Department

Psychology

Course Code

PSYB01H3

Professor

Anna Nagy

Chapter 4 – Studying Behavior

Variables

•Variable – an event, situation, behavior, or individual characteristic that varies

(ie. Word length, gender, age).

•Each of these variables represents a general class within which specific instances

will vary. The specific instances of these is called the levels or values of the

variables. A variable must have two or more levels or values.

•Some 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. Mesure the mean).

•Some 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.

•Variables can be classified in four general categories

1.Situational variables describe characteristics of a situation or environment

(ie. The spatial density of a classroom, the credibility of a person who is

trying to persuade you).

2.Response variables are the responses or behaviors of individuals (ie.

Reaction time, performance on a cognitive task).

3.Participant or Subject variables are characteristic differences of the

individuals (ie. Age, gender, intelligence, personality traits).

4.Mediating variables are psychological processes that mediate the effects

of a situational variable on a particular response (ie helping is less likely

when there are more bystanders to an emergency…the mediating variable

is diffusion of responsibility).

Operational Definitions of Variables

•A variable is an abstract concept that must be translated into concrete forms of

observation or manipulation, thus a variable such as “aggression” must be defined

in terms of the specific method used to measure or manipulate it.

•Operational definition – a definition of a variable in terms of the operations or

techniques the researcher used to measure or manipulate it.

•Variables must be operationally defined so they can be studied empirically.

•“cognitive task performance” may be operationally defined as the number of

errors detected on a proofreading task during a 10 minute period

•There may be several levels of abstraction when studying a variable. (ie stress is a

very abstract variable. When researchers study stress, they might focus on a

number of stressors – noise, crowding, school, etc. a researcher would probably

choose one stressor to study then develop an operational definition of that type of

stressor. They would then carry out research both pertaining to that specific stress

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and the more general concept of stress). The key point is that researchers must

always translate variables into specific operations to manipulate or measure them.

•Operationally 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.

•Operational 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.

•There is 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.

•Because no one method is perfect, understanding a variable entirely involves

studying the variable in a variety of operational definitions.

Relationships Between Variables

•The 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?

•We will begin by looking at two quantitative variables. Different “shapes” can

describe their relationship.

•The four most common relationships found:

1.Positive linear relationship – increases in the value of one variable is

accompanied by the increase in the values of the second variable (ie.

height vs. weight). The variables have a positive, systematic relationship.

2.Negative linear relationship – increases in the value of one variable are

accompanied by decreases in the values of the other variable (ie amount of

time spent studying v. grades). The variables have a negative, systematic

relationship.

3.Curvilinear relationship – aka nonmonotonic function. Increases in the

values of one variable are accompanied by both increases and decreases in

the values of the other variable (ie the amount of money spent on

advertising by a company v. the profit of that company). The direction of

the relationship changes at least once.

4.No relationship – The variables vary independently of one another. The

graph is simply a flat line.

In a graph, the independent variable is placed on the x-axis and the dependant variable is

placed on the y-axis (graphs available on page70.

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•The positive and negative relationships described are examples of monotonic

relationships – the relationship between the variables is always positive or

always negative.

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

strictly linear.

•Remember these are general patterns – just because a positive linear relationship

exists does not mean that everyone who scores high on one variable will

necessarily score high on the second variable.

•It 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.

•Correlation Coefficient – the numerical index of the strength of a relationship

between variables.

•Strong correlation – two variables are strongly related and there is little deviation

from the general pattern

•Weak correlation – two variables are not strongly related because many

individuals deviate from the general pattern.

Relationships and Reduction of Uncertainty

•When we detect a relationship between variables, we reduce uncertainty of the

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

•Uncertainty implies there is randomness in events; scientists refer to this as

random variability or error variance

•Research is aimed at reducing random variability by identifying systematic

relationships between variables.

•Variability is called random or error. It is called “error” because we do not

understand it.

•If you flipped a coin and guessed at the outcome you would have to make a

random guess – and you would probably be right about 50% of the time.

However, 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.

•The relationship between variables is stronger when there is less random

variability.

Nonexperimental Versus Experimental Methods

•There are two general approaches to the study of relationships, the experimental

method and the nonexperimental method.

•Nonexperimental method – relationships are studied by making observations or

measures of the variables of interest as the behavior naturally occurs. This can be

done by asking people to describe their behavior, direct observation, examining

various public records such as a census, etc. A relationship between variables is

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