Research Methods
What is the difference between qualitative and quantitative research methods?
Qualitative research focuses on understanding why people behave a certain way, while quantitative research focuses on measuring and generalizing data.
Name and define the four types of data.
The four types of data are nominal (categories with no inherent order), ordinal (categories with a specific order), interval (numerical data with equal intervals), and ratio (numerical data with a true zero point)
What is an advantage and disadvantage of surveys as a data collection method?
Surveys gather a lot of data quickly but might not go deep or be completely honest.
Define descriptive statistics and give an example.
Descriptive stats summarize data like averages and trends.
Why is data cleaning essential before analysis?
Cleaning data is important to remove mistakes that could lead to wrong conclusions.
Explain the purpose of a case study in research methodology.
A case study is used to deeply study a single person, group, or event to find out why things happen.
Differentiate between primary and secondary data.
Primary data is collected by the researcher, while secondary data is data collected by others.
Explain the concept of sampling bias.
Sampling bias happens when the group chosen for a study doesn't represent the larger group accurately.
What is the purpose of inferential statistics?
Inferential stats make predictions about a larger group based on a smaller sample.
Explain the term "outliers" in data cleaning.
Outliers are unusual data points that can affect the results if not handled carefully.
What is the significance of random sampling in research studies?
Random sampling helps make sure the people chosen for a study represent a larger group, making the results more reliable.
Provide an example of categorical data and numerical data.
Categorical data groups things like colors, while numerical data counts things like ages.
Describe observational data collection methods.
Observational data collection is watching and recording behaviors or events in their natural setting.
Explain the significance of correlation analysis in data interpretation.
Correlation analysis shows how two things are related and how strong that relationship is.
Describe the process of handling missing data in a dataset.
Handling missing data includes estimating values or removing incomplete cases.
Define triangulation in research and its importance in data analysis.
Triangulation means using different methods to check if findings are true, making research results more trustworthy.
What is the difference between discrete and continuous data?
Discrete data is specific values, while continuous data can be any value within a range.
Discuss the importance of validity and reliability in data collection.
Validity ensures measurements are correct, and reliability means getting similar results each time.
Discuss the difference between parametric and non-parametric tests.
Parametric tests assume data follows a pattern, while non-parametric tests are more flexible.
What are some common data quality issues encountered during the data cleaning process?
Common data issues are duplicates, format inconsistencies, and errors during cleaning.
Discuss the strengths and weaknesses of experimental research designs.
Experimental research shows cause and effect relationships but may not apply to everyone and can be influenced by the researcher.
How is ordinal data different from nominal data?
Ordinal data has a specific order, while nominal data is just categories without order.
How can interviews be structured to gather reliable data?
Structured interviews use the same questions for all to collect consistent data and reduce bias.
How does regression analysis help in understanding relationships between variables?
Regression analysis helps see how variables are connected and how strong that connection is.
How can data normalization improve the quality of a dataset?
Normalizing data puts it on the same scale to make comparisons fair and improve data quality.