* Good research, bad research
* Involves connecting theory and data.
* Maximising leverage by using very few variables to explain many effects. * Reports on the degree of certainty of results.
* Shows true causal relationship, not just correlation. * Provides accurate data and accounts for other variables. * Determines if the relationship is unidirectional.
* Purpose of research
* To establish a relationship between two or more variables * To demonstrate that the results are generally true in the real world and not in just a particular context. * To reveal whether one phenomenon precedes another in time, establish time order * To eliminate as many alternative explanations for the observed finding as possible * Choice of design
* What affects
* Is research exploratory, descriptive or explanatory * What are the practical limitations in investigating hypothesis. * Experimental design:
* Classical experimental design, 2 groups, pre and post test, randomisation, * Simple post test: only post test
* Repeated measure design, measure how long effect takes to start. Multiple pre and post tests. * Multigroup design, more than 2 groups, can compare different levels of experimental variable. * Randomised Field experiments, applies logic of randomisation and variable manipulation * Non-randomised quasi-experiments. Purposeful selection, target a certain group. * Non-experimental design: single group, no control over assignment and application of IV, inability to measure DV. * Case study: small N designs. Provide detailed explanation * Comparative analysis: compare two or several units in relative detail * Focus group: gather information about reaction to certain IV. * Surveys: large number of people measured to find causal relationships. * Aggregate data analysis: variables are averages or percentages of geographical areas, find causal relationships. * Longitudinal designs, time span.
* Trend analysis: measurement on same variables at different time periods to examine changes. * Panel analysis: follows a group of participants. * Intervention analysis: measurement of change in the DV is observed and taken before and after. No interaction, mere observation. * Ethnographies: form of data collection through participant observation, interviews and questionnaires. Field studies * Content analysis: textual analysis, study of recordings, written. * What they have in common
* They all share the basic objectives of research design despite having different levels of internal and external validity. Using several designs together will cover each other’s shortfall. * They all attempt to draw sound conclusions supported by observable evidence * Terms
* Causal vs spurious
* Both show correlation between IV and DV, but in spurious the change in DV because 3rd factor caused changed in both. Causal is a direct relationship. 5 different relationships. Multiple causes without chain. Multiple causes with chain. Multiple causes that affect DV, but are changed with the introduction of another variable. Spurious causality with antecedent variable. Chain causality with intervening variable. * Covariation
* Demonstrates that the IV does in fact covary with DV. Not causal relationship yet. * Time order
* Show that the IV precedes DV. Effect cannot appear before cause. * Alternative causes
* Confounding factors. Factors that possible cause a change in DV as well. * Randomised controlled experiments
* Experiments that allow the researcher to control the exposure to the IV through assignments to groups. Selection and grouping all randomised. *...
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