The quantitative process
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The quantitative process



Looks at Quantative Research and Methods

Looks at Quantative Research and Methods



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  • Experimental most powerful in terms of examining causal linkages Quasi – next Most social resaerchers consider that two variables are causally related, that is one causes the other if: The cause preceeds the effect in time There is an empirical correlation between them The relationship is not found to be the result of some third variable effecting the two initally measured variables Reliability – the extent to which a measure produces consistent result – internal consistency, rides nicely with parallel test, shows in test and re-tests Can be improved by a careful selection of measures, use of objective criteria, multiple observations, large samples, pilot studies and triangulation Validity: the extent to which a meaus reflects what it is intended to measure Internal validity (various types – content is one) – improved by careful selection of measures, real-life situations, good experimental design and control of extraneous variables External validity (generalisability) improved by representative samples, replication Observational with a focus on descriping cause – weakest design

The quantitative process Presentation Transcript

  • 1. The Quantitative ResearchThe Quantitative Research Process:Process: Techniques for ResearchTechniques for Research Dr Fiona M Beals
  • 2. Lecture AimsLecture Aims Review key research methods brought to quantitative research by experimental designs Outline the role of the quantitative researcher Introduce and look at methods of: – Testing – Surveys – Observation and Interviewing
  • 3. The SettingThe Setting  Experimental research vs Quasi-Experimental Research  The need for empirical data  Sampling is key (stratified random or purposive)  Key words are reliability and validity (internal and external)  Significance is important  Eliminate bias  Remember variables
  • 4. TestingTesting
  • 5. Why Test?Why Test? Established tests tend to have measures of reliability and validity Testing before and after an intervention can show evidence of change (and the direction of change) Tests for significance can occur (ANOVA, Chi Square)
  • 6. What to testWhat to test  Psychometric variables  Biological/Physiological changes  Educational Changes (IQ etc)
  • 7. How?How? Don’t create your own test instead find established tests which have measures of reliability and validity
  • 8. Survey ResearchSurvey Research
  • 9. Survey ResearchSurvey Research  Types – Cross-sectional surveys (inc. Census) – Longitudinal surveys (trend, cohort, panel)  How/What – Text/Document Surveys (primary and secondary sources) – Questionnaires inc open/closed items, branching and clear layout
  • 10. Traps in Questionnaire DesignTraps in Questionnaire Design  Ambiguity – unclear questions  Assumptions – Multiple responses when really only one is wanted – Memory stretching – Knowledge demands  Double questions  Leading questions  Presuming questions  Hypothetical questions  Overlapping categories
  • 11. Getting it rightGetting it right  Remember most people don’t want to write or type  So quick ticks and clicks work  Follow the KISS principle  Use likerts for measuring variability in responses  Connect the question to the response  NEVER ask two questions in one!!!  Keep the survey to under seven minutes  PILOT, PILOT, PILOT
  • 12. Observation and InterviewingObservation and Interviewing
  • 13. Observation and InterviewingObservation and Interviewing Observation can have an important function in quantitative designs but tends to focus on descriptive elements Interviewing needs to be structured Both observation and interviewing should only be used for triangulation of data and results
  • 14. The role of statisticsThe role of statistics
  • 15. Know the basicsKnow the basics  Nominal – =/ ≠ – Dichotomous (Gender)/Non-dichotomous (nationality) – Mode – Qualitative  Ordinal (rank order without degree of difference) – =/ ≠, </> – Dichotomous (truth, beauty, health) – Non-dichotomous (opinion) – Median (psychological tests do tend to break this rule) – Qualitative  Interval (degrees of difference but without ratios) – =/ ≠, </>, +/- – Date, Latitude, Temperature – Arithmetic mean (average using sum – what usually happens) – Quantitative with an arbitrary point of origin (0)  Ratio – =/ ≠, </>, +/-, ×/÷ – Age, mass, length, duration, energy etc. – Geometric mean (average using product and the nth root) – Quantitative with a unique and non-arbitrary zero
  • 16. Know a little moreKnow a little more  Correct use of percentages  Data sets need to be over 30  Basic tests for significance – Chi square – T test – ANOVA  Read research critically!!! – Read for bias – Read for incorrect use of statistics – Read so you don’t make the same mistakes