Formulating a Hypothesis
- Seale (p. 344-347)
- Hypothesis is made up of a:
- Dependent variable
- Independent variable
- Intervening variable
- 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
- 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
- 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.
- Positively skewed distributions: distributions which have few extremely high
values (mean>median) (average is greater than the number positioned in the
- Negatively skewed distributions: distributions which have few extremely low