UNIT I(slides copyright D. McCann, 2006)

PSYCHOLOGY is ……

PSYCHOLOGY is ……

“the scientific study ofbehavior and mental

processes”

SCOPE OF THE SUBJECT

Blowfly

Wilder Penfield

Attitude change

Mob Violence (deindividuation)

GOALS OF SCIENCE

Describe

Explain

Predict

Control & change

SCIENTIFIC METHOD – topursue goals Research Theory Research reports

Replication Hypothesis testing

difficulties/examples (ONLY YES/NO QUESTIONS ALLOWED FOR

DATA)

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(ONLY YES/NO QUESTIONS ALLOWED FORDATA)

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Riddles are like Scientific Method similarities & characteristics

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RESEARCH – complicated bysubtle biases Hypothesis: “York Brand” T.P. is the

best test U of T York Western

result: thus, “ other examples:

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RESEARCH APPROACHES – 5RESEARCH 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

Second Goal (& 4th research approach)

B : PREDICTION 4) Correlational Research

is there an association between two variables variable is:

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correlation coefficient (is a statistic, a numberrepresenting the degree of association)

its characteristics: - -

PROBLEM: IT DOES NOT IMPLYCAUSATION!

EXAMPLE: measure variables A & B if A & B are correlated (are associated) then possible explanations are:

Example Correlation of + .70

As the number of storks in winter resting on roofs increases, thenumber of human births 9 months later increases

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Both A & B increase (at the same time, so seemto be related) but neither causes the other, theyare caused (independently) by C

EXAMPLE: Height & weight + . 78

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

Negative Correlations A (anxiety) & B (test performance)

-.65 Thus,

- Finally, correlation does not imply

causation

EXPERIMENTS if well done, allow for causal statements Hypothetical Example: Alcohol and

memory Hypothesis: Independent Variable –

Levels of the independent variable createexperimental“conditions” Levels: 0 beer, 1 beer, 8 beer

(thus, three conditions) Participants:

Dependent Variable

-here,

RESULTS: (numbers represent average recall of

participants in that condition) 0 Beer 1 Beer 8 Beer - (also called thecontrol condition)

CONCLUSIONS:-

Important Concepts Hypothesis Independent Variable Dependent Variable & Extraneous Variables

Example: Height and Reaction Time - - -

Perhaps…

You can be…

RT = Reaction time, speed ofreaction

Hypothesis: - RESULTS

- Interpretation:

- Comments:??

Important Concepts Hypothesis, Independent Variable, Dependent

Variable, & Extraneous Variables

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Good Experiments: - - -

Explanations (some of them..)

could be or,

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EXAMPLE O BEER 8 BEER

Ss 10 females 10 males Recall

Causes (interpretation): independent variable - -

here, is an extraneous variable

EXAMPLE 2

Hypothesis – pleasant scentattraction Independent – - Dependent – Participants – first year undergrad

males

Problems… INDEPENDENT EXTRANEOUS RESULT

No scent

Mildly Pleasant

Very Pleasant

Explanation Differences in Differences in

Types of extraneousvariables: 2

A) problem with how expt. conducted- ??? (which one)

B) subject characteristics

Solutions: A)- B) -

e.g.,.. personality, age, IQ, religion, ethnicbackground, height, education, … etc. …

How to equalize…. Random Assignment:

-“controls for extraneous variables bymaking chances or probability equalthat each characteristic will berepresented in each condition”

e.g., flip a coin to decide whichcondition a participant is in whenthey arrive (random)

now, done by computer

Important Concepts cont’d

Validity: Internal – External – (generalizability)

relationship to “real world”

Psych labs real worldParticipants broader world-

research tool Descriptive * - describe the results

in your sample of participants Inferential – go beyond your

sample, to the population -

DESCRIPTIVE STATISTICS Experimental Example: imagery &

recall Conditions:

1. Control – (No image group) –remember the words

2. Image – try to visualize the wordswhile remembering them

Procedure: -

Results(Note: 25 words in the list, 10 participants per condition)

Image No Image 20 5

24 9 20 5 18 9 22 6 19 11 20 8

19 11 17 7 21 9

Presentation Frequency

Distribution – seee.g.

Bar Graph – see e.g. Frequency Polygon –

see e.g.

Presenting Sample Results:Descriptive Statistics In describing the shape of frequency

polygons: A. Symmetricality

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B. Kurtosis (peakedness) - - - (see examples…)

Descriptive Stats: Measures of CentralTendency

one number gives a concise,description of the condition, can beused to compare two (or more)conditions mean:

e.g.,

mode: = e.g.,

median:

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, 11 - -

these measures of “centraltendency” provide a more concise,descriptive statement of groupthat the freq. distribution

Descriptive Stats: Measures of variability

(a measure of how much the scoresvary, or differ): e.g. – temperature of Albuquerque & San.

Fran. for a year lowest highest X

ALBER SAN. FRAN. thus,

range: (highest score – lowest) AL = SF =

however, Can be a problem example: age variability in two classes

M R A 19, 19, 19, 19, 19, 20, 25 B 17, 17, 17, 20, 23, 23, 23

neither age or range differentiatesclasses

but, for A,

use the variance: VAR =

Standard Deviation = variance

EXAMPLE: No-image group (variance)

Score S – mean (S – M)2

8 8-8=0 02 = 0 11 11-8=3 9 6 -2 4 7 -1 1 5 -3 9 9 1 1 5 -3 9 9 1 1 9 1 1 11 3 9 44 Variance = 44/10 = 4.4 Standard Deviation = var = 2.1

Descriptive statistics summarize our“sample” of subjects/participants

Central Tendency mean * mode median

Variability range variance standard deviation*

Inferential Stats:

However, we want to make a moregeneral statement In our experiment

Image X = 20 No-image X = 8 Thus, Image> No-image

how do we go beyond our sample??

We want to assume the same thingwould happened no matter whatgroup of participants we chose, -

Inferential Statistics

are a set of mathematical operationsthat… 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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