HMPYC 80:
Research
Methodology
Section C
Chapter 13 –
Quantitative Data Analysis &
HMPYC 8
Interpretation
, ➢ 1. Introduction:
• Before getting to data analysis, you will have performed a literature review, decided within theoretical appro
one or more research questions and design the proper method for collecting the data.
- You will also have chosen an appropriate research design and the sampling approach (probability or non-pr
and have gained access to the population that you wish to sample.
- You may have decided to collect (primary) data or use existing data – i.e., data collected by someone else.
• The ethical principles of voluntary and informed participation, confidentiality, anonymity, non-harm and pos
feedback of important research findings to the sample groups afterwards will also have been negotiated.
• Ideally, a data analysis plan should be constructed before doing the actual data collection and analysis.
• Without determining very early in the design process the depth and types of analysis that are to be done, yo
the wrong questions, or asking them in a way that produces the wrong kinds of answers, or expecting data to
results that cannot logically be produced.
- Therefore, data analysis procedures are mostly determined by the chosen research design and the nature o
that were collected.
• Quantitative data analysis comprises those techniques by which you convert data to a numerical form and su
statistical analysis.
• The purpose of analysis is thus to reduce data to an intelligible and interpretable form so that the relationshi
research problems can be studied and tested, and conclusions can be drawn.
, ➢ 1. Introduction:
• Monette et al, view statistical analysis as procedures for assembling, classifying, tabulating and summarizing
data to obtain meaning or information.
• Data analysis, in the quantitative paradigm , does not in itself provide the answers to research questions.
- Answers are found by way of interpreting the results of statistical analysis.
• If the sample is relatively small, some statistical analysis can be performed manually with calculators, or with
spreadsheet application.
- However, data or mostly analyzed by computer using statistical software applications, such as R or IBM SPSS
➢2. Fundamentals of Measurement & Analysis:
➢2.1.Measurement Levels:
• Strict rules apply when choosing an appropriate analysis technique; This choice depends on the measurem
the data that were collected.
- Deer are variables that the note categories, while other variables give measurements for counts.
, ➢ 2.1.Measurement Levels:
• Levels of Measurement:
Level: Description: Examples:
Nominal Classify into categories. Sex, gender, race , province, marit
status, religion.
Ordinal Order by rank or magnitude. Employment status, disability stat
agree/disagree scales.
Interval Distance between values is Aptitude tests, intelligence tests,
meaningful, but without an absolute personality tests , knowledge tests
zero.
Ratio Distance between values is Age, height, weight, distance ,
meaningful, and there is an absolute number of dependents, annual
zero point. income.
