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Wilfrid Laurier University
Communication Studies

Formulating a Hypothesis - Seale (p. 344-347) - Hypothesis is made up of a: - Dependent variable - Independent variable - Intervening variable Variables - Are the product of the concept-indicator link - Values of an indicator vary according to case - Univariate analysis – study one variable - Ex. Gender: how many people are male? (the analysis is only of one variable) - We usually have more than one variable. - Bivariate analysis: two variables - Multivariate analysis: more than two variables (like your content analyses) - Ex. Gender relating to laptop use. Gender and laptop - Proper use of the laptop during class time is determined by the gender of the user. - You need to make a choice and then make a statement as to which variable is independent and which is dependent. - Our statement says that gender is the independent variable and laptop use is the dependent variable - Concept: 1. Gender 2. Laptop use - Indicators: 1. Male and female and 2. What sites are you on during class? How often are you on the WebCT site? How much use is course content? - You may have an intervening variable such as the colour of the laptop. - You may find that there is a positive relationship between gender and laptop use - Females use their laptops more appropriately than males - The higher the female population the more appropriate the laptop use - Don’t worry about the findings, just make sure to know dependent, independent, and intervening variables make up a hypothesis. Measure of central tendency Mean – average of the distribution of the variable Median – number positioned in the middle of the distribution Mode – most frequent occurring value in a distribution Standard deviation – how far the data deviate from the mean Normal distribution - mean, median, and mode are all the same Shape of Distribution - Distribution can be either symmetrical or skewed, depending on whether there are more frequencies at one end of the distribution that the other. Skewed Distribution - Positively skewed distributions: distributions which have few extremely high values (mean>median) (average is greater than the number positioned in the middle) - Negatively skewed distributions: distributions which have few extremely low values (Means
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