group_designs

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Group Quantitative Designs

First, let us consider how one chooses a
design. There is no easy formula for choice
of design. The choice of a design should be
based on overall considerations of the study,
that is, the theoretical framework, the
problem, the hypotheses, the treatments,
measures, settings, costs, feasibility, and
time, to name a few.
Correlational research

Assesses strength of a relationship
between two or more variables.


Cannot imply causality
However it can:
Help with prediction of future events
 Provide data that is consistent or inconsistent
with a particular scientific theory.
 Correlational research can’t prove a theory, but
it can disprove/negate a theory.

Differential Research Methods


Differential research compares 2 or more groups that
are differentiated by some preexisting variable. No
manipulation – only measurement.
Group differences existed before the study was
conducted.



IV: Classification
DV: Behavior measured
Differential design is not the same as experimental:



Differential design groups individuals by pre-existing
conditions (e.g., race or gender).
Experimental design groups are determined by random
assignment.
Causality cannot be inferred from a differential design
Cross-sectional vs.
Longitudinal

Cross-sectional

Eg: groups of individuals at different ages are
examined on a particular variable


Cohort Effect (e.g. living through Great Depression)
Longitudinal

Follow same people over time to observe
developmental changes (controls for cohort
effects)

Artifact: an apparent effect of an independent variable
that is actually the result of something else – thus a
confound.
Correlation Analysis

Pearson product-movement correlation


Spearman rank-order correlation


Used if both variables are at least on an interval
scale
Used if one variable is ordinal and the other is at
least ordinal.
Range is from –1.00 (perfect negative
relationship) to +1.00 (perfect positive
relationship). Correlation of .00 means no
relationship whatsoever.
Interpretation

Need to know whether the correlation is
significant:

p-values


If the correlation is low (close to zero) then it is likely
that you will not have a significant correlation.
Coefficient of determination:

Computed by squaring the correlation



Eg: if r = .50 then r2 = .25.
So a correlation of .50 indicates that 25% of the
variability of the first variable can be accounted for (or
explained by, predicted by) by knowing the scores on the
second variable.
Sample size must be large enough for this to be
meaningful.
Differential Analyses

Type of statistical test used depends on # of
groups and the scale of measurement



If DV is at least interval and there are 2 groups a
t-test for independent groups is usually used.
If more than 2 groups…ANOVA is generally used.
If the data are ordinal or nominal: nonparametrics are used (such as Mann-Whitney U
test for ordinal and chi-square for nominal)
Validity

Validity of procedures & conclusions


“appropriateness or soundness”
Validity problems can occur at any level of
research.


Researchers must anticipate these threats to
validity
AS WELL AS
Create procedures to eliminate or reduce them
Types of Validity

Statistical Validity


Construct Validity


Degree to which the theory or theories behind research
provide best explanation for results observed.
External Validity


Accuracy of p-value
Generalizability to other people, places or conditions.
Internal Validity

How confident we are that the observed changes in the DV
were due to the effects of the IV and not extraneous
variables.
Confounding Variables









Maturation
History
Testing
Instrumentation
Regression to the Mean
Selection
Attrition
Diffusion of Treatment
Sequence effects
Pre-Experimental Designs

One-Shot Case Study (barely research)
X
O

One Group Pretest-Posttest Design
O1
X
O2

Intact (static) Group Comparison
X
O1
--------------
O2
Experimental Designs

Posttest-Only Control Group Design
R
X
O1
R
O2

Pretest-Posttest Control Group Design
R
O1
X
O2
R
O3
O4
Solomon Four-Group Design
Tx
Control
Pretest
X
X
Intervention
X
0
Posttest
X
X
Tx
Control
0
0
X
0
X
X
This design is used to control for the effects of the
pretest on the intervention and postteest.
Quasi-Experimental Designs

Nonequivalent Control Group Design
O1
X
O2
--------------------------
O3

O4
Separate Sample Pretest-Posttest Designs
O1
X
O2
-------------------------------O3
O4
X
O5
Quasi-Experimental Designs

Interrupted Time-Series Design
O1 O2 O3 O 4
X
O5 O6 O7 O8

Recurrent Institutional Cycle Design
(institutional cohort design)
X
O1
-----------------------O2
X
O3
Ex Post Facto Designs


One-Shot Case Study
One-Group Pretest-Posttest Design

Co-Relational Study
O1
O2

Static Group Design
X
O
--------O
Recap

What is main purpose of Correlational
research?

What information can be obtained from
correlational research?

Can correlational research determine
causality?

How can correlational research help to
validate or invalidate a theory?
Recap

What is the main purpose of differential
research?

What type of independent variable is used in
differential research?

What are artifacts? How do the affect
differential research?

How is differential research similar to
experimental research?
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