Textbook Notes (270,000)

CA (160,000)

UTSC (20,000)

Psychology (10,000)

PSYB01H3 (600)

Anna Nagy (200)

Chapter 4

Department

PsychologyCourse Code

PSYB01H3Professor

Anna NagyChapter

4This

**preview**shows pages 1-3. to view the full**9 pages of the document.**Chapter 4

Variables

oA variable is any event, situation, behaviour, or individual characteristic

that varies

oE.g. cognitive task performance, word length, spatial density, intelligence,

gender, reaction time, rate of forgetting, aggression, speaker credibility,

attitude change, anger, stress, age, self-esteem

•Each of these variables represents a general class w/in which specific

instances will vary

•These specific instances are called the levels or vales of the variable

oMust have 2 or more levels or values

oSome variables will have numeric values – hence they will be quantitative (ie.

Age, your IQ). They differ in amount or quantity. Algebra can be applied to

such variables (ie. Measure the mean)

oSome variables are not numeric and instead identify categories – hence they

are categorical (ie. Gender, occupation). These variables differ, but not by

quantity, and algebra cannot be applied to them

oClassified into 4 general categories

•Situational variables

Describe characteristics of a situation or environment

The length of words that you read in a book, the spatial density

of a classroom, the credibility of a person who is trying to

persuade you, the number of bystanders to an emergency

•Response variables

The responses or behaviours of individuals

Reaction time, performance on a cognitive task, helping a

victim in an emergency

•Participant or subject variables

Individual differences, characteristics of individuals

Including gender, intelligence, personality traits such as

extraversion

•Mediating variables

Psychological processes that mediate the effects of a situational

variable on a particular response

Operational Definitions of Variables

oA definition of the variable in terms of the operations or techniques the

researcher uses to measure or manipulate it

o“Cognitive task performance” may be operationally defined as the number of

errors detected on a proofreading task during a 10 minute period

oThere also may be several levels of abstraction when studying a variable

www.notesolution.com

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

•A variable such as word length is concrete and easily operationalized

in terms of numbers of letters or syllables, the exact words of the study

must still be selected

oOperationally defining variables causes researchers to discuss abstract

concepts in concrete terms, a process that can lead to the realization that the

variable is in fact too vague to study. This doesn’t mean the concept is

useless, but systemic research is not possible until the concept can be

operationally defined.

oOperational variables also help us communicate our ideas to others. For

example, when someone is talking about aggression, you need to know exactly

what is meant by “aggression” because there are many ways of operationally

defining it.

oThere are a variety of methods to operationally define variables, each with

advantages and disadvantages. Researchers must decide on the best one to

use given the problem of study, goals of research, ethics, etc.

oBecause no one method is perfect, understanding a variable entirely involves

studying the variable in a variety of operational definitions.

Relationships Between Variables

oThe relationship between two variables is the general way in which the

different values of one variable are associated with the different values of

another variable. That is, do the levels of two variables vary systematically

together?

o4 most common relationships found in research

•Positive linear relationship

Increases in the values of one variable are accompanied by

increases of the second variable

E.g. height/weight

•Negative linear relationship

Increases in the values of one variable are accompanied by

decreases in the values of the other variable

E.g. time studying/grades

•Curvilinear relationship

Increases in the values of one variable are accompanied by both

increases and decreases in the values of the other variable

The direction of the relationship changes at least once

Sometimes referred to as a nonmonotonic function

E.g. the amount of money spent on advertising by a

company/the profit of that company

•No relationship

Flat line, variables are independent of each other

oThe positive and negative relationships described are examples of

monotonic relationships – the relationship between the variables is always

positive or always negative

www.notesolution.com

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

oFigure 4.2 on page 72 illustrates a positive monotonic relationship that is not

strictly linear

oIt is also important to know the strength of the relationship between two

variables. That is, we need to know the size of the correlation between the

variables.

oCorrelation Coefficient – the numerical index of the strength of a

relationship between variables

oStrong correlation – two variables are strongly related and there is little

deviation from the general pattern

oWeak correlation – two variables are not strongly related because many

individuals deviate from the general pattern

Relationships and Reduction of Uncertainty

oWhen we detect a relationship between variables, we reduce uncertainty of

the world by increasing our understanding of the variables we are examining

oThe term uncertainty implies that there is a randomness in events; scientist

refer to this as random variability or error variance in the events that occur

in the world

oAsk 200 students if they like shopping, 100 said yes, 100 said no

•This variability is called random or error variance, called error b/c we

don’t understand it

•If you walked up to anyone at your school and tried to guess whether

the person likes shopping, you would have to make a random guess—

you would be right half the time and wrong half the time, if we could

explain the variability it would no longer be random

•So how can random variability be reduced? By identifying the

relationship between the variables

•Reduce by gender, how you would be right a higher % of time

oThe relationship between variables is stronger when there is less random

variability

Nonexperimental Vs. Experimental Methods

oNonexperimental method

•Relationships are studied by making observations or measures of the

variables of interest

•Behaviour is observed as it occurs naturally

•May be done by asking people to describe their behaviour, directly

observing their behaviour, recording physiological responses,

examining various public records such as census data

•A relationship b/w variables is established when the 2 variables vary

together

•E.g. students who work more hours have a lower grades

oExperimental method

•Involves direct manipulation and control of variables

www.notesolution.com

###### You're Reading a Preview

Unlock to view full version