© 2001 Dr. Laura Snodgrass, Ph.D.1 Conducting Experiments Choosing methods Sampling and sample size Independent variables Dependent variables Controls Debugging

© 2001 Dr. Laura Snodgrass, Ph.D.2 Choosing Methods Laboratory experiments can be artificial –too much control Field experiment –more natural setting –lose some control Ethical and practical concerns Participant variables –quasi-experimental designs

© 2001 Dr. Laura Snodgrass, Ph.D.3 Choosing Description and prediction can be done without casual concerns Human complexity –number of interacting causal variables Neglect of individual differences –averaged across groups Social responsibility –objectivity –values –Gergen’s paradigm II

© 2001 Dr. Laura Snodgrass, Ph.D.4 Sampling Generalization requires adequate sampling Populations –you define population “of interest” Why sample –cost-benefit analysis - law of diminishing returns –destruction of items tested –infinite populations –may increase accuracy

© 2001 Dr. Laura Snodgrass, Ph.D.5 Participant Sampling Psychology as the study of white rats and college sophomores Generalization from –different species –different groups students have different pressures and performance anxiety volunteers differ from non-volunteers

© 2001 Dr. Laura Snodgrass, Ph.D.6 Sampling Techniques Systematic random sampling Stratified random sampling Cluster sampling Haphazard or convenience sampling Quota sampling

© 2001 Dr. Laura Snodgrass, Ph.D.7 Other Types of Samples Experimenters as samples –gender, age, ethnicity, behavior, dress Stimulus sampling –representative of pop of stimuli –random or controlled Condition sampling Response sampling –number of dependent measures –number of trials

© 2001 Dr. Laura Snodgrass, Ph.D.8 Sample Size Tradition –look in journals Expected variability in results –consistency within and between participants Planned statistical analysis –parametric versus nonparametric –significance level –size of difference between means expected –do a power analysis

© 2001 Dr. Laura Snodgrass, Ph.D.9 Independent Variables Setting the stage –informed consent –brief explanation of what is expected Types of manipulations –straightforward –staged to create a psychological state to simulate a real world situation use confederates

© 2001 Dr. Laura Snodgrass, Ph.D.10 Independent Variables Strength of manipulation - choosing levels –number of levels –range –how close together are the levels Combining variables –incomplete or unbalanced designs (leaving out some cells) redundant or illogical data from literature too many cells to fill

© 2001 Dr. Laura Snodgrass, Ph.D.11 Independent Variables Confounding –environmental confounds the “Hawthorne Effect” –participant confounds equality of groups Cost of manipulation

© 2001 Dr. Laura Snodgrass, Ph.D.12 Dependent Measures Types of measures –self-report rating scales –behavioral reaction time error rate –physiological GSR, heart rate

© 2001 Dr. Laura Snodgrass, Ph.D.13 Dependent measures Sensitivity –ceiling effect - too easy –floor effect - too hard –no effect Multiple Measures –e.g. time perception Ethics of measures (e.g. privacy) Cost

© 2001 Dr. Laura Snodgrass, Ph.D.14 Other Controls Participant Effects –loss of subjects –volunteers –social desirability –demand characteristics deception filler items placebo groups

© 2001 Dr. Laura Snodgrass, Ph.D.15 Controls Experimenter effects –experimenter bias or expectancy effects subtle coaching recording errors teacher expectancy Solutions –training –run conditions simultaneously –single-blind –double-blind

© 2001 Dr. Laura Snodgrass, Ph.D.16 Debugging Research proposals –getting feedback from others Pilot studies Manipulations checks –especially in pilot study –can explain non-significant results

© 2001 Dr. Laura Snodgrass, Ph.D.17 Data Defects Missing data –some statistics allow for missing daat –can replace with averaging techniques –SPSS have several missing data options Extreme score or outliers –techniques for discarding –replace with averaging Appropriate Statistics!!!!!!!!!!!!!!!!!