Data Analysis In Social Sciences Research Articles

A Comparison of Neural Network and Expert Systems Algorithms with Common Multivariate Procedures for Analysis of Social Science Data

New computer techniques for data analysis, notably the algorithms associated with neural networks and with expert systems, have not caught on to a significant extent in social science. To appraise these developments, an empirical assessment is conducted in which expert systems and neural network approaches are compared with multiple linear regression, logistic regression, effects analysis, path analysis, and discriminant analysis. A simple method of partitioning neural network output layer connections in terms of input nodes (corresponding to independent variables) is also presented, allowing neural net analysis for modeling as well as classification purposes. It is concluded that back-propagation (neural networks) is more effective than other procedures, sometimes strikingly so, in correctly classifying the dependent, even when the amount of noise in the model is high. Back-propagation was of less help, however, in causal inference. None of the techniques performed well by this important criterion. The ID3 algorithm is found to provide a useful mode of knowledge representation quite different from other procedures. While this may be preferred by some analysts for certain types of research, ID3 is not consistently superior to procedures in the multiple linear general model (MLGH) family in terms of effectiveness, either for classification or for causal inference. Keywords: statistical inference, computers, modeling, simulation, regression, discriminant analysis, effects analysis, path analysis, expert systems, ID3, neural networks, back-propagation.

The Analysis of Social Science Data with Missing Values

Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable. Two more principled approaches, namely maximum likelihood under a model for the data and missing-data mechanism and multiple imputation, are applied to the bivariate problem. General properties of these methods are outlined, and applications to more complex missing-data problems are discussed. The EM algorithm, a convenient method for computing maximum likelihood estimates in missing-data problems, is described and applied to two common models, the multivariate normal model for continuous data and the multinomial model for discrete data. Multiple imputation under explicit or implicit models is recommended as a method that retains the advantages of imputation and overcomes its limitations.

The first 10 years of a working career: An illustration of event-history analysis with West German mobility data

This article presents a flexible framework for the application of stochastic processes to social science data analysis. This task becomes more and more important as large longitudinal data sets are available. We demonstrate the applicability of our methodology with an analysis of job mobility data from a cohort of young men entering the West German labor market in the early sixties. We estimate multivariate and time-dependent rate models using maximum likelihood and partial likelihood techniques to evaluate different job change patterns between upward and downward mobility. A survey of available software for event-history analysis concludes our discussion.

Teaching Social Science Data Analysis: A Political Scientist's View

The paper argues that it is necessary to teach social scientists `data analysis' rather than `statistics'. Instruction in statistical principles needs to be closely integrated with the use of appropriate software packages and secondary data sets. It is also suggested that a discussion of interval level techniques such as correlation and regression should precede the introduction of the supposedly simpler technique of cross tabulation. The merits of intensive short course module teaching are also briefly examined.

IMPRESS

IMPRESS (Interdisciplinary Machine Processing for Research and Education in the Social Sciences) is an integrated set of computer programs and social science data sets designed to allow students and faculty who have very little computer expertise to analyze and manipulate social science data in a time-sharing environment. The user of IMPRESS need not know computer programming in order to access the 64 data sets currently on disc in the IMPRESS public library, nor is it necessary to prepare data decks or instruction cards. The user's only task is to type in simple English commands at his console and await the return of the tables, plots, and statistics he has requested. Indeed, even the most naive first-time user of IMPRESS can generally produce some sort of intelligent output within an hour or so of his introduction to the system. This interactive capability means that undergraduates can learn non-trivial social science data analysis at the freshman and sophomore level, and that both faculty and students can regularly do their own quantitative research, and incorporate that research in their courses.

Project IMPRESS, several perspectives: Interactive computing and data analysis

Project IMPRESS is an interactive social science data analysis system used extensively at Dartmouth College and throughout the DTSS network. The programming techniques used to make it an unobtrusive time-sharing job and the user interface design considerations used to make it a system easy for both students and experienced researchers to run are described and their pedagogical and research values discussed.

SIXTH ESSEX SUMMER SCHOOL IN SOCIAL SCIENCE DATA ANALYSIS 9 JULY to 19 AUGUST 1973

European Journal of Political ResearchVolume 1, Issue 1 p. 102-102 SIXTH ESSEX SUMMER SCHOOL IN SOCIAL SCIENCE DATA ANALYSIS 9 JULY to 19 AUGUST 1973 First published: April 1973 https://doi.org/10.1111/j.1475-6765.1973.tb01285.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume1, Issue1April 1973Pages 102-102 RelatedInformation