Title: UGQuants (SSPS)

Section 1
  News forum
  (untitled)
Maths Self Test
  Basic mathematical procedures and conventions
  Basic signs and symbols
  Fractions
  Adding, subtracting, multiplying and dividing fractions
  Proportions, percentages and probabilities
  Proportions
  Percentages
  Examples of calculated percentages
  Showing increases and decreases with percentages.
  Probabilities
  Powers and indices (exponents)
  Some arithmetical and mathematical conventions
  Equations and formulae
  Common statistical notation
  Self test: Basic maths check
Introduction to Quantitative Analysis
  Welcome!
  Introduction to quantitative analysis
  Are there right and wrong answers in quantitative analysis?
  Why is quantitative analysis useful?
  Theory, evidence and measurement
  The operationalisation of social class
  Cases, variables and values
  Variables
  Values
  Putting cases, variables and values together in SPSS
  Levels of Measurement and different types of variable
  Nominal
  Ordinal
  Interval
  Ratio
  Ambiguities in defining levels of measurement
  Continuous, categorical and dummy variables
  Categorical variables
  Dummy variables
  Self test: Levels of measurement
  Forum: Intro to Quantitative Analysis
  Tutorial activity
Univariate Analysis
  Introduction: Univariate Analysis
  Data = fit + residual
  Measures of central tendency
  The Mean
  The Median
  The Mode
  Comparing measures of central tendency
  Measures of spread
  Variance
  Standard deviation
  Visualising the spread of data
  Ranges, Quartiles and Percentiles
  Visualising ranges and extreme values: boxplots
  Frequency tables
  Simplifying and editing frequency tables
  Some practice with frequency tables
  ‘Recoding’ variables
  Some practice of recoding
  An introduction to missing data
  'Missing data'
  Conventions for dealing with missing data
  Implications of missing data for analysis
  Video summary
  Self-tests and exercises
  Self test: Measures of central tendency using software
  Tutorial activity: Univariate Analysis
Associations Between 2 Variables
  Introduction: Associations between 2 variables
  Association between variables – key points
  Independent and dependent variables
  Analysing associations
  Column percentages
  Row percentages
  Cell (Total) percentages
  Contingency tables as indicators of association
  More complex contingency tables
  Some practice of contingency tables
  Association between categorical (nominal or ordinal) and ratio/interval variables
  Comparing means
  Example: Why are non-voters on average younger than voters?
  Comparing Means continued
  Comparing other measures of distribution
  Self test - Associations between 2 variables
  Tutorial exercises
  Associations between 2 variables - Summary
Inference
  Introduction to inference
  The confidence interval
  Type I + II errors and significance
  Hypothesis formulation and testing in random samples - A summary
  Exploring relationships in samples
  Exploring relationships using samples (continued)
  Chi Sq. and statistical software
  Conclusion
  Self test: Inference
  Tutorial Exercise
Association for Nominal and Ordinal Variables
  Introduction: Association for nominal and ordinal variables
  Good Table Manners
  Using tables for measures of association
  Yule’s Q
  Chi Square
  How to calculate Chi Square in SPSS
  Correlation coefficients using Chi square results
  Phi
  Cramer’s V
  Proportional Reduction of Error (PRE) Measures of Association in Tables
  Lambda
  Tau
  Gamma
  Using software to obtain measures of association
  Measures of association: Things to consider
  Conclusion
  Self test: Measures of association
  Tutorial activities: Measures of association
Correlation and Regression
  Introduction: Correlation and regression
  Bivariate relationships among two interval / ratio level variables
  Using scatter plots to assess correlation
  Assessing Correlation: The Pearson product moment correlation coefficient ‘r’
  Strength of association - How strong is strong?
  Correlation matrix
  A Golden Rule: correlation is not causation
  From correlation to regression
  Minimising prediction errors
  When can we use regression?
  Understanding SPSS regression output
  Understanding coefficients
  Sources of error
  Conclusion
  Self test: Correlation and regression
  Tutorial activity: Correlation and Regression
Causality
  Introduction: Causality
  Developing causal theories
  The concept of control
  An example of the concept of control
  The concept of control
  The idea of control in experimentation
  Alternatives to experimentation
  What assumptions do we need to make to infer cause from correlation?
  Introducing a control variable to a cross-tabulation
  Family Planning
  Do you trust your parliament?
  Causal pathways for more than two variables
  Causality is complex: An illustration
  Testing models empirically: An example
  Testing models empirically: Assessing relationships
  Testing models empirically: Path diagram for two variables
  Testing models empirically: Adding the control variable
  Testing models empirically: Path diagram with control variable
  Testing models empirically: Estimating effect sizes
  FAC! (Fallacy of Affirming the Consequent)
  Conclusion
  Tutorial Activity: Causality
  Causality Appendix
Probability
  Introduction: Probability
  Chaos, order and regularities
  Structure and probability
  Structure, coincidence and chance
  The idea of a 'limit'
  The normal curve
  The distance between chance and structure
  Probability, populations and samples
  Some basics of sampling inference
  The normal curve and the probability of getting accurate estimates from a sample
  The standard normal curve
  Using the standard normal distribution to make descriptive summary statements
  Repeated samples are normally distributed
  SPSS and the normal curve
  The accuracy of sample estimates from populations
  Estimating the value of a characteristic
  Categorical variables: estimating the proportion of a characteristic
  Weights
  Conclusion: Probability
  Self test: Probability
  Tutorial Activity: Probability
  50 samples syntax
Multiple Regression
  Introduction: Multiple regression
  Regression line
  Error and coefficient
  Control
  Nominal and ordinal variables
  Ordinary Least Squares
  Multiple Regression and causation
  Calculations
  Step 1: From a generable idea to a testable model
  Step 2: Exploring the shape of our data
  Step 3: Transformation
  Step 4: Obtaining the values for the regression equation
  Step 5: Improving the model
  Outliers
  Step 6: Working with more than one independent variable
  STEP WISDOM
  Dummies
  Step 7: Checking for Multicollinearity
  Step 8: Checking the model
  Heteroscedasticity: as nasty as it sounds?
  Multiple regression: a checklist
  Summary
  Self test: Multiple regression
  Tutorial Exercise: Multiple regression
Logistic Regression
  Introduction: Logistic regression
  Logistic regression: modelling probabilities of a value rather than the value itself
  From probability to odds to logits
  Probabilities, Odds & Odds Ratios
  Odds and Logits
  Logistic regression results
  Building a model
  Building a model - the effects of age?
  Building a model - SPSS outputs
  Building a model continued - controlling variables page
  Building a model - Controlling variables continued
  Analysis of residuals
  Outliers and influential cases
  Presentation of results
  Conclusion
  Tutorial Exercise: Logistic regression
Data Visualisation
  Introduction: data visualisation
  In the Beginning (A Brief History of Visualisation)
  The golden rules of visualisation
  Visualisation Gallery
  Example 1: Health and internet use
  Example 1: Discussion
  Example 2: Retirement age
  Example 2: Discussion
  Example 2: Discussion (2)
  Different techniques for different data
  Visualisation of a single variable
  Continuous variable: The histogram
  Continuous variable: box plots
  Density Plot Syntax SPSS
  Categorical variable
  Categorical variable: pie charts
  Categorical variable: bar charts and column charts
  Visualisation of two variables
  Two continuous variables
  Two Continuous Variables continued.
  Two continuous variables: scatterplots
  Two Continuous Variables: An Example
  One Continuous Variable and One Categorical Variable
  Density Plot Syntax (Groups) for SPSS
  Two categorical variables
Survey Design
  Introduction: Survey design
  Strengths and weaknesses of survey design
  Research designs in survey research
  The place of surveys in research design
  What sorts of research topics can surveys help us with?
  Secondary analysis
  2. What or who to survey?
  Probability samples
  Sampling
  Non-probability samples
  Convenience sampling, purposive sampling and quota sampling
  3. Tools of the trade – methods of data collection
  Interviews
  Face-to-face interviews
  Telephone interviews
  Self completion
  Advantages and disadvantages of self completion methods
  4. Asking questions
  Operationalisation – developing indicators for concepts
  Principles of questionnaire design
  Types of questions
  Question wording
  Response structure
  Closed questions
  Thinking ahead to analysis: Levels of measurement
  Questionnaire structure and layout
  Pre-testing and piloting
  5. Cognitive interviewing
  Think-aloud interviews
  Verbal probe interviews
  6. From words to numbers: data coding
  Forming and coding response categories
  Forming and coding response categories (2)
  Summary
  Self Test: Survey Design
Beyond data collection and analysis
  Introduction: Beyond data collection & analysis
  The social construction of data
  Statistical literacy
  Who funds data collection and why?
  Who funds data collection and why? (2)
  The data collectors
  The Office for National Statistics
  NatCen & ScotCen
  Private sector research agencies
  Survey management and data management
  Survey management systems
  Data management for primary data
  Sharing data
  Data management planning
  Documenting data
  Data storage and backup
  Data security, protection and confidentiality
  Preparing analysis and reporting our findings
  Deriving analysis variables
  Choosing a method of analysis
  Reporting findings
  Summary
  Self Test Beyond Data Collection and Analysis