Class Notes (786,168)
Psychology (3,171)
PSYC 690J3 (29)
Juan Wang (29)
Lecture 14

# lecture 14.doc

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School
McGill University
Department
Psychology
Course
PSYC 690J3
Professor
Juan Wang
Semester
Fall

Description
Epidemiological research  Epidemiology is the study of the frequency and distribution of a disordered in a population  Data re gathered about the rates of a disorder and its possible correlates in a large samples or population  Epediologioical research focuses on determining three features of a disorder:  1) prevalence- the proportion of a population that has the disorder at a given point or period fo time  2) incidence- the # of new cases of the disorder that occur in some period of time usually a year  3) risk factors- conditions or variables that if present increase the likelihood of developing the disorder  knowing this stuff is imp for planning health care facilities, services, etc  depression is twice as common in women than men. Thus gender is a risk factor and this knowledge led to a theory of depression that suggests its due to a style of coping with stress that is more common in women than men The correlational method  correlational method - establishes whether there is a relationship between or among two or more variables  it is often employed in epidemiological research  in correlatioanl research the variables being studied are measured as they exits in nature  this feature distinguishes the method from experimental research in which variables are actually manipulated and controlled by the researcher  ex: national study of Canadian preschoolers showed that behaviour problems were higher among kids from less affluent neighbourhoods Measuring correlation  1 thing in determining a correlation is to obtain parts of observations of the variables in question. Ex: height and weight  Then the strength of the relationship between the two sets of observations can be calculated to determine the correlation coefficient denoted by the symbol r. the statistic may take any value between –1.00 and +1.00 and it measures both the magnitude and the direction of a relationship  The higher the absolute value of r, the larger or stronger the relationship between the two variables.  An r of either +1.00 or –1.00 indicates the highest possible or perfect relationship whereas an r of 0.00 indicates that the variables are unrelated  If the sign of r is positive the two variables are said to be positively related –as the values for variable x increase those for variable y also tend to increase. Ex: correlation between height and weight is +.88 this would indicate a very strong positive relationships as height increased so does weight  When the sign of r is negative, variables are said to be negatively related as scores on one variable increase those for the other tend to decrease. Ex: # of hours spent watching t.v is negatively correlated with grade point average  Scatter diagrams of positive and negative correlations  In perfect relationships all points fall on a straight line  The values tend to scatter increasingly and become dispersed as the correlation becomes lower  When the correlation reaches 0.00 knowledge of a persons score on one variable tells us nothing about his or her score on the other Statistical significance  The magnitude of a correlation coefficient tells us the strength of a relationship between two variables  Statistical significance refers to the likelihood that the results of an investigation are due to chance  A statistical significance correlation is one that is not likely to have occurred by chance  A correlation is considered statistically significant if the likelihood or probability that it is a chance finding is 5 or less in 100. this level of significance is called the .05 level, commonly written as p =0.5 (p stands for probability)  In general as the size of the correlation coefficient increases the result is more and more likely to be statistically significant  Whether a correlation attains statistical significance depends on the # of observations made.  The greater the # of observations the smaller r (the correlation) needs to be to reach statistical significance Applications to psychopathology  Whenever we compare ppl given one diagnosis with those given another or with ppl without a psychological diagnosis the study is correlational  When the correlational method is used in research on psychopathology one of the variables is typically diagnosis  To calculate a correlation between this variable and another one diagnosis is quantifies so that having an anxiety disorder is designated by a score of 1 and not having one is score of 2  The diagnosis variable can then be correlated with another variable such as the amount of stress that has been recently experiences  Variables such as having anxiety disorder or not are called classificatory variables  other ex are age, sex, social class and body build. These variables are naturally occurring patterns and are not manipulated  most research on the causes of psychopathology is corrleational problems of causality  correlatioanl method has a drawback: it does not allow determination of cause-effect relationships  a sizeable correlation between 2 variable tells us only that they are related or tend to co vary with each other but we do not really know which is cause and which is effect or if either variable is actually the cause of the
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