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Chapter 4

# PSYB01H3 Chapter Notes - Chapter 4: Social Loafing, Operational Definition, Mental Chronometry

by OC5831

Department

PsychologyCourse Code

PSYB01H3Professor

Anna NagyChapter

4This

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

Variables

A Variable is any event, situation, behavior, or individual characteristic that varies (ie. Cognitive task

performance, word length, gender, age, self-esteem, etc)

Each of these variables represents a general class within which specific instances will vary. The specific

instances are called the levels or values of the variable. 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, etc.). They

differ in amount or quantity. Algebra can be applied to such variables (ie. Measure 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 length of words

you can read in a book, the spatial density of a classroom, the credibility of a person who is trying to

persuade you, etc.)

2.Response variables are the responses or behaviors of individuals (ie. Reaction time, performance on a

cognitive task, etc.)

3.Participant or Subject variables are individual differences; they are the characteristics of individuals

(ie. Gender, intelligence, personality traits, etc.)

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 of a variable – a definition of a variable in terms of the operations or techniques

the researcher uses 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, 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 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

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that the concept is useless, but that 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

Much research studies the relationship between two 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 the 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 in research (also see figure 4.2, pg 69)

1.Positive linear relationship – increases in the value of one variable are accompanied by increases in

the value 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. Number of people in a group vs. group efficiency [called social

loafing]). The variables have a negative, systematic relationship.

3.Curvilinear relationship (aka nonmonotonic function or an inverted-U) - Increases in the value of

one variable are accompanied by both increases and decreases in the value of the other variable (ie.

the amount of money spent on advertising by a company vs. 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

The positive and negative relationships described are examples of monotonic relationships – the

relationship between the variables is always positive or always negative.

Figure 4.3 on page 71 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.

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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 about 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 variance. It is called “error” because we do not understand it.

If you sampled 200 people at a school and found that 100 like to shop and 100 don’t, and then you go up

to another person and guess their preference, you’d 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 variables that are related to attitudes towards

the phenomena (in this case, shopping preference)

Now let’s say you break down your sampling into males and females, and you find that 70 females (out

of 100) like to shop whereas only 30 males (out of 100) like to shop. You’ve reduced the random

variability.

The relationship between variables is stronger when there is less random variability.

Nonexperimental Versus Experimental Methods

How can we determine whether variables are related?

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, using direct observation, examining various public records such as a census data, etc. A

relationship between variables is established when the two variables vary together (ie. the number of

hours a student studies related to their GPA).

Experimental method – Involves direct manipulation and control of the variables. The researcher

manipulates the first variable of interest, and then observes the response. Here, the two variables do not

merely vary together; one variable is introduced first to see if it affects the second variable.

Nonexperimental Method:

Also called the correlational method

Suppose a researcher is interested in the relationship between exercise and anxiety.

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