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ST3001 Assessment Template Prepared by: Endurance Sunday Date: 5/21/2024

Describing and Graphing Data Part 1 — Identifying Variables in a Published Study Choose an article from the list provided. All articles are available in full text in the Walden Library. You may also read the abstract for each by copy and pasting the full DOI address into a web browser. Read the abstract for your chosen article then answer the questions that follow. Be sure to provide an explanation for each of your answers and include supporting evidence from the text and/or Learning Resources. Perming, C., Thurn, Å., Garmy, P., & Einberg, E.-L. (2022). Adolescents’ experience of stress: A focus group interview study with 16-19-year-old students during the COVID-19 pandemic . International Journal of Environmental Research and Public Health, 19 (15). https://doi.org/10.3390/ijerph19159114 Write responses to address the following in paragraph form by inserting your answers directly beneath the questions. 1. In your own words, briefly describe the study and what it covered as if explaining it to someone who has not read the abstract information. The Study titled "Adolescents' experience of stress: A focus group interview study with 16-19- year-old students during the COVID-19 pandemic" by Perming, C., Thurn, A., Garmy, P., & Eisenberg (2022) is qualitative research. The Study aims to explore the experiences of stress among adolescents aged 16-19 during the COVID-19 pandemic. The researchers used a focused group interview method to gather data. This method involves conducting group interviews where participants are encouraged to interact with each other, allowing for a more in-depth exploration of their experiences and perspectives. The Study is significant as it provides insight into the psychological impact of the pandemic on adolescents, a group that is often overlooked in public health discussions. The findings in this Study can be used to inform interventions and policies aimed at supporting adolescents' mental health during times of crisis. 1. What sample was used in the Study? Explain your answer and provide supporting evidence from the textbook or other resources. In the Study by Perming, Thurn, and Garmy, the sample used was a group of upper secondary school students aged (16–19 years old, n = 41). The Study aimed to investigate stress among adolescents (16–19 years old) during the coronavirus disease 2019 (COVID-19) pandemic (Perming et al., 2022). The choice of this specific age group as the sample for the Study is supported by research that suggests adolescence is a critical period of development characterized by significant changes in Page 2 of 9

sleep patterns and increased exposure to screens. According to the textbook "Research Methods in Psychology" by Shaughnessy, Zechmeister, and Zechmeister (2015), adolescence is marked by social, emotional, and cognitive development changes that can impact sleep patterns and screen time behavior. 1. What population does this sample best represent? Explain your answer and provide supporting evidence from the textbook or other resources. The sample represents two schools from two municipalities that agreed to participate in the Study. The schools had 350–450 students each and provided university preparatory courses and vocational education (Perming et al., 2022). 1. Identify at least one variable examined in this Study. Classify this variable as quantitative or qualitative. Explain your answer and provide supporting evidence from the textbook or other resources. The variable of self-reported anxiety levels in the Study by Perming, Thurn, and Garmy (2015) can be classified as qualitative due to its subjective nature and reliance on descriptive qualities. By understanding the nature of this variable, researchers can better interpret and analyze the results of their Study to gain insights into adolescents' psychological well-being. 1. Explain the level of measure for the variable identified in Section 4. Explain your answer and provide supporting evidence from the textbook or other resources. In the Study by Perming, Thurn, and Garmy (2015), the measure of self-reported anxiety levels can be considered ordinal. Ordinal or categorical variables represent categories with a natural order or ranking. In the case of self-reported anxiety levels, individuals may report their anxiety on a scale ranging from low to high or from mild to severe. While the intervals between the categories may not be evenly spaced, there is a clear progression from lower levels of anxiety to higher levels. 1. Determine if the Study is overall quantitative or qualitative. Justify your reasoning using at least two key aspects for the type of Study you chose. Explain your answer and provide supporting evidence from the textbook or other resources. In the case of Perming, C., Thurn, Å., and Garmy, P.'s Study (2015), the key aspects that suggest it is a qualitative study are the use of interviews and the focus on an in-depth exploration of the topic. Qualitative research often relies on interviews to gather detailed data about participants' experiences, perspectives, and beliefs. Based on the use of interviews and the focus on an in-depth exploration of the topic, it can be determined that Perming, C., Thurn, Å., and Garmy, P.'s (2015) study is qualitative. The Page 3 of 9

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researchers utilized qualitative research methods to gather and analyze their data, emphasizing understanding and exploration over hypothesis testing and statistical analysis. By considering these critical aspects of qualitative research, it is clear that this Study falls within the qualitative research paradigm. Part 2 — Data Analysis The following should be based on the data set you created from the larger BODY DATA data set using the instructions provided. Creating Graphical Displays of Data 1. Using the column labeled Smoker (not sorted by smoking status), create an appropriate graphical display to clearly show the breakdown of smokers and nonsmokers in your data set. Smoker Non Smoker 56.4 56.6 56.8 57 57.2 57.4 57.6 57.8 58 58.2 Smoking Status Smoking Status Frequency Figure1: Comparison Between Numbers of Smokers and Non-Smokers. 2. Copy and paste your graph from Excel here. Write a one-sentence interpretation of what the graph display. Page 4 of 9

Smoker Non Smoker 56.4 56.6 56.8 57 57.2 57.4 57.6 57.8 58 58.2 Smoking Status Smoking Status Frequency Figure 2: Comparison Between Numbers of Smokers and Non-Smokers. From the above descriptive statistics graph, 57 were smokers while 58 were non-smokers. 3. Create a histogram for the column labeled BMI (not broken down by smoking status). Use a bin width of 2. Start the bins at your minimum data point as appropriate for your data set. Write a one-sentence interpretation of what the graph display. 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 0 2 4 6 8 10 12 14 16 18 20 BMI FOR SMOKERS AND NON-SMOKERS BMI Frequency Figure 3: Histogram of BMI (not Broken Down by Smoking Status) Page 5 of 9

Majority from my data set had a BMI of 27-29. This was a combination of smokers and non- smokers. 4. Create two modified box plots for BMI, one for smokers one for nonsmokers. Write a one-sentence interpretation of what the graph display. Figure 4: Modified Box Plots for BMI for Smokers and Non-Smokers Table 1: Calculation from Distribution for Modified Box Plot for BMI for Smokers and Non- Smokers Based on the box plot data above, for smokers BMI, the minimum data point was 17.9, the first quartile was 24.7, the median data point was 26.6, the third quartile data point was 31.2, the maximum data point was 45.9 and the mean data point was 28.4, the interquartile range was Page 6 of 9

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6.5, the lower data point limit was14.95, while the upper data point was 41. There were two upper outliers for smokers 49.5 and 43.8. For the non-smoker BMI, the minimum data point was 18.7, the first quartile was 24.2, the median data point was 28.9, the third quartile data point was 35.25, the maximum data point was 56.8 and the mean data point was 30.0, the interquartile range was 11.1, the lower data point was 40.8, while the upper data point was 52. There were two upper outliers 56.2 and 52.6. Descriptive statistics Replace this text with your Excel output created using the following instructions: Use the data analysis tool pack to create two tables of descriptive statistics, one for smokers and one for nonsmokers using the columns created in step 7 in the preparation section. Copy and paste those statistics here. Comparing data Replace this text with your response to the following: Use these statistics to answer the following questions comparing smokers to nonsmokers. Be sure to provide values from your Excel output to support your reasoning. BM I Smoker BMI Non smoker Mean 28.3561 4 Mean 30.3448 3 Standard Error 0.71816 7 Standard Error 1.12052 9 Median 26.6 Median 28.85 Mode 25.2 Mode 23.3 Standard Deviation 5.42204 6 Standard Deviation 8.53369 3 Sample Variance 29.3985 8 Sample Variance 72.8239 2 Kurtosis 1.66352 3 Kurtosis 0.93240 4 Skewness 1.15064 3 Skewness 1.02727 7 Range 28 Range 38.1 Minimum 17.9 Minimum 18.7 Page 7 of 9

Maximum 45.9 Maximum 56.8 Sum 1616.3 Sum 1760 Count 57 Count 58 Table 2: Excel Output Comparing smokers to Non-smokers 1. Which group has an BMI that is typically higher? Be sure to write at least one sentence justifying your reasoning including values from the excel output. Non-smokers have higher BMI with a mean of 30.3.4±8.5 compared to smokers, whose BMI is 28.4±5.4 2. Which group has greater variation in their BMI? Be sure to write at least one sentence justifying your reasoning including values from the excel output. Non-smokers have a more significant variation than smokers. This is determined by comparing the standard deviation values for the two groups. The standard deviation for non-smokers is 8.5, while that of smokers is 5.4. Smokers, therefore, have BMI values spread far apart from the mean, while values for smokers are closer to the standard than non-smokers. 3. Do you suspect any outliers are present in the BMI for each group? Be sure to justify your reasoning. There were upper outliers in both BMI groups. The data points for non-smokers (56.2 and 52.6) were higher than the mean at 30 and also had a more significant variance. The distribution for the smokers with a data point of (49.5 and 43.8) with a mean of 28, making the data shape positively skewed to the right. Outliers can significantly affect the skewness of the distribution. If there are high-value outliers, they can make the distribution right-skewed (Shaughnessy, Zechmeister, and Zechmeister (2015),). Skewness can be calculated using the formula: Skewness = (3 * (Mean - Median)) / Standard Deviation. The distribution is highly skewed if the skewness is less than -1 or greater than 1. The distribution is moderately skewed if the skewness is between -1 and -0.5 or between 0.5 and 1. The distribution is approximately symmetric if the skewness is between -0.5 and 0.5 (Triola, 2022). References Perming, C., Thurn, Å., Garmy, P., & Einberg, E.-L. (2022). Adolescents’ experience of stress: A focus group interview study with 16-19-year-old students during the COVID-19 Page 8 of 9

pandemic . International Journal of Environmental Research and Public Health, 19 (15). https://doi.org/10.3390/ijerph19159114 Shaughnessy, J. J., Zechmeister, E. B., & Zechmeister, J. S. (2015). Research methods in psychology. McGraw-Hill Education. Triola, M. F. (2022). Elementary statistics using Excel (7th ed.). Boston, MA: Pearson. Page 9 of 9

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