ProcData: An R Package for Process Data Analysis

Xueying Tang, Susu Zhang, Zhi Wang, Jingchen Liu () and Zhiliang Ying
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Xueying Tang: University of Arizona
Susu Zhang: University of Illinois at Urbana-Champaign
Zhi Wang: Columbia University
Jingchen Liu: Columbia University
Zhiliang Ying: Columbia University

Psychometrika, 2021, vol. 86, issue 4, No 13, 1058-1083

Abstract: Abstract Process data refer to data recorded in log files of computer-based items. These data, represented as timestamped action sequences, keep track of respondents’ response problem-solving behaviors. Process data analysis aims at enhancing educational assessment accuracy and serving other assessment purposes by utilizing the rich information contained in response processes. The R package ProcData presented in this article is designed to provide tools for inspecting, processing, and analyzing process data. We define an S3 class ‘proc’ for organizing process data and extend generic methods summary and print for ‘proc’. Feature extraction methods for process data are implemented in the package for compressing information in the irregular response processes into regular numeric vectors. ProcData also provides functions for making predictions from neural-network-based sequence models. In addition, a real dataset of response processes from the climate control item in the 2012 Programme for International Student Assessment is included in the package.

Keywords: process data analysis; multidimensional scaling; autoencoder; sequence model (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11336-021-09798-7

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