- Research interests
- Publications
- Books
- Data Management in R: A Guide for Social Scientists
- Introduction
- Building Blocks of Data
- Data Frames and their Management
- The Structure of Data Frames
- Accessing and Changing Variables in Data Frames
- Manipulating Data Frames
- Creating subsets of data frames
- Merging data frames: An example with data from the British Election Study
- Merging data frames: An example with artificial data
- Appending data frames
- Reshaping data frames: An example with artificial data
- Reshaping data frames: An example with data from the British Election Study
- Sorting data frames
- Aggregating Data Frames
- Groupwise computations within Data Frames
- Importing Data into Data Frames
- Data Tables and the “Tidyverse”
- Social Science Surveys
- Data from Complex Samples
- Dates, Times, and Time Series
- Spatial and Geographical data
- Text as Data
- Data Management in R: A Guide for Social Scientists
- Articles in peer reviewed journals
- Multilevel Analysis with Few Clusters: Improving Likelihood-based Methods
- Method Factors in Democracy Indicators
- Social Cleavages and Electoral Behaviour in Long-Term Perspective
- A Dynamic State-Space Model of Coded Political Texts
- Bounded Volatility in the Dutch Electoral Battlefield
- Stability or Decline? Class, Religion and the Vote in Germany
- Social Divisions, Party Positions, and Electoral Behaviour
- Ignoramus, Ignorabimus? On Uncertainty in Ecological Inference
- The Decline of Social Cleavages in Western Europe Revisited
- Politicisation, Economic Development, and Political Interest in Europe
- Book contributions
- All Gone? Change and Persistence in the Impact of Social Cleavages on Voting Behavior in Germany since 1949
- Der Einfluss des politischen Kontextes auf die Ausformung einer Parteiidentifikation
- Replicationsmaterial
- Research Reports
- Books
- Working papers
- Transformation of Religious Cleavages in Western Europe
- On Models for Categorical Dependent Variables
- The RiLe Index of Party Ideology - A Reductio
- Education and Voting in Germany in Long-Term Perspective
- Valence or Position? Unified Conception of Party Competition
- Political Context and the Formation of Party Identification in the United States
- A New Approach to the Analysis of Tactical Voting
- Strategic Voting and Ticket-Splitting in Germany
- Party Families and Conceptions of Party Ideology
- The Political Demobilisation of the Working Class in Germany
- Campaign Dynamics and Strategic Voting in UK Parliamentary Elections
- On the Distinctiveness of Party Families
- Measuring Politcal Knowledge: The Problem of Cross-National Equivalence
- Data
- Teaching
- Software
- memisc: Management of Survey Data and Presentation of Analysis Results
- "memisc" on CRAN
- "memisc" on GitHub
- Documentation
- User guides
- Handling Questionnaire Items
- Importing Data from SPSS and Stata
- Creating tables of model estimates
- Generating Tables of Descriptive Statistics
- Combining Tables
- Example: The American Election Study 1948
- Manual pages
- Introduction to the ‘memisc’ Package
- Conditional Evaluation of an Expression
- Vectors of Univariate Sample Statistics
- Loop over Variables in a Data Frame or Environment
- Operate on grouped data in data frames and data sets
- Convert Annotations, and Value Labels between Encodings
- Create a list and conveniently supply names to its elements
- Reshape data frames or data sets
- A Dimension Preserving Variant of “sapply” and “lapply”
- Substitutions in Language Objects
- One-Dimensional Table of Frequences and/or Percentages
- Write Codebooks and Variable Descriptions into a Text File
- Adding Annotations to Objects
- Apply a Formatting Template to a Numeric or Character Vector
- Converting Data Frames into Arrays
- Construction of Lists of Symbols
- Distinguish between Cases Specified by Logical Conditions
- Generate a Codebook of a Data Set
- Describe structure of Data Sets and Importers
- Collect Objects
- Convenience Methods for Setting Contrasts
- Data Set Objects
- Manipulation of Data Sets
- Handle duplicated labels
- Change dimnames, rownames, or colnames
- Check for and report duplicated labels
- Format Objects in HTML, show the HTML Format or Write it to a File
- Format Codebooks as HTML
- Format “Flattened Tables” as HTML
- Combining flattened tables.
- Generic Tables and Data Frames of Descriptive Statistics
- Get Model Summaries for Use with “mtable”
- Building Blocks for HTML Code
- Object Oriented Interface to Foreign Files
- Survey Items
- How Survey Items Are Converted into “Ordinary” Data Vectors
- Value Labels
- Levels of Measurement of Survey Items
- Deprecated Functions in Package memisc
- Comparative Table of Model Estimates
- Format for ‘mtable’ Objects for Writing into File
- HTML Formatting for ‘mtable’ Results
- Format ‘mtable’ Results for LaTeX
- Print Format for ‘mtable’ Objects
- Negative Match
- Table of Percentages with Percentage Base
- Easy Creation of Tables of Percentages
- Query an Object for Information
- Recode Items, Factors and Numeric Vectors
- Change labels of factors or labelled objects
- Change Names of a Named Object
- Reorder an Array or Matrix
- Retain Objects in an Environment
- Take a Sample from a Data Frame-like Object
- Convenience Methods to Sort Data Frames and Data Sets
- Convenience wrappers for common statistical functions
- Formatting Styles for Coefficients, Factor Contrasts, and Summary Statistics
- Syntactic Sugar for Setting Annotations and Attributes
- Interface to Packages ‘tibble’ and ‘haven’
- Convert an Array into a Data Frame
- Additional Methods for LaTeX Representations for R objects
- Named Lists, Lists of Items, and Atomic Vectors
- Value Filters
- A Generic Viewing Function
- HTML Output for ‘view.
- Table of frequencies for unlabelled codes
- Add Alternative Variance Estimates to Models Estimates
- Index
- User guides
- Recent news
- Examples
- mclogit: Multinomial Logit Models, with or without Random Effects or Overdispersion
- mclogit on CRAN
- Baseline-category logit models
- Conditional logit models
- Random effects in baseline logit models and conditional logit models
- Documentation of the Package
- Manual pages
- Choice of Means of Transport
- Overdispersion in Multinomial Logit Models
- Class, Party Position, and Electoral Choice
- getSummary Methods
- Baseline-Category Logit Models for Categorical and Multinomial Responses
- Conditional Logit Models and Mixed Conditional Logit Models
- Internal functions used for model fit.
- Control Parameters for the Fitting Process
- Predicting responses or linear parts of the baseline-category and conditional logit models
- Simulating responses from baseline-category and conditional logit models
- Index
- Technical documentation
- Manual pages
- References
- munfold: Metric Multidimensional Unfolding in R
- mpred: Generic Predictive Margins
- manifestos: Spatial Modelling of Party Manifestos (and other political texts)
- iimm: Improved Inference for Multilevel Models with Few Clusters
- EMfit: An Infrastructure for Latent Variable Model Fitting using EM Algorithms
- memisc: Management of Survey Data and Presentation of Analysis Results
- About
Social Science Surveys¶
This chapter introduces the extension package memisc, which is specifically designed to address several of the challenges that were discussed in the previous chapter. It shows how a the system memory can be saved by importing subsets of variables and observations; how variables can be renamed so that results are more easy to interpret; how certain metadata can be used, that are not provided for by a basic R installation, such as value labels and user-defined missing values. The chapter also provides examples for more complex recodings of variables, e.g. for the construction of Goldthorpe class categories for households from ISCO-coded occupations of survey respondents and the creation of codebooks.
Below is the supporting material for the various sections of the chapter.
Importing survey data¶
Importing data from the British Election Study in SPSS format
Importing ASCII format data from the American National Election Study
Recoding and other transformations¶
Recoding data from the British Election Study
Combining variables using case distinctions