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# 524 Lectures

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### 524 Lectures

1. 1. Graduate Research and Statistics Lab<br />CEDP 524<br />
2. 2. Single Subject (small n)<br />Problems with group analysis<br />Individual differences are ignored<br />These are important factors!<br />This logic is based on farming analysis!<br />The logic of single subject is high levels of internal validity (control) then replication.<br />Systematic,<br />Direct<br />
3. 3. Experimental Single Subject Design<br />Using a subject as their own control<br />If we establish a pattern of responding and then change that pattern of responding at the exact moment the IV is present we conclude that it was the IV that had the effect<br />
4. 4. Reversal design<br />Shifting conditions – must be something that is not a permanent change<br />Baseline (Stability)<br />Treatment (stability)<br />Baseline (stability)<br />Treatment (stability)<br />ABA designs, ABAB designs, ABAC<br />Showing effects across people<br />Ethics<br />
5. 5. Multiple Baseline<br />Several individuals<br />Baselines overlap to control for environmental variables.<br />The treatment is then introduced at different times for each<br />If the behavior changes in each person at the treatment time, then the IV has the effect.<br />
6. 6. Changing criterion<br />Modify the criteria as it is reached until a given level is desired<br />Step down/up approach<br />
7. 7. Quasi Experimentation<br />Internal vs. external validity<br />Internal allows you to detect effects<br />But is generally low in applicability<br />External validity makes the study applicable<br />But decreases chance to detect effect.<br />
8. 8. Archival studies<br />Looking at events in history and measuring their effect<br />Looking at records<br />Looking at changes before and after.<br />No ability to detect cause.<br />Naturalistic observations combined with sound data collection.<br />
9. 9. Time series design<br />Repeated measures<br />Time one and time two are the minimum<br />Can be an experimental treatment<br />Pre M X post M<br />Single group pre-post design.<br />Remember – no control here – so cannot truly conclude if the X made the changes.<br />
10. 10. Interrupted time series<br />Adding more pre and post tests <br />Allows us to assess changes that we cant see with a single shot.<br />M M M M X M M M M M <br />
11. 11. Multiple time series<br />Just like multiple but uses a control group!<br />No random assignment though.<br />Adds some control – but still cant detect cause<br />
12. 12. Non-equiv before after design<br />When your groups are different before the study starts.<br />Or you expect they are!<br />M X M<br />M M <br />Compare differences in terms of groups to themselves.<br />
13. 13. Ex-post facto design<br />Retrospective comparisons<br />An early event – with a later event<br />Participating in debate – becoming a leader<br />Medical or behavior problems associated with later issues (heart disease)<br />Identify group of interest (those serial killers)<br />Look for patters using various predictors<br />Compare to a selected control group<br />Could be randomly chosen people from the year book<br />
14. 14. Correlational designs<br />Designs that are relating variables<br />But not seeking to see which causes the other<br />Used not to establish cause<br />But to show a relationship to then allow for further study<br />Prevents us from wasting time and resources<br />
15. 15. Naturalistic study<br />Simply observing and describing<br />Darwin on the HMS Beagle<br />Started as simple observations<br />Went on to develop ideas<br />Eventually a theory<br />Now tests of that theory<br />Now it exists as a definition of life.<br />Hawthorne effect (reactance)<br />Being observed causes changes<br />Use unobtrustive observations<br />Selective perception<br />We choose to focus on certain things<br />We are influenced by what we expect to see<br />Boredom – leads you to looking for boredom!<br />