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The Research Context • Research is not done in a vacuum – Build on prior findings – Build on and contribute to theory • Good design is crucial – Statistics cannot overcome flawed design • Ethical issues – – – – Treatment of participants Treatment of data Treatment of manuscript – as author or reviewer Comply with laws Ethical Treatment of Participants • Institutional Review Board (IRB) established and operated as matter of Federal law – http://www.umresearch.umd.edu/IRB/ • Research ethics became a focus after WWII – National Research Act became law July 12, 1974 – Belmont Report issued some 4 years later • Became the basis for codifying ethical treatment of human participants – APA adopted a code of research ethics in 1982 and has amended it on occasion since then 1 • Belmont Report – Respect for Persons • people are autonomous agents and those with diminished autonomy require extra protection – Beneficence • Maximize benefit and minimize harm to individuals – Justice • Except when it can be specifically justified, research participation and benefits should be fairly distributed • Operationalizing these principles – – – – – Obtain informed consent Assess risks/benefits Select participants fairly Freedom to discontinue Assure privacy and confidentiality Types of Research • Experimental – Involves random assignment of individuals to conditions (or to combinations of conditions) – For example, • Two group design: experimental vs. control • Three group: group 1 vs. group 2 vs. group 3 • Factorial: Cross 3 levels of A with 3 levels of B A1 A2 A3 B1 B2 B3 – Absolutely necessary for inferring cause-effect 2 – – – – Naturalistic observation Y Group comparisons Correlational studies Combinations of the above 1 2 3 4 5 6 • Observational 1 2 3 4 5 6 7 8 9 10 X • Does not involve random assignment • Done outside or inside the lab • Can never support cause-effect inferences Types of Variables • Experimental research – Independent – manipulated by experimenter – Dependent – variable(s) that is (are) measured or counted • Observational research – Sometimes terms above are used – Sometimes predictor and outcome (or criterion) – Sometimes just X and Y ************************************* • Quantitative vs. qualitative • Measured vs. counted • Discrete vs. continuous 3 Additional Terms • Population – The complete set of scores of interest • Can be finite or infinite • Numerical characteristics are called parameters • Sample – A subset of the population • Is finite • Numerical characteristics are called statistics • Descriptive statistics – Characterize properties of a sample • Inferential statistics – Draw probabilistic inferences about populations Sample Description to Population Inference • If every element in the population is observed, no inferences are required – Data can simply be summarized and displayed • Otherwise – Gather, display, and summarize data with respect to a randomly selected sample – Assume a probability model of the population – Use this model along with the sample statistics to draw probabilistic conclusions about the population 4 Sampling • Simple random sampling is the ideal for most (but not all) research – Each observation (or set of N observations) is equally likely to be drawn – This is virtually impossible in practice • Depends on definition of population • May be prohibitively expensive in time or money • Some types of sampling schemes – Convenience – Stratified (sometimes used to intentionally oversample specific groups) Within-Subject, Between-Subject, and Mixed Designs • Within-subject – Also called repeated measures – Each participant receives each treatment – Advantages and disadvantages • Each participant is his/her own control • Observations are not independent (often control order) • Between-subject – Participants randomly assigned to conditions – Advantages and disadvantages • Often less sensitive than within-S design • Independence of observations is assured • Mixed designs (e.g., time series) 5 Measurement Scales • Measurement is the assignment of numbers to objects such that certain numerical relations represent certain empirical relations within the construct being scaled. – An important often misunderstood issue • The type of scale is determined by the specific empirical relations that are represented – Affects the meaningful statements that can be made about the underlying construct • Consider temperature to measure heat or weight to measure mass – Our interested is in constructs, not measurements • Nominal scales – Classify observations into mutually exclusive and exhaustive categories (e.g., male/female) – Any 1:1 transformation is allowable • Ordinal scales – Rank order observations (e.g., scratch scale of hardness; degree of learning by number of errors) – Any ordinal transformation is allowable 6 • Interval scales – Information about ratios of differences is meaningful (rare to have this in psychology – IQ is not an interval-scale measure of intelligence) – Linear transforms, y’=a+by (a any constant, b>0), are allowable • Ratio scales – 0 truly means the absence of the variable – Information about ratios is meaningful (even more rare in psychology – time is not a ratio-scale measure of learning) – Ratio transforms, y’=by (b>0), are allowable 7