Experimental Design. 1. Randomized assignment 2. Pre-test ( O1) 3. Independent Variable ( X ) 4. Post-Test ( O2 )



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Transcription:

Experimental Design 1. Randomized assignment 2. Pre-test ( O1) 3. Independent Variable ( X ) 4. Post-Test ( O2 )

Randomized Trial Group R O1» X O2

Randomized Control Group R»O1 O2

True Experimental Designs Classical Experimental Design More Complex Versions Solomon Four Group Design Latin Squares Design Factorial Design

Pre-Experimental Designs Here something is missing from the true experimental design What is usually missing is the control group Pre-Experimental Design is used when we have time series or longitudinal data 01 02 03 X 04 05 06 01 x 02 x 03 x 04 x 05

True Experimental Design 1. Basic Research ( pure research ) 2. Explanatory 3. Cross-section of time (t1, Tu) Maturation is not considered a key variable In physical science it is assumed Time is Universal In social science it is one very specific time t1 Sometimes tn is assumed to be Tu

Pre- & Quasi- ED versus non-ed Sometimes we cannot have any kind of experimental design, not even pre- or quasiexperimental design In order to do experiments on human subjects it would often involve unethical procedures Historical social change cannot be manipulated experimentally without enormous human costs True Experimental Design is impossible in human history since there cannot be a truly randomized control group

Non-Experimental Design Research Design that is not true experimental design and/or pre- & quasi- Most social science research is not experimental ( true, pre-, or quasi-ed) Hence, we use a way of trying to simulate what an experiment might have shown The statistical techniques used for non-ed are ultimately based on assumptions from true ED The most commonly used statistic in all natural and social sciences is Pearson s r

Correlation is not Causation When we calculate a Pearson s r we obtain a co-relationship or correlation Such correlations are NEVER a substitute for theoretical understanding of causation Unless you have True ED you cannot use the data to determine causation Even with True ED (Classical ED) you must interpret the findings Interpretation of data always requires theoretical understanding of the research design

More than one IV? The Classical ED always has one and only one IV The IV (Independent Variable) is designated as X X is understood in medicine (e.g. a flu shot will improve the probability of not dying from influenza, in most cases) X is also fairly clear in agronomy and crop science X is also easy to understand in very simple examples (e.g. When King Louis XVI was taken to the guillotine- 1793-the chopping off of his head caused him to die. But, was X really a heart attack?) Crime Scene Investigation (CSI) plots often hinge on X not really being the causal factor (e.g. bullet comes from far away on a very long trajectory)

A Second IV? The IV is designated as X A second IV is often designated as Z Then we can start with A, B, C, etc. That is because Y is often the letter used for the Dependent Variable In True (Classical) ED it is X --- Y In Pre- & Quasi ED we still use X ---- Y But when there is no ED we use x y or x & z --- y, or x, y & a ---- y, etc.

History of Statistics Fechner, Gustav Theodor (1801-1887) 1860 book Elemente der Psychophysik Psycho-physics of human sensitivity to various stimuli (e.g. sound, light, heat) Weber-Fechner Law (E. H. Weber!) Fisher, Ronald Aylmer (1890-1962) 1935 Design of Experiments (Edinburgh)

Latin Square Design Suppose you want to know whether the order of X, Z and A matters You could test X first, Z second and A third But you could also test Z first and X second, with A third Latin Squares takes all logical combinations of three IVs E.g. types of training exercises (X, Z, A)

Solomon Four-Group Design Combines: Classical and Pre-ED Classical Experimental Design ie. Randomized Two Group Pre- and Post- Test, and Pre-Experimental Design i.e. Randomized Two Group Post-Test only Adding the Pre-ED helps to cover the possibility that the Pre-Test affects Y

Review: Classical ED v. Special ED The Special EDs can be either Pre-ED or Quasi-ED Special ED is also a covering label for combinations of Classical (True) ED and one or more of the others In the literature authors often use the term Quasi to cover both Pre-ED and Quasi-ED The Logic of Method (Methodology) of True, Classical ED (and extensions of it) is what many authors mean by the Scientific Method, meaning the Methodology of Positivist Science

Research Design The term Research Design is used to mean even more general considerations than Experimental Design The Methodology of any research involves some kind of Research Design which, in turn, may involve some kind of Experimental Design as well But some RDs do not involve EDs! In Social Science we often do not use any kind of ED because we often do not do experiments! However, the Methodology of ED is basic

Research Design without Experiments? Most natural science and social science in the 21 st century does NOT use ED (tt) The Scientific Method(ology) is no longer limited to ED (in all its forms) Statistical ideas are now based on more than ways to do something like a TED Yet, in the background, the logic of the TED Methodology is important for all Methodologies and Research Designs