“Quantitative” methodologies (a) Assumptions underpinning research Associate Professor Rob Cavanagh October 22, 2008 Fraenkel, J.R. & Wallen, N.E. (2003). How to design and evaluate research in education (5th ed). New York: McGraw-Hill, Inc

ASSUMPTIONS UNDERPINNING RESEARCH METHODOLOGIES Assumptions in positivistic research Assumptions in modern scientific human research Assumptions in interpretive research

1. Hypothesising Preference for precise hypotheses stated at the outset Preference for precise hypotheses stated at the outset A model based on/comprising hypotheses is proposed for testing A model based on/comprising hypotheses is proposed for testing Preference for hypotheses that emerge as the study develops Preference for hypotheses that emerge as the study develops

2. Operational definitions Preference for precise definitions stated at the outset Preference for precise definitions stated at the outset Existing definitions are presented on the assumption they require clarification Existing definitions are presented on the assumption they require clarification Preference for definitions in context or as study progresses Preference for definitions in context or as study progresses

3. Nature of data Data reduced to numerical scores Data reduced to numerical scores Units of measurement are developed from raw data Units of measurement are developed from raw data Preference for narrative description Preference for narrative description

4. Reliability Much attention to assessing and improving reliability of scores obtained from instruments Much attention to assessing and improving reliability of scores obtained from instruments Invariant (context and person independent) measures are created Invariant (context and person independent) measures are created Preference for assuming that reliability of inferences is adequate Preference for assuming that reliability of inferences is adequate

5. Validity Assessment of validity through a variety of procedures with reliance on statistical indices Assessment of validity through a variety of procedures with reliance on statistical indices The instrument of data collection is developed/refined to elicit data that conforms to the theory – that is data-to-model fit The instrument of data collection is developed/refined to elicit data that conforms to the theory – that is data-to-model fit Assessment of validity through cross-checking of sources of information (triangulation) Assessment of validity through cross-checking of sources of information (triangulation)

6. Sampling Preference for random techniques for obtaining meaningful samples Preference for random techniques for obtaining meaningful samples The criteria for sample selection and sample characteristics are considered The criteria for sample selection and sample characteristics are considered Preference for expert informant (purposive) samples Preference for expert informant (purposive) samples

7. Explaining procedures Preference for precisely describing procedures Preference for precisely describing procedures The logic of procedures is explained both literally and mathematically The logic of procedures is explained both literally and mathematically Preference for narrative/ literary description of procedures Preference for narrative/ literary description of procedures

8. Control of variables Preference for design or statistical control of extraneous variables Preference for design or statistical control of extraneous variables Whether or not a variable is ‘extraneous’ is revealed in analysis of the data-to-model fit Whether or not a variable is ‘extraneous’ is revealed in analysis of the data-to-model fit Preference for logical analysis in controlling or accounting for extraneous variables Preference for logical analysis in controlling or accounting for extraneous variables

9. Procedural bias Preference for specific design to control for procedural bias Preference for specific design to control for procedural bias Piloting of data collection instruments and analyses can reveal procedural bias leading to design modification Piloting of data collection instruments and analyses can reveal procedural bias leading to design modification The researcher can deal with procedural bias The researcher can deal with procedural bias

10. Summarising results Preference for statistical summary of results Preference for statistical summary of results Statistical comparison of data on persons and variables Statistical comparison of data on persons and variables Preference for narrative summary of results Preference for narrative summary of results

11. Holism vs reductionism Preference for breaking down complex phenomena into specific parts for analysis Preference for breaking down complex phenomena into specific parts for analysis Complex phenomena are initially de-constructed, and later re-constructed to provide a holistic description Complex phenomena are initially de-constructed, and later re-constructed to provide a holistic description Preference for holistic description of complex phenomena Preference for holistic description of complex phenomena

12. Intervention Willingness to manipulate aspects, situations or conditions in studying complex phenomena Willingness to manipulate aspects, situations or conditions in studying complex phenomena Longitudinal, intervention or ‘one shot’ designs may be used, but in each methodology, the instrument of data collection is refined to elicit data that better explains the phenomena Longitudinal, intervention or ‘one shot’ designs may be used, but in each methodology, the instrument of data collection is refined to elicit data that better explains the phenomena Unwillingness to tamper with naturally occurring phenomena Unwillingness to tamper with naturally occurring phenomena