Posted by
Mike on
Jul 14, 2016; 5:01pm
URL: http://spssx-discussion.165.s1.nabble.com/Non-Equivalent-Control-Groups-Design-Quasi-Experimental-Design-tp5732723p5732732.html
From what the OP has said, he *COULD* have a randomized
block (3 levels of BMI as blocks) and with the 2nd independent variable
being the "C" variable (3 levels of pictures) IF he had randomized
the picture conditions within each Block. Let's think about this
in ANOVA terms:
Main effect of blocks: because this variable is an attribute
of the subject/participant, differences among the 3 blocks
may or may not be significant but if it is significant, one cannot
explain WHY one has a difference because the three blocks
differ on many background variables (a propensity score analysis
and/or use of additional covariate would be appropriate to better
understand why the differences exist). IF the people within the
blocks do not represent a random sample that poses the problem
of whom the results apply to.
Main effect of Images ("C"): if subjects within a block were
randomly assigned to one of the three image conditions, then
a significant main effect for images would indicate that the type
of image had an effect on person. One way to randomize
assignment is within each block assign a subject a unique
number between 1-27 and after assignment, randomly distribute
the 27 numbers among the three image groups, allocating
nine numbers (subjects) per group. Apparently this was not
done which means that type of image is now confounded with
Block level (and any variables producing differences among
blocks). If one obtained a significant main effect for type of
image, one would not be able to explain why the differences
occurred because the differences could depend upon many
different causes (randomization would have helped to clear
this up).
Interaction of Block by Image: it should be clear from the above
that even if one obtained a significant interaction, one would
not be able to explain what it means.
One source on this type of analysis -- along with a worked exampled --
but which has randomized assignment for the variable that is
"image type" above (Edwards uses the letter "B") is the following:
Edwards, A. L. (1968). (3rd Ed) Experimental design in psychological
research. See Section 13.8 on pages 260-262.
You can look at more recent books for coverage of this material
but the example by Edwards seems to fit your situation best (but he
does it correctly). For a more comprehensive review of Randomized
Block designs (including designs where the independent variables
are fixed, random, or a combination), how it compares to Completely
randomized designs, and related issues see chapter 8 "Randomized
Block Designs" in Kirk's 4th edition (2013) text "Experimental
Design". His Figure 8.1-2 shows how the Sum of Squares are
divided up differently in Randomized Block design versus the
Completely Randomized design. The key word in both designs
is "randomized" which is what you should have kept in mind
when designing your study. What texts/sources did you use to
come up with your design and how to implement it?
-Mike Palij
New York University
[hidden email]
----- Original Message -----
From: "Maguin, Eugene" <
[hidden email]>
To: <
[hidden email]>
Sent: Thursday, July 14, 2016 9:33 AM
Subject: Re: Non-Equivalent Control Groups Design/ Quasi-Experimental
Design
As I understand your procedure, you have two between factors: model
weight category and BMI category. From your reply to David, the
dependent variables seem to be "continuous". Computationally, this fits
an ANOVA, GLM in spss. I'd say you should be interested in the
interaction. You'll get F values and associated p values. Depending on
the audience for your work, I think the problem you'll have will be
defending your results against alternative explanations such as those
David pointed out.
Gene Maguin
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
kat92
Sent: Thursday, July 14, 2016 5:17 AM
To:
[hidden email]
Subject: Re: Non-Equivalent Control Groups Design/ Quasi-Experimental
Design
Hi David,
Sorry I didn't ask clearly in the previous message. By "body image", I
was referring to "body satisfaction". I used Stunkard's Figure Rating
Scale before and after the picture-viewing, by asking the participants
to choose their current body shape and ideal body shape among the 9 body
shapes provided. Therefore, the body image changes is measured by
calculating the difference of ideal and current body shape (for pre- and
post-test), the greater the difference indicates the greater body
dissatisfaction one has.
And my hypothesis was that participants (especially those who were
overweight) will have lowered body dissatisfaction after viewing
pictures of models with similar body shape with them.
"Weight loss desire" is measured as one's ideal weight subtracted by
one's current weight. I hypothesized that the participants will have
lowered weight loss desire (i.e. smaller difference between ideal weight
and current weight in post-test compared to pre-test) after viewing
over-weight or normal weight model pictures.
As I did not randomly assign the participants to the groups for
picture-viewing (I made sure each picture-viewing condition had equal
no. of participants in each BMI classification), I think it should not
be considered as random assignment? One of my concerns was that
participants in each condition had different level of weight loss desire
and body satisfaction due to their own weight. By having 3 conditions (9
participants in each BMI classification x 3 classifications = 27
participants each condition), the difference within and between group
might be unpredictable, so I wonder if there's a way to control any (DVs
affected by weight).
Thanks!
--
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