Computer-aided evaluation of racial representation in jury selection

Cornput.. Environ. andMan

Systems, Vol. 16, pp. W-155,1992

Printed in the USA. All rights resewed.

0196-971992 $6.00 + .OO Copyright 8 1992 Pergamon Press Ltd.

COMPUTER-AIDED EVALUATION OF RACIAL REPRESENTATION IN JURY SELECTION Edgar W. Butler Department of Sociology, University of California, Riverside Hiroshi Fukurai Board of Studies in Sociology, University of California, Santa Cruz

ABTRACT. This paper examines a computer-aided evaluation of racial representation in jury selection. The main thrust of the paper is to present a technical comparison of two methods of selecting jury panels utilizing both spatial analyses and covariance structural modelings (LISREL). The computer-aided geographic analysis of racial representation in jury selection is further illustrated by the use of both spatial display of racially segregated residential patterns and inferential statistical methods in substantiating disproportionate racial representation in jury panels. The research site is the Long Beach Superior Court District, Los Angeles County, CA. The spatial analysis demonstrates that if representative jury panels are to be obtained, court officials must recognize the existence of racial residential segregation. Minority and ethnic groups are unequally distributed in neighborhoods and consequently within the area served by the court. Thus, the selection process must incorporate racial and socioeconomic patterns affecting residential segregation into the selection process.

INTRODUCTION The courts, as a legal institution, have resisted change of blatant inequities surrounding place-to-place variations in laws and jury selection procedures which have a profound influence upon all legal outcomes, from misdemeanors to death sentences. There are substantial spatial affects in the administration of justice which are in part a ramification of residential location with respect to the site of crimes and the geographical and social composition of juries. Spatial influences are particularly important in respect to where crimes are committed, where trials are held, and when race, income, national origin, and occupation are reflected in attitudes of jurors (Brunn, 1975; Harries & Brunn, 1978), as well as in respect to gender and age (Fukurai, Butler, & Krooth, 1991b). Reprint requests should be sent to Edgar W. Butler, Department of Sociology, University of California, Riverside, Riverside. CA 92502. 131

132

E. W. Butler and H. Fukurai

The Federal Jury Selection and Service Act passed in 1968 recognized the importance of the implications of juries by guaranteeing that “all litigants in Federal courts entitled to trial by jury shall have the right to grand and petit juries selected at random from a fair-cross-section of the community” (U.S. 1968, Section 1861).l The Federal Act requires that selection procedures “‘ensure that each county, parish or similar political subdivision within the district or division is substantially proportionally represented in the master jury wheel for that judicial district, division, or combination of divisions” (U.S. 1968, Section 1863 (b)(3)). Current federal law attempts to ensure this goal by specifying two key requirements for forming the jury venire. During panel selection procedures, there must be (a) a “random” selection of jurors and (b) selection from an area that includes special geographic districts in which a particular court convenes (U.S. 1968, Section 1861).2 At the state level, a similar standard applies. For example, California law specifically states that persons listed for service in the court “shall be fairly representative of the population in the area served by the court and shall be selected upon a random basis” (Section 9,203). However, jury research has thrown doubt on the ability of these procedures alone to produce representative juries (Hemandez v. Texas, 1954; United States v. Femandez, 1973).3 Despite the requirement that jury panels be composed of a fair cross-section of the community, racial and ethnic minorities have been consistently under-represented in the vast majority of both federal and state courts (Carp, 1982; Brady, 1983; Hastie, Penrod, & Pennington, 1983; Fukurai, 1985; Hans & Vidmar, 1986; Wishman, 1986; Wrightsman, 1987; Kassin & Wrightsman, 1987; Fukurai, Butler, & Krooth, 1991a; Butler, Fukurai, & Huebner-Dimitrius, in press). The significant under-representation of race/ethnic minorities has been observed in both state and federal courts in spite of the fact that the law specifically gives gender, race, and socioeconomic factors “cognizable” status for protection in jury selection procedures. Geographical living areas are also given cognizable status in order to serve as further protection against nonrepresentative juries (Fukurai, Butler, & Krooth, 1991~) Explaining racially balanced juries was the focus of many jury challenges and research studies in the late 1970s and 1980s (see Fukurai et al., 1991a). In some of these evaluations there was an argument that “human capital” factors such as race, socioeconomic status, educational achievement, and occupational level and other individual characteristics generated differences in jury representation (Kairys, 1972; Staples, 1975; Alker, Hosticka, Mitchell, 1976; Heyns, 1979; Diamond, 1980; Carp, 1982; Brady, 1983). For example, persons with specific human capital factors such as higher income, higher education, and white racial background were more likely to be represented on juries because they were more inclined to register to vote, and at that time most jury panels were derived exclusively from registered voters lists (see Fukurai, Butler, & Huebner-Dimitrius, 1987, for variation in racial/ethnic variation in voting registration rates and cartographic and statistical illustrations of the consequences of variation in voting registration). Further, such persons could take time off from work to serve on juries (Hastie et al., 1983; Starr & McCormick, 1985; Fukurai et al., 1991~). More recently, however, other factors have come to be recognized as influencing jury selection, especially institutionalized factors within the jury selection process. lSee U.S. 1%8, Section 1861 and House Report at 1801. For more information, see Thiel v. Southern Pacific Co., 328 U.S. 217 (1946); State v. Holstrom 43 Wis. 465, 168 N.W. 2d 574 (1969); and State v. Cage 337 So. 2d 1123 (La. 1976). 2In the U.S., the historic background for a panel of jurors drawn from the community is known. The fist provision for a jury trial in a vicinage (jurisdictional area served by the court) is found in Article III, Section 2 of the Constitution. 3See.Fukurai et al. (1991~) for a presentation of the total process of jury selection procedures and Butler et al., 1992 (in press) for an analysis of the total process as it impacted one particular case.

Computer-Aided Evaluation of Racial Representation in Jury Selection

133

FIGURE 1. Los Angeles County, 1990. Potential Black Jurors by Census Tract.

RESIDENTIAL FACTORS AND JURY SELECTION In addition to the impact of socioeconomic and demographic characteristics of individual jurors, research has indicated that the presence of residential segregation affects the chances of an individual being selected for jury service. Thus, in order to obtain a representative list of prospective jurors, the jury selection process must control for the existence of racial residential segregation and the socioeconomic and other factors associated with such residential segregation. Figures 1 and 2 illustrate the extent of spatial segregation of blacks and Hispanics in Los Angeles County as of 1980. When 1990 census data become available, the extent of segregation is expected to be even greater than shown on these two maps. These ma& demonstrate that race/ethnic minorities are not equally distributed over space. Thus, if areas of high concentration of potential black and Hispanic jurors are not included equally with other neighborhoods, there will be severe under-representation of blacks and Hispanics on jury panels. As a result, these jury panels would not reflect a fair cross-section of the area served by the court as specified by both state and federal laws. Traditional methods of jury selection, which are based upon random selection from Registrar of Voters (ROV) lists and, in some jurisdictions, Department of Motor Vehicle (DMV) lists, generate nonrepresentative juror pools. This lack of representativeness is reflected in unequal representation of geographic areas - residential living areas/neighborhoods - because of variation in voting registration and DMV lists. Minorities, especially, are then substantially under-represented on juror panels since they are residentially as well as spatially segregated. Thus, even with simple random sampling procedures, there is a strong likelihood that persons living in minority neighborhoods will be summoned less often than people from dominantly white areas (for example, see Fukurai et al., 1991~). The result, then, is a racially disproportionate jury representation, i.e., an under-representation of minorities and an over-representation of whites. In addition, because of characteristics associated with being a minority, jury

134

E. W. Butler and H. Fukurai

FIGURE 2. Los Angeles County, 1980. Potentlal Hlspanlc Jurors by Census Tract.

panels then primarily consist of white, middle- and upper-class persons with middle to higher level education and incomes. Similar results of racially demarcated juries have been observed in other superior courts in Los Angles, Orange, Riverside, Sacramento, and Sonoma Counties in California, and in other states including Nevada and Oregon (Butler 1980a, 1980b, 1981; Fukurai, 1985). Further, the research also pointed out that younger people are less represented than are the middle-aged and elderly persons. Therefore, jury panels are not representative of the geographical area served by the court. This is so despite the fact that a fair cross-sectional representation is required by federal law and most state laws, including California. The purpose of the remainder of this paper is twofold: (a) to demonstrate how current jury selection procedures result in disproportionate representation of minority jurors and (b) to propose a geographically-based jury selection system that can ensure the cross-sectional representation of the community in jury panels. In addition to a fair cross-section of race/ethnicity, we anticipate that the jury selection procedures proposed here will result in a fair-cross-section of all population characteristics, including age, gender, socioeconomic status, and the geographic area served by the court. It is our contention that cluster sampling of smaller geographic areas with the probability proportionate to population size (PPS) will eliminate the effect of spatial biases on jury representation. Utilizing cartographic and statistical analysis techniques, this paper, then, has the following three specific objectives: (a) To evaluate jury representation based on the currently used procedure of simple random sampling, (b) to demonstrate how an alternative cluster sampling with PPS strategy can create a more representative jury system, and (c) to show the extent to which the PPS method can help ensure racially egalitarian jury venires (as well as for other characteristics). A MODEL OF JUROR REPRESENTATION The evaluative model to examine two different sampling approaches and the extent to which racial segregation effects jury pools is shown in Figure 3. The model includes four cognizable

Computer-Aided Evaluation of Racial Representation in Jury Selection

El

Ob~rvcd Awcp~e

135

Imdka~ors

NOTE: Xl. Male; X2. Black X3. Spanish; X4. College; X5, Poverty; X6 Distance; Y 1, No. of Peoples; YZ, No. of panels *: All the observed indicators are aggregate.census variables by census tracts.

FIGURE 3. Evaluative Model of Jury Representation:

specified by federal law in jury selection procedures: (a) gender, (b) race, (c) socio-economic position, and (d) distance in the sense that the area served by the court represents distance. Distance is especially important since discrimination in jury representation often takes place by jury commissioners not calling persons some distance from the courthouse or those for whom it takes some time to get to the courthouse. If residential segregation impacts jury representation, the paths from the cognizable characteristics should be large and statistically significant. Nonsignificant paths, however, would demonstrate that residential segregation - neighborhood/area of residence - does not unduly influence jury representation. The examination presented here allows a comparison of the extent of possible discrimination and anomalies of simple random selection as opposed to an alternative cluster sampling model with sampling probability proportionate to size. Recently developed covariance structures and LISREL maximum-likelihood estimates were utilized to assess the jury representation model (Hayduck, 1987; Matsueda & Bielby, 1986). The &i-square statistic and the likelihood ratio indices of both delta and rho were employed in comparing the goodness-of-fit of the jury representation model (Bentler & Bonett, 1980; Bollen, 1989). Failure to reject the null hypothesis may be taken as an indication that the model is consistent with the data; however, alternative models also may be consistent with the data (Joreskog & Sorbom, 1985). Because the &i-square test is affected by sample size, a general null model based on modified independence among variables also was used to provide an additional reference point for the cluster evaluation of covariance structure models (Long, 1983; Matsueda & Bielby, 1986).

characteristics

DATA

Two data sets and a computer simulation were linked to serve as a foundation for evaluating the adequacy of current jury selection procedures and the proposed cluster sampling proce-

136

E. W. Butler and H. Fukurai

dures. The first data set consisted of actual jury impaneiment lists for a 1985 trial in California, People v. Harris4 lmpanelment lists covering a period of ten weeks, from 4 April 1985 through 12 June 1985, were obtained to identify neighborhoods (census tracts) from which jurors were being drawn to the Long Beach Superior Court in Los Angeles County. The second data set utilized was 1980 U.S. Census Bureau information for Los Angeles County census tracts. For this analysis, consistent with Los Angeles County judicial procedures, census tracts were used as the basis for delineating the judicial district as per several different regional definitions utilized at various times by the court. Jury impanelment lists were then compared with county and smaller area demographic data to determine whether or not jury panels represented a fair cross-section of the various jurisdictions defined for the Long Beach Superior Court District. Hispanics (Latinos) were identified by the use of the Spanish Surname list developed by the Census Bureau. The jury impanelment lists in combination with census tract data provided information on (a) frequency of jury representation from each tract and (b) the racial/ethnic composition of neighborhoods from which potential jurors were being drawn. There were 1,250 impaneled jurors during the ten-week period. These ten panels were typical, based upon panel data available for other time periods that we had previously analyzed (see, for example, Fukurai et al., 1991b). Population and housing data were used to determine if there were disparities between the population composition and jurors at the jury impanelment stage. Eight variables were selected from the 1980 U.S. Census to evaluate the jury representation model. Gender was the proportion of male potential jurors in a given census tract. Racelethnicity was measured by the proportions of black and Hispanic ~pulation in a ne~gh~rh~. S~i~cono~c status was based on the proportion of potential jurors with a college education and the proportion of households under the poverty line. Distance was the absolute distance between the courthouse and the centroid of each census tract. Finally, two measures of jury representation were calculated: (a) The number of times that a census tract was chosen and (b) the number of potential jurors from each census tract that were summoned to the courthouse. Those variables are important in measuring the level of jury representation because a tract might be chosen once but have 100 potential jurors summoned, chosen once with one potential juror summoned, or, alternately, not chosen at all,

GEOGRAPHIC AREA SERVED BY THE COURT One of the basic problems in evaluating jury representationis determining the area served by the court. While many jurisdictions are delineated specifically, many others are. not. In the example used here, Los Angeles County and its Long Beach Superior Court, the “law” relating to the geographic area served by the court has been variously interpreted, almost invariably to the advantage of the prosecution. Other instances of such geographic variation exist. For instance, the same appellate judge has ruled for contrary boundaries in San Diego County depending upon what the prosecution has asked for (see Daily Appellate Report, 1984 and 1986a). ln this example, the Long Beach Superior Court District of Los Angeles County has been delineated in five different ways: (a) Los Angeles County, (b) a 20-mile region, (c) a 1% mile region, (d) the Long Beach Judicial District, and (e) the geographic areas from which the jurors actually came. 4&e Peoplev. Harris,36 Cal. 3d 36; 201 Cal. Rptr. 782 679 F! 2d 433,1984. EmpiricaI anaIyses of the original jury panels were carried out by us. The motion of respondent for leave to proceed in forma pauperis was granted; however, the Writ of Certiorari by the prosecution to the U.S. Supreme Comt was denied on 29 October 1984, effectively requiring a retrial based upon the lack of representative jnry panels.

Computer-Aided Evaluation of Racial Repiesentation in Jury Selection

137

FIGURE 4. Los Angeles County and Long Beach Superior Court 2OMle Region.

“Areas” 2-4 are within Los Angeles County, but their boundaries are not conterminous. Finally, during the course of the Harris retrial, a sixth area served by the court emerged. This specifically bounded area had never been previously referred to in trials throughout Los Angeles County. This sixth area delineated by the court is used to assign cases to one of the superior court jurisdictions in Los Angles County; thus, when a crime is committed within these specific boundaries, the case is assigned to a specific superior court even though jurors are selected from a variety of areas. All of these bounded areas are within Los Angeles County, but otherwise they have nothing in common. Figure 4 shows Los Angeles County and the 20mile region for the Long Beach Superior Court. While the California legal code specifically states that the region from which jurors are to be called in Los Angeles County is within 20-miles of the courthouse, the California Supreme Court in fact has vacillated on the boundary in deciding upon whether or not jury panels meet the legal mandate of a fair cross-section. This inability to make a definitive decision in its boundary undoubtedly is due to the fact that none of the areas examined in various jury challenges have been found to adequately represent any region served by the court in Los Angeles County. Similar results have been obtained elsewhere, which has led to the conclusion that there are very few, if any, representative jury panels. A consequence of this lack of representation is that defense attorneys in California and elsewhere constantly challenge jury panel composition based upon the lack of definitive boundaries and/or perceived and then determined by research of the under-representation of minorities and other cognizable categories gender, SES, and age. Geographic definitions become quite important if one is concerned about whether there is a fair cross-section of minorities on jury venires. Figure 5 illustrates the importance of geographic boundaries in determining whether impaneled Hispanic potential jurors in areas served by the Long Beach Superior Court represent a fair cross-section of the community as mandated by

138

E. W. Butler and H. Fukurai

Actusl Pm&

summons Mea

15-h4ileRadius

26.7

Los Angeles County ~

23*g

0

20

10 Percent

30

Hlspanlo

FIGURE 5. Juror Panels and 1980 U.S. Census Data: Geographical Area and Percent Hispanic.

federal and state law. The actual panels in the Harris retrial contained 5.8% Hispanics. Census tracts from which the jurors were summoned for the trial, had a population that was 15.9% Hispanic. The 20-mile region specified by California law contained 26.7% Hispanic population. However, the jury commissioner’s office of Los Angeles County after utilizing the 20mile region for a number of years decided that in fact, the area served by the court should actually be 15miles since a direct route to the courthouse was generally not possible. Finally, in the original Harris trial, the California Supreme Court said that the county as a whole was the proper comparison basis (People v. Harris, [1984] 36 Cal R. 3d 36). For each of these definitions given to judicial boundaries, there is great disparity between the Hispanic population on the actual panels as opposed to the areas that have been variously defined by the court. Figure 6 presents somewhat similar results for the black population except that in this instance the actual jury panels and areas from which they were summoned more or less match. However, there is a great disparity between these two bounded areas and the three remaining definitions. While in the original Harris trial, the California Supreme Court specified that Los Angeles County as a whole was the proper comparison criterion, the prosecution argued just the opposite, that in fact the smaller areas of either the summons area or the even more limited Long Beach Superior Court District from which its cases were derived represent the proper definition for the district served by the court. The difference is important because if any of the other three regions are used in determining disparity, rank discrimination exists; however, if the summons or District are used as the comparison base, black jurors represent a fair cross-section of the community! This, of course, is why the prosecution preferred that definition and why the defense wanted the larger base of the E-mile or 20-mile regions or Los Angeles County as a whole. RESULTS -

SIMPLE

RANDOM

SAMPLE

SELECTION

Figures 7 and 8 illustrate the observed jury representation under the simple random sampling method currently used by the court and its 20-mile region as specified by California law. The

Computer-Aided Evaluation of RacialRepresentation in Jury Selection

Actual Panel8

SummorN kep

15Mle

Radlur

20-k&

Radius

LosAngeles County 10 Percent Black

FIGURE 6. Juror Panels and 1980 U.S. Census Data: Geographlcal Area and Percent Black.

FIGURE 7. Long Beach Superior Court PO-MileRegion: Simple Random Jury Selectlon.

139

E. W. Butler and H. Fukurai

FIGURE 8. Long Beach Superior Court 20.Mile Region: Simple Random Selection.

maps substantiate the argument that social and racial composition of the neighborhoods were strongly associated with the op~~u~ty to serve on a jury. Neigh~rh~s adjacent to economically prosperous Orange County had a population that was 1.14% black and 12.50% Hispanic. Thus, these residents were predominantly white with middle-class or higher incomes and had higher educational attainment. The Long Beach Judicial District, the smallest of the areas and one closest to the courtroom, had 16.4% blacks and 20.8% Hispanics. As shown in Figure 7, most neighborhoods in the 20-mile judicial district had no one on the panels. Nomepresented areas included those with the highest percentage of blacks and Hispanics; some of these completely neglected areas were quite close to the courthouse. Table 1 showed that one census tract was represented by potential jurors 22 times, while 117 tracts were represented four or fewer times, and 319 tracts had no representation whatsoever (also see Figures 7 and 8). Almost half (47.1%) of the potential jurors came from 39 (7.2%) of the 538 total tracts in the 20-mile region. The proportion of black and Hispanic potential jurors in the completely nonrepresented 319 areas was higher than in the much smaller Long Beach Judicial District, The census tract with the highest representation had only 0.2% black and 5.9% Hispanic, far below the average for the 2Omile region. Thus, nonrepresented tracts had far below average minority representation on impanelment lists than the average for the judicial district (19.3 and 26.6%, respectively, for black and Hispanic residents). Identi~cation of the neigh~rh~d with median represen~tion subst~tiated that jury representation was also highly discriminatory (see Table 2). For example, 26.1% of residents in neighborhoods with below median jury representation were black, in contrast to 6.0% black in neighborhoods with representation above the median. A disproportionate representation also was found for Hispanics with 28.4% in under-represented neighborhoods and 18.0% in neighborhoods above the median representation. A source of the significant minority ~der-representation explained by court representatives and prosecutors is that their jury qualification rates were significantly lower than the whites.

141

Computer-Aided Evaluation of Racial Representation inJury Selection TABLE 1. Census Tracts Representation

on Ten Panels:

The Long Beach Judlclal District

MINORITY COHPOSITION NO. OF TIMES CENSUS TRACTS REPRESENTED THE LONG

FREQUENCY

PERCENT1

CWUL4TIVE PERCENT

BEACH JUDICIAL DISTRICT -. 24.2 11.4 9.6 8.2 6.4 7.8 6.4 4.1

BUCK PERCENT

20.9

_m 24.2 35.6 45.2 53.4 59.8 67.6 74.0 78.1

16.6 31.7 34.8 19.5 7.7 5.1 10.6 5.6 5.0

35.0 35.7 21.1 29.0 20.8 26.5 28.8 23.1

326 53 25

9 10 11 12 13 14

9 1 3 6 3 4

4.1 0.5 1.4 2.7 1.4 1.8

82.2 82.6 84.0 86.8 88.1 90.0

11.8 0.6 3.6 5.6 0.9 4.2

:6' 17 19

5 x

2.3 Z

0.9 2.2 4.8

20

1 2 1

0.5 0.9 0.5

94.5 92.2 97.3 98.2 98.6 99.5 100.0

f:

21 22

PERCENT

16.4

0 1 2 3 4 5 6 7 8

14 17 14 9

HISPANIC

11.1 17.1 8.1 9.6 10.8 3.9 9.0 12.7 6.9

6.8 0.3

10.2 9.2 3.6

4.5 0.2

13.5 5.9

MEDIUM - 4. WZAN

-

10.28.

1: Percent is computed by the represented census tracts.

TABLE 2. Average Representation

of Census Tracts on Ten Panels: Black and Hlspanlc

Potential Jurors HIUN

RACE

JUDICIAL NOT DSTRICT REPRESENTED

HEDIUH

10 TIHES 11 TIXBS 4 TIHES 5 TIMES OR LESS OR MORE OR LESS OR MORE

16.62Y

19.362

3.602

26.16X

6.01%

HISPANIC 20.90

35.02

26.67

9.59

28.49

18.04

HISPANIC1 SURNAMES 17.84

-_

77.08

22.92

34.37

BLACK

16.40%

65.63

1:96outof538hadatleast onejurorwiththe Hispanicsurnameimpaneled;136outof 1250impaneledjurorshad Hispanic surnames(ll.O%).

142

E. W. Butler and H. Fukurai TABLE3. Potential and Qualified Jurors RACE

POTENTIALJURORS QUALIFIEDJURORS DISTRICT JUDICIAL DISTRICT

N

243,274

IHPANELED DISTRICT 62,758

x

x

N

100.0%

46,436

19.08%

100.0

12,459

19.85

BUCK

HISPANIC

16.4%

20.9%

9.7

6.7

1: 96 out of 538 had at least one juror with the Hispanic surname impaneled; 136 out of 1250 impaneled jurors had Hispanic surnames (11.0%).

The argument presented is that juror qualification criteria, such as U.S. citizenship, English language proficiency, residency requirements, and no prior felony convictions eliminate more racial/ethnic minorities than whites. Thus, it was assumed that eligibility requirements reduced the rate of minorities eligible for jury service but enhanced the white eligibility rate. However, our analysis reported in Table 3 shows quite clearly that the qualification rate of potential jurors does not explain the geographical biases found here. While 19.8% of the population in the impaneled neighborhoods (212 tracts) were qualified in the screening process, an approximately equal percentage (19.0%) of persons who resided in the 20-mile region (538 tracts) were also qualified. Thus, juror qualification rates do not explain the representation of minorities. To assess whether place of residence influences a potential juror’s chances of being included in the jury pool, the results of the current jury selection procedures were statistically analyzed. The two sets of coefficients of correlation reported in Table 4 are for the overall judicial district (538 tracts) and the tracts from which the impaneled jurors actually came (212 tracts).5 The overall goodness-of-fit of the model for the entire judicial district reflects the extent to which residence impacted an individual’s chance of being impaneled. The goodness-of-fit for impaneled tracts reflects the effect of racial segregation only for selected panelists. Thus, the similarity and difference between these two samples shows the overall goodness-of-fit of jury representation for the simple random sample model currently used by Los Angeles superior courts. (X-square goodness-of-fit tests are shown in Table 5 (also see Figures 8 and 9). For both overall and impaneled neighborhoods, the model illustrated in Figure 3 did not fit the observed covariance matrix. After six unique factor correlations were allowed, the model shown was an excellent fit with the model explaining 99.3% of chi-square values; it also explained 91.6% of &i-square values after controlling for the degrees of freedom. The model, for the impaneled areas, explained 98.0% and 71 .l% of &i-square values. Tables 6 and 7 demonstrate that the standardized parameter estimates for both the overall and impaneled districts for the four cognizable groups defined by federal law (gender, race/ethnicity, socioeconomic status, and distance) were significant. This indicates that residential characteristics significantly influence one’s chance to be on juries. As previous jury analyses have consistently found, race had a statistically s&i&ant negative impact upon the opporhmity to serve on a jury (Fukurai et al., 1991a, 1991b). That is, the greater the proportion of black and Hispanic residents in a given neighborhood, the less the chance for residents in that neigh5The variables, Xl through X5, represent the proportions of those cognizable groups for given census tracts. Gender and racial variables were created by dividing those proportions of the total population. The percentage of college graduates was computed by the total number of college graduates divided by the population over 25 years of age. The proportion poverty was judged by the number of houeholds divided by the total households in each census tract.

Computer-Aided

Evaluation

of Racial Representation

143

in Jury Selection

TABLE 4. Simple Random Selectlon: Coefficients of Correlation among Causal Variables In Represented’ (above dlagonal) and Total Census Tracts (below dlagonal)2 VARIhBLes

xl

X2

x3

X4

X5

X6

-.339

-.063

Yl

Y2

HEAN S.D’

Xl: MALK -.435 .241

.113

.132

.150

,475

.055

X2: BUCK -.294 -.187 x3: SPANISH .109 -.263 X4: COLLEGE .109 -.227 -.653 x5: POVERTY -.153 .598 .316 X6: DISTANCE -.049 .177 .272 X7: NO. OF PEOPLE .015 -.175 -.263 X8: NO. OF PANELS .013 -.174 -.265

S.D

.185 .309

-.253 .643 .168 -.362 -.405 -.631

,267 .341 -.350 -.335 .278 .177 -.585 -.389 .606

.068 -.403 -.403 .146 .121

-.621 -.206 .296

.588 .318 .177

.152

-.386 -.432 12.94 4.95

-.233 -.470

.927 2.32 4.31

.266 -.220 -.505 .956

1.49 2.44

.474 .176 .208

.355 .132 10.28 5.90

.026 .291 .183

.154 .llO 4.96 5.11 2.53

3.80

- 212. - 538. 3: Standard deviations.

borhood to participate in jury service. Distance from the courthouse also had a significant negative impact on representation, initially suggesting that impaneled neighborhoods were mostly located near the courthouse. Note, however, that the utility of Figures 7 and 8 is to demonstrate

FIGURE 9. Long Beach Superlor Court 20-Mile Region: Chl-Square Dlstrlbutlon Selectlon.

Simple Random

144

E. W. Butler and H. Fukurai TABLE 5. Simple Random Testing: Chl-Square Goodness-of-Fit Test of the Juror Representation Model

DEGREES OF FREEDOM

MODEL

x*

P.LEVEL

RHO (Z) DELTA (X)

TOTAL NEIGHBORHOODS* (1) HODEL IN FIGURE 2

250.66

.ooo

69.32 X

66.56

.ooo

84.18

97.61

18.61

.ooo

91.60

99.33

76.98

-000

88.03

97.24

__

__

(2)

HODEL WITH CORRS" X2-X4,X2-XS x3-x4,x3-x5 4 (3) HODEL WITH CORRS X1-X2,X1-X3 L

(4) MODEL WITH THE RESTRICTION:Es - 0 6 (5) NULL MODEL' 28

2,796.60

.ooo

91.03 x

REPRESENTEDNEIGHBORHOODS* (1) MODEL IN FIGURE

2

8

(2) MODEL WITH CORRS X1-X4,X2-X4 4 X4-X6,X5-X6 (3) MODEL WITH CORRS X1-X2.X1-X3 2 (4) MODEL WITH THE RESTRICTION:Bs - 0 6 (5) NULL HODEL 28

186.64

. 000

40.56

82.71

60.15

.ooo

62.63

94.43

21.39

.ooo

71.19

98.01

64.06

.000

87.55

96.84

1.080.03

. 000

__

NOTE: Xl-MALE; X2-BIACK; X3-SPANISH; X4-COLLEGE;XS-POVERTY; X6-DISTANCE;Yl-PEOPLE; Y2-PANELS; *: N - 538. t*: N - 212. 1: Residual correlationsfor observed variables. 2: See Bentler amd Bonett (1980) for further references.

quite clearly that many minority neighborhoods adjacent to and/or near the courthouse were disproportionately represented or excluded all together. The analyses presented here illustrate that simple random jury selection, as currently being administered in Los Angeles County, fails to control for spatial biases related to residential segregation. As a result, jury impanelment lists show severe under-represenation of minorities and do not reflect the overall characteristics of the judicial district. Consequently, we decided to apply a methodology ensuring equal representation of minorities, as well as other population segments, including the young and lower social class individuals with lower incomes and lower level educations. The jury selection procedure we applied via computer simulation was cluster sampling with the probability proportionate to size (PPS).

Computer-Aided Evaluation of Racial Representation in Jury Selection

145

TABLE 6. Simple Random Selection: Standardized Parameter Estimates for the Measurement Model

TOTALAREAS(N - 538) MODEL(3) FACTORS

FACTOR

AND

VARIABLES

REPRESENTED AREAS(N - 212) MODEL(3) FACTOR

STANDARD

LOADINGS

STANDARD

LOADINGS

ERROR

1.001

f

1.00

f

f

.54 .37

f .03

.99

f .06

ERROR

SEX

Xl RACE

x2 x3

S12 .76

SES3 X4

.04

X5

.87 - .71

.03

DISTANCE X6

1.00

f

1.00

.62 .65

f -04

.68 .70

f -

.61

f

JUROR

REPRESENTATION Yl Y2

f

.05

NOTE: Xl-MALE; X2-BIACK; X3-SPANISH; X4-COLLEGE;X5-POVERTY: X6-DISTANCE;Yl-PEOPLE; YP-PANELS; 1: Factor loadings are estimated by fixing the loadings with standard deviations of respec-j tive variables. 2: Factor loadings are estimated by fixing the loadings to 1.0. 3: Socio-economic status. f: Fixed.

CLUSTER

SAMPLING

-

PROBABILITY

PROPORTIONATE

TO SIZE

The cluster sampling method with probability proportionate to size (PPS) results in selection of prospective jurors within a jurisdiction in accordance with an equal-probability basis. First utilized in surveys by Kish (1965), PPS is an efficient method of developing a multistage cluster sample. Whenever the areas sampled are of greatly differing sizes and compositions, it is appropriate to use PPS. Cluster sampling with PPS assumes that each unit is given an equal chance for selection proportionate to its size. For example, each geographical unit, such as a census tract, has a different number of potential jurors. A fixed number of jurors is selected, say two. Cluster sampling with PPS ensures that the selection procedure results in each potential juror having the same probability of selection overall. Further, this results in the selection of a fair cross-section of the community population - the entire geographical territory of the judicial district served by the court. Cluster sampling of prospective jurors consists of two steps carried out in the following manner. First, geographical units from which jurors are to be drawn are randomly selected. In carrying out this process each unit (census tract) within the area served by the court is given a unique number. Then, a series of random numbers are generated, corresponding to the number

146

E. W. Butler and H. Fukurai TABLE 7. Simple Random Selection: Standardized Parameter Estimates for the Structural Model

TOTAL AREAS (N - 538) MODEL(3) FACTORS AND

VARIABLES

REPRESENTEDAREAS (N - 212) MODEL(3)

FACTOR STANDARD CRITICAL LOADINGS ERROR RATIO

FACTOR STANDARD CRITICAL LOADINGS ERROR RATIO

FACTOR

CORREIATIONS R sex-race R sex-ses* R sex-dlst* R race-ses R race-dist R ses-dirt REGRESSION WEIGHTS B sex-rep B race-rep B ses-rep B dist-rep

- .18 .30 - .09

.03

5.80

- .24

.05

4.61

.02 .02

13.04 3.33

.47

.04

10.93

- .12

.53 - .32

.03 .03 .03

27.67 15.14 10.32

- .96 .61 - .30

.04 .OS .05 .OS

2.85 18.82 10.89 5.76

- .25 - .23 .25 - .87

.oo .Ol .04 .04

45.45 12.77 11.01 19.77

- .36 - .27 .54 - .47

.02 .04 .OS .06

6.42 5.86 9.63 6.86

.78

.04

19.12

.72

-

.94

RESIDUAL VARIANCES Representation

* : Socio-economic *: Distance.

.04

14.79

status.

of areas that need to be selected. This process is repeated according to the number of jury panels to be assembled. Then, the frequency for each tract is computed. The frequencies become equivalent to the numbers of jurors expected from each of the corresponding census tracts. Second, prospective jurors are randomly selected within chosen geographical units. Stratifying jury selection by geographical units eliminates potential selection biases derived from residential segregation. Further, it enhances the probability of assembling a more representative jury pool. As example of the proposed process, assume that a judicial district has 1,000 census tracts and 1,000,000 potential jurors living in the area served by the court. If 1,000 jurors are to be called for jury service, each person has a 1,000/1,000,000 or .OOl chance of being selected. In the first stage 500 census tracts could be randomly selected. Each census tract, then, would have approximately two jurors to be selected to impanel 1,000 persons. At the second stage, consider a tract with 1,000 potential jurors. The tract has a probability of selection equal to: 500 (tracts identified by random selection) X

1,000 (potential jurors in the tract) 1,O,OOO (potential jurors in the district)

= .5

Computer-Aided

Evaluation

of Racial Representation

in Jury Selection

147

If this tract is represented, each potential juror has a second-stage probability of selection equal to: 2 (to be selected in the tract) 1,000 (potential jurors in the tract) = .002 Multiplying .5 times .002 yields an overall probability of selection equal to .OOl, as required. Now consider a census tract with only 200 eligible potential jurors. The tract’s chance of selection is only 500 x 200/l ,OOO,OOO, or .lO, much less than the previous example. If this tract is selected, each potential juror has a chance of 2/200 or .Ol of selection at the second stage. Overall, each potential juror in this tract has a probability of selection of .lO times .Ol, or .OOl; the same as the previous example and as demanded by the overall sample design. The only difference between the two examples is the number of potential jurors in the tracts, but that number appears in both numerator and denominator, thus cancelling itself out. Thus, no matter what the population size, then, the overall probability of a potential juror being selected is equal to 500 times 2/1,000,000 or .OOl. The assumed advantage of cluster sampling with PPS is that it selects relatively few potential jurors in each census tract, thus avoiding the current problem of selecting a large number of jurors from a very few tracts with a homogeneous population and no jurors from most tracts. The heterogeneity of a large number of clusters (i.e., tracts) increases sample variability, which in turn reduces sampling error (Sudman, 1976) but increases the probability of equal representation of minorities, the young, etc. Thus, the cluster sampling design with the small cluster size and the large number of clusters is particularly important since residential characteristics and racial representativeness are likely to be homogeneous and affect sampling variability. For instance, the cluster size of five usually is considered sufficient in the context of a large sample (Babbie, 1989) because the ratio of cluster to simple random sampling error remains minimal (Sudman, 1976). Obviously, a large number of potential jurors in a single tract would improve the description of the tract slightly, but the description of the judicial district as a whole will be improved more by adding tracts to the sample rather than adding potential jurors in fewer tracts as per current procedures. Given that the court needs only 1,000 impaneled potential jurors, it would be better to select two juror clusters from each of 500 tracts than to selected 20 each from 50 tracts, that is, if a fair cross-section of the area served by the court is the goal. The First Stage The first stage of cluster sample selection assumes that one person is randomly selected from each identified census tract for a jury panel. Generating 125 numbers identifies 125 prospective jurors. In our simulation, we performed this procedure ten times, since there were ten impanelment lists that needed to be generated. Then, the frequency was computed for each selected tract. Thus, using the cluster sampling procedure, we recreated a pool of 1,250 potential jurors. The geographical distribution of these potential jurors is shown on Figures 10 and 11. The difference in jury representation between the random sample selection procedure currently used by the courts shown in Figure 7 and PPS illustrated in Figure 10 is abundantly clear. The PPS method draws persons from the entire judicial district creating a pool of potential jurors that at least reflects the geographic area served by the court and presumably the population of the judicial district. The next question examined was “Do residential factors significantly impact an individuals’ chances of being selected for jury representation, thus creating imbalanced juries?” As reported

148

E. W Butler and H. Fukurai

FIGURE 10. long Beach SUperlor Court PO-Mile Region: Cluster Sampling Jury Selection Stage.

First

in Table 8, the goodness-of-fit for the PPS model demonstrated that 98.5% and 99.8% of total &i-square values were explained; after degrees of freedom were controlled, 99.3% and 93.6% of total &i-square values for the impaneled neighborhoods were explained. Out of 538 census tracts in the judicial jurisdiction, 492 (91%) were chosen by PPS while only 212 (7.2%) were

FIGURE 11. Long Beach Superior Court 20-Mile Region: Cluster Sampling Jury Selection First Stage.

The

Computer-Aided Evaluation of Racial Representation in Jury Selection

149

TABLE 8. A Cluster Sampling Strategy: Chi-Square Goodness-of-FM Test of the Juror

Representation Model

MODEL

DECREES OF FRBEDOX

x2

P.WEL

RHO (2) DELTA (X)

TOTAL NEIGHBORHOODS* (1) MODEL IN FICDRE 2 a (2) MODEL WITH CORRS x*-x4, x*-x5 X3-X&. x3-x5 (3) MODEL WITH CORRB'4 x1-x*,x1-X3 3 (4) KODEL WITH THE RESTRICTION: Bs - 0 7

230.01

.ooo

68.09

90.66

45.51

.ooo

88.07

98.15

4.51

.2Ll

98.55

99.81

.052

99.09

99.49

12.49

(5) NULL MODEL* 28

2.463.79

.ooo

__

VW

REPRESENTED NEICHBORHOODSf* (1) tiODELIN FIGURE 2

a

(2) MODEL WITH CORRS X1-X4,X2-X4 X4-X6,x5-X6 4 (3) KODEL WITH CORR Xl-X2 3 (4) MODEL WITH THE RESTRICTION: Bs - 0 7

345.98

.ooo

44.22

83.90

59.14

-000

81.80

97.24

17.46

.ooo

93.63

99.31

38.75

.ooo

94.01

98.19

2,149.23

.ooo

(5) NULL HODEL 28

__

__

Xl-HALE; X2-BLACK; X3-SPANISH; X4-COLLEGE; XS-POVERTY; X6-DISTANCE; Yl-PEOPLE; Y2-PANELS; *: N - 538. **: N - 492. 1: The addition of the unique factor correlation, X1-X3. did not reduce X2 values Thus, this residual correlation was not respecified in the model estimation. 2: See Bentler and Bonett (1980) for further references. NOTE:

represented by the simple random jury selection techniques currently being used in Los Angeles County. Tables 9 and 10 present statistical analyses that determined if an individual’s area of residence would affect his or her chance of being selected for jury service by PPS. Standardized parameters of PPS were found to be statistically insignificant. This finding suggests that the residential characteristics including the composition of gender, race, SES, and distance would not effect the representativeness of jury pools if cluster sampling were used. Thus, every potential juror in the district would have a more or less equal chance of being included on a jury panel. This analysis further demonstrates that the PPS procedure is superior to the simple random procedure in two ways: (a) racially segregated residential patterns have no bearing on chances

150

E. W. Butler and H. Fukurai

TABLE 9. A Cluster Sampling Strategy: Standardized Parameter Estimates for the Measurement Model

TOTAL AREA8 (N - 538) HODKL (3) FACTORS AND VARfABLES

REPRESENTEDAREAS (N - 492) HODKL (3)

FACTOU

STANDARD

LQNIXNGS

ERROR

1.001

f

.512 .75

f .02

-24 .13

f .Ol

.91 - .67

f .03

.90

- .52

f -03

1.00

f

1.00

f

f .02

-68 .56

f ‘03

FACTOR LUADXNCS

STANDARD ERROR

SEX

Xl

1.00

f

RACE

x2 x3 SES3 X4 xs DISTANCE X6

JUROR REPRESENTATION Yl .41 Y2 .S4 NOTE:

Xl-MALE;X2-BIACK;X3-SPANISH;X4-COLLEGE;X5-POVERTY; X6-DISTANCE;Yl-PEOPLE;YZ-PANELS;

1: Factor loadingsare estimatedby fixing the loadingswith standard deviationsof respectivevariables. 2: Factor loadingsare estimatedby fixing the loadingsto 1.0. 3: Socio-acono~~fcstatus. f:

Fixed.

for jury participation; that is, there would be no “systematic selection or discrimination” based on neighborhood characteristics and (b) the cfuster sampling method ensures the equal probability of juror representation within the defined boundary of the judicial district, The Second Stage At the second stage of the cluster sampling method, once census tracts have been identified, the probability p~po~ionate to size selection of potential jurors within them is carried out. Evaluation of the PPS to produce a representative panels focused on two questions. First, does PPS accurately reflect the racial and SES compositions of each geographical unit (i.e., census tract)? Second, does PPS yield a more egalitarian pool of prospective jurors for the entire judicial district - the entire bounded area served by the court? To answer the question of individual juror representation, the Z-test statistic examined representativeness within each census tract (see Table 11). The Z-score tests whether the characteristics of a sample are consistent with the population from which it is drawn. That is, if the sample (e.g., jury panels) contains a fair cross-section of the population. In this instance, this would mean that the proportion of Hispanics selected would be similar to the proportion of Hispanics in the tract (Ott, Larson, & Mendenall, 1987).

Computer-Aided

Evaluation

of Racial Representation

151

in Jury Selection

TABLE 10. A Cluster Sampling Strategy: Standardized Parameter Estimates for the Structural Model

TOTAL AREAS (N - 538) MODEL (3) FACTORS AND

FACTOR

VARIABLES

LOADINGS

STANDARD CRITICAL FACTOX STANDARD CRITICAL LOADINGS ERROR RATIO RATIO ERROR

FACTOR CORRELATIONS R sex-ram - .19 R sex-ses* .29 R sex-dint** - .lO R race-sea

- .91

R race-dist R sea-dist

.50 - .31

REGRESSION WEIGHTS B sex-rep B race-rep B acs-rep B dist-rep

.03 .02 .02 .03 .03 .03

6.33 13.18 3.70 26.00 14.28 10.00

.oo

- .Ol

.04 .04

6.00 5.00 2.20 0.22

RESIDUAL VARIANCES Representation -91

.04

22.19

- .03 - .09

.Ol

.OS

* : Soclo-economic **: Distance.

REPRESENTED AREAS (N - 492) MODEL (3)

- .59 .37 - .15 - .95 .71 - .45

.03 .02 .02 .03 .03 .03

.oo - .oo .oo - .oo

.Ol -02 .03 .04

-

.99

18.54 14.06 5.83 30.44 20.71 14.14

.ll .03 .17 -19

.02

34.13

status.

TABLE 11. Chl-Square and Z-Score Dlstrlbutlons for Slmple Random SelectIon and Cluster Sampling Strategies

STATISTICAL INDX

HEAH

STANDARD DEVIATION

KINIXUM

MAXIHUH

SIUPLE RANDOX SAMPLING1 CM-SQUARE z SCORE

2.21

2.75

0.00

21.20

- 0.95

1.40

- 10.26

4.70

CLUSTERSAMPLING2 UiI-SQIJARX' Z SCORE

1.86’ - 0.24

1.94 0.62

0.00 -

4.41

13.06

1.81

1: The correlation coefficient between X2 and Z is - 0.781. 2: The correlation coeffkient between X2 and Z is - 0.143. 3: N = 421. 4: When the analysis included expected Hispanic jurors who were assumed to be zero for the given census tract, the mean and standard deviation would be reduced to 1.46 and 1.72, respectively.

152

E. W. Butler and H. Fukurai

For the question of the overall representation of cognizable groups, the goodness-of-fit chisquare test, which examines whether observed probabilities depart from expected probabilities, is an appropriate test. In this example, observed and expected proportions of potential jurors of different racial/ethnic groups in the entire judicial district are evaluated. Expected probabilities are derived from the U.S. population census (Kairys, 1972). The chi-square then determines whether the observed probabilities for representation of individuals of various racial/ethnic backgrounds derived from PPS. are consistent with the racial/ethnic makeup of the entire judicial district. Thus, a significant chi-square test indicates that the observed racial/ethnic composition of the panel is statistically different (i.e., nonrepresentative) from the district as a whole. In this example, then, there would be systematic selection, bias, or discrimination (Ott et al., 1987). For comparative purposes, first a chi-square distribution of juror representation using the simple random selection method was computed. The analysis shown in Table 11 indicates that areas adjacent to downtown Long Beach (the south part of the district) has the greatest concentration of large &i-squares, suggesting that there were greater disparities in Hispanic representation of jury panels there than elsewhere in the judicial district. Since the Z-test shows the strength and direction of disparity, it was determined that areas with many jurors represented had a statistically significant under-representation of Hispanics, i.e., there was systematic exclusion of Hispanics with a substantial over-representation of whites. Similar results held for the black population. In the PPS analysis, the &i-square distribution is substantially smaller than in the simple random sampling procedure (see Figure 12). In addition, individual tract Z-scores were more equal over the entire judicial district. The average Z-score for cluster sampling was -0.24 in contrast to -.95 for simple random samples. The negative Z-scores for the PPS analysis may be attributable to the underestimation of actual Hispanic potential jurors. In our analysis, Hispanics were identified by the U.S. Bureau of the Census’s Spanish Surname list rather than by self-identification. Generally, the Census surname list undercounts people of Hispanic origin (Bean & Tienda, 1987; Fukurai et al., 1991b). Nevertheless, the Z-score for Hispanic representativeness was not statistically significant for the PPS method demonstrating that it is superior to simple random sample methodology. Utilization of the PPS. then, results in a Hispanic and black representation that is comparable to census tracts within the judicial district and to the judicial district as a whole. Our conclusion is that under simple random sampling, as currently applied in Los Angeles County, there is a statistically significant under-representation of potential Hispanic and black

FIGURE 12. Long Beach Superior Court 2tI-Mile Region: Chi-Square Distribution, Cluster Sampling Jury Selectlon.

Computer-Aided Evaluation of Racial Representation in Jury Selection

153

jurors, the less educated and the poor at two levels: (a) for the entire judicial district and (b) within each individual census tract. For example, &i-square values indicated statistically significant under-representation of Hispanics for census tracts under simple random sampling (R = -0.78).6 Using PPS, there was not such systematic under-representation (R = -0.14) (also see Fukurai et al., 1991b). CONCLUSIONS Maintenance of the jury as an institution in the United States depends on commitment to its democratic principles. The jury is the embodiment of the belief that only by gathering together persons from a fair cross-section of the community is it possible to ensure that all relevant perspectives have been considered and that the verdict represents the community’s collective judgment on a controversial issue. Any other source for these decisions undermines their legitimacy in the eyes of citizens. Here we focused on procedures that result in systematic exclusion of race/ethnic minorities, as well as young adults with differing levels of education, etc. This process results in (a) selected tracts being selectively clustered in regions with high concentrations of whites, and (b) as a result, Hispanic and black prospective jurors are systematically excluded, i.e., they are substautially under-represented on jury panels. We rejected the notion that the reason for this under-representation was due to jury qualification rates since they were equal in both the included and excluded tracts. Thus, our analyses demonstrate that by current procedures, geographic areas of residence and racial/ethnic origin significantly influence a person’s chance of being selected for jury service. To correct the discrimination that is derived from procedures, we proposed an alternative sampling technique which our computer simulation analysis demonstrated would obtain more egalitarian jury pools. The proposed alternative utilizes ail geographic areas within a judicial district. A statistical analysis and computer generated maps substantiated that PPS is far superior to current procedures because minorities have a substantially greater opportunity of being represented on panels. The generalizability findings, of course, is an empirical question. We believe, however, that PPS would enhance any community’s cross-sectional representation of potential jurors and of all legally cognizable categories - gender, race/ethnicity, age, and socioeconomic status. The analysis presented here was for only part of a year and thus might be considered static. A dynamic jury selection process involves selecting jurors periodically throughout the year. Thus, a synchronic system involves qualifying jurors and selecting them throughout the year. However, PPS is not limited to a single selection; and, in fact, our simulation was for ten different panels. Thus, the process could be carried out continually as it is now with the current system. The PPS also can be utilized in any applicable judicial district as long as there is a definitively bounded judicial district. Acknowledgments: The authors wish to thank Adlai E. Stevenson and Merrill Colleges at the University of California, Santa Crux and the Department of Sociology at the University of California, Riverside, for supporting the research reported in this paper. The authors are especially appreciative to Jon Alston and Letitia Alston in the Department of Sociology at Texas A & M University, John Childs, John Kitsuse. and Dane Archer in the Board of Studies in Sociology, University of California, Santa Crux, and Richard Appelbaum, the Department of Sociology at the University of California. Santa Barbara. who read the earlier draft and made valuable suggestions.

6Ihe R represents the correlation coefficient between the Z-score and a &i-square value. The coefficient is merely used to as an index showing the relationship between the discrepancies in expected and observed Hispanic representation in the census tract and the direction of the Hispanic representation (over and under) in the judicial district. The coefftcient. however, does not offer a test statistic.

154

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The appreciation is also extended to Richard Krooth in the Department of Sociology at the University of California, Berkeley, and Jo-Ellan Huebner-Dimitrius at the California State University, Los Angeles, for their helpful comments and valuable suggestions.

REFERENCES Alker, H. R., Jr., Hosticka, C.. & Mitchell, M. (1976). Jury selection as a biased process. The Law andSocieryReview, 9.9-41.

Babbie, E. (1989). The practice of social research. Belmont, CA: Wadsworth Publishing Company. Bean, E D., & lienda, M. (1987). The Hispanic population of the United States. New York: Russell Sage Foundation. Bentler, P. M., & Bonnett, D. G. (1980). Significance tests and goodness-of-fit in the analysis of covariance structure. Psychological Bulletin, 88,588~606.

Bollen, K. A. (1989). Structural equations with latent variables. New York: John Wiley & Sons. Brady, J. (1980). Fair and impartial railroad: The jury, the media, and political trials. Journal of Criminal Justice, 11. 241-263.

Brunn, S. D. (1975). Jury selection, justice, and geography. The Pennsylvania Geographer, XIII, 23-32. Butler, E. W. (1980a). Torrance superior court panels and population analysis. University of California, Riverside. Butler, E. W. (198Ob). Van Nuys superior court panels and population analysis: May 7, 1979 through September 24, 1979. University of California, Riverside. Butler, E. W. (1981). The 1980 Los Angeles county jury selection study: Compton superior court. University of California, Riverside. Butler, E. W., Fukurai, H., Huebner-Dimitrius, J. E., & Krooth, R. (in press). Anatomy of the McMartin case. New Brunswick, NJ: Rutgers University Press. Carp, R. A. (1982). Federal grand juries: How true a “cross section” of the community. The Justice System Journaf, 7, 257-277. Daily Apppellate Report. (1984). 83 Daily Journal D.A.R. 58. Black defendant is entitled to representative jury vebire.

Johnson v. Superior Court of San Diego. Daily Apppellate Report. (1986a). 86 Daily Journal D.A.R. 1080. Jury venire may be limited to court’s judicial district.

Eddie O’Hare v. Superior Court of San Diego. Daily Apppellate Report. (1986b). 86 Daily Journal D.AR. 807. Jury chosen from 20-mile zone provides fair cross-

section. Edward Williams v. The Superior Court of Los Angeles County. Diamond, W. H. (1980). Federal remedies for racial discrimination in grand jury selection. Columbia Journal of Low and Social Problems, 16.85-117.

Fukurai, H. (1985). Institutional racial inequality: A theoretical and empirical examination of the jury selection process. Unpublished doctoral dissertation, University of California, Riverside. Fukurai, H., Butler, E. W., & Huebner-Dimitrius, J.-E. (1987). Spatial and racism imbalances in voter registration and jury selection. Sociology and Social Research, 77.33-38. Fukurai, H., Butler, E. W., & Krooth, R. (1991a). Cross-sectional jury representation or systematic jury representation? Simple random and cluster sampling strategies in jury selection. Journal of Criminal Justice, 19,31-48. Fukurai, H., Butler, E. W.. & Krooth, R. (1991b). Where did black jurors go? A theoretical synthesis of racial disenfranchisement in the jury system and j&y sel&ion. Journal of B&k Studies. Fukurai. H.. Butler. E. W.. & Krc&, R. (1991c). Race and the iurv. New York: Plenum. Hans, i P.,‘& Vi&ar, N..(1986). Judging the jhry. New York-Plenum. Harries, K. D., & Brunn, S. D. (1978). The geography of laws and justice: patial perspectives on the criminal justice J-ystem.New York: Praeger Publishers. Hastie, R., Penrod, S. D., & Pennington, N. (1983). Inside the jury. Cambridge: Harvard University Press. Hayduck, L. A. (1987) Structural equation modeling with LISREL: Essentials and advances. Baltimore: Johns Hopkins Press. Heyns, B. (1979). 1979 jury analysis. Superior Court, County of Los Angeles, No. A-344097, Joseph Piazza, defendant. Joreskog, K. G., & Sorbom, D. (1985). LISREL VI: Analysis of linear structural relationships by the method of maximum liklihood. Chicago, IL: National Educational Resources. Kairys, D. (1972). Juror selection: The law, a mathematical method of analysis and a case study. American Criminal Law Review, 12,771-806.

Kairys, David, Joseph B. Kadane, and John P Lehoczky. 1977. “Jury Representiveness: A Mandate For Multiple Source Lists.” Calgornia Law Review. 65.776-827. Kish, L. (1965). Survey samples. New Yolk: John Wiley & Sons. Long, J. S. (1983). Confrnrutory factor analysis. Beverly Hills, CA: Sage Publications. Matsueda, R. L., & Bielby, W. T. (1986). Statistical power in covariance structure models. Sociology Methodology, 120-158.

Ott, L., Larson. R. F., & Mendenall, W. (1987). Statistics: A toolfor the social sciences. Boston, MA: Duxbury Press.

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Staples, R. (1975). White racism, black crime, and American justice. Phylon. 36.14-22. Starr, V. H., & McCormick, M. (1985). Jury selecrion: An nftorney’s guide ro jury hw and methods. Boston: Little, Brown and Company. Sudman, S. (1976).Ap$ed sampling. New York Academic Press. Wisbman, S. (1986). Anatomy of a jury: The system on trial. New York: Times Books. Wrightsman, L. S. (1987). Psychology an& the legol systems. Monterey, CA: Brooks/Cole Publishing Company.