Soc 3306aLecture 3

Developing the Research Question

Research questions and objectives (adapted from Fig. 3.1 Blaikie 2000)

‘What’ and ‘Why’ questions

What? Descriptive questions

What types of people are involved? What is their characteristic behaviour? What are consequences of their behaviour?

Why? To find causes for or reasons why

Why do they think/act this way? Why does this behaviour have particular

consequences?

Using statistics to answer research questions Statistics are mathematical tools

used to organize, summarize, and manipulate data.

Data gathered through survey items and are the scores on the variables used in a statistical analysis. Data is simply information expressed

as numbers (quantitatively).

Statistical Applications

Two main statistical applications: Descriptive statistics Inferential statistics

Descriptive Statistics Univariate descriptive statistics

summarize the information on one variable at a time. %, mean, standard deviation, histogram

Bivariate descriptive statistics summarize the relationship (i.e. describe the strength and direction) of the relationship between two variables Gamma, correlation coefficient,

scatterplot

Descriptive Statistics

Multivariate descriptive statistics describe the relationships between three or more variables. Multiple correlation, two-way ANOVA,

multiple regression. Can describe the complex and multi-

layered relationships in the social world.

Inferential Statistics

Can use to make inferences from a random sample statistics in order to generalize the results to a population.

Can also use to test for significant differences in group means or proportions.

This is “hypothesis” testing Z or T-tests, one-way ANOVA (F), Chi-

square statistic

Variables….

Used to calculate statistics Are concepts in numerical form

that can vary in value Have traits that can change values

from case to case. Examples:

Age, Gender, Race, Social class change for different individuals in a survey study

Case

The entity from which data are gathered Can be individuals, groups,

organizations, nations etc. The “unit of analysis” In the datasets you will be using,

each case is an individual survey respondent

Reliability and Validity Validity = truthfulness of a measure

Is it measuring what the researcher thinks it is measuring?

Reliability = the consistency or dependency of a measure Does the measure consistently give

the same results? Reliability can be assessed numerically

with statistics like Cronbach’s alpha

Measurement Error Systematic: the measure (ie survey

item) is flawed or distorted and does not reflect a respondent’s true attitude or behaviour this is validity

Random: lack of agreement between repeated uses of a measure This is reliability

Precision error: related to the “level of measurement” of a variable

Important aspects of Quantitative Variables:

a) Independent vs dependent variables Antecedent (control) and Intervening

b) Continuous vs. discrete categories Mutually exclusive and exhaustive

attributes c) Levels of measurement:

nominal ordinal interval/ratio

Independent and Dependent Variables

In causal relationships: CAUSE EFFECTindependent variable X dependent variable Y

Independent variables (“causal” or “explanatory”) are those that are manipulated. Can use more than one in a multivariate analysis (X1, X2, X3….)

Dependent (“outcome” or “response”) variables are measured for variation in response to X.

Discrete and Continuous

Discrete variables are measured in units that cannot be subdivided. Example: Gender

Continuous variables are measured in a unit that can be subdivided infinitely. Example: Age

Level Of Measurement

The mathematical quality of the scores of a variable. Nominal - Scores are labels only,

they are not numbers. Ordinal - Scores have some

numerical quality and can be ranked. Interval-ratio - Scores are numbers

All mathematical operations possible. Most “precise” level of measurement

Survey items as variables

Survey items are questions used to measure demographic, behavioural, or attitudinal aspects of individuals (cases)

Each item is a variable Response categories should have

mutually exclusive and exhaustive attributes

Items can be combined to create composite measures (i.e. Index, Scale)

Using one of the datasets to answer ‘what’ and ‘why’ Look at the variables in one of the

datasets available for your research to see which variables might be suitable to answer ‘what’ and ‘why’ questions

Examine the actual questions asked and the level of measurement of answers

Which variables are of interest to you? What type of questions can you ask?

Hypothetical problem statement: (Uses variables from a 2007 CCHS dataset)

“How is overall health affected by the food choices that are made?”

Is this problem important? Why? Other questions related to this problem:

What factors determine one’s food choices? What are the consequences of the food

choices people make? What role do doctor/dentist visits play?

Other related questions What age groups or gender make

healthier food choices? What are the consequences of less

healthy food choices? What group is more likely to visit

the doctor/dentist regularly? Does ability to chew affect food

choices and consequently, overall health?

Identify specific variables in a dataset that can be used to solve the problem….

Respondent’s Age Respondent’s Gender Consumption of fruit (index) Consumption of vegetables (index) # of doctor, dentist visits Respondent has trouble chewing Respondent’s overall health status

Relations Between Variables

Hypothesis: a statement that describes the relationship between two or more variables.

Null hypothesis: What is actually tested in a statistical test

Alternate hypothesis: The research hypothesis. “Rejection” of the null builds up evidence for the research hypothesis

Testable Hypotheses Hypotheses are stated in terms of

your chosen specific variables:

1. Higher consumption of fruit and vegetables leads to better overall health (main hypothesis)

2. Ability to chew increases the likelihood of higher fruit/vegetable consumption

3. # of dentist visits is related to the ability to chew

The Causal Model(See Figure 4.7 in Gray and Guppy)

Possible model to answer research questions and test hypotheses above:

DVOverall Health Status

Main IVConsumption of Fruit and Vegetables

AV (Control)Gender

AV# of Dentist visits

Antecedent Variable (control)Age

Intervening variableDoctor visits

AVAbility to chew