Analytics, Basic Statistics And Metrics Training

Introduction to Social Science Research Principles and Terminology

• Populations, samples, parameters and statistics
• Inferential and descriptive statistics
• Sampling issues
• Types of variables and scales of measurement
• Research design
• Making sense of distributions and graphs
• Wrapping up and looking forward
• Work problems

Measures of Central Tendency

• Measures of central tendency in depth
• Example: the mean, median and mode of skewed distributions
• Writing it up
• Wrapping up and looking forward
• Work problems

Measures of Variability

• Range
• Variance
• Standard deviation
• Measures of variability in depth
• Why have variance?
• Examples: examining the range, variance, and standard deviation
• Worked examples
• Wrapping up and looking forward
• Work problems

The Normal Distribution

• Characteristics of the normal distribution
• Why is the normal distribution so important?
• The normal distribution in depth
• The relationship between the sampling method and the normal distribution
• Skew and Kurtosis
• Example 1: applying normal distribution probabilities to a normal distribution
• Example 2: applying normal distribution probabilities to a nonnormal distribution
• Wrapping up and looking forward
• Work problems

Standardization and z Scores

• Standardization and z scores in depth
• Interpreting z scores
• Examples: comparing raw scores and z scores
• Worked examples
• Wrapping up and looking forward
• Work problems

Standard Errors

• What is standard error?
• Standard errors in depth
• How to calculate the standard error of the mean
• The central limit theorem
• The normal distribution and t distributions: comparing z scores and t values
• The use of standard errors in inferential statistics
• Example: sample size and standard deviation effects on the standard error
• Worked examples
• Wrapping up and looking forward
• Work problems

Statistical Significance, Effect, Size, and Confidence Intervals

• Statistical Significance in depth
• Limitations of statistical significance testing
• Effect size in depth
• Confidence intervals in depth
• Example: statistical significance, confidence interval. and effect size for a one-sample t test of motivation
• Wrapping up and looking forward
• Work problems

t Tests

• What is a t test?
• t distributions
• The one-sample t test
• The independent samples t test
• Dependent (paired) samples t test
• Independent samples t tests in depth
• The standard error of the difference between independent sample means
• Determining the significance of the t value for an independent samples t test
• Paired of dependent samples t tests in depth
• Example 1: comparing boys’ and girls’ grade point averages
• Example 2: comparing fifth- and sixth- grade GPAs
• Writing it up
• Worked examples
• Wrapping up and looking forward
• Work problems

One-Way Analysis of Variance

• ANOVA vs. independent t tests
• One-way ANOVA in depth
• Deciding if the group means are significantly;y different
• Post-Hoc tests
• Effect size
• Example: comparing the sleep of 5-, 8-, and 12-year-olds
• Writing it up
• Worked example
• Wrapping up and looking forward
• Work problems

Factorial Analysis of Variance

• When to use factorial ANOVA
• Some cautions
• Factorial ANOVA in depth
• Interpreting main effects in the presence of an interaction effect
• Testing simple effects
• Analysis of covariance
• Illustration of factorial ANOVA, ANCOVA, and effect size with real data
• Example: performance, choice, and public vs. private evaluation
• Writing it up
• Wrapping up and looking forward
• Work problems

Repeated-Measures Analysis of Variance

• When to use each type of repeated-measures technique
• Repeated-measures ANOVA in depth
• Repeated-measures analysis of Covariance (ANCOVA)
• Adding an independent group variable
• Example: changing attitudes about standarized tests
• Writing it up
• Wrapping up and looking forward
• Work problems

Correlation

• When to use correlation and what it tells us
• Pearson correlation coefficients in depth
• A brief word on other types of correlation coefficients
• Example: the correlation between grades and test scores
• Writing it up
• Wrapping up and looking forward
• Work problems

Regression

• Simple vs. multiple regression
• Variables used in regression
• Regression in depth
• Multiple regression
• ExampleL Predicting the use of self-handicapping strategies
• Writing it up
• Worked examples
• Wrapping up and looking forward
• Work problems

The Chi-Square Test of Independence

• Chi-square test of independence in depth
• Example: generational status and grade level
• Writing it up
• Worked example
• Wrapping up and looking forward
• Work problems

Factor Analysis and Reliability Analysis: Data Reduction Techniques

• Factor analysis in depth
• A more concrete example of exploratory factor analysis
• Confirmatory factor analysis: a brief introduction
• Reliability analysis in depth
• Writing it up
• Work Problems
• Wrapping up