Data coding, descriptive statistics, and data visualization

Question 1

What is the primary purpose of data coding in research?

Answer 1

Data coding standardizes data to enable objective comparisons between units of analysis, facilitating analysis and interpretation.

Question 2

Differentiate between interval and ordinal variables. Provide an example of each.

Answer 2

• Interval variables have meaningful and equal differences between values but lack a true zero point (e.g., temperature).
• Ordinal variables have ordered categories, but the differences lack meaning (e.g., education level).

Question 3

Explain the concept of data cleaning and why it’s important in data analysis.

Answer 3

Data cleaning involves detecting and correcting errors, inconsistencies, and irrelevant data. This ensures data quality, improves accuracy in analysis, and prevents misleading results.

Question 4

When constructing a frequency distribution table, what key information should be included?

Answer 4

A frequency distribution table should include :

• values of the variable,
• their corresponding frequencies (counts),
• and often percentages for clarity.

Question 5

Explain the difference between a bar chart and a histogram.

Answer 5

• bar chart = categorical data,
• histogram = the distribution of continuous numerical data grouped into intervals.

Question 6

What is the main purpose of a contingency table in analyzing data?

Answer 6

A contingency table examines the relationship between two or more categorical variables, revealing patterns and associations.

Question 7

Define an independent variable and a dependent variable in the context of research.

Answer 7

The independent variable is manipulated or changed to observe its effect on the dependent variable, which is the outcome measured in response.

Question 8

What are the three common measures of central tendency? When might you choose one over the others?

Answer 8

Mean, median, and mode measure central tendency.

• mean = used for symmetrical data,
• median = for skewed data,
• mode = for categorical or nominal data.

Question 9

What does standard deviation measure? How is it useful in interpreting data?

Answer 9

Standard deviation quantifies data spread around the mean, indicating data variability. A larger standard deviation implies greater dispersion.

Question 10

Why are summary statistics a crucial first step in data analysis?

Answer 10

Summary statistics offer a quick overview of data characteristics, highlight trends, reveal potential relationships, and inform subsequent analysis.