomly, but somehow on the basis of homogeneous financial backgrounds of the families from which the students belong. The students from fifth, sixth and seventh grades were selected in a stratified random sampling technique. Guardians of the wards were also interviewed for their outlook on their children’s extracurricular activities, and especially about their knowledge about the possible impact of social media on their kids.INSTRUCTION: Insert the text here.

 2. Explanatory and Response Variables

Demographic details of the children were collected. Age, gender, and socioeconomic status of the family were assessed to be three important explanatory factors. Total time spent on social media websites, especially, any particular choice of timing for surfing the internet was also considered as explanatory variables from the survey. The financial condition of the family was also one of the explanatory variables in the study. Other than social networking, students were evaluated for activities based on music, outdoor and indoor games, and adventure sports activities. Interest levels of the students for the activities were assessed from the performance and time taken to perform the activities. The time spent on social media and on extracurricular activities was considered as the dependent or outcome variable. INSTRUCTION: Insert the text here.

3. Sampling Method

Explanatory and Response Variables

The current study has expanded previous works and tested the use of time with the children in fifth to seventh grade. The first objective was to identify the characteristics of the child and the family in relation to children’s activities after school. The second goal was to investigate relationships within two years between social, emotional and academic activities and rules for children. The longitudinal survey was conducted with two types of sampling techniques. First, the schools were selected randomly from the Texas province of the United States. Next, the students were selected from their respective classes by using a stratified sampling method. The stratified sampling was done considering the three classes as three strata.INSTRUCTION: Insert the text here.

Data Collection Method and Justification

The scholar sent letters describing the study to the parents of fifth, sixth and seventh class students from nine transition schools. The study was formalized in an environmental perspective, and parents spoke of a more recent experience to adapt to methodologies taught in school. They were asked to return the form with information about basic demographic and children’s information about their family.

The researchers chose schools because they had a high proportion of children who were Americans and whites. In addition, students were available after school, either in schools or in adjoining areas. 50% of the family (n = 96) returned the forms.

Of those who agreed to provide information about their child, 78 percent agreed to participate. The demographic profile of the volunteer children in each school was established with respect to the parental race, gender and education. From the ready-to-do interview pool, all American families (n = 78) are selected as participants. The scholar chose white and non-Hispanic children from the groups of available participants, using a stratified random sampling plan that provided roughly the same number of girls and boys.

There was no difference between the children selected for education and those who were not selected in terms of gender, lunch scholarships or school transfer. 48% were American. 52% were girls. 55% lived in the same family home. The parents also provided the weekly schedule of their kids, and the total time invested in extracurricular activities was cross verified from the students.

The sampling methodology and data collection procedures can be justified based on the structure of the research. The strata of schools were chosen to explore and establish the results based on the difference between American and other children.

Sampling Method

Also, frequent access to social media for the majority of the students was also an essential component of research. It is to be noted that scholar also wanted some students from non-American communities, who have the less financially affluent background and access to social media sites. Also, random sampling was essential to avoid any possible bias due to the homogeneity of the sample subjects. The type of extracurricular activities was also an important aspect in data collection method. The scholar wanted specifically to collect the data on time invested in the sports and sports-related activities.

You may replace subsection headings with a more appropriate title. Remember to update the ‘Table of Contents’ page after making any changes in your report.

For Part B of Assignment 1, you will need to perform some exploratory analyses on one of the provided example datasets. Follow the outline below to present a tidy written assignment which includes your numerical work, your graphs, and your interpretations.

1. Provide a short description of the dataset (what it represents, number and type of variables, number of observations, type of data).
2. List appropriate graphs and descriptive statistics you would use for graphical and numerical exploration of your data, justifying your choice.
3. Draw appropriate graphs and give a short summary of what they show.
4. Present a summary of the descriptive statistics calculated and a short description of what they tell you.
5. Summarise the results of your exploratory analyses. What follow-up investigations do you propose?

You must include in an appendix of this report screenshots of software output. Each screenshot should show only the part of the screen that is relevant to a particular task. Insert relevant graphs with a caption below each graph in the body of your report. Tables with a caption above each table in the body of your report should be formatted according to APA style requirements.

The alcohol-related mortality rate is estimated in several populations, but few studies have yielded reliable results in mortality and age and gender-related to alcohol. The death register contains information on the main and associated causes of death and provides an individual assessment of the proportion of alcohol consumption in mortality.

The data for life expectancy with alcohol consumption has been used to investigate the effect of alcohol, and to assess the influence of over-consumption of alcohol on the population of the sample countries in the sample data. The data set summarizes information from 45 countries about the average life expectancy of citizens. The study examined the question of whether the average life expectancy of citizens at three levels (low, medium, high) or alcohol consumption per adult citizen per year was different.

The present article researches on the impact of alcohol consumption on the average life expectancy in several countries. Interest was also revealed for the descriptive summary of consumptions of all the counties. Also, a one-way ANOVA model was constructed to find the difference between life expectancies for three levels of alcohol consumption. A post-hoc analysis for pair-wise comparison was also done to find the significant difference in life expectancy for the alcohol consumption levels.  

Data Collection Method and Justification

Statistical Enquiry Process

The sample data was scrutinized for the type of the variables. The variable indicating level of alcohol consumption was converted from a string level variable to a numerical variable by introducing a categorical variable with three values. High level of consumption was denoted by “3” and the low level was assigned “1”. The ordinality nature of the categorical variable was preserved. The researcher tried to assess the difference in average life expectancy for three different levels of alcohol consumptions. The research question was framed as whether excess alcohol consumption adversely impacts the average life expectancy of the people across different counties in the world.

A set of hypotheses were constructed for inferential analysis of the research objective. The null hypothesis was constructed as follows,

H0: Alcohol consumption level had no impact on the average life expectancy of people

The alternate two-tailed hypothesis to be tested at a 5% level of significance was constructed as follows,

HA: Alcohol consumption level had a statistically significant effect on the average life expectancy of people. Hence, there exists at least one group of countries with different alcohol consumption than the other two groups.

2. Dataset description

According to the data received, 33.33% percent (N = 45) of all alcohol-related cases were for all three reported levels of consumption. Probable deaths caused alcohol consumption was responsible for the loss of life expectancy of people from 45 countries across the globe. In the low alcohol consumption category the average life expectancy was 82.53 years (SD = 6.81). The average life expectancy at average consumption level was 70.45 years (SD = 9.05), and for high consumption group, the average life expectancy was 63.27 Years (SD = 5.74).

Normality of the life expectancy was checked by Shapiro-Wilk test. At 5% level of significance the variable was found to be significantly normally distributed (W = 0.965, p = 0.195). The choice of the dependent variable as average life expectancy was evident. The control or independent variable was the categorical alcohol consumptions. No missing values were observed, and the descriptive summary revealed that the mean of average life expectancies was 73.76 years (SD = 10.77). Median of the distribution was 73 years. Hence, 50% of the countries had an average life expectancy less than 73 years. The 95% confidence interval for the mean life expectancy of the total data set was evaluated for the population mean as 70.52 years to 76.99 years.

Statistical Enquiry Process

3. List of graphs and descriptive statistics for data exploration

The normality of the life expectancy was explored by histogram fitted normal curve.

A side-by-side box plot was constructed to visually represent the difference in central tendency of life expectancy and the spread for three levels of alcohol consumptions.

Mean and standard deviations were calculated to descriptively summarize the life expectancy of the sample.

Shapiro-Wilk test statistic was used to check the normality of the average life expectancy.

A one-way ANOVA with F-statistics was used to assess the difference in life expectancies for three consumption levels.

Figure 1 below reflected that the life expectancy data for 45 countries was normally distributed with mean = 73.76 years (SD = 10.77). provides a clear display of the central location of the life expectancies for three categories of alcohol consumptions. It was noted that average life expectancy (LE) was normally distributed and the median LE across the globe was way higher for low alcohol consumption. The spread or the middle 50% observations was greater for average consumption level. A positive skewness can also be identified from the box plot of average category. For a high level of alcohol consumption, the median LE was near to 65 years of age with a high positive skewness. Hence, differences in life expectancies were found to depend on the consumption level of alcohols.

Note:  SD has been used to denote standard deviation

From Table 1 the estimated life expectancy bracket for the low level of consumption was considerably higher than that of the high alcohol consumption level. The low standard deviation at a low level also signified the accumulation of the data around the mean value. The spread or standard deviation is higher in the average category. A significant difference in the estimated range of life expectancies was visible from the range of 95% confidence intervals.

A one-way analysis of variance was conducted to infer the descriptive summary of life expectancy. It was found that there existed at least one category of alcohol consumption for which average life expectancy is statistically different. (F = 26.50, p < 0.01) at 5% level of significance. A post-hoc analysis with Tukey’s test is conducted. From Table 2 it became apparent that there were statistically significant differences between life expectancies for comparison between every pair of consumption category at 5% level of significance.

7. Suggested Follow-up Investigations

The results illustrated that high alcohol consumption was a primary reason for less life expectancy and early deaths. A future research could also include the demographical details of the countries. The average age of the residents, gender ratio in the country could include interesting comparative analysis with pinpoint assertion and cause of early deaths. The parallel impact may then reduce the significance of alcohol consumption amount for early deaths or low life expectancy. The results provide a overall estimate of the average life expectancies of the countries, and province wise division of countries would help in deriving detailed results.