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Y603
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Lecture
#2
1. Review of Experimental
Design --What is it? What are the activities (facets)
involved?
2. An overview of experimental
designs (Chapter 2 in Kirk)
Modern inferential
statistical procedures, especially ANOVA, are largely
due to the contributions of R. A. Fisher, J. Neyman,
and E.S. Pearson who laid the groundwork in the early
20th century. These statisticians promoted the ideas
of (a) randomization, (b) general linear model
underlying the data, and (c) hypothesis-testing plus
interval estimation as two primary approaches to
classical statistical inference-making.
Three basic experimental
designs or building block designs:
completely randomized design (or CR-p design),
randomized block design (or RB-p design), and
Latin-square design (or LS-p design).
The completely randomized
design is illustrated on page 31 in Figure 2.2-2. The
randomized block design is illustrated on pages 34 and
35. The Latin-square design is illustrated on page 37.
Other designs are derivatives of these three basic
designs.
Factorial designs are designs
in which there are two or more main effects (or
independent variables or factors). Consequently it is
possible to study the "interaction" between these main
effects. These factorial designs are also referred to
as two-way or three-way,..., multi-way
designs.
All designs share these
common features:
(1) All designs are
constructed from three basic designs;
(2) There are only four kinds of variations in
ANOVA: total variation, between-group variation,
within-group variation, and interaction
variation.
(3) All error terms, i.e., the denominator of the F
statistic, are formed from either the error
variation or the interaction variation.
(4) The numerator of an F statistic should always
estimate one more source of variation than the
denominator, and that source of variation should be
the one that is under testing by H0
.
Other terms:
Completely
crossed -- A design which includes each and
every combination of all the levels of A and B
factors.
Nested
design-- A design which cannot employ all
possible combinations but have levels of one factor
nested within selected levels of another factor,
such as classrooms are nested within school
buildings.
Balanced
designs -- Designs in which equal number
(or proportional number) of subjects were assigned
to each treatment condition.
3. Research Strategies and the
Control of Nuisance Variables (Chapter 1 in
Kirk)
Acceptable Research
Hypotheses in experimental and quasi-experimental
research --
Questions that are in the
form of "if A then B".
Scientific principles in
inquiry: (a) the phenomenon must be quantifiable, (b)
the phenomenon to be studied must be passive, (c)
science cannot study things from the past, (d)
scientific experiments must be repeatable, (e) the
observer or the scientist must be at least as
intelligent as the objects of his/her scientific
inquiry, (f) scientific results are neutral but the
application of the result is not, and (g) science
cannot solve moral problems.
To test a research
hypothesis, several forms of inquiry are possible (1)
experiment, (2)quasi-experiments, (3) surveys, (4)
case studies, and (5) naturalistic observations plus
others (read pages 9-16).
The purposes of these forms
of inquiry are to explore, describe or classify,
establish relationships, and to establish
causality.
Four types of threats to
valid conclusion based on experimental and
quasi-experimental data:
(A) statistical
conclusion validity (page 17)
(B) internal validity --
the link between the independent variable and the
dependent variable
(read pages
18-19).
(C) external validity --
generalizability (page 19)
(D) construct validity of
causes and effects -- operational definitions of
constructs
Other threats are listed on
pages 19-21.
Controlling Nuisance
variables and minimizing threats (pages
22-24).
4. Assignments:
(1) Review Chapters 1
and 2 in Kirk.
(2) Do Questions 1,2,3,4, 8,
and 13 in Chapter 1 of Kirk.
Do Questions 1, 2, 4, 5, 6,
7, 9, 10, 12, 13, and 19 in Chapter 2 of
Kirk.
(3) Preview Section 3.1 in
Chapter 3 of Kirk.
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