NUR 705 Assignment 4.1: Organize Data in JASP

Value: 15 points

Introduction

In this assignment, you will use the JASP statistical software. If you have not yet downloaded JASP, please download and install JASP (Links to an external site.). I also recommend that you download this free PDF guide to using JASP (PDF) (Links to an external site.). It can help answer questions as they come up.

Assignment Guidelines

You will download the NUR705 Week 4 (CSV) dataset (Excel) (Links to an external site.) file for use in this assignment. Save this file in an easily accessible location on your computer’s hard drive.

Note: The Edge browser sometimes makes it difficult to download files. Consider switching to Chrome (or another browser) if, when downloading the Health dataset, you get a screen with cryptic cyber-gibberish such as that shown in the following image.

Part One

Using the NUR705 Week 4 dataset, complete the following using JASP:

1. Locate the three horizontal lines on the top left of the JASP page and use this dropdown menu to open a file on your computer.
2. Choose the NUR705 Week 4 Dataset (it is a CSV file).
3. Run descriptive statistics for the variable: age. Include the mean, median, mode, standard deviation, range, minimum, and maximum.
4. Run a frequency distribution for the variable: Gender
5. Run a frequency distribution for the variable: Race
6. Run a histogram for the variable: number of drinks per week.
7. Run a frequency distribution for the variable: Smoking/Use

For part one of the assignment, submit screenshots of the items 3, 4, 5, 6, 7 above. It is best to copy these and put them in a Word document.

Part Two

For part two of the assignment:

1. Prepare a short narrative to describe the sample and your analysis. Your narrative should include the following: the use of APA formatting, the distribution of age ranges (also include the mean and standard deviation), gender, race, and smoking/tobacco use.
2. This narrative should be approximately one paragraph, double-spaced.

Submission

Submit your assignment and review full grading criteria on the Assignment 4.1: Organize Data in JASP page.

Week 4: Descriptive & Inferential Statistics, Measures of Central Tendency

Lesson 1: Descriptive & Inferential Statistics, Measures of Central Tendency

Introduction

Quantitative research studies must have a preliminary data analysis completed in order to convey to a research consumer the characteristics of the sample. Preliminary data analysis is also needed to ensure that the appropriate statistical test is conducted on the collected data.

Another key component of effectively analyzing and communicating the characteristics of the sample is to properly organize and display data. This week, you will explore descriptive statistics. You will also briefly examine inferential statistics and measures of central tendency.

Week 4 Introduction Transcript (Links to an external site.)

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Learning Outcomes

At the end of this lesson, you will be able to:

• Utilize statistical software to run an analysis on data.
• Transfer a statistical description into a narrative for an audience.
• Understand the difference between descriptive and inferential statistics.
• Understand principles of normal distribution when analyzing data.
• Conduct a critique of a systematic review of the literature on a topic related to the DNP project.

Before attempting to complete your learning activities for this week, review the following learning materials:

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Learning Materials

Read the following in your Kim, Mallory, & Vallerio (2022) Statistics for evidence-based practice in nursing textbook:

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• Chapter 2, “Statistical Essentials I”
• Chapter 5, “Organizing and Displaying Data”
• Chapter 6, “Descriptive Statistics”

Normal Distributions Transcript

The normal distribution is a theoretical concept of how large samples of ratio or interval-level data will look once plotted. Since many variables tend to have approximately normal distributions, it is one of the most important concepts in statistics. The normal curve allows for probabilities to be calculated. In addition, many inferential statistics require that data are distributed normally. If your data is not normal, be careful what statistical tests you use with it. In a normal distribution, measures of central tendency, including the mean, median, and mode all fall at the same midline point. The mean, median, and mode are all equal. The calculation of these measures of central tendency are covered in another video.

Normal distributions share several key features. They are unimodal, meaning that there’s only one peak in the distribution. When divided at the mean, a normal distribution takes the form of a symmetrical bell-shaped curve. Standard deviations are used to measure how much variation exists in a distribution. Low standard deviations mean values are close to the mean, whereas high standard deviations mean that values are spread out over a large range.

In a normal distribution, approximately 34% of scores fall between the mean and one standard deviation above the mean. Therefore, based on its symmetry, approximately 68% of scores fall between one standard deviation above and one standard deviation below the mean. Approximately 95% of scores fall between two standard deviations above and two standard deviations below the mean. Approximately 99.7% of scores fall between three standard deviations above and below the mean. Z-scores are used to measure how many standard deviations above or below the mean a particular score is. These scores allow for comparison and probability calculations.

Not all samples approximate a normal curve. To understand more about distributions, it is important to understand modality, symmetry, and peakedness. A distribution can have more than one peak. The number of peaks contained in a distribution determines the modality of the distribution. Most distributions are normally distributed and have only one main peak, meaning they are unimodal. However, it is possible to have distributions with two or more peaks. Distributions with two peaks are bimodal. Distributions with more than two peaks are multimodal. Symmetry and modality are independent concepts. If two halves of a distribution can be superimposed on each other where each half is a mirror image of the other, distribution is said to be symmetrical.

Sometimes data are not symmetrical. If the peak is off-center, one tail of the distribution will be longer than the other, meaning it is skewed. Skewness is a measure of symmetry of distributions. Pearson’s Skewness Coefficient provides a non-algebraic quick estimation of symmetry. Recall that normal distributions are symmetrical and bell-shaped. In a perfect distribution, the skewness coefficient will be equal to zero because the mean equals the median.

Positive skewness means that there’s a pileup of data to the left, leaving the tail pointing to the right side of the distribution. The tail has been pulled in the positive direction. The data is skewed to the right. In this case, the mean is to the right of the median. Interestingly, positive skews are more common than negative ones. Negative skewness means that there’s a pileup of data to the right, with a long tail to the left side. The tail has been pulled in the negative direction. In this case, the mean is to the left of the median. To remember the meaning of a positive and a negative skew, think of pulling on tails. Remember that the tail points toward the direction of the skew. The mean is also pulled in the direction of the long tail of the skew.

Kurtosis is the measure of the shape of the curve. It measures if the bell of the curve is normal, flat, or peaked. Since it’s calculation’s tedious, it is typically done by a computer. Using Fisher’s Measure of Kurtosis, a normal distribution would receive a coefficient of zero and be called mesokurtic. If the calculation of excess kurtosis results in a large positive number, the distribution is too peaked to be considered normal. This type of data is called leptokurtic. The curve is taller and skinnier than a normal distribution. The beginning of the word kind of sounds like “leapt”, so think of a skinny guy who leapt high in the air.

If the calculation of excess kurtosis results in a negative number, it is too flat to be normal. It would be called platykurtic. The curve is shorter and fatter than a normal distribution. One way to remember this is that the beginning of the word kind of sounds like a flat plateau. If a distribution is skewed, there’s no need to calculate kurtosis since the distribution is already not normal.

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APA Writing Checklist
Use this document as a checklist for each paper you will write throughout your GCU graduate program. Follow specific instructions indicated in the assignment and use this checklist to help ensure correct grammar and APA formatting. Refer to the APA resources available in the GCU Library and Student Success Center.
☐ APA paper template (located in the Student Success Center/Writing Center) is utilized for the correct format of the paper. APA style is applied, and format is correct throughout.
☐ The title page is present. APA format is applied correctly. There are no errors.
☐ The introduction is present. APA format is applied correctly. There are no errors.
☐ Topic is well defined.
☐ Strong thesis statement is included in the introduction of the paper.
☐ The thesis statement is consistently threaded throughout the paper and included in the conclusion.
☐ Paragraph development: Each paragraph has an introductory statement, two or three sentences as the body of the paragraph, and a transition sentence to facilitate the flow of information. The sections of the main body are organized to reflect the main points of the author. APA format is applied correctly. There are no errors.
☐ All sources are cited. APA style and format are correctly applied and are free from error.
☐ Sources are completely and correctly documented on a References page, as appropriate to assignment and APA style, and format is free of error.
Scholarly Resources: Scholarly resources are written with a focus on a specific subject discipline and usually written by an expert in the same subject field. Scholarly resources are written for an academic audience.
Examples of Scholarly Resources include: Academic journals, books written by experts in a field, and formally published encyclopedias and dictionaries.
Peer-Reviewed Journals: Peer-reviewed journals are evaluated prior to publication by experts in the journal’s subject discipline. This process ensures that the articles published within the journal are academically rigorous and meet the required expectations of an article in that subject discipline.
Empirical Journal Article: This type of scholarly resource is a subset of scholarly articles that reports the original finding of an observational or experimental research study. Common aspects found within an empirical article include: literature review, methodology, results, and discussion.
Adapted from “Evaluating Resources: Defining Scholarly Resources,” located in Research Guides in the GCU Library.
☐ The writer is clearly in command of standard, written, academic English. Utilize writing resources such as Grammarly, LopesWrite report, and ThinkingStorm to check your writing.