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Non-Experimental designs

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Presentation on theme: "Non-Experimental designs"— Presentation transcript:

1 Non-Experimental designs
Psych 231: Research Methods in Psychology

2 Journal Summary 2 due this week

3 Stages of survey research cont.
Stage 6) Designing the survey instrument Question construction: How the questions are written is very important Clearly identify the research objectives Do your questions really target those research objectives? Take care wording of the questions Keep it simple, don’t ask two things at once, avoid loaded or biased questions, etc. How should questions be answered? Stages of survey research cont.

4 Survey Questions Question types
Open-ended (fill in the blank, short answer) Can get a lot of information, but Coding is time intensive and potentially ambiguous Close-ended (pick best answer, pick all that apply) Easier to code Response alternatives are the same for everyone Rating scales Used for “how much” judgments e.g., Likert scale – measures attitudes, agree/disagree Take care with your labels Range of scores, anchors Survey Questions

5 Stages of survey research cont.
Stage 7) Pre-testing the survey instrument Fix what doesn’t seem to be working Stage 8) Selecting and training interviewers For telephone and in-person surveys Need to avoid interviewer bias Stage 9) Implementing the survey Stage 10) Coding and entering the data Stage 11) Analyzing the data and preparing a final report Stages of survey research cont.

6 Quasi-experiments What are quasi-experiments?
Almost “true” experiments, but with an inherent confounding variable Design includes a quasi-independent variable Examples An event occurs that the experimenter doesn’t manipulate Interested in subject variables Time is used as a variable Quasi-experiments

7 Quasi-experiments What are quasi-experiments?
Almost “true” experiments, but with an inherent confounding variable Advantages Allows applied research when experiments not possible Threats to internal validity can (sometimes) be assessed Quasi-experiments

8 Quasi-experiments What are quasi-experiments? Disadvantages
Almost “true” experiments, but with an inherent confounding variable Disadvantages Threats to internal validity may exist (which can not be addressed) Be careful when making causal claims Statistical analysis can be difficult Most statistical analyses assume randomness Quasi-experiments

9 Quasi-experiments What are quasi-experiments?
Almost “true” experiments, but with an inherent confounding variable Common types Nonequivalent control group designs Program evaluation Interrupted time series designs Quasi-experiments

10 Quasi-experiments Nonequivalent control group designs
With pretest and posttest (most common) participants Experimental group Control Measure Non-Random Assignment Independent Variable Dependent Variable But remember that the results may be compromised because of the nonequivalent control group Quasi-experiments

11 Quasi-experiments Program evaluation
Research on programs that is implemented to achieve some positive effect on a group of individuals. e.g., does abstinence from sex program work in schools Steps in program evaluation: Needs assessment - is there a problem? Program theory assessment - does program address the needs? Process evaluation - does it reach the target population? Is it being run correctly? Outcome evaluation - are the intended outcomes being realized? Efficiency assessment- was it “worth” it? The the benefits worth the costs? Quasi-experiments

12 Quasi-experiments Time series designs treatment
Basic method: Observe a single group multiple times prior to and after a treatment treatment Obs Obs Obs Obs Obs Obs The pretest observations allow the researcher to look for pre-existing trends The posttest observations allow the researcher to look for changes in the trends Is it a temporary change, does it last, etc.? Quasi-experiments

13 Quasi-experiments Time series designs treatment
A variation of basic time series design Addition of a nonequivalent no-treatment control group time series Obs treatment Obs Quasi-experiments

14 Developmental designs
Used to study changes in behavior that occur as a function of age changes Age typically serves as a quasi-independent variable Three major types Cross-sectional Longitudinal Cohort-sequential Developmental designs

15 Developmental designs
Cross-sectional design Groups are pre-defined on the basis of a pre-existing variable Study groups of individuals of different ages at the same time Use age to assign participants to group Age is subject variable treated as a between-subjects variable Developmental designs

16 Developmental designs
Cross-sectional design Advantages: Can gather data about different groups (i.e., ages) at the same time Participants are not required to commit for an extended period of time Developmental designs

17 Developmental designs
Cross-sectional design Disavantages: Individuals are not followed over time Cohort (or generation) effect: individuals of different ages may be inherently different due to factors in the environment Example: are 5 year old different from 13 year olds just because of age, or can factors present in their environment contribute to the differences? Cannot infer causality due to lack of control Developmental designs

18 Developmental designs
Longitudinal design Follow the same individual or group over time Age is treated as a within-subjects variable Rather than comparing groups, the same individuals are compared to themselves at different times Repeated measurements over extended period of time Changes in dependent variable likely to reflect changes due to aging process Changes in performance are compared on an individual basis and overall Developmental designs

19 Developmental designs
Longitudinal design Advantages: Can see developmental changes clearly Avoid some cohort effects (participants are all from same generation, so changes are more likely to be due to aging) Can measure differences within individuals Developmental designs

20 Developmental designs
Longitudinal design Disadvantages Can be very time-consuming Can have cross-generational effects: Conclusions based on members of one generation may not apply to other generations Numerous threats to internal validity: Attrition/mortality History Practice effects Improved performance over multiple tests may be due to practice taking the test Cannot determine causality Developmental designs

21 Developmental designs
Cohort-sequential design Measure groups of participants as they age Example: measure a group of 5 year olds, then the same group 5 years later, as well as another group of 5 year olds Age is both between and within subjects variable Combines elements of cross-sectional and longitudinal designs Addresses some of the concerns raised by other designs For example, allows to evaluate the contribution of generation effects Developmental designs

22 Developmental designs
Cohort-sequential design Advantages: Can measure generation effect Less time-consuming than longitudinal Disadvantages: Still time-consuming Still cannot make causal claims Developmental designs

23 Small N designs What are they?
Historically, these were the typical kind of design used until 1920’s when there was a shift to using larger sample sizes Even today, in some sub-areas, using small N designs is common place (e.g., psychophysics, clinical settings, expertise, etc.) Small N designs

24 Small N designs One or a few participants
Data are not analyzed statistically; rather rely on visual interpretation of the data Observations begin in the absence of treatment (BASELINE) Then treatment is implemented and changes in frequency, magnitude, or intensity of behavior are recorded Small N designs

25 Small N designs Baseline experiments – the basic idea is to show:
when the IV occurs, you get the effect when the IV doesn’t occur, you don’t get the effect (reversibility) Before introducing treatment (IV), baseline needs to be stable Measure level and trend Small N designs

26 Small N designs Level – how frequent (how intense) is behavior?
Are all the data points high or low? Trend – does behavior seem to increase (or decrease) Are data points “flat” or on a slope? Small N designs

27 ABA design ABA design (baseline, treatment, baseline)
The reversibility is necessary, otherwise something else may have caused the effect other than the IV (e.g., history, maturation, etc.) ABA design

28 Small N designs Advantages
Focus on individual performance, not fooled by group averaging effects Focus is on big effects (small effects typically can’t be seen without using large groups) Avoid some ethical problems – e.g., with non-treatments Allows to look at unusual (and rare) types of subjects (e.g., case studies of amnesics, experts vs. novices) Often used to supplement large N studies, with more observations on fewer subjects Small N designs

29 Small N designs Disadvantages
Effects may be small relative to variability of situation so NEED more observation Some effects are by definition between subjects Treatment leads to a lasting change, so you don’t get reversals Difficult to determine how generalizable the effects are Small N designs

30 Some researchers have argued that Small N designs are the best way to go.
The goal of psychology is to describe behavior of an individual Looking at data collapsed over groups “looks” in the wrong place Need to look at the data at the level of the individual Small N designs

31 Statistics (Chapter 14) Next time

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