Slide 1Chapter 2 - The Ways and Means of Psychology Chapter 2 The Ways and Means of Psychology.

Chapter 2 - The Ways and Means of Psychology Slide 1

Chapter 2

The Ways and Means of Psychology


The 3 Major Types of Scientific Research are:

Naturalistic Observations involve observing people or animalsin their natural environment with as little involvement as possible

Correlational Studies are often observational, but also involve the measurement and examination of variables that may berelated to certain behaviours

Finally, experiments use active manipulation to gain a betterunderstanding of how certain variables effect behaviour

The Scientific Method


1. Identify the problem and formulate cause-effect hypotheses2. Design the experiment (PsyB01)3. Perform the experiment4. Evaluate the hypothesis on the basis of the obtained data (PsyB07)5. Communicate the results (PsyC02)

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Rules of the Scientific Method



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Steve shows you his “vacation video” of a shark diveas a way of illustrating the continuous nature of naturalistic observations

Via the “chainmail” suit, explains how naturalistic observations can lead the formation of a hypothesis

Follows this up with the “shark electrical experiment video” to show how an experiment can then be done to test the hypothesis

Examples



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Some Basic Terminology (in sharky context)

Variable - Anything that can take on multiple values

Independent Variable - A variable that is manipulated by the experimenter (presence or absence of an electric field)

Dependent Variable - A variable that is measured during an experiment … typically an index of behaviour (some measure of avoidance behaviour)

Designing an Experiment


Often experiments involve a contrast between the behaviour of subjects in a control condition (a group or condition in which the critical manipulation is not present) with an experimental group (a group or condition where the critical manipulation is present)

For example, the experimental condition of the shark study was when the electric field was on, the control condition was when it was off

Designing an Experiment


Often our theory involves general ideas, but experiments require specific variables … thus we must define our concepts operationally, in terms of some variables that can be measured or manipulated

e.g., students who are encouraged to actively participatein classes will learn better

Operational Definitions


Often our theory involves general ideas, but experimentsrequire specific variables … thus we must define ourconcepts operationally, in terms of some variables that can be measured or manipulated


Making course notes available?Verbally encouraging students to participate?



Often our theory involves general ideas, but experimentsrequire specific variables … thus we must define ourconcepts operationally, in terms of some variables that can be measured or manipulated


Final marks?Student evaluations of learning?



The validity of an experiment refers to how well the operational definitions capture the concept of interest

Manipulation checks can be used to test the validity of independent variables

The validity of an experiment is also strengthened if other operational definitions of the same concept(s) provide similar results (i.e., converging evidence)

Validity of an Experiment


In order to attribute the results of the experiment to the independent variable that was manipulated, it is important that the critical conditions were systematically different only in terms of that variable

If some other variable also differed across conditions, then the two variables are said to be confounded, and it is impossible to attribute the results to either variable in a convincing manner

e.g. - can you tell me how to get to Sesame Street?

Confound it!


Sometimes it is necessary to use counterbalancing techniques to avoid confounds

For example, the Stroop example we did last class actually included a confound … practice (subjects usually get faster at any task with practice on that task)

Solution … half the subjects get the congruent list followed by the incongruent … and half get the incongruent followed by the congruent

Counterbalancing to Avoid Confounds



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If you measure your height over and over, you’ll tend to get the same height each time … rulers are reliable

What if you wrote the “same” test over and over, would you expect to always get the same mark? And what if the conditions of testing changes with each test?

It is important that variables be measured as reliably as possible when an experiment is performed … the more reliable a variable can be measured, the higher the chance of finding a difference/relation if it exists

Reliability


Some variables can be measured objectively (e.g., reactiontimes), others are more subjective (e.g., babies reactions)

It is harder to reliably measure a subjective variable, and often one must use multiple raters and examine inter-rater reliability to insure reliability of measurement

This is done by seeing how often independent raters agreein their measures … low agreement = poor reliability

Subjective Measures and Inter-rater Reliability


Note the difference between reliability and validity

Reliability is the likelihood of getting the same result if some variable is measured on multiple occasions

Validity refers to how well some variable reflects somepsychological concept

e.g., IQ tests of Intelligence

Reliability versus Validity


Often independent variables are confounded because of subject differences across the groups

The best way to avoid subject differences is to use random assignment

If random assignment is combined with fairly large sample sizes, the groups should not be systematically different in terms of the variables you know to be relevant AND the ones you don’t know about

The Importance of Random Assignment


Subjects hardly ever just go along with an experiment …rather, they never trust the experimenter, and almost always believe there is more to the experiment than the experimenter lets on (sometimes they are right!)

As a result, they do not behave “naturally”, but instead they try to figure the study out, and their behaviour is partially a function of their hypotheses about the exp

An experiment must do all that is possible to avoid such expectations to bias the results of their study

Those Pesky Subjects!


One way to try preventing a subject’s expectations from “causingan effect” is to not allow the subject to figure out what conditionof an experiment they are in:

e.g., subliminal perception “weight loss” tapes

… placebo idea

Thus, subjects in both the experimental and control groups mightexpect something, but there is no reason to believe that expectationswould be different across the conditions

… food is crap ...

Single Blind Studies


Sometimes it is not enough to have just the subject “blind”to what condition they are in, as it is possible that theexperimenter might react differently to subjects dependingon which condition they are in

In those cases it is sometimes optimal to have the experimenter“blind” as well throughout the course of the experiment

I have no idea!Where am I?

Double-Blind Experiments


> Subjects cannot be coerced into participating in experiments (hence course credit in A01)> Subjects must be told the ethically relevant aspects of the experiment, then give their informed consent

Deception is justified, as long as:> participant is debriefed after the study> does not otherwise harm the subject either

psychologically or physically> the dignity of the subject is maintained

Confidentiality of data is also necessary

A Question of Ethics - Human


Animals must be treated “humanely” in research settings> no unnecessary suffering> approved procedures for surgery and the like

Animal research should only be conducted if it is worthwhile> what is worthwhile?> the trend towards “simulated” rats

Issue of the treatment of pets versus research animals

A Question of Ethics - Non-Human



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In my statistics class (PsyB07) I give the students a questionnaireon the first day, and use the data from this questionnaire throughoutthe class to demonstrate concepts

One of the things I ask them is how old they are. Here are some data

21, 20, 23, 21, 23, 22, 20, 21, 22, 22, 21, 25, 19, 22, 21, 23, 26, 24

OK, now what?

Statistics


Usually the first step to analyzing the data is to describe thedata by boiling down to certain descriptors (describe someone)

The usual suspects:

Frequency Tables and HistogramsMeasures of Central Tendency - Mean, Median, ModeMeasures of Variability - M.A.D. and Variance

Sometimes when we measure more than one variable, we wantto know something else too: Are the variables related?

Scatterplots, correlations

Descriptive Statistics


Age Frequency

19 1 20 2 21 5 22 4 23 3 24 1 25 1 26 1 0

1

2

3

4

5

19 20 21 22 23 24 25 26AGE

Fre

quen

cy

Frequency Tables and Histograms all you to depict all the data ina fairly concise way

Frequency Tables and Histograms


Sometimes we want to communicate the data in an even brieferform … one characteristic we often choose to communicate issome measure of “middle” to show a representative point

Mean - The point that is the minimum possible distance from all the other points in the sample

Median - The point that have the data points lie above, and half lie below

Mode - The most frequently occurring data point (or observation)

Measures of Central Tendency


Another characteristic we often which to quantify is the extentto which our data points lie close to the middle point

e.g., 18, 19, 20, 21, 22 vs. 5, 12, 20, 28, 35

M.A.D. - The mean absolute deviation of each data point from the mean of the numbers

Variance - The average squared deviation of each data point from the mean

Measures of Variability


Quite often our end goal is not merely to measure some variable but, rather, we wish to compare different groupsto see if they differ in terms of some variable

The tests we do to look for differences fall underthe heading termed “inferential statistics” and include suchthings as t-tests, z-tests, analysis of variance, and correlations

These are statistical procedures we use to eventually make inferences about how some variable affects the behaviour ofour population of interest

Inferential Statistics


Observed Difference

Difference That Would be Expected by Chance

> If large, we have an effect> If not, we don’t

A Taste of Statistical Logic


Observed Difference



Difference between mean height of 6 males vs. 6 females



Observed Difference



Difference between mean height of 6 males vs. 6 females

Average difference between mean height of 6 randomlyselected people, and 6 other randomly selected people




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Publications- APA formatted manuscripts- The peer-review process- lag

Conference Presentations - Typical Format

Invited Talks

Communicating the Results

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