Ways of knowing
Agreement
Tradition (advantages & disadvantages)
Authority (advantages & disadvantages)
Experience (advantages & disadvantages)
Errors in human inquiry
Errors in observation (inaccurate & selective)
Errors in reasoning (overgeneralization, discounting, illogical reasoning)
Errors in the process (premature closure, private)
Overview of scientific approach (empiricism & logic)
Safeguards employed in scientific discovery
Everything is open to debate
Knowledge is based upon logic and empiricism
Observations are detailed and systematic
Logic guides the interpretation of observations
Process is never complete
Process is public
Advantages and disadvantages of scientific approach
Scientific process of inquiry
Goal of social science research
Variables (attributes)
Relationships (causal vs. correlational)
Overview of scientific process of inquiry
Theory
Research questions
Observations
New knowledge
Inductive vs. deductive
Steps involved in quantitative research
Come up with question needs to be empirically answered (hypothesis vs. research questions)
Determine unit of analysis
Select a research method (survey, content analysis, experiment)
Operationalization
Sampling and populations
Make your observations
Analyze observations
Interpret results.
Conceptualization
Operationalization
Reliability
Validity
Selecting samples
Key Concepts (sampling, population, study population, sampling frame, sampling unit, sample, statistic, parameter)
Probability sampling
Sampling theory
Key concepts (sampling distribution, normal distribution, 68-95-99 rule, standard deviation, sampling error, confidence level, confidence ratio)
Types of representative samples (advantages & disadvantages)
Random samples
Systematic sampling
Stratified sampling
Multistage clustering
Types of non-representative samples (advantages & disadvantages)
Purposive sampling
Convenience sampling
Quota Sampling
When to use different types
Types of research methods
Comparison between qualitative and quantitative methods
Similarities
Differences
Questions addressed
Words vs. numbers
Importance of insider perspective
Emphasis of induction or deduction
Naturalistic vs. artificial settings
Tools used to collect observations
Advantage and disadvantage of using two approaches
Criticism often leveled qualitative approach
Idiomatic
Studying the obvious
Criticism often leveled qualitative approach
Artificial settings
Measurement issues
Overview of quantitative research methods
Primary goal of quantitative research (dependent variables vs. independent variables, relationships -- causal (three necessary features, causal vs. correlational)
Variables are measured
Deterministic/probabilistic model
Levels of measurement
Properties and differences between (nominal, ordinal, interval, ratio measures)
Reliability (test/retest method, internal consistency, inter-coder method)
Validity (face validity, content validity, criterion validity – concurrent and predictive)
Describing Data
Distributions (frequency)
Distributions (population, sample, sampling)
Measures of central tendency (mode, median, mean)
Measures of dispersion (range, standard deviation)
z-scores
Survey research
Goals (simple description and identifying patterns)
Primary advantages and disadvantages of using surveys
Types of surveys
Mail or self-administered questionnaires
Computer-Internet Surveys
Telephone
Face to face interviews
Advantages and disadvantages of each.
Guidelines for writing questions
Use very simple words and grammar.
Avoid leading questions.
Be care in the words you chose.
Avoid double-barreled questions.
Avoid negative items.
Order Effects (Consider order of items)
Decide if you should use a direct or indirect question (single or multiple item).
Overview of Hypothesis Testing
Goal
Null versus Research Hypothesis
Statistics used with surveys
Describing results of surveys
Single item
Always report how many people filled out the survey.
Report the appropriate measure of central tendency for each variable.
Multiple item questionnaire.
Always report how many people filled out questionnaire.
Determine which items should be added together.
Item analysis
Identifies which items to keep and which items to drop.
Describes how consistent the kept items are with each other.
Cronbach’s alpha or coefficient alpha.
Factor analysis
Identifies the number of variables actually being measured.
Identifies which items should be grouped together.
Interpretation of variables being measured is a judgment call.
Correlation
Exploring relationships among variables
Correlation coefficient (symbolized by "r")
Direction (positive-direct, negative-inverse, independent)
Magnitude of the coefficient (Guilford’s guidelines, coefficient of determination)
Steps involved in a correlation analysis
State hypothesis
Calculate the correlation coefficient.
Calculate the d.f.
Look up the critical value in the table.
Multiple correlation vs. partial correlation
Regression
Goal (prediction)
Simple linear regression.
Line of best fit
Provides two important pieces of information.
Y-intercept and slope
Determining if slope is significant
Accuracy of prediction (r2)
Multiple linear regression:
More than one independent variable.
Regression line changes to reflect additional slopes for each independent variable.
Accuracy of prediction is now symbolized by R2