Testing Specific Research Hypotheses - Pairwise Comparisons

Testing Specific Research Hypotheses - Pairwise Comparisons

Multiple Group Designs Limits of 2 condition designs Kinds of Treatment Conditions Kinds of Control Conditions 2 Kinds of Causal Research Hypotheses Limitations of 2-cond Designs 2-cond designs work well to conduct basic treatment evaluations they allow us to investigate whether or not a specific treatment has an effect usually by comparing it to a no treatment control e.g., does a new treatment program work to help socially anxious clients (compared to no treatment)? However as research questions/hypotheses become more sophisticated and specific, we often require designs that have multiple IV conditions Kinds of Conditions to Include in Research Designs

Tx Conditions Ways treatment conditions differ amount of treatment receiving therapy once vs. twice each week getting 0, 1, 5 or 10 practice trials before testing kind of treatment receiving Cognitive vs. Gestalt clinical therapy whether or not there is feedback on practice trials combinations of treatment components Receivingdrug vs. talk therapy vs. combined drug & talk therapy receiving 10 practices without feedback vs. 2 practices with feedback The Secret is to be sure the selection of conditions matches the research hypotheses you started with !!! Different Kinds of Control Conditions No Treatment control

Asks if the Tx works better than nothing Standard Tx control Asks if the Tx works better than usual Best Practice Control Asks if the Tx works better than the best known Pseudo Tx Control Asks if TX works without a specific component The Secret is to be sure the selection of conditions matches the research hypotheses you started with !!! Of course Any multiple conditions design could be reproduced by the right combination of 2-conditions studies TX1

TX1 C TX2 TX2 C C TX1 TX2 While more expensive and time-consuming than running multipeconditions studies this pairwise approach does provide more replications. An important point to remember... Not every design needs a no treatment control group !!!! Remember, a design needs to provide an comparison of appropriate conditions to provide a test of the research hypothesis !!! What would be the appropriate control group to answer each of the following ? Group receiving the

My new Tx works better than the currently behavioral therapy. used behavioral therapy technique My new Tx works better than no treatment Group receiving no treatment. My new Tx works because of the combo of the usual and new behavioral components Pseudo-Tx group My new TX works better when given by a Ph.D. than by a Masters-level clinician Groups receiving the Tx from the two types of clinicians. The Secret is to be sure the selection of conditions matches the research hypotheses you started with !!!

Causal Hypotheses for Multiple Condition Designs Sometimes there is more than one component to a treatment, and so, there are multiple differences between the IV conditions. When this happens, you must distinguish.. Causal Hypotheses about treatment comparisons -- hypothesis that the difference between the DV means of the IV conditions is caused by the combination of treatment component differences Causal Hypotheses about identification of causal elements -- hypothesis that the difference between the DV means of the IV conditions is caused by a specific (out of two or more) treatment component difference The Secret is to be sure the condition comparison matches the specific type of causal research hypotheses !!!! For example I created a new treatment for social anxiety that uses a combination of group therapy (requiring clients to get used to talking with other folks) and cognitive self-appraisal (getting clients to notice when they are and are not socially anxious). Volunteer participants were randomly assigned to the treatment condition or a no-treatment control. I personally conducted all the treatment conditions to assure treatment integrity. Here are my results using a DV that measures social context tolerance (larger scores are better). Group therapy

Cx & self-appraisal F(1,38) = 9.28, p = .001, Mse = 17.3 Which of the following statements will these results support? 52 25 Here is evidence that the combination of group therapy & cognitive selfappraisal increases social context tolerance. ??? Yep -- treatment comparison causal statement You can see that the treatment works because of the cognitive self-appraisal; the group therapy doesnt really contribute anything. Nope -- identification of causal element statement & we cant separate the role of group therapy & self-appraisal Same story... I created a new treatment for social anxiety that uses a combination of group therapy (requiring clients to get used to talking with other folks) and cognitive self-appraisal (getting clients to notice when they are and are not socially anxious). Volunteer participants were randomly assigned to the treatment condition or a no-treatment control. I personally conducted all the treatment conditions to assure treatment integrity. What conditions would we need to add to the design to directly test the second

of these causal hypotheses... The treatment works because of the cognitive self-appraisal; the group therapy doesnt really contribute anything. Group therapy & self-appraisal Group therapy Selfappraisal No-treatment control Lets keep going Heres the design we decided upon. Assuming the results from the earlier study replicate, wed expect to get the means shown below. Group therapy & self-appraisal 52

Group therapy 25 What means for the other two conditions would provide support for the RH: Selfappraisal No-treatment control 52 25 The treatment works because of the cognitive self-appraisal; the group therapy doesnt really contribute anything. Another example The new on-line homework Ive been using provides immediate feedback for a set of 20 problems. To assess this new homework I

compared it with the online homework I used last semester which 10 problems but no feedback. I randomly assigned who received which homework and made sure each did the correct type. The DV was the % score on a quiz given the day the homework was due. Here are the results ... F(1,42) = 6.54, p = .001, Mse = 11.12 Old Hw 72 New Hw 91 Which of the following statements will these results support? Here is evidence that the new homework is more effective because it provides immediate feedback! Nope -- identification of causal element statement -- with this design we cant separate the role of feedback and number of problems The new homework seems to produce better learning! Yep -- treatment comparison causal statement Same story... The new on-line homework Ive been using provides immediate feedback for a set of 20 problems. To assess this new homework I compared it

with the online homework I used last semester which 10 problems but no feedback. I randomly assigned who received which homework and made sure each did the correct type. What conditions would we need to add to the design to directly test the second of these causal hypotheses... Here is evidence that the new homework is more effective because it provides immediate feedback! Hint: Start by asking what are the differences between the new and old homeworks -- what are the components of each treatment??? New Hw 20 problems w/ feedback 20 problems w/o feedback 10 problems w/ feedback

Old Hw 10 problems w/o feedback Lets keep going Heres the design we decided upon. Assuming the results from the earlier study replicate, wed expect to get the means shown below. New Hw 20 problems w/ feedback 91 20 problems w/o feedback 75 What means for the other two conditions would provide support for the RH:

10 problems w/ feedback 89 Old Hw 10 problems w/o feedback 72 Here is evidence that the new homework is more effective because it provides immediate feedback!

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