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Chapter 4


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University of Toronto Scarborough
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 emergencythe 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 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 doesnt 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. 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 woul
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