Budgeting Strategy

Cross-Sectional versus Longitudinal Models

Bibliographic Data

ID7362076
AuthorsHarvey J Tucker (corresponding author)
Year1981
Volume41
Issue6
Pages644-644
Publication date1981-11-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenuePublic Administration Review (JOURNAL)
Journal identifiersISSN: 0033-3352 • E-ISSN: 1540-6210
PublisherWiley (PUBLISHER • GB)
DOI10.2307/975740
OpenAlexW2312884307
LanguageEN
Citations received6

ment not only on its own practices but also on those of other executive agencies and the legislature. Also, no attempt was made to verify the responses; the research team did not try to validate the extent to which any given state was truly conducting policy analyses. This data has had broad exposure to practitioners and researchers, however, and has been widely reported upon for other purposes. See Mushkin, op. cit.; Robert D. Lee, Jr. and Raymond J. Staffeldt, Executive and Legislature Use of Policy Analysis in the State Budgetary Process: Survey Results, Policy Analysis, 3 (Summer, 1977), pp. 395-504; and Robert D. Lee, Jr. and Raymond J. Staffeldt, Educational Characteristics of State Budget Officers, Public Administration Review, 36 (July/August, 1976), pp. 424-428. Given the structure of the questions, the numerous cross-validating uses reported in the literature, and a lack of reason to challenge the reliability of state budget officers, the authors assume that substantial validity and reliability can be attributed to the data. 22. Because of the nature of the dependent variables, different statistical analyses were required. The variables pertaining to executive decision making allowed for interval level analysis such as multiple regression. The categorical data for the legislative and agency dependent variables required a more robust multivariate method, such as discriminant analysis which allows for the examination of nominal and interval level independent variables. This method also corresponds to regression for the executive analysis. For a more detailed description of the uses and advantages of discriminant analysis over bivariate analysis, see Norman H. Nie, et al., SPSS: Statistical Package for the Social Sciences, 2nd ed. (New York: McGraw-Hill, 1975), pp. 435-444

Cross-sectional study · Statistics · Local Government Finance and Decentralization · Mathematics

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