Watch Types of Data: Nominal, Ordinal, Interval/Ratio – Statistics Help (6:19), then answer the following discussion questions.

https://www.youtube.com/watch?v=hZxnzfnt5v8

Types of Data: Nominal Ordinal Interval/Ratio
Data is central to statistical analysis. When we wish to find out more about a phenomenon or process we collect data. Usually, we collect several measures on each person or thing of interest. Each thing we collect data about is called an observation. If we are interested in how people respond, then each observation will be a person. OR an observation could be a business or a product, or a period in time, such as a week. Variables record the measurements we are interested in. Age, sex, and chocolate preference can all be stored as variables. For each observation, we record a score or value for each of the variables. When we store this data in a spreadsheet or database, each row corresponds to a single observation and each column is a variable.

Level of Measurement
The level of measurement used for a variable determines which summary statistics, graphs, and analyses are possible and sensible. The Nominal level is the most basic level of measurement.
Nominal is also known as categorical or qualitative. Examples of nominal variables are sex, preferred type of chocolate, and color. These are descriptions or labels with no sense of order.

Nominal values can be stored as a word or text or given a numerical code. However, the numbers do not imply order. To summarise nominal data we use a frequency or percentage. You can not calculate a mean or average value for nominal data.

The next level of measurement is Ordinal.
Examples of ordinal variables are rank, satisfaction, and fanciness! Ordinal variables have a meaningful order, but the intervals between the values in the scale may not be equal. For example, the gap between first and second runners in a race may be small, whereas there is a bigger gap between second and third. Similarly, there may be a big difference between satisfied and unsatisfied, but a smaller difference between unsatisfied and very unsatisfied.

Like Nominal data, ordinal data can be given as frequencies. Some people state that you should never calculate a mean or average for ordinal data. However, it is quite common practice, particularly in research regarding people’s behavior to find mean values for ordinal data. You should be careful if you do this to think about what it means and if it is justifiable.

The most precise level of measurement is interval/ratio.
This label includes things that can be measured rather than classified or ordered, such as number of customer’s weight, age and size. Interval ratio data is also known as scale, quantitative or parametric. Interval/Ratio data can be discrete, with whole numbers or continuous, with fractional numbers. Interval/Ratio data is very mathematically versatile. The most common summary measures are the mean, the median, and the standard deviation. The way data should be represented in a graph or chart depends on the level of measurement.

Nominal data can be displayed as a pie chart, column or bar chart or stacked column or bar chart. In most cases, the best choice for a single set of nominal data is a column chart. Ordinal data must not be represented as a pie chart, but is best shown as a column or bar chart. Interval/ratio data is best represented as a bar chart or a histogram. For these, the data is grouped. Box plots illustrate the summary statistics for a variable in a neat way. Data that occurs over time is best displayed as a line chart.

Here is an example using different types of data:
Helen sells choconutties. Helen is interested in developing a new product to add to her line of choconutties. She develops a questionnaire and asks a random sample of 50 of her customers to fill it out. She asks them their age and sex, how much they spend on groceries each week, how many chocolate bars they buy in a week, and which they like best out of dark, milk, and white chocolate. She asks them how satisfied they are with choconutties: very satisfied, satisfied, not satisfied, very unsatisfied. And she asks them how likely they are to buy a whole box of 10 packets of choconutties.

Helen enters the data in a spreadsheet. Each row has responses from one customer. Each column contains the measurements or scores for one variable. The type of chocolate preferred is nominal data. This can be shown in a pie chart or bar chart.

We can summarise by saying that 46% of customers prefer Dark chocolate, 40% prefer milk chocolate, and 14% prefer white chocolate. The measures of satisfaction and likelihood are ordinal level data. These should not be shown in a pie chart. The values should be put in a logical order in a column chart. We could say that 32% are very satisfied with choconutties and 72% of people are satisfied or very satisfied and 72% of people are satisfied or very satisfied.
The average satisfaction score comes to 2.06, which could be interpreted as satisfied. However, it is debatable whether it is sensible to calculate a mean satisfaction score. Age, the amount spent on groceries, and the number of chocolate bars are all interval/ratio data. These can be displayed on bar charts or histograms. We can say that for the customers in the sample, t

Protection of Human Participants in Epidemiological

Ethical issues must be considered when carrying out research that involves human subjects. There are several ways in which human participants must be protected to ensure they are free from risks and have a possibility of benefiting from the study. Ethical standards have been set, which researchers are expected to meet.

Describe the various ways in which epidemiology research participants must be protected

Understanding social factors and their impact on curriculum development is critical for a curriculum developer. Critically analyze three social factors that can influence the curriculum and discuss how you will address them when developing your curriculum

Summarize Hemophilia A and Cystic fibrosis in terms of etiology, pathogenesis, clinical picture, diagnosis, and treatment rationale.

 

The 5 General Topics of Disease Summary

1. Etiology & risk factors

2. Pathogenesis: The molecular mechanism of the disease process (How the disease process evolves)

3. Clinical Picture: Signs& Symptoms, Sequelae, and Complications

4. Diagnosis: Labs & tests (as well as screening tests and follow-up tests whenever applicable)

5. Pathophysiologic rationale of treatment

The summary should cover the following 3 genetic diseases:

• Hemophilia A is an example of X-linked recessive genetic disease
• Huntington’s Disease as an example of autosomal dominant genetic disease
• Cystic fibrosis as an example of autosomal recessive genetic disease

 

• For each disease you need to cover topics through 5. In the above-mentioned table.
• Etiology: talk about the gene responsible for the disease (which chromosome, the exact location of the gene on the chromosome, and the type of mutation)
• Pathogenesis: talk about the mechanism by which the mutant abnormal gene leads to the development of the clinical picture of each disease
• Clinical Picture: S&S, sequalae and complications of each disease
• Diagnosis: Tests done to diagnose each disease
• Pathophysiologic rationale of treatment: e.g we use recombinant factor VIII for treating patients with Hemophilia A since they lack that clotting factor because of a genetic mutation……and so on.

 

• For each disease talk about the Mode of inheritance and recurrence risk: e. what’s the possibility of having normal kids/diseased kids/carrier kids…..etc

 

This is the textbook for information:

McCance, K.L., Huether, S. E. (2018) Pathophysiology: The Biological Basis for Disease in Adults and Children. (8th Ed) St. Louis, MO. Elsevier Mosby ISBN-13: 978-0323583473 ISBN-10: 9780323583473