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Quantitative methods
Research Aptitude
Useful for: OSSC Art Education Instructor, OSSC Economics, OSSC English, OSSC Foundation Course, OSSC Geography, OSSC History, OSSC Mathematics, OSSC Odia
Overview
Quantitative research measures variables numerically and uses statistics to describe patterns and test hypotheses.
Key points
- Methods: surveys, experiments and correlational studies.
- Probability sampling (simple random, stratified, cluster, systematic) supports generalisation.
- Tools: standardised tests, questionnaires, rating scales.
- Descriptive statistics summarise data; inferential statistics test hypotheses.
- Reliability and validity of tools are essential.
- Large samples improve precision.
Explanation
- Statistical significance does not always mean practical importance.
- Effect size shows the magnitude of difference.
- Pilot testing improves tools.
- Missing data must be handled carefully.
- Quantitative results can be combined with qualitative insights.
Step by step
- Identify the concept, method or policy asked.
- Recall the definition, thinker, year or rule.
- Apply it to the situation and eliminate extreme options.
Examples
- A state-wide survey of 5,000 students’ learning levels → quantitative.
- Stratified sampling by district and gender → probability sampling.
- A t-test comparing two groups’ means → inferential statistics.
Traps and advanced points
- Trap: Generalising from a convenience sample.
- Trap: Confusing significance with importance.
- Trap: Unreliable tools.
Quick revision
- Surveys, experiments, correlational.
- Probability sampling.
- Descriptive vs inferential.
Practice MCQs
Which is probability sampling?
- snowball
- convenience
- simple random
- purposive
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Answer: (c) simple random. Also stratified, cluster, systematic.
Probability sampling supports —
- only case studies
- no analysis
- bias
- generalisation
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Answer: (d) generalisation. Representativeness.
Dividing a population into groups and sampling from each is —
- snowball sampling
- quota only
- stratified sampling
- cluster sampling
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Answer: (c) stratified sampling. Strata.
Selecting every 10th name from a list is —
- snowball
- systematic sampling
- cluster sampling
- purposive sampling
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Answer: (b) systematic sampling. Systematic.
Selecting whole schools as units is —
- cluster sampling
- stratified sampling
- snowball sampling
- quota sampling
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Answer: (a) cluster sampling. Clusters.
Inferential statistics are used to —
- collect data
- design tools
- test hypotheses
- only describe data
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Answer: (c) test hypotheses. Generalise.
Descriptive statistics —
- summarise data
- test hypotheses
- select samples
- replace data
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Answer: (a) summarise data. Mean, SD, etc.
Larger samples generally improve —
- errors
- precision
- bias
- cost savings
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Answer: (b) precision. Smaller sampling error.
A questionnaire must be —
- random
- reliable and valid
- long
- secret
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Answer: (b) reliable and valid. Tool quality.
Correlational studies show —
- cause and effect proven
- only themes
- only narratives
- relationships between variables
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Answer: (d) relationships between variables. Correlation ≠ causation.
More topics in this chapter
Notes and questions are written by Kundali IQ for practice. They are not official papers; always check the latest official notification for the syllabus and pattern.