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Computer Power and Legal Reasoning: A Case Study of Judicial Decision Prediction in Zoning Amendment Cases

Published online by Cambridge University Press:  20 November 2018

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Abstract

While social scientists have long advocated the use of statistical methodology in legal analysis, its practical application has not been tested. Statistical models based on social science theories have been used to predict judicial decisions and explain court behavior, but the legal profession has failed to develop statistical models based on traditional legal theories and using data familiar to the lawyer. This article seeks to demonstrate by practical application of statistical methodologies, coupled with traditional legal research methods, that such research can produce important insight into a court's decision making and provide a useful model for predicting the probability of a favorable decision. The zoning amendment decisions of the Connecticut Supreme Court are the data base of this study, which also provides a comprehensive explanation of zoning amendment law in Connecticut as a backdrop against which to evaluate the insights gained by statistical analysis.

Type
Research Article
Copyright
Copyright © American Bar Foundation, 1977 

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References

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144 Conn. Gen. Stat. sec. 8–2 (1971) specifically requires that “regulations shall be made with reasonable consideration as to the … peculiar suitability” of a district for “particular uses,” with a view to “encouraging the most appropriate use of land.”Google Scholar

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171 Stiles v. Town Council, 159 Conn. 212, 268 A.2d 395 (1970).Google Scholar

172 Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966); Senior v. Zoning Comm'n, 146 Conn. 531, 153 A.2d 415 (1959), appeal dismissed, 363 U.S. 143 (1960).Google Scholar

173 Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 172 A.2d 619 (1961).Google Scholar

174 Malafronte v. Planning & Zoning Bd., 155 Conn. 205, 230 A.2d 606 (1967). The police and fire departments had reviewed the plan for conformance to safety standards; Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966). Here the court was concerned with the unavailability of water, light, power, transit, and access roads.Google Scholar

175 Jarvis Acres, Inc. v. Zoning Comm'n, 163 Conn. 41, 301 A.2d 244 (1972).Google Scholar

176 146 Conn. 321, 327, 150 A.2d 312, 315 (1959).Google Scholar

177 148 Conn. 500, 504, 172 A.2d 619, 621 (1961).CrossRefGoogle Scholar

178 Samp Mortar Lake Co. v. Town Plan & Zoning Comm'n, 155 Conn. 310, 231 A.2d 649 (1967).Google Scholar

179 Metropolitan Homes, Inc. v. Town Plan & Zoning Comm'n, 152 Conn. 7, 10, 202 A.2d 241, 243 (1964) (“facilitate[s] provision for water, sewage, schools, parks …”).Google Scholar

180 Jarvis Acres, Inc. v. Zoning Comm'n, 163 Conn. 41, 301 A.2d 244 (1972); Wilson v. Planning & Zoning Comm'n, 162 Conn. 19, 291 A.2d 230 (1971); Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966); Brustein v. Zoning Comm'n, 151 Conn. 101, 193 A.2d 523 (1963); Whalen v. Town Plan & Zoning Comm'n, 146 Conn. 321, 150 A.2d 312 (1959).Google Scholar

181 162 Conn. 425, 430, 294 A.2d 582, 584, cert. denied, 408 U.S. 924 (1972); see also 54 A.L.R.3d 1278 (1973).Google Scholar

182 Samp Mortar Lake Co. v. Town Plan & Zoning Comm'n, 155 Conn. 310, 231 A.2d 649 (1967); Metropolitan Homes, Inc. v. Town Plan & Zoning Comm'n, 152 Conn. 7, 202 A.2d 241 (1964).Google Scholar

183 Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 314, 197 A.2d 770, 779 (1964).Google Scholar

184 150 Conn. 129, 131, 186 A.2d 802, 804 (1962).Google Scholar

185 Jarvis Acres, Inc. v. Zoning Comm'n, 163 Conn. 41, 301 A.2d 244 (1972); Wilson v. Planning & Zoning Comm'n, 162 Conn. 19, 291 A.2d 230 (1971); Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966); Brustein v. Zoning Comm'n, 151 Conn. 101, 193 A.2d 523 (1963); Whalen v. Town Plan & Zoning Comm'n, 146 Conn. 321, 150 A.2d 312 (1959).Google Scholar

186 163 Conn. 41, 43, 301 A.2d 244, 245 (1972).CrossRefGoogle Scholar

187 Id. at 51, 301 A.2d at 249.Google Scholar

188 Faubel v. Zoning Comm'n, 154 Conn. 202, 210–11, 224 A.2d 538, 543 (1966).Google Scholar

189 Lurie v. Planning & Zoning Comm'n, 160 Conn. 295, 307, 278 A.2d 799, 805 (1971); see also 49 A.L.R.3d 476 (1973).Google Scholar

190 Stiles v. Town Council, 159 Conn. 212, 227, 268 A.2d 395, 403 (1970); Furtney v. Simsbury Zoning Comm'n, 159 Conn. 585, 598, 271 A.2d 319, 325 (1970).Google Scholar

191 Ferndale Dairy, Inc. v. Zoning Comm'n, 148 Conn. 172, 169 A.2d 268 (1961).Google Scholar

192 Furtney v. Simsbury Zoning Comm'n, 159 Conn. 585, 271 A.2d 319 (1970); Lake Garda Improvement Ass'n v. Town Plan & Zoning Comm'n, 151 Conn. 476, 199 A.2d 162 (1964).Google Scholar

193 Nielson v. Zoning Comm'n, 149 Conn. 410, 180 A.2d 754 (1962).Google Scholar

194 Strom v. Planning & Zoning Comm'n, 153 Conn. 339, 216 A.2d 623 (1966); Tarasovic v. Zoning Comm'n, 147 Conn. 65, 157 A.2d 103 (1959); Mallory v. Town of West Hartford, 138 Conn. 497, 86 A.2d 668 (1952).Google Scholar

195 Sheridan v. Planning Bd., 159 Conn. 1, 266 A.2d 396 (1969).Google Scholar

196 Furtney v. Simsbury Zoning Comm'n, 159 Conn. 585, 271 A.2d 319 (1970); Couch v. Zoning Comm'n, 141 Conn. 349, 106 A.2d 173 (1954).Google Scholar

197 Florentine v. Town of Darien, 142 Conn. 415, 115 A.2d 328 (1955).Google Scholar

198 Nielson v. Zoning Comm'n, 149 Conn. 410, 180 A.2d 754 (1962).Google Scholar

199 Lake Garda Improvement Ass'n v. Town Plan & Zoning Comm'n, 151 Conn. 476, 199 A.2d 162 (1964).Google Scholar

200 Lathrop v. Planning & Zoning Comm'n, 164 Conn. 215, 319 A.2d 376 (1973); Stiles v. Town Council, 159 Conn. 212, 268 A.2d 395 (1970); Damick v. Planning & Zoning Comm'n, 158 Conn. 78, 256 A.2d 428 (1969); Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966).Google Scholar

201 Langer v. Planning & Zoning Comm'n, 163 Conn. 453, 313 A.2d 44 (1972); Stiles v. Town Council, 159 Conn. 212, 268 A.2d 395 (1970); Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966).Google Scholar

202 Morningside Ass'n v. Planning & Zoning Bd., 162 Conn. 154, 292 A.2d 893 (1972); Hawkes v. Town Plan & Zoning Comm'n, 156 Conn. 207, 240 A.2d 914 (1968); Malafronte v. Planning & Zoning Bd., 155 Conn. 205, 230 A.2d 606 (1967); Pierrepont v. Zoning Comm'n, 154 Conn. 463, 226 A.2d 659 (1967).Google Scholar

203 152 Conn. 262, 266, 205 A.2d 774, 777 (1964).Google Scholar

204 Id. at 268, 205 A.2d at 777.Google Scholar

205 164 Conn. 215, 222–23, 319 A.2d 376, 381 (1973).Google Scholar

206 159 Conn. 212, 225, 268 A.2d 395, 402 (1970).CrossRefGoogle Scholar

207 Id. at 226, 268 A.2d at 402.Google Scholar

208 Sheridan v. Planning Bd., 159 Conn. 1, 20, 266 A.2d 396, 406 (1969).Google Scholar

209 Anderson v. Zoning Comm'n, 157 Conn. 285, 293, 253 A.2d 16, 21 (1968).Google Scholar

210 Florentine v. Town of Darien, 142 Conn. 415, 115 A.2d 328 (1955).Google Scholar

211 Anderson v. Zoning Comm'n, 157 Conn. 285, 253 A.2d 16 (1968).Google Scholar

212 155 Conn. 205, 210, 230 A.2d 606, 609 (1967).Google Scholar

213 Damick v. Planning & Zoning Comm'n, 158 Conn. 78, 84, 256 A.2d 428, 431 (1969).Google Scholar

214 DeForest & Hotchkiss Co. v. Planning & Zoning Comm'n, 152 Conn. 262, 268, 205 A.2d 774, 778 (1964).Google Scholar

215 Eden v. Town Plan & Zoning Comm'n, 139 Conn. 59, 89 A.2d 746 (1952).Google Scholar

216 163 Conn. 453, 460, 313 A.2d 44, 48 (1972).CrossRefGoogle Scholar

217 Morningside Ass'n v. Planning & Zoning Bd., 162 Conn. 154, 159, 292 A.2d 893, 897 (1972).Google Scholar

218 Anderson v. Zoning Comm'n, 157 Conn. 285, 293, 253 A.2d 16, 21 (1968).Google Scholar

219 Damick v. Planning & Zoning Comm'n, 158 Conn. 78, 256 A.2d 428 (1969); DeForest & Hotchkiss Co. v. Planning & Zoning Comm'n, 152 Conn. 262, 205 A.2d 774 (1964); Kimball v. Court of Common Council, 148 Conn. 97, 167 A.2d 706 (1961).Google Scholar

220 See id.; Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964); Suffield Heights Corp. v. Town Planning Comm'n, 144 Conn. 425, 133 A.2d 612 (1957).Google Scholar

221 142 Conn. 415, 422–23, 115 A.2d 328, 331 (1955).Google Scholar

222 Furtney v. Simsbury Zoning Comm'n, 159 Conn. 585, 599, 271 A.2d 319, 325 (1970); see also Suffield Heights Corp. v. Town Planning Comm'n, 144 Conn. 425, 133 A.2d 612 (1957); Eden v. Town Plan & Zoning Comm'n, 139 Conn. 59, 89 A.2d 746 (1952).Google Scholar

223 Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 172 A.2d 619 (1961); Mills v. Town Plan & Zoning Comm'n, 145 Conn. 237, 140 A.2d 871 (1958).Google Scholar

224 Lake Garda Improvement Ass'n v. Town Plan & Zoning Comm'n, 151 Conn. 476, 199 A.2d 162 (1964).Google Scholar

225 145 Conn. 237, 241, 140 A.2d 871, 874 (1958); see also Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 172 A.2d 619 (1961); Village Builders, Inc. v. Town Plan & Zoning Comm'n, 145 Conn. 218, 140 A.2d 477 (1958).Google Scholar

226 For another example of improper motive, see Village Builders, Inc. v. Town Plan & Zoning Comm'n, 145 Conn. 218, 218–19, 140 A.2d 477, 478 (1958), where the supreme court, though deciding the case on a technical issue (admissibility of evidence before the court of common pleas), was obviously disturbed about a suggestion made at the executive session of the zoning commission “that if there is too much opposition it will always be possible to compromise on R-30.” Ironically, though the zone change was reversed in Village Builders, the court had a chance to review the case on its merits in Metropolitan Homes, Inc. v. Town Plan & Zoning Comm'n, 152 Conn. 7, 202 A.2d 241 (1964). There the court upheld the decision of the commission to change the zone to R-30 with a minimum lot size of 30,000 square feet, the “compromise” position that bothered the court in the earlier case.Google Scholar

227 Vece v. Zoning & Plan Comm'n, 148 Conn. 500, 502–3, 172 A.2d 619, 620 (1961).Google Scholar

228 Norris v. Planning & Zoning Comm'n, 156 Conn. 592, 244 A.2d 378 (1968); Samp Mortar Lake Co. v. Town Plan & Zoning Comm'n, 155 Conn. 310, 231 A.2d 649 (1967); Scovil v. Planning & Zoning Comm'n, 155 Conn. 12, 230 A.2d 31 (1967) (remanded); Andrew C. Peterson, Inc. v. Town Plan & Zoning Comm'n, 154 Conn. 638, 228 A.2d 126 (1967); Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964) (reversed); Young v. Town Planning & Zoning Comm'n, 151 Conn. 235, 196 A.2d 427 (1963); Senior v. Zoning Comm'n, 146 Conn. 531, 153 A.2d 415 (1959), appeal dismissed, 363 U.S. 143 (1960); Yurdin v. Town Plan & Zoning Comm'n, 145 Conn. 416, 143 A.2d 639, cert. denied, 358 U.S. 894 (1958); Florentine v. Town of Darien, 142 Conn. 415, 115 A.2d 328 (1955); Couch v. Zoning Comm'n, 141 Conn. 349, 106 A.2d 173 (1954).Google Scholar

229 Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964).Google Scholar

230 Lake Garda Improvement Ass'n v. Town Plan & Zoning Comm'n, 151 Conn. 476, 480, 199 A.2d 162, 166 (1964).Google Scholar

231 Damick v. Planning & Zoning Comm'n, 158 Conn. 78, 256 A.2d 428 (1969); Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 172 A.2d 619 (1961); Nowicki v. Planning & Zoning Bd., 148 Conn. 492, 172 A.2d 386 (1961).Google Scholar

232 Fletcher v. Planning & Zoning Comm'n, 158 Conn. 497, 264 A.2d 566 (1969).Google Scholar

233 146 Conn. 397, 151 A.2d 327 (1959).CrossRefGoogle Scholar

234 148 Conn. 492, 499, 172 A.2d 386, 390 (1961).CrossRefGoogle Scholar

235 There was also a special meeting of the board which the court decided not to evaluate since it was able to reverse on the ground stated above.Google Scholar

236 Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 503, 172 A.2d 619, 620 (1961).Google Scholar

237 George LaCava & Sons, Inc. v. Town Plan & Zoning Comm'n, 154 Conn. 309, 225 A.2d 198 (1966).Google Scholar

238 Eden v. Town Plan & Zoning Comm'n, 139 Conn. 59, 89 A.2d 746 (1952).Google Scholar

239 Cascio v. Town Council, 158 Conn. 111, 256 A.2d 685 (1969).Google Scholar

240 Damick v. Planning & Zoning Comm'n, 158 Conn. 78, 256 A.2d 428 (1969).Google Scholar

241 Hahn v. Zoning Comm'n, 162 Conn. 210, 293 A.2d 9 (1972).Google Scholar

242 158 Conn. 497, 506, 264 A.2d 566, 571 (1969).Google Scholar

243 Burke v. Board of Representatives, 148 Conn. 33, 166 A.2d 849 (1961); Leslie v. Board of Representatives, 148 Conn. 44, 166 A.2d 855 (1961).Google Scholar

244 Young v. Town Planning & Zoning Comm'n, 151 Conn. 235, 196 A.2d 427 (1963); Pecora v. Zoning Comm'n, 145 Conn. 435, 144 A.2d 48 (1958).Google Scholar

245 See cases cited supra note 228.Google Scholar

246 Young v. Town Planning & Zoning Comm'n, 151 Conn. 235, 243, 196 A.2d 427, 431 (1963).Google Scholar

247 Norris v. Planning & Zoning Comm'n, 156 Conn. 592, 244 A.2d 378 (1968).Google Scholar

248 Woodford v. Zoning Comm'n, 147 Conn. 30, 156 A.2d 470 (1959).Google Scholar

249 Josephson v. Planning Bd., 151 Conn. 489, 199 A.2d 690 (1964); see also 10 A.L.R.3d 687 (1966).Google Scholar

250 See cases cited supra note 228.Google Scholar

251 Eden v. Town Plan & Zoning Comm'n, 139 Conn. 59, 89 A.2d 746 (1952).Google Scholar

252 Southern Burlington County NAACP v. Township of Mount Laurel, 67 N.J. 151, 336 A.2d 713 (1975); Golden v. Planning Bd., 30 N.Y.2d 359, 285 N.E.2d 291, 334 N.Y.S.2d 138 (1972); Construction Indus. Ass'n v. City of Petaluma, 375 F. Supp. 574 (N.D. Cal. 1974), rev'd, 522 F.2d 897 (1975), cert. denied, 424 U.S. 934 (1976).Google Scholar

253 Nowicki v. Planning & Zoning Bd., 148 Conn. 492, 172 A.2d 386 (1961); Wolfpit-Villa Crest Ass'n, Inc. v. Zoning Comm'n, 144 Conn. 560, 135 A.2d 732 (1957).Google Scholar

254 Senior v. Zoning Comm'n, 146 Conn. 531, 535, 153 A.2d 415, 417 (1959), appeal dismissed, 363 U.S. 143 (1960).Google Scholar

255 See appendix 2, p. 764 infra, for a complete listing of the 167 variables.Google Scholar

256 Perhaps the easiest way to conceptualize cross-tabulation is with the use of a 2 × 2 table, as follows: Here, we seek to test the relationship between two variables: the approval or denial of the zone change and the need for the use. We assume that the decision to approve or deny depends to some extent on need for the use and that need for the use can, to an extent yet to be determined, help predict the decision to approve or deny. In this example, need appears to have some influence on the outcome, for the zone change is approved 75 percent of the time if the use is needed; but if it is not needed, 75 percent of the time the change is denied. This cross-tabulation is then analyzed with a chi-square test to determine the statistical significance of the relationship between the two variables by comparing the cell frequencies that would be expected if no relationship were present between the variables with the frequencies actually observed (in the above example, 15, 10, 5, and 30 are the four cell frequencies). The mathematical measure used for this comparison is defined as: where Oi refers to the observed frequency in cell i, and Ei refers to the expected frequency in cell i. The expected frequency, Ei , is calculated as: is the respective column total and r i is the respective row total of cell i, and N stands for the total number of cases. As can be seen, the larger the differences between the observed and expected frequencies, the larger the chi-square becomes. The evaluation of the chi-square is dependent upon the total number of cases observed (N). Tables have been calculated which enable the researcher to infer the existence of a statistical relationship between the two variables under study. For a more complete description of the chi-square test, see Hubert M. Blalock, Social Statistics 275–86 (2d ed. New York: McGraw-Hill Book Co., 1972), or any other introductory statistical text.Google Scholar

257 Even with this loose test, it is possible that variables may have been eliminated which, had we controlled for the effects of other variables, might have been useful in predicting the court's behavior. And, conversely, it is possible that some variables that were further analyzed were not as important as the cross-tabulation indicated. Nevertheless, the chi-square test is useful because variables that have a primary and direct effect on the outcome are retained for further analysis. Many of the 40 retained turned out to have little predictive value when used together with other variables, at least in the models we developed.Google Scholar

258 A correlation analysis of the original data was made and a matrix of correlation coefficients was calculated for a set of 32 of the relevant variables. The other 8 were eliminated for reasons that appear in note 259 infra. With this information, factor analysis techniques enabled us to see whether some underlying pattern of relationships existed among the variables which would allow the data to be reduced to a smaller set of “factors.” These “factors” are conceptual variables or indices that utilize most of the information in the larger number of variables in an aggregated form. Cases are then analyzed in terms of their factor scores. A detailed explanation of factor analysis will be found in Harry H. Harman, Modern Factor Analysis (Chicago: University of Chicago Press, 1960); Godfrey H. Thomson, The Factorial Analysis of Human Ability (5th ed. London: University of London Press, 1951); Herbert Solomon, A Survey of Mathematical Models in Factor Analysis, in H. Solomon, ed., Mathematical Thinking in the Measurement of Behavior 269 (Glencoe, Ill.: Free Press, 1960). Both the Thomson and Harman discussions give a balanced treatment of the important factor models. Harman's is long, but readable, and has an extensive bibliography. For an over-view, the reader should refer to the Solomon discussion. Three other discussions of the methodology may be useful: Raymond B. Cattell, Factor Analysis: An Introduction to Essentials: I. The Purpose and Underlying Models; II. The Role of Factor Analysis in Research, 21 Biometrics 190, 405 (1965); Donald F. Morrison, Multivariate Statistical Methods (New York: McGraw-Hill Book Co., 1967) (see ch. 8); Norman H. Nie et al., Statistical Package for the Social Sciences (2d ed. New York: McGraw-Hill Book Co., 1975). Cattell provides a useful introductory description of factor analysis; Morrison gives a brief mathematical exposition of the technique; and Nie describes in detail the computer program utilized in factor analysis.Google Scholar

259 Eight of the 40 variables found significant by using a chi-square test were not included in the factor analysis. They were: Var. 012 –ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash;ndash; zoning authority decision Var. 011 – court of common pleas decision Var. 015 – developer plaintiff Var. 016 – neighbor plaintiff Var. 013 – type of zoning authority Var. 047 – court found warrant in the record Var. 048 – court did not find warrant in the record Var. 049 – court found record equivocal. Since, as a matter of judgment, we wanted to include Var. 012 and Var. 011 in the regression independently, they were excluded from the factor analysis. Var. 015 and Var. 016 were so highly correlated with each other and with Var. 012 that one variable sufficed as a proxy for all three, and accordingly Var. 012 was used in the regression. If either Var. 015 or Var. 016 had been left in the factor analysis, one of the scales would have been so highly correlated with Var. 012, which was to be included independently, that it would have made interpretation of Var. 012 in the regression analysis highly unreliable. The type of zoning authority, although statistically correlated with the outcome variable, was excluded because the special legislation applicable to certain towns under the zoning laws of Connecticut made interpretation of this variable quite difficult. Finally, we believed the court's view of the adequacy of the record (vars. 047, 048, and 049) was too conclusory in nature and too much a reflection of the court's judgment on other factors to be subject to meaningful interpretation as part of the regression. Furthermore, two of the variables included in the factor analysis had insignificant loadings on the scales finally selected for use in the regression analysis and, therefore, do not appear in the list of variables in table 2. The two variables dropped at this stage were: Var. 074 – departure from large uniform blocks Var. 079 – area had numerous nonconforming uses.Google Scholar

260 The factors defined by the computer are not arbitrary but can be reproduced by anyone using the same data and methodology. They reflect inherent relationships among the variables making up the scales. It is not inevitable that the scales will be meaningful in commonsense terms-i.e., that the inherent relationships will be such that they can be described in other than mathematical terms. But the scales that emerged from our factor analysis did in fact make sense intuitively, sufficiently so for us to give them meaningful labels (see table 2), though they were not necessarily grouped in the same way that we would have grouped them on the basis of intuition, traditional legal knowledge, and subjective judgment. Indeed, before applying the statistical technique we had developed 16 factors on the latter basis without the benefit of the computer, but our factors proved less useful in predicting the court's decisions than those the computer gave us. Human judgment cannot be ignored, however; we used only groupings that were meaningful and reasonable in the subsequent regression analysis. Those the computer supplied us were indeed meaningful. A specific and detailed explanation of the process used in this analysis is available in Nie et al., supra note 258, secs. 24.2.2 & 24.3.2. aThe strength of each variable within a scale can be compared using the values given, but variables in different scales cannot be compared at this point. Direct comparison is provided in the Summary Table (table 1, p. 657 supra). bVariable identification numbers, as given in appendix 1, p. 752 infra.Google Scholar

261 Given a set of data made up of observations of the relationship between a dependent variable and an independent variable, what mathematical function can best describe it? In a two-variable case, this question can be answered graphically by determining the curve that best fits the data in a two-dimensional space. In general, regression analysis seeks to identify the relationship between a dependent variable, y, and a set of n independent variables, x 1, x 2, …x n. Such a relationship cannot easily be depicted graphically if n is greater than 2, but it can be expressed algebraically. In the present analysis, this relationship is assumed to be linear and is derived by using least square estimation. In the bivariate case (i.e., where there are only two variables), the dependent variable y is assumed to vary in proportion to the independent variable x plus a constant. This may be written as: The parameters a and b are estimated from the data. Then Ŷ= a + bx where Ŷ is the estimate of y given a particular value of x. The technique of least squares is used to ascertain a and b so that the difference between the estimated values Ŷi and the actual values y i, when squared and added over all cases in the sample(s), is minimized. That is, if e i=y iy i, then will be as small as possible for the straight line that is chosen to approximate the data. Hence, the name “least squares.” This line is said to have the “best fit” to the observed data, in the least squares sense. The analogy of the bivariate case with the multivariate case (i.e., where there is more than one independent variable) is straightforward. The equation y=a+b 1 x 1+b 2 x 2+…+b n x n is fitted to the data and the parameters a, b 1 b 2b n are determined to minimize The high-speed computer makes it possible to solve this problem without difficulty. The following introductory texts are recommended for a detailed but simple review of the techniques described here: Thomas H. Wonnacott & Ronald J. Wonnacott, Introductory Statistics (2d ed. New York: John Wiley & Sons, 1972); Blalock, supra note 256; Ralph E. Beals, Statistics for Economists: An Introduction (Chicago: Rand McNally & Co., 1972). The computer program package used is described in detail in Nie et al., supra note 258.Google Scholar

262 The model was actually tested in two ways. First, the model that best described the entire set of 55 later cases (1960 to 1975) was used to estimate the probability of approval for the entire set of 79 cases. Ninety percent of the cases were correctly predicted. Of the 8 cases not correctly predicted, 6 were decided before 1960 and 2–Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964) (holding floodplain zoning a taking), and Pelchat v. Planning & Zoning Comm'n, 162 Conn. 603, 291 A.2d 239 (1971) (memorandum opinion)–after 1960. The analysis used the following criterion for predicting the outcome of a case: an approval of the zone change was predicted if the calculated probability of approval was greater than .50; otherwise, a denial of the zone change was predicted. Using this criterion, the model correctly predicted 95 percent of the cases after 1960. Second, the coefficients of this model were reestimated using 51 cases (all but the last 4); the outcomes of the last 4 cases were then estimated, and all 4 were correctly predicted.Google Scholar

263 For a more extended discussion of the methodology, see appendix 1, p. 752 infra.Google Scholar

264 The problem of model specification can easily be seen by comparing the results of two other regressions varying only slightly from the model presented above. If Scale 9 is added to the model or if Scale 10 is dropped from the model, then the coefficients associated with each of the other scales shift:Google Scholar

265 In formulating the probability model used here, certain assumptions were made and they are explained in appendix 1, p. 752 infra.Google Scholar

266 For an explanation of this concept see pp. 676–79 supra.Google Scholar

267 This total may exceed .99 or be less than .01 in extremely weak or strong cases, but in those cases the probability should be considered as .99 or .01.Google Scholar

268 This probably means at least 30 acres or larger. See p. 681 supra.Google Scholar

269 Moreover, the strength of these variables is so slight that their effects may be masked by those of other variables with which they covary. The statistical analysis we have used is not robust enough for us to place substantial weight on these numbers. It is these variables that are most likely to disappear altogether or even change signs if other models are developed by further regressions.Google Scholar

270 Stiles v. Town Council, 159 Conn. 212, 218–19, 268 A.2d 395, 399 (1970).Google Scholar

271 In formal terms this is a “decision-flow diagram,” and an extensive discussion of its utility for decision analysis is found in Howard Raiffa, Decision Analysis: Introductory Lectures on Choices Under Uncertainty (Reading, Mass.: Addison-Wesley, 1970).Google Scholar

272 Cases decided on procedural grounds are not included because in these the legal issue, i.e., whether the change of zone was in accordance with a comprehensive plan, is not directly confronted by the court. All the cases in the “decision tree” were decided between 1950 and 1975.Google Scholar

273 Whether the plaintiff is a developer (owner) or neighbor in the action before the court depends directly on the decision of the zoning authority. Because of the virtually reciprocal relationship between the two variables, they cannot be treated separately in the regression equation. Technically this problem is called multicollinearity; its presence biases the values of the other variables in the equation. To eliminate (or reduce) this problem the equation must be specified using a single variable that is a suitable substitute for the collinear variables. Here the model was specified using zoning authority denies change as the single independent variable, since this variable adequately represents the decision of the zoning authority and the fact that the developer, rather than the neighbor, was the plaintiff.Google Scholar

274 The two cases that they won were Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964), and Suffield Heights Corp. v. Town Planning Comm'n, 144 Conn. 425, 133 A.2d 612 (1957). In Dooley the court held that placing the owner's land in a floodplain zone, leaving no reasonable use, constituted a “taking” under the Fourteenth Amendment. “Taking” is dispositive, though it appears in few cases. See pp. 703–4 supra. In Suffield Heights the court found that the property adjacent to an existing shopping center was unsuited for residential use because of size, shape, and location, and that the only appropriate use for it was a parking lot for the shopping center.Google Scholar

275 Reversed between 1950 and 1959 (9 of 21 cases): Eden v. Town Plan & Zoning Comm'n, 139 Conn. 59, 89 A.2d 746 (1952); Miller v. Town Planning Comm'n, 142 Conn. 265, 113 A.2d 504 (1955); Suffield Heights Corp. v. Town Planning Comm'n, 144 Conn. 425, 133 A.2d 612 (1957); Guerriero v. Galasso, 144 Conn. 600, 136 A.2d 497 (1957); Magnin v. Zoning Comm'n, 145 Conn. 26, 138 A.2d 522 (1958); Mills v. Town Plan & Zoning Comm'n, 145 Conn. 237, 140 A.2d 871 (1958); Gordon v. Zoning Bd., 145 Conn. 597, 145 A.2d 746 (1958); Whalen v. Town Plan & Zoning Comm'n, 146 Conn. 321, 150 A.2d 312 (1959); Woodford v. Zoning Comm'n, 147 Conn. 30, 156 A.2d 470 (1959). Reversed between 1960 and 1975 (11 of 58 cases): Kimball v. Court of Common Council, 148 Conn. 97, 167 A.2d 706 (1961); Nowicki v. Planning & Zoning Bd., 148 Conn. 492, 172 A.2d 386 (1961); Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 172 A.2d 619 (1961); Brustein v. Zoning Comm'n, 151 Conn. 101, 193 A.2d 523 (1963); Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964); DeForest & Hotchkiss Co. v. Planning & Zoning Comm'n, 152 Conn. 262, 205 A.2d 774 (1964); Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966); Damick v. Planning & Zoning Comm'n, 158 Conn. 78, 256 A.2d 428 (1969); Wilson v. Planning & Zoning Comm'n, 162 Conn. 19, 291 A.2d 230 (1971); Pelchat v. Planning & Zoning Comm'n, 162 Conn. 603, 291 A.2d 239 (1971) (memorandum opinion); Jarvis Acres, Inc. v. Zoning Comm'n, 163 Conn. 41, 301 A.2d 244 (1972).Google Scholar

276 Although there are technical objections, based on statistical theory, to comparing the coefficients derived from two separate sets of data using the same model, the following comparison between the model estimated with data from the late cases and the model estimated with data from the early cases is very strong evidence of a change in the underlying decision-making process of the court:Google Scholar

277 Scale 7: Physical Services Inadequate –ndash;ndash;ndash;completion of necessary improvements not likely .96 –creates a physical hazard 1.17 –streets inadequate 1.53Google Scholar

278 Scale 9: Good Planning Practice –public experts consulted 1.10 –physical hazards reduced .86 –proposed zone change followed master plan recommendation .44Google Scholar

279 Scale 5: Compatibility from an Economic Perspective –positive or neutral impact on value of adjacent lots 1.35 –needed in area or town .68 –aesthetically compatible .40 –no alternative use for site .23Google Scholar

280 As a result of this dramatic shift, the predictive model shown in the Summary Table, supra p. 657, is based only on the last 55 cases. The 24 cases decided before 1960 were discarded because their inclusion tended to bias the model by placing undue emphasis on variables that are no longer dealt with by the court in the same way. It should be apparent, of course, that if the lawyer's task were predicting outcomes prior to the zoning authority's decision, the shift in the appellate court's reviewing posture would not be very meaningful; regression equations that looked vastly different would then emerge.Google Scholar

281 This situation occurred several times. A variable would have a significant (X2 .05) statistical relationship to the dependent variable when the dependent variable was defined as approval or denial of the zone change by the supreme court; but when the dependent variable was redefined to indicate that the supreme court reversed or did not reverse the zoning authority, that statistical significance was lost. The two variables are clearly different. Except for the eight variables listed in note 259 supra, all variables that showed a statistically significant relationship to the dependent variable were included in the factor analysis; many of these were not significant in the regression analysis, but they were not prematurely dropped.Google Scholar

282 See table 2, p. 713 supra.Google Scholar

283 If the significant variables in table 1 (Summary Table, p. 657 supra) are compared with the statutory standards set out in part I (pp. 660–68 supra), the importance of the statutory directives is easily seen.Google Scholar

284 Load is a shorthand way of saying that the statistical relationship between the variable and the scale was strong enough to include the variable as part of that particular scale. A variable may relate to several scales. Scale 8: Compatibility Indicated by Large Uniform Blocks –ndash;not a departure from large uniform blocks 2.90 –existing zoning line a natural boundary –.09 –aesthetically compatible –.31Google Scholar

285 Sixteen of the 20 cases where the decision of the zoning authority was reversed involved predominantly commercial (12) and industrial (4) uses; 2 proposed single-family zones, 1 an apartment zone, and 1 a floodplain zone. This is not significant when subjected to the rigors of statistical analysis.Google Scholar

286 Whether the court would stand by its decision in Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964), that floodplain zoning is a “taking without just compensation” remains an open question, particularly in view of the strong federal policy reflected in the Flood Disaster Protection Act of 1973, Pub. L. No. 93–234, 87 Stat. 975, and the judicial approval given to such zoning in other states.Google Scholar

287 See Jarvis Acres, Inc. v. Zoning Comm'n, 163 Conn. 41, 301 A.2d 244 (1972); Wilson v. Planning & Zoning Comm'n, 162 Conn. 19, 291 A.2d 230 (1971); Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966); Whalen v. Town Plan & Zoning Comm'n, 146 Conn. 321, 150 A.2d 312 (1959).Google Scholar

288 Whalen v. Town Plan & Zoning Comm'n, 146 Conn. 321, 150 A.2d 312 (1959).Google Scholar

289 See Jarvis Acres, Inc. v. Zoning Comm'n, 163 Conn. 41, 301 A.2d 244 (1972)* Wilson v. Planning & Zoning Comm'n, 162 Conn. 19, 291 A.2d 230 (1971)* Faubel v. Zoning Comm'n, 154 Conn. 202, 224 A.2d 538 (1966)' Brustein v. Zoning Comm'n, 151 Conn. 101, 193 A.2d 523 (1963); Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 172 A.2d 619 (1961); Whalen v. Town Plan & Zoning Comm'n, 146 Conn. 321, 150 A.2d 312 (1959)* Gordon v. Zoning Bd., 145 Conn. 597, 145 A.2d 746 (1958); Eden v. Town Plan & Zoning Comm'n, 139 Conn. 59, 89 A.2d 746 (1952). (Asterisk indicates that courts also found completion of necessary improvements unlikely.)Google Scholar

290 Since the need for a use is nevertheless frequently mentioned as a reason for approval or denial of a zone change, and since most litigation concerns areas experiencing rapid growth, we thought it would be useful to attempt to pin down this elusive factor. We looked beyond facts given in the cases to objective indicators of need obtained from census information. Seven variables were fed into the computer: 1. the population of the town 2. the rate of growth of the population of the town 3. the density of the population of the town 4. the median income of the standard metropolitan statistical area (SMSA) 5. the unemployment rate of the SMSA 6. the median age of the population of the SMSA 7. the size of the town. Each of these variables was correlated with the variables relating to need which seemed most pertinent–ndash;need for use in the town or neighborhood and need for jobs or housing–and to the decision of the zoning authority. While the variable need correlates highly with the decision of the zoning authority, it does not correlate with any of the census information. In fact, only two of the census indicators correlate with the zoning authority's decision–median income and unemployment rate. As the income of the SMSA rose, the frequency of approval went down; and as unemployment rose, so did the frequency of approval. The data are too gross for us to make any strong inferences along this line, but they do suggest the elusiveness of the concept of need as used by the court. Given what has been said before about the use of “need” as a rationalization rather than a reason, this is not too surprising.Google Scholar

291 E.g., see Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964), and Suffield Heights Corp. v. Town Planning Comm'n, 144 Conn. 425, 133 A.2d 612 (1957). The importance of this factor (“taking”) was clear from the legal analysis but was not revealed in the statistical analysis because of the infrequency with which it occurred.Google Scholar

292 See Scales 1, 5, and 10 in table 2, p. 713 supra.Google Scholar

293 In summary form: Opposition by Adjacent Property Owners Zoning similar on one side 14 of 26 cases 54% Zoning similar on two sides 7 of 21 cases 33% Zoning not similar 2 of 9 cases 22% This pattern of opposition may explain why the conventional wisdom about the appropriateness of extending an existing zone is not operable in the supreme court.Google Scholar

294 The interesting question, not explored because of time limitations, is what other factors or variables are affected by the opposition of neighbors. At least two have been suggested–aesthetic compatibility and economic impact.Google Scholar

295 In Mills v. Town Plan & Zoning Comm'n, 145 Conn. 237, 140 A.2d 871 (1958), after denying the application for a local shopping center two months earlier, the zoning authority reversed itself (splitting 3–2) and approved a “regional” shopping center following private negotiation with the applicant. Since the agreement also included a reduction in the number of parking spaces originally proposed for the shopping center, despite the alleged broader market area, the court found it difficult to accept the reasons given for the change and reversed the zoning authority's decision. (It should be noted that the property was prone to flooding as well, and that had been the basis for denial of the zone change two months earlier.) In Village Builders, Inc. v. Town Plan & Zoning Comm'n, 145 Conn. 218, 140 A.2d 477 (1958), the court of common pleas had reversed the decision of the zoning authority to increase the zoning limits for the site from a -acre minimum lot size to a 1-acre minimum. Though the appeal to the supreme court was based on the issue of the admissibility of evidence in the court of common pleas, and not on the conclusions of the trial court, the supreme court was obviously disturbed that at an executive session of the zoning commission it was suggested “that if there is too much opposition it will always be possible to compromise on R-30.”Id. at 218–19, 140 A.2d at 478.Google Scholar

296 Dooley v. Town Plan & Zoning Comm'n, 151 Conn. 304, 197 A.2d 770 (1964). Even here the court had nothing but praise for the intended purposes of the change, but it found that the hardship to the owner was not outweighed by the public benefit.Google Scholar

297 See p. 704 et seq.Google Scholar

298 198 U.S. 45, 76 (1905).Google Scholar

299 See discussion in part 1, pp. 696–98 supra, and part II, pp. 735–38 supra.Google Scholar

300 Nowicki v. Planning & Zoning Bd., 148 Conn. 492, 172 A.2d 386 (1961); Vece v. Zoning & Planning Comm'n, 148 Conn. 500, 172 A.2d 619 (1961).Google Scholar

301 While we recognize the theoretical and practical deficiencies of the concept of comprehensive planning that is currently the rage in academia, practicing professionals also recognize the need to narrow the gap between current practices and what is possible in terms of focusing on community problems and opportunities involving an integrated, functional comprehensive plan.Google Scholar

302 Several states have already adopted such legislation (Hawaii, Florida, Vermont, Maine, Oregon, etc.), and the American Law Institute in its proposed official draft of the Model Land Development Code suggests improvements in this entire development process.Google Scholar

303 For a more extensive discussion of the evolution of judicial research, with emphasis on the innovations provided by social scientists, see C. H. Pritchett, The Development of Judicial Research, in Joel B. Grossman & Joseph Tanenhaus, eds., Frontiers of Judicial Research (New York: John Wiley & Sons, 1969).Google Scholar

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317 Rohde & Spaeth, supra. note 313.Google Scholar

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326 Schubert, The Judicial Mind Revisited, supra note 315.Google Scholar

327 See note 258 supra.Google Scholar

328 See note 259 supra.Google Scholar

329 Specific and detailed explanation of the entire process used in this analysis is available in Nie et al., supra note 258, sec. 24.Google Scholar

330 See Nie et al., supra note 258, for a more detailed explanation. Although all of the texts listed in note 258 cover regression analysis, the mathematical theory underlying these techniques is discussed in John E. Freund, Mathematical Statistics (2d ed. Englewood Cliffs, N.J.: Prentice-Hall, 1971); N. R. Draper & H. Smith, Applied Regression Analysis (New York: John Wiley & Sons, 1966). David L. Sjoquist, Larry D. Schroeder, & Paula E. Stephan, Interpreting Linear Regression Analysis: A Heuristic Approach (Morristown, N.J.: General Learning Press, 1974) discuss regression analysis in a manner that is comprehensible to readers unfamiliar with statistics.Google Scholar