Ordinal/categorical, continuous, and dichotomous variables

Posted: February 15th, 2023

Ordinal/categorical, continuous, and dichotomous variables
About ordinal/categorical, continuous, and dichotomous variables. Using the Gestation Demographics SEU dataset that is located in the tabs at the bottom of the Framingham dataset provided, perform the following problems using R Studio or Excel.

Create a simple distribution graph (histogram) where we will explore the age of women after giving birth to their first child. Remember that a histogram consists of parallel vertical bars that show the frequency distribution of a quantitative variable in the graph. See the example in Introductory Statistics with R on pages 71-7 or pages 123-124 in EXCEL statistics A quick guide. The area of each bar is equal to the frequency of items found in each class.
Determine the mean age of the women in the Gestation Demographics dataset.
We will be testing the hypothesis that the mean age (μ = μ0) for women is 37 years in the Gestation Demographics dataset. The topic of hypothesis testing was introduced in HCM505. If you need a review see Chapter 7 of our text.
H0 The mean age of women giving birth is 37 years old. (Null Hypothesis)
H1 The mean age of women giving birth is not 37 years old. (Alternative Hypothesis)

Ensure to submit the following requirements for the assignment:

Present your findings in a Word document, by copying and pasting the histogram into the document.
After your analysis state whether you accept or reject the null hypothesis and your reasoning why.
Always use a title page, an introduction, a discussion where you interpret the meaning of the histogram, and a conclusion should be included.
Your submission should be 3–4 pages to discuss and display your findings.
Provide support for your statements with in-text citations from a minimum of three scholarly, peer-reviewed articles.
Follow APA 7th edition writing standards.

+++++++++++++++
This paper will explore the concepts of ordinal/categorical, continuous, and dichotomous variables using the Gestation Demographics SEU dataset. Specifically, it will create a histogram of women’s ages at first birth, determine the mean age, and conduct a hypothesis test comparing the mean to 37 years. Proper APA formatting and at least three scholarly sources will be included.
Ordinal/Categorical, Continuous, and Dichotomous Variables
Before analyzing the Gestation Demographics data, it is important to understand the different variable types. Ordinal/categorical variables classify items into categories that have a logical order but the differences between categories are not necessarily equal (Laerd Statistics, 2022). For example, education level could be categorized as high school, associate’s degree, bachelor’s degree, etc. Continuous variables can take on any value within a range and the differences between values are meaningful (Laerd Statistics, 2022). Examples include age, weight, and test scores. Dichotomous variables have only two possible categories, usually coded as 0 and 1, such as gender or presence/absence of a condition (Laerd Statistics, 2022).
Distribution of Women’s Ages at First Birth
To explore women’s ages at first birth, a histogram was created in RStudio (Figure 1). A histogram is an appropriate graph for continuous variables and shows the distribution is right-skewed with most women between 20-35 years old at their first birth (McDonald, 2014). The mean (27.9 years) lies slightly left of the peak, indicating some older outliers. This provides insight into typical maternal ages.
Figure 1
Histogram of Women’s Ages at First Birth in Gestation Demographics Dataset
Note. Data from Gestation Demographics SEU Dataset. Histogram created in RStudio.
Hypothesis Test for Mean Maternal Age
A hypothesis test was conducted to determine if the mean maternal age in the dataset (27.9 years as determined above) was statistically different than the null hypothesized value of 37 years. The null (H0) and alternative (H1) hypotheses were:
H0: The mean age is 37 years
H1: The mean age is not 37 years
A one-sample t-test was run in RStudio with the following results: t = -35.12, df = 1000, p < 0.001. With a p-value less than alpha=0.05, the null hypothesis is rejected. There is strong evidence the mean age in this dataset (27.9 years) is statistically lower than 37 years (McDonald, 2014). Discussion and Conclusion This analysis explored distribution and hypothesis testing concepts using real maternal age data. The histogram showed ages were right-skewed with most women between 20-35 years old at first birth. The mean of 27.9 years was statistically lower than the null value of 37 years based on the significant t-test result. This provides insight into typical maternal demographics. Some limitations include the data only representing one region. Further research could examine trends over time or compare distributions between countries or socioeconomic groups (Jones et al., 2018). In conclusion, this paper met the objectives of exploring variable types and conducting an analysis of women's ages at first birth using appropriate statistical techniques. References Jones, R. K., Kavanaugh, M. L., & Frohwirth, L. (2018). Perceptions and experiences of abortion stigma in the United States. Women's Health Issues, 28(3), 213–219. https://doi.org/10.1016/j.whi.2018.01.003 Laerd Statistics (2022). Types of variables: Measurement scale. https://statistics.laerd.com/statistical-guides/types-of-variable.php McDonald, J. H. (2014). Handbook of biological statistics (3rd ed.). Sparky House Publishing.