Chapter 4.docx

Chapter 4: Fundamental Research Issues *(take out pages 77-83, and 86-90) Validity  Validity= truth or accuracy  Three types of validity: construct validity, internal validity and external validity Variables  A variable is any event, situation, behavior, or individual characteristic that varies  Any variable must have two or more levels or values  Ex. Depression, intelligence, stress, etc Operational Definitions of Variables  The operational definition of a variable is the set of procedures used to measure or manipulate it  Ex. Cognitive task performance, self-esteem and word length (can be measured and manipulated)  A variable must have a o.d to be studied empirically  Help researchers communicate ideas with other  Construct validity refers to the adequacy of the operational definition of variables: Does the operational definition of a variable actually reflect the true theoretical meaning of the variable?  If you wish to scientifically study the variable of extraversion, you need some way to measure that variable Relationships Between Variables  Ex. Does playing violent video games result in greater aggressiveness?  four common relationships found in research: positive linear relationship, negative liner relationship, curvilinear relationship and no relationship Positive Linear Relationship  increases in the value of one variable are accompanied by increases in value of another  ex. Higher speech rates are associated with attitude change Negative Linear Relationship  increases in the values of one variable, are accompanied by decreases in the value of another  ex. Increasing the number of a people working on a task reduces the productivity Curvilinear  increases in the values of one variable are accompanied by systematic increases and decreases in the values of the other variable (the direction of the relationship changes at least once)  can be called a nonmonotonic function (direction of the relationship changes)  inverted U relationship (U faces down on a graph)  ex. Relationship between age of 40 and happiness No Relationship  when there is no relationship between the two variables and the graph is simply a flat line  ex. Relationship between crowding and task performance  monotinic (always positive of always negative) – curved line going up or down on a graph, not exactly linear  A numerical index of the strength of relationship between variables is called a correlation coefficient  Correlation coefficients are very important because we need to know how strongly variables are related to one another 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  The term uncertainty implies that there is randomness in events; scientists refer to this as random variability in events that occur  Research wants to reduce random variability  If could explain variability then something would no longer be random  You are right 50% of the time when things are random  Finding more variables= decreases variability/random variability Non-experimental vs. Experimental Methods  How can we determine whether variables are related?  Two methods: non-experimental and experimental  Non-experimental is relationships are studied by making observations or measures of the variables of interest  Ex. By asking people to describe their behaviour, or by observing someone’s behaviour  Experimental methods, involves direct manipulation and control of variables (researcher manipulates first variable of interest and observes the response)  Ex. Why anxiety may impair performance (wrote a math test on feelings about how they feel about writing a test) Non-experimental Method  can operationally define variables  non-experimental method allows us to observe covariation between variables, another term that is frequently used to describe this procedure is the correlational method  With this method, we examine whether the variables correlate or vary together  There are two problems with making causal statements when the non-experimental method is used: (1) it can be difficult to determine the direction of cause and effect and (2) researchers face the third- variable problem—that is, extraneous variables may be causing an observed relationship Direction of the Cause and Effect  Non-experimental method difficult to determine which variable causes the other  If something covary or correlate, don’t know which variable causes the other  Third variable problem exists more in this method Third Variable Problem  When the non-experimental method is used, there is the danger that no direct causal relationship exists between the two variables (spurious relationship)  there may be a relationship between the two variables because some other variable causes both exercise and anxiety. This is known as the third-variable problem  A third variable is any variable that is extraneous to the two variables being studied (any variable can be responsible for the relationship between two variables)  Third variable produces alternative explanations for the reduce the overall validity in a study  When we actually know that an uncontrolled third variable is operating, we can call the third variable a confounding variable  If two variables are confounded, they are intertwined so you cannot determine which of the variables is operating in a given situation  Experimental method would provide us with a way of controlling this effect (third variable) 