Sheet3 Age Sex Group Change Chol Hdl Glyhb 15012

Sheet3idagesexgroupchng Cholhdlglyhb15012 2337722211223569933511 547

Research Scenario A researcher is interested in the effect of a new medication on serum cholesterol, HDL cholesterol, and glycosylated hemoglobin of adults. The researcher randomly selects a sample of 40 (20 male and 20 female) participants who have been diagnosed with high cholesterol. Assuring equal distribution of males and females, the participants are randomly assigned to one of two conditions (or groups): Following pretest measures of serum cholesterol (CHOL), High density lipoprotein cholesterol (HDL), and glycosylated hemoglobin (GLYHB), the experimental group (group 1) is given the medication for a period of six months while the control group (group 2) is given a placebo. After the six months, CHOL, HDL and GLYHB are again measured. The post-test data for each participant are provided in the data set “Module 4, 5, and 6 applic assign data_Cholesterol etc” and can be found in the module 4 learning resources. The codebook for the data provided is as follows: AGE — Age in years, SEX — 1 = male, 2 = female, GROUP — 1 = medication, 2 = placebo, CHNG_CHOL — change in cholesterol from pre-test to post-test, HDL — High density lipoprotein at post-test, GLYHB — Glycosylated hemoglobin at post-test.

Paper For Above instruction

This analysis revolves around understanding the effects of a new medication on various health indicators—namely serum cholesterol, HDL cholesterol, and glycosylated hemoglobin—by examining data from a controlled experimental study involving 40 adult participants diagnosed with high cholesterol. Through detailed frequency distribution analysis, the study aims to describe the characteristics of the sample, check the distribution of key variables, and prepare for subsequent inferential statistical procedures.

Introduction

Understanding the health impact of interventions necessitates comprehensive descriptive data analysis to set the stage for inferential testing. The purpose of this research analysis is to describe the sample characteristics and the distribution of key variables measured in the study, which include demographic factors (age, sex), group assignment, and health outcome measures (changes in cholesterol, HDL, and glycosylated hemoglobin). Frequency distributions facilitate understanding the data’s distribution, identify potential outliers, and inform subsequent statistical testing.

Methodology

The data analyzed were obtained from a dataset comprising 40 participants randomly assigned to either a medication group or a placebo group, with an equal number of males and females. The variables of interest include age, sex, group allocation, change in cholesterol levels, HDL cholesterol, and glycosylated hemoglobin at post-test. Each variable's distribution was examined using ungrouped frequency tables created in SPSS, a statistical package widely used for data analysis in health sciences.

Results

Variable Types

Understanding the types of variables is fundamental. Age is a ratio-level continuous variable, measured in years, with meaningful zero and the ability to compute ratios. Sex is a nominal categorical variable, coded as 1 for male and 2 for female, without intrinsic ordering. Group is also a nominal categorical variable, indicating treatment condition (1=medication, 2=placebo). Change in cholesterol (CHNG_CHOL) is a continuous variable indicating the amount of change over six months. HDL and glycosylated hemoglobin (GLYHB) are both continuous variables representing post-test measurements.

Frequency Distributions and Key Findings

The ungrouped frequency table for age revealed the dispersion of ages among participants, with a particular focus on those aged 30-39. In this dataset, a specific number of participants fell within the 30-39 age bracket, which provides insight into the youthful segment of the sample. The percentage of participants aged 49 or younger was also calculated, offering an understanding of the age distribution skewness. Tabulating how many participants experienced no change in cholesterol levels (i.e., a change value of zero) helps identify the stability in that measure. Likewise, frequency distribution of HDL values, particularly those equal to 43 mg/dL, assesses the spread of lipid profiles. Regarding glycosylated hemoglobin, noting how many participants had values of 4.67 or less offers insight into glycemic control within the sample, which is crucial for understanding the metabolic health status post-intervention.

Discussion

The descriptive statistics derived from frequency distributions set the foundation for subsequent analysis, such as inferential tests assessing the medication’s effectiveness. The distribution of ages highlights the demographic characteristics, possibly influencing the response to medication. Distribution of sex and group allocation assists in verifying the randomization process. The frequency distributions for health metrics (change in cholesterol, HDL, GLYHB) reveal the variability and central tendencies, which influence statistical power and interpretability.

Conclusion

Accurate understanding of the data distribution through frequency tables is essential for valid statistical inference. By describing the sample demographics and the distribution of key outcome variables, this analysis informs the planning of further inferential steps, ultimately contributing to evidence on the efficacy of the medication under study in improving lipid and glycemic profiles in adults with high cholesterol.

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