# Practical Statistics for UX and Customer Research Course

\$1,500.00 for 1 year

## Product Description

Welcome to our extensive online course on Practical Statistics for UX and Customer Research. This is a 9-module, 19-part course will cover the history, math, and application of hypothesis testing, sample sizes, correlations, multiple regressions, analysis of variance, factor analysis, cluster analysis, and more. Follow along as well with the extended SPSS and Excel examples of these key statistical areas for UX and customer research.

## 1. Introduction to Data Types

###### Topics for this lesson (25 min):
• Data types taxonomy
• Qualitative vs quantitative
• Levels of measurement
• nominal, ordinal, interval & ratio
• Pragmatic approach to levels of measurement

## 2. Confidence Intervals

###### Confidence Intervals: Intro & Rating Scales (48 min):
• Introduction to confidence intervals
• Three ingredients to a confidence interval
• Quick statistics review
• Confidence intervals applied to rating scale data
###### Confidence Intervals: Binary & Time Data (28 min):
• Confidence intervals for binary data (completion rates)
• Confidence intervals for task time data
• Review of confidence intervals & discussion

## 3. Hypothesis Testing

###### Hypothesis Testing Introduction (36 min):
• An introduction to the Null Hypothesis Significance Testing (NHST) framework
• Working with the NHST framework
• To p or not to p < .05
• Assessing practical significance
###### Hypothesis Testing: Conducting Tests (12 min):
• Considerations when conducting statistical tests
• Data types and between- vs. within-subjects

## 4. Power & Sample Estimation

###### Power & Sample Size Estimation: Intro & Problem Discovery (39 min):
• Three ways to determine the right sample size
• And more…
###### Power & Sample Size Estimation: Precision (33 min):
• How precise do you really need to be?
• And more…
###### Power & Sample Size Estimation: Comparison (37 min):
• Three ways to determine the right sample size
• And more…

## 5. Correlation & Regression

###### Correlation & Regression (29 min):
• Overview of Advanced Research Methods
• Introduction and Application of the Correlation (the basis for many advanced methods)
• An Introduction to Linear Regression
• The Influential History of Regression
• The Mechanics of Linear Regression

## 6. Multiple Regression

###### Multiple Regression & Key Driver Analysis (19 min):
• Overview of Multiple Regression and Key Driver Analysis (KDA)
• Visualizing Key Drivers
• Multiple Regression in SPSS
###### Multiple Regression & KDA SPSS Example (19 min):
• SUPR-Q survey of auto insurance websites
• Key Driver Analysis of low-level predictors influence on SUPR-Q

## 7. Analysis of Variance

###### Analysis of Variance (24 min):
• History of Analysis of Variance (ANOVA)
• Overview of ANOVA output
• How to conduct an ANOVA in SPSS/Excel Calculator
• Interaction Effects in SPSS
###### Analysis of Variance Extended SPSS Example (13 min):
• Example of 1-way ANOVA with multiple comparisons
• And more…

## 8. Factor Analysis

###### Factor Analysis (31 min):
• History of factor analysis
• Exploratory (EFA) vs. Principal Components (PCA) vs. Confirmatory Factor Analysis (CFA)
• Conducting an exploratory factor analysis (EFA)
• Examples of EFA, PCA, and CFA
###### Factor Analysis Extended SPSS Example (9 min):
• Auto insurance example
• Examine correlation matrix
• And more…

## 9. Cluster Analysis

###### Cluster Analysis (9 min):
• History of cluster analysis
• Different types of cluster analysis
• Preview of the steps for conducting a cluster analysis
###### Cluster Analysis Extended SPSS Example (10 min):
• Auto insurance example
• Select data
• Start hierarchical cluster analysis
• And more…
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