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# Lecture 8: Quasi-experiments

Lecture 8: Quasi-experiments. Aims &amp; Objectives To differentiate between true and quasi-experiments To discuss the nature of random allocation To examine threats to experimental validity To examine some basic quasi-experimental designs. Type of general approaches to design. Descriptive

## Lecture 8: Quasi-experiments

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### Presentation Transcript

1. Lecture 8: Quasi-experiments • Aims & Objectives • To differentiate between true and quasi-experiments • To discuss the nature of random allocation • To examine threats to experimental validity • To examine some basic quasi-experimental designs

2. Type of general approaches to design • Descriptive • What, where, when and to whom • Relational • Co-variaton • Experimental • Causal analysis via random allocation • Quasi-experimental • Causal statements when groups are not equivalent – no random allocation

3. Random allocation • Every potential subject has an equal chance of being in any condition • Simple randomisation • Block randomisation • Blocks A&B, produce sequences e.g., AABB, ABAB. Sequences are selected at random and subjects selected at random into that block • Stratified randomisation • Select on a characteristic that influences the groups and have block randomisation lists within those blocks

4. Internal validity: I • Ruling out a third cause • Randomisation controls for • History effects • Maturation effects • Mortality • Statistical regression to the mean • Randomisation does not control for • Effects equalising groups • Diffusion of treatment effects • Compensatory rivalry • Compensatory equalisation • Effect separating groups • Resentful demoralisation

5. Statistical validity • Risk of making a type 1 error • Power • Fishing • Reliability of measures, treatments • Random irrelevance • Random heterogeneity of respondents

6. External validity:generalisation • Is the effect stable • Over time • Across individuals • Across IVs & DVs • Across places

7. Mook • Research is not always about generalizability of findings • Conceptualisation of generalizability are base don an agricultural model • Experiments are about generalizability of theory not findings

8. Construct validity • Experimenter effects • Structural • Mono-operation bias • Mono-method bias • Poor explication of constructs • Interpersonal • Demand characteristics • Apprehension evaluation • Rosenthal effect

9. Quasi-experiments Nomenclature X = a treatment O = Observation … = Not randomly assigned

10. Uninterrupted designs One group pre- post test design O X O Threats = history, maturation regression

11. Non-equivalent groups Untreated control group with pre and post test O X O ………… O O

12. Reverse treatments O X+ O ……………. O x- O

13. ITSDs OOOOXOOOO ……………….. OOOO OOOO OO OOOXOOO OOXOOO OOO Withswitch replication

14. ARIMA • OOOXOOO • 456 Upward drift • 444 Upward constant • 466 Gradual upwards • 333 333 No change

15. Regression discontinuity Depression Poverty Short Long

16. Randomized field trials • Randomisation by independent group • Make seek treatment elsewhere • Within condition effects • Placebo-control

17. Experiments: the last word • Experiments are important because they allow us to show what can or ought to happen • Bio feedback • Milgram • Sherrif’s boys camp study

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