DNP

Module 2: 
Assignment 1

Directions:

Using the information from the two studies referenced in this week’s lecture:

1. Identify the variables being measured in each survey and explain which measure of central tendency would be used to evaluate them.

2. Write the results for study I and extrapolate to the COVID19 vaccination information for the state of Florida.

Submission Instructions:

· Submit your assignment by 11:59 pm on Sunday.

· It should include at least 2 academic sources, formatted and cited in APA.

Module 2: 
Displaying data

Data visualizations are the culmination of all data supposed to take long numeric lists and present them in an intuitive, easy-to-understand way. Different data types can be collected depending on the study type and the number of variables (Scott & Mazhindu, 2014). The two most common forms of data display are graphs and tables. Both aim to summarize and present the data in a manner that is easy to absorb and understand.

Tables are visual displays composed of columns and rows in which numbers, text, or a combination thereof are presented.

Parts of a table include:

·   Title number and title

·     Divider rules

·     Spanner heads

·     Stub heads

·     Column heads

·     Row titles

·     Cells

·     Footnotes

Frequency distributions of numerical variables can be displayed in a table, a histogram chart, or a frequency polygon chart. For discrete variables, it is possible to present the number of observations according to the different values found in the study (Duquia et al., 2014).

Frequency table: it is a table with two columns. One column lists the categories and another for the frequencies with which the items in the categories occur (number of items (count) fit into each category).

Below are two examples with information from Table 2 from this week’s lecture, presented to summarize the data with descriptive statistics. In Table 3, we have a representation of the variable (score) in the row and the descriptive statistics that explain the different measures of central tendency for that variable: the mean, median, and mode, along with the standard deviation.

Table 3: Descriptive statistics with selected information from Table 2.

Table 4 also shows the variable in the first column and the different summarized information in terms of frequency and percentage for each value of the variable depression grade.

Table 4: Score classification frequency and percentage.

We could also represent Table 4 information with a graph.

A graph is another way to represent data. Graphs are used to display data because it is easier to see trends in the data when shown visually compared to when displayed numerically in a table. Complex data can often be displayed and interpreted more easily in a graph format than in a data table (Bartee et al., 2017).

In a graph, the X-axis runs horizontally (side to side), and the Y-axis runs vertically (up and down). Typically, the independent variable will be shown on the X-axis, and the dependent variable will be shown on the Y-axis (Bartee et al., 2017).

There are a variety of graph types. The main types you will see are frequency charts, histograms, bar charts, pie charts, scatter graphs, and line graphs.

Bar charts: A bar graph is a graph that displays a bar for each category, with the length of each bar indicating the frequency of that category.

A pie chart is a circle with wedges cut of varying sizes marked out like slices of pie or pizza. The wedges’ relative sizes correspond to the categories’ relative frequencies.

How do You Choose an Appropriate Way of Displaying Data?

To choose an appropriate graph, the first thing is to consider the objective of the presentation of data (Infogram, 2019):

Comparison: Use comparison charts to compare one or more datasets. They can compare items or show differences over time.

Relationship:  These types of charts are used to show a connection or correlation between two or more variables.

Composition: For this kind of objective, the charts display parts of a whole that change over time.

Distribution: The charts are used to show how variables are distributed over time, helping identify outliers and trends.

Pyramids and pie charts display parts of a whole. Line, bar, and column charts represent change over time. While scatter plots and tree maps are helpful if you have a lot of data to visualize (Infogram, 2019).

Lies, Damned Lies, and Statistics

1. What are the ways statistics can be used to mislead?

2. How to avoid being misled by poor statistics. 

Statistics have a lot of power. So much so that people and organizations based some of their most important decision on statistics. Statistics are extensively used in studies, organizations, media, and politics. People say numbers do not lie; that might be true. However, sometimes, statistics can be misleading, and the same data can show the opposite trend depending on how it is used. Most of the time, statistics are presented without the background information necessary for their correct interpretation. Knowing the different types of ways statistics can be misused is vital so that you can identify them and not make decisions based on biased or incorrect data (Wendy, 2021).

· Statistics mislead

· Statistics misrepresent research findings

Statistics can generate misleading results for several reasons, such as poor research design, inappropriate sample size, or erroneous choice of statistical test, for example, can all generate misleading results. A group of issues should be known to assure a good interpretation of statistical data to avoid misleading statistical data. According to Scott & Mazhindu (2014), they are:

·
Measures of Central Tendency
To decide which measure of central tendency to use is linked to the type of data being collected and analyzed when a measure of central tendency is presented, it is also normal to include a measure of the variation. If an inappropriate measure of central tendency is used to describe a particular sample of data, the readers can be misled is if.

·
“Meaningless” Mean
An average (the mean, median, or mode of a set of numbers) is useful as a single-parameter characterization of a more or less quantitative variable type. For bi-modal distributions, an average has little descriptive value and obscures the number set’s nature. For example, there would not be much value in calculating the average age of a family consisting of two parents and several children. Let us say the parents are aged 33 and 35, and the children are ages 1,3, 5, and 7. The average (mean) age in the family is 14, suggesting that the “average” family member is a teenager (Deriel, 2021).

·
Percentage and Proportion
Percentages are used to express proportions. This is especially important concerning sample size; that is why you should be wary when a percentage is quoted without an indication of the sample size.

·
Absolute and Relative Risk
These two statistics can be used to report risk, but they can have very different values even when the data they are based on is the same. Absolute refers to the actual probability that an event will occur. Relative risk, on the other hand, is a comparison of the risk experienced by one group compared to another.

·
Percentages, Proportions, Probability, and Ratios
Percentages, proportions, and ratios are related mathematical terms. However, the interesting question is whether or not they instinctively similarly interpreted each of these values. Proportion refers to the number of items in a sub-category about the total number of items in the main category, and percentages are used to express proportions.

·
Error of Measurement
Other issues related to statistics’ misleading uses are extrapolation beyond the reasonable and extreme, sampling, and data display. When there are two groups to compare, the difference between the two groups is potentially small. You need to ensure that your measurement tool is accurate and that the error generated by the measuring tool does not swamp the difference between the two groups.

How to Avoid Being Misled by Poor Statistics

The following elements, according to (Spire et al., 1998 & Weatherburn, 2011), should be reviewed with care in the case of poor statistics:

· The source is a subject matter expert, not a statistics expert.

· The source is a statistician, not a subject matter expert.

· The subject being studied is not well defined.

· Data quality is poor.

· The mainstream media has limited expertise and mixed motives.

References

Bhandari, P. (2020). 
Descriptive statistics, definitions, types, examples. Scribbr. 
to an external site.

CDC. (2022). 
Prevaccination checklist for COVID-19 vaccines for vaccine recipients.  
to an external site.

Glen., S. (2021). 
Odds ratio calculation and interpretation. Statistics how to. 
to an external site.

Mcleod, S. (2018). 
Questionnaire: definition, examples, design, and types. Simplypsychology.org; Simply Psychology. 
to an external site.

Scott, I., & Mazhindu, D. (2014). 
Statistics for healthcare professionals: an introduction. Sage.

Spirer, H., Spirer, L., & Jaffe, A. J. (1998). 
Misused statistics. (2nd ed.). 
CRC Press.

Sun, Y., Fu, Z., Bo, Q., Mao, Z., Ma, X., & Wang, C. (2020). The reliability and validity of PHQ-9 in patients with major depressive disorder in a psychiatric hospital. 
BMC Psychiatry, 
20(1). 
to an external site.

Weatherburn, D. (2011). Uses and abuses of crime statistics. 
Crime and justice bulletin: Contemporary issues in crime and justice, NSW bureau of crime statistics and research, 
153, 1 – 16.