14.2 Stop words: Removing noise in the data.14.1 The tidy process of working with textual data.14 Mixed-methods research: Analysing qualitative data in R.13.4 Other regression models: Alternatives to OLS.13.3.3 Centering predictors: Making \(\beta\)s more interpretable.13.3.1 Regressions with control variables.13.2.3 Multicollinearity: The dilemma of highly correlated independent variables.13.2.2 Standardised beta ( \(\beta\)) vs. unstandardised beta ( \(B\)).13.2.1 Outliers in multiple regressions.
13 Regression: Creating models to predict future observations.12.4.3 A final remark about comparing groups based on categorical data.12.4.2 Paired groups of categorical variables.12.4.1 Unpaired groups of categorical variables.12.4 Comparing groups based on factors: Contingency tables.11.4 Concluding remarks about power analysis.11.1 Ingredients to achieve the power you deserve.11 Power: You either have it or you don’t.10.4.3 Simpson’s Paradox: When correlations betray you.10.4.1 Correlations are not causal relationships.10.3 Significance: A way to help you judge your findings.9.5.4 Concluding remarks about outliers.9.5.2 Detecting outliers using the interquartile range (IQR).9.5.1 Detecting outliers using the standard deviation.9.4 Homogeneity of variance (homoscedasticity).9 Sources of bias: Outliers, normality and other ‘conundrums’.8.5.2 The skimr package for descriptive statistics.8.5.1 The psych package for descriptive statistics.8.5 Packages to compute descriptive statistics.8.4.6 Standard deviation: Your average deviation from the mean.8.4.5 QQ plot: A ‘cute’ plot to check for normality in your data.8.4.3 Density plots: Your smooth histograms.8.4.2 Histogram: Do not mistake it as a bar plot.8.4.1 Boxplot: So much information in just one box.8.4 Indicators and visualisations to examine the spread of data.8.3 Central tendency measures: Mean, Median, Mode.8.2 Frequencies and relative frequencies: Counting categorical variables.7.9 Once you finished with data wrangling.7.8.4 Reversing items with opposite meaning.7.8.2 Checking internal consistency of items measuring a latent variable.7.8 Latent constructs and their reliability.7.7.5 Main takeaways regarding dealing with missing data.7.7.4 Two more methods of replacing missing data you hardly ever need.7.7.3 Replacing or removing missing data.7.7.2 Identifying patterns of missing data.7.6.1 Converting dates and times for analysis.7.6 Handling dates, times and durations.7.4 Data types: What are they and how can you change them.7.3 Cleaning your column names: Call the janitor.7.1.3 Importing data using functions directly.7.1.2 Importing data from the Environment pane.
5.4.3 Install all necessary R packages for this book.5.4.2 Installing packages via RStudio’s package pane.5.4.1 Installing packages using install.packages().4.4 The Files / Plots / Packages / Help / Viewer window.4.3 The Environment / History / Connections / Tutorial window.3.4 Updating R and RStudio: Living at the pulse of innovation.2.4 Learning any programming language will help you learn other programming languages.2.3 Programming languages allow you to look at your data from a different angle.2.2 Programming languages enhance your conceptual thinking.2.1 Learning new tools to analyse your data is always essential.2 Why learn a programming language as a non-programmer?.1.4 Understanding the formatting of this book.1.3 A ‘tidyverse’ approach with some basic R.1.1 A starting point and reference book.The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. The cookies is used to store the user consent for the cookies in the category "Necessary".
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