scholarly journals Tabulation of Multiple Responses

2005 ◽  
Vol 5 (1) ◽  
pp. 92-122 ◽  
Author(s):  
Ben Jann
Keyword(s):  
Data Structure ◽  
Survey Research ◽  
Drug Addiction ◽  
Multiple Response ◽  
Significance Tests ◽  
Multiple Responses ◽  
Interview Question ◽  
Effective Analysis ◽  
Response Variables

Although multiple-response questions are quite common in survey research, Stata's official release does not provide much capability for an effective analysis of multiple-response variables. For example, in a study on drug addiction an interview question might be, “Which substances did you consume during the last four weeks?” The respondents just list all the drugs they took, if any; e.g., an answer could be “cannabis, cocaine, heroin” or “ecstasy, cannabis” or “none”, etc. Usually, the responses to such questions are stored as a set of variables and, therefore, cannot be easily tabulated. I will address this issue here and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results. In addition, tools to create graphs of multiple-response distributions are presented.

Enhancement of juvenile Caribbean spiny lobsters: an evaluation of changes in multiple response variables with the addition of large artificial shelters

Oecologia ◽  
2006 ◽  
Vol 151 (3) ◽  
pp. 401-416 ◽  
Author(s):  
Patricia Briones-Fourzán ◽  
Enrique Lozano-Álvarez ◽  
Fernando Negrete-Soto ◽  
Cecilia Barradas-Ortiz
Keyword(s):  
Multiple Response ◽  
Spiny Lobsters ◽  
Response Variables

Improving survey research using diagrams of data structure

2012 ◽  
Vol 65 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Hsi-Lin (Wayne) Liu ◽  
Yann-Jou Lin ◽  
Yu-Wen Wang ◽  
Wu-Chung Wu
Keyword(s):  
Data Structure ◽  
Survey Research

scholarly journals Interaction of p-synephrine with p-chloranil: experimental design and multiple response optimization

RSC Advances ◽  
2016 ◽  
Vol 6 (69) ◽  
pp. 64967-64976 ◽  
Author(s):  
Marwa S. Elazazy ◽  
K. Ganesh ◽  
V. Sivakumar ◽  
Yasser H. A. Huessein
Keyword(s):  
Experimental Design ◽  
Factorial Design ◽  
Performance Characteristic ◽  
Present Approach ◽  
Multiple Response ◽  
Multiple Responses ◽  
Characteristic P ◽  
Multiple Response Optimization ◽  
Response Optimization

A multivariate factorial design was proposed for determination ofp-synephrine. Novelty of present approach stems from consolidating multiple responses into a unified performance characteristic.

s-Level Multiple-Response Main-Effects Factorial Plans

1992 ◽  
Vol 42 (3-4) ◽  
pp. 237-246
Author(s):  
U. Batra ◽  
M.L. Aggarwal
Keyword(s):  
Dispersion Matrix ◽  
Multiple Response ◽  
Relative Importance ◽  
Factorial Experiments ◽  
Response Variable ◽  
Main Effects ◽  
Observation Vector ◽  
Response Variables

This paper deals with construction of plans for s-level factorial experiments in which there are p response variables and each respose is affected by one or more factors. The plans are orthogonal for each response variable. Estimates of the parameters in the models for such plans are obtained when Σ, the dispersion matrix of an observation vector is known. The properties of these estimates can be of help in designing the experiment so that the variances of estimates of the parameters can be influenced by their relative importance.

scholarly journals Speaking Stata: On Structure and Shape: The Case of Multiple Responses

2003 ◽  
Vol 3 (1) ◽  
pp. 81-99 ◽  
Author(s):  
Nicholas J. Cox ◽  
Ulrich Kohler
Keyword(s):  
Data Management ◽  
Multiple Response ◽  
Multiple Responses ◽  
Response Data ◽  
Multiple Response Data ◽  
New Variables ◽  
Frequent Problem

A frequent problem in data management is that datasets may not arrive in the best structure for many analyses, so that it may be necessary to restructure the data in some way. The particular case of multiple response data is discussed at length, with special attention to different possible structures; the generation of new variables holding the data in different form; valuable inbuilt string and egen functions; using foreach and forvalues to loop over lists; and the use of the reshape command. Tabulations and graphics for such data are also reviewed briefly.

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