Advanced categorical statistics: Issues and applications in communication research

被引:9
作者
Denham, BE [1 ]
机构
[1] Clemson Univ, Dept Speech & Commun Studies, Clemson, SC 29634 USA
关键词
D O I
10.1111/j.1460-2466.2002.tb02537.x
中图分类号
G2 [信息与知识传播];
学科分类号
05 ; 0503 ;
摘要
Communication scholars frequently, use cross-tabulation and chi-square analysis in conducting research, Advanced categorical statistics such is log-linear modeling and logistic regression analysis are less often employed. This article addresses the use of advanced analyses, focusing on their application and why they are important tools. Because variables are instance, log-linear and logistic regression procedures allow scholars to fit both theoretically driven and mathematically parsimonious models to the data under study and to avoid making statistical errors common to bivariate comparisons.
引用
收藏
页码:162 / 176
页数:15
相关论文
共 47 条
[1]  
Agresti A., 1990, CATEGORICAL DATA ANA
[2]  
Aldrich J. H., 1984, Linear Probability, Logit, and Probit Models
[3]  
[Anonymous], 1983, COMMUNICATION YB
[4]  
[Anonymous], 1986, The history of statistics: The measurement of uncertainty before 1900
[5]   MULTI-VARIATE PROBIT ANALYSIS [J].
ASHFORD, JR ;
SOWDEN, RR .
BIOMETRICS, 1970, 26 (03) :535-&
[6]  
Bishop Y.M., 2007, DISCRETE MULTIVARIAT
[7]   The method of probits [J].
Bliss, C. I. .
SCIENCE, 1934, 79 (2037) :38-39
[8]   The calculation of the dosage-mortality curve [J].
Bliss, CI .
ANNALS OF APPLIED BIOLOGY, 1935, 22 (01) :134-167
[9]  
CHAFFEE S, 1986, HUMAN COMMUNICATION, V13, P77
[10]   DISCRIMINATION BETWEEN ALTERNATIVE BINARY RESPONSE MODELS [J].
CHAMBERS, EA ;
COX, DR .
BIOMETRIKA, 1967, 54 :573-&