copyright D. McCann, 2006) PSYCHOLOGY is

Transcription

1 UNIT I(slides copyright D. McCann, 2006) PSYCHOLOGY is

2 PSYCHOLOGY is the scientific study of behavior and mental processes

3 SCOPE OF THE SUBJECT Blowfly Wilder Penfield Attitude change Mob Violence (deindividuation)

4 GOALS OF SCIENCE Describe Explain Predict Control & change

5 SCIENTIFIC METHOD to pursue goals Research Theory Research reports Replication Hypothesis testing difficulties/examples (ONLY YES/NO QUESTIONS ALLOWED FOR DATA)

6 - (ONLY YES/NO QUESTIONS ALLOWED FOR DATA) -

7 - -Answers - - -

8 Riddles are like Scientific Method similarities & characteristics

9 RESEARCH complicated by subtle biases Hypothesis: York Brand T.P. is the best test U of T York Western

10 result: thus, other examples: - - -

11 RESEARCH APPROACHES 5 RESEARCH GOALS 3 A,B,C A: DESCRIBE 1. Naturalistic Observation - Deindividuation 2. Case Study clinical psychology, e.g., Freud 3. Survey Research -polls -representative sample

12 Second Goal (& 4 th research approach) B : PREDICTION 4) Correlational Research is there an association between two variables variable is: - - correlation coefficient (is a statistic, a number representing the degree of association) its characteristics: - -

13 PROBLEM: IT DOES NOT IMPLY CAUSATION! EXAMPLE: measure variables A & B if A & B are correlated (are associated) then possible explanations are:

14 Example Correlation of +.70 As the number of storks in winter resting on roofs increases, the number of human births 9 months later increases

15 Both A & B increase (at the same time, so seem to be related) but neither causes the other, they are caused (independently) by C EXAMPLE: Height & weight

16 If the correlation = 0.0?? - E.G.: - - -

17 Negative Correlations A (anxiety) & B (test performance) -.65 Thus, - Finally, correlation does not imply causation

18 EXPERIMENTS if well done, allow for causal statements Hypothetical Example: Alcohol and memory Hypothesis: Independent Variable

19 Levels of the independent variable create experimental conditions Levels: 0 beer, 1 beer, 8 beer (thus, three conditions) Participants: Dependent Variable -here,

20 RESULTS: (numbers represent average recall of participants in that condition) 0 Beer 1 Beer 8 Beer - (also called the control condition) CONCLUSIONS: -

21 Important Concepts Hypothesis Independent Variable Dependent Variable & Extraneous Variables Example: Height and Reaction Time - - -

22 Perhaps You can be

23 RT = Reaction time, speed of reaction Hypothesis: - RESULTS - Interpretation: - Comments:??

24 Important Concepts Hypothesis, Independent Variable, Dependent Variable, & Extraneous Variables - Good Experiments: - - -

25 Explanations (some of them..) could be or,

26 EXAMPLE O BEER 8 BEER Ss 10 females 10 males Recall Causes (interpretation): independent variable - - here, is an extraneous variable

27 EXAMPLE 2 Hypothesis pleasant scent attraction Independent - Dependent Participants first year undergrad males

28 Problems INDEPENDENT EXTRANEOUS RESULT No scent Mildly Pleasant Very Pleasant Explanation Differences in Differences in

29 Types of extraneous variables: 2 A) problem with how expt. conducted -??? (which one) B) subject characteristics Solutions: A)- B) - e.g.,.. personality, age, IQ, religion, ethnic background, height, education, etc.

30 How to equalize. Random Assignment: - controls for extraneous variables by making chances or probability equal that each characteristic will be represented in each condition e.g., flip a coin to decide which condition a participant is in when they arrive (random) now, done by computer

31 Important Concepts cont d Validity: Internal External (generalizability) relationship to real world Psych labs Participants - real world broader world

32 research tool Descriptive * - describe the results in your sample of participants Inferential go beyond your sample, to the population -

33 DESCRIPTIVE STATISTICS Experimental Example: imagery & recall Conditions: 1. Control (No image group) remember the words 2. Image try to visualize the words while remembering them Procedure: -

34 Results (Note: 25 words in the list, 10 participants per condition) Image No Image

35 Presentation Frequency Distribution see e.g. Bar Graph see e.g. Frequency Polygon see e.g.

36 Presenting Sample Results: Descriptive Statistics In describing the shape of frequency polygons: A. Symmetricality B. Kurtosis (peakedness) (see examples )

37 Descriptive Stats: Measures of Central Tendency one number gives a concise, description of the condition, can be used to compare two (or more) conditions mean: e.g., mode: = e.g., median:

38 To calculate the median: rank order scores from smallest to largest then identify score that splits group in half (may be real, or imaginary score) no-image group: 5, 5, 6, 7, 8, 9, 9, 9, 11, 11 median is if the scores were, 5, 5, 6, 7, 8, 9, 9, 9,

39 these measures of central tendency provide a more concise, descriptive statement of group that the freq. distribution

40 Descriptive Stats: Measures of variability (a measure of how much the scores vary, or differ): e.g. temperature of Albuquerque & San. Fran. for a year lowest highest X ALBER SAN. FRAN. thus,

41 range: (highest score lowest) AL = SF = however, Can be a problem example: age variability in two classes A 19, 19, 19, 19, 19, 20, 25 B 17, 17, 17, 20, 23, 23, 23 M R

42 neither age or range differentiates classes but, for A, use the variance: VAR = Standard Deviation = variance

43 EXAMPLE: No-image group (variance) Score S mean (S M) =0 0 2 = = Variance = 44/10 = 4.4 Standard Deviation = var = 2.1

44 Descriptive statistics summarize our sample of subjects/participants Central Tendency mean * mode median Variability range variance standard deviation*

45 Inferential Stats: However, we want to make a more general statement In our experiment Image X = 20 No-image X = 8 Thus, Image> No-image how do we go beyond our sample??

46 We want to assume the same thing would happened no matter what group of participants we chose, -

47 Inferential Statistics are a set of mathematical operations that tells us the probability of getting the same results if we took another sample, another, we accept an error probability of p<.05 = - - -

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