Wissenschftlichen Arbeiten. Research methods (II) Zhuanghua Shi

Transcription

1 Wissenschftlichen Arbeiten Research methods (II) Zhuanghua Shi!1

2 Last week Descriptive method case studies, survey, developmental study, follow-up study, documentary analysis, correlation analysis Experimental method variables and conditions Experimental biases Theory and hypothesis construct operational definition!2

3 Variables Independent Variable (IV) Dependent Variable (DV) Extraneous variable!3

4 Experimental methods Cause-and-effect relationship Cause Operational definition effect Independent variables (Unabhängige Variablen) Dependent variables (Abhängige Variablen) Extraneous variables (Störvariablen)!4

5 Experimental biases Two major experimental biases Subject bias Experimenter bias Control methods Placebo methods Blind experiment Double blind experiment!5

6 Variance control Max Independent variables (Unabhängige Variablen) Extraneous variables (Störvariablen) Control Min Dependent variables (Abhängige Variablen)!6

7 Variance/Variability In statistics, variance is a measure of how far a set of samples are spread out from each other Large variance small variance!7

8 Variance between groups Group 1 Group 2 Group 1 Group 2 small variance between groups Large variance between groups!8

9 Variance within groups Group 1 Group 2 Group 1 Group 2 small variance within groups Large variance within groups!9

10 Variance control Max Independent variables (Unabhängige Variablen) Min Dependent variables (Abhängige Variablen) Extraneous variables (Störvariablen) Control!10

11 Maximize experimental Experimental variance variance The variance of the dependent variable influenced by the independent variable(s) MAX criteria Make experimental conditions as DIFFERENT as possible Pull two or multiple treatments (conditions) apart as much as possible!11

12 Example Hermann grid illusion Independent variable Curvature Dependent variable strength of illusion MAX increase the curvature, such that the illusion disappears Difference between two conditions is maximized.!12

13 Cautious of MAX criterion Cautious of the control group Do NOT deliberately make the control group inferior Example To test German literature knowledge of Bavarian high school students. One compares it against one group from Asian high school. To test German literature knowledge of Bavarian high school students. One compares it against high school students from East Berlin (or from single family)!13

14 Cautious of MAX criterion Anti-depressant example by Siegfried 2010 One study on anti-degressant, Paxil, showed a higher rate of suicidal incidents than patients in the placebo group Another study showed that another anti-depressant, Proaz, had fewer suicidal incidents than the placebo group The truth is two different placebo groups Fewer suicidal incidents on placebo group in the Paxil experiment than those on placebo in the Proaz experiment!14

15 Variance control Max Independent variables (Unabhängige Variablen) Min Dependent variables (Abhängige Variablen) Extraneous variables (Störvariablen) Control!15

16 Control extraneous variance Why do we need to control extraneous variance? Group 1 Group 2? Variable 1 Variable 2 Effect 1 Effect 2 Confounding changes of the dependent variables (effects) cannot be traced by the changes of the independent variables!16

17 Confounding Example: Influence of loudness on the reaction time Loud tone facilitates the response However, loud tone was a loud high-pitch tone... The results was confounding since it cannot be determined if the loudness or tone frequency is the main factor Example: Wait duration on moods and helping Do something --> to kill the time or to change the mood?!17

18 Control extraneous variance Randomization important method to reduce confounding The extraneous variable as a factor or a covariate e.g. If age could be a potential factor Matching subjects Repeat measures Within subject design!18

19 Variance control Max Independent variables (Unabhängige Variablen) Min Dependent variables (Abhängige Variablen) Extraneous variables (Störvariablen) Control!19

20 Minimize error variance Error variance Uncontrollable e.g. Guessing, bad mood, inattentive blinking Methods Repetition extraneous variable as a covariate Improve the reliability of measure!20

21 Experimental designs Within-subject design Between-subject design One factorial design Multi-factorial design!21

22 Within-subject design Every subject is tested in all conditions of independent variables e.g. Investigating the difficulty of learning foreign languages (a) French (b) Spanish (c) Chinese 8 weeks learning per language French Spanish Chinese Subject 1 x x x Subject 2 x x x!22

23 Between-subject design For different conditions (levels of independent variable), different subjects are used e.g. Language learning French group vs. Spanish group vs. Chinese group French Spanish Chinese Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject !23

24 Within/between subject design Important concerns When should we use within-subject design? And when should we use between-subject design? What potential problems should we take care of?!24

25 Within-subject design e.g. Investigating the difficulty of learning foreign languages (a_ French (b) Spanish 8 weeks learning per language Week 1-8 Week 9-16 Subject A French Spanish Subject B French Spanish Potential problem transfer effect: Learning the first language may transfer to the second language!25

26 Within-subject design Ways to reduce transfer effect Counter-balance the conditions A-B & B-A e.g. Group1: A-B, Group 2: B-A Note: Counter-balance only works when Transfer effect from A to B is equal to transfer effect from B to A. What if, when transfer effect is large from spanish to french than vice versa?!26

27 Between-subject design e.g. French Subject A Subject C Spanish Subject B Subject D Advantages and disadvantages Advantages Free from transfer effect Disadvantages Low statistic power Possible confounding effect from subject difference!27

28 Between-subject design How to overcome the disadvantages? Balance the subjects (gender, age,...) Randomization Increase the number of pairs Control other random variables...!28

29 One/multi-factorial design One factorial design one independent variable Multi-factorial design multiple independent variables Independent variables (Unabhängige Variablen) Dependent variables (Abhängige Variablen) Extraneous variables (Störvariablen)!29

30 One factorial design Modulate only one independent variable, and observe the changes of the dependent variable e.g. subjects are randomly assigned to two conditions: Experimental condition and control condition Experimental group was manipulated (treated) while the control group is not.!30

31 Example: Stroop effects Cause Effect Congruent Incongruent RT Neutral!31

32 Variance control in on Randomization factorial design Reduce the systematical subject biases Parallelization (Matched pairs) Make subjects compariable between groups Particularly for between-subject design Keep other random variables constance among conditions!32

33 Variance control in on factorial design Matched pairs Not random assign subjects between conditions Rather control and match subjects between conditions Subject pairs are manually selected and matched Gender Age Education...!33

34 Variance control in on factorial design Matched pairs Advantages Reduce the confounding induced by subject variances Avoid transfer effect Problem What criteria should be taken into account?!34

35 Pre-Post-Test design Experimental group Pretest& Treatment/ Manipula1on& Pos3est& Control group Pretest& No&treatment& Pos,est& Measure the difference between pre- and posttests!35

36 Multi-factorial design Designs with two or more factors Allow systematically investigate of influences of multiple factors!36

37 Example Research question Motivation and blood donors Operation definition of motivation Material (Factor A) 50%: 20 euros/person 50%: 5 euros/person Attitude (Factor B) 50%: distribute booklet of the necessary of blood donation 50%: no booklet!37

38 Example Two factors with two levels Booklet No Booklet 20 Euro 25% 25% 5 Euro 25% 25%!38

39 Hypothetical results of the Number of donors example Attitude With booklet Without booklet Sum Material 20 Euro 12/100 7/100 19/100 5 Euro 10/100 2/100 12/100 Sum 22/100 9/100 31/100!39

40 Possible outcomes Main effects Material Attitudes Interaction of two factors Material and attitudes may interact with each other and have different effects at different level!40

41 Two-way interaction 14" 12" 10" 8" 6" Without"booklet" Booklet" 4" 2" 0" 20"Euro" 5"Euro"!41

42 Possible outcomes of twofactorial design Effect Effect Effect B1 B2 B1 B2 B1 B2 A1 A2 A1 A2 A1 A2 Factor A: No effect Factor B: effect Interaction: No Factor A: effect Factor B: effect Interaction: No Factor A: effect Factor B: effect Interaction: Yes!42 Additive effect Two factors are independent with each other

43 Checklist of the experimental design Research question formulation Operational definition Define independent variable(s) What other random variables may influence the DV? How can we control those random variables?!43

44 Checklist of the experimental design How can we control the experimental variances Max, Con, Min What design should we select? One/two/multi-factorial design Within- / Between-subject design Potential outcomes How can we interpret the results?!44

45 Next week (home work) No class Form groups Select an interesting psychological phenomenon Literature research Construct a hypothesis (theory) Design a hypothetical experiments You will present your topic and design at the end of the semester as your final report!45

46 Examples People often believe his/her own IQ/intelligence is better than average People often believe the world is just and therefore people get what they deserve Women have better color perception than men...!46