Contemplating Courts

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Edited by: Lee Epstein

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    Dedication

    For Harold

    Preface

    A few years ago, one of my former doctoral students asked me if I knew of any good readers for an undergraduate course on the judicial process. He was just getting settled in a new academic position and realized that he would need to order books for the coming semester. I responded with a few suggestions but, in the end, none of the current offerings satisfied either of us.

    What we both wanted was something akin to Lawrence C. Dodd and Bruce I. Oppenheimer's Congress Reconsidered—a book of original, thought-provoking, and timely essays tailored to the needs of political science students; a book that would give students a compelling sample of our discipline's scholarship without the unnecessary jargon; a book whose contents could be appreciated by scholars and students alike. Unfortunately, nothing like that existed for the study of the judicial process; there was no Courts Reconsidered.

    When conversations with others confirmed this void, I resolved to fill it by bringing the equivalent of Congress Reconsidered to law and courts-oriented classes. This meant, for one thing, aiming at something short of comprehensiveness. Just as Congress Reconsidered does not cover every aspect of legislative politics, Contemplating Courts does not provide an in-depth look at every dimension of judicial politics. That is because instructors typically use these kinds of books in conjunction with other texts or readings. By the same token, just as Congress Reconsidered presents many approaches to the study of legislative politics, I wanted the book to reflect the pluralism of the field of law and courts. Scholars of judicial politics are a varied lot, focusing on diverse substantive topics and invoking myriad research strategies. So I thought it especially important that the book convey to students the wide array of contemporary thinking on judicial processes.

    With these goals in mind, Contemplating Courts was developed. The scope of the resulting book is revealed in its part titles—“Actors in the Legal System,” “Trial Courts,” “Lower Appellate Courts,” “The U.S. Supreme Court,” and “The Impact of Courts.” These are subjects covered in virtually every judicial politics course, and instructors will be able to integrate the essays from each of the parts, in any order they choose, into their syllabi. One of the advantages of a book of readings, of course, is the flexibility it provides.

    The choice of essayists also reflects my goals about the scope of the volume, for they are as varied as the field itself. Some of the contributors use numerical data to make their points; others take more contextual and historical routes. What is more, because the contributors range from Harold J. Spaeth—a founder of the modern-day study of judicial politics—to Lauren Bowen—a young but impressive Ph.D.—the chapters reflect the thinking of more than just a generation of legal analysts. They encapsulate older perspectives that have withstood the test of time and newer ones that seem quite promising.

    My suggestions to the essayists were simple. First, their chapters needed to be accessible to college students. Although graduate students will find the book useful, I did not want the contributors to lose sight of the fact that undergraduates were a primary audience. As they mapped out their chapters, then, I hoped that they would consider how upper-division students would react to their work. I also asked them to avoid long literature reviews or other information that textbooks often provide.

    Second, I advised the authors to use statistics or mathematics if their work required them. After all, it is important to show students how political scientists go about answering interesting substantive questions; and many scholars use abstract formalizations to represent political processes mathematically, invoke statistical techniques to test their expectations against data, and so forth. I did, however, ask the authors who used statistics or mathematics in their chapters to take great care to explain their procedures clearly.

    The chapters more than exceeded my expectations. The authors raise interesting research questions, use appropriate analytic strategies, and convey their messages in accessible prose without jargon. In the end, I had to make only two adjustments to the original plan. First, I supplemented the essays with a chapter on research design. “Studying Law and Courts” (Chapter 1) provides students with an overview of the key elements contained in most sociolegal research and defines important terms used in other chapters, such as “variables,” “statistical significance,” and so forth. Second, I added methodological appendices that specifically address the logic of regression, probit, and logit models. Statistically speaking, these are the most sophisticated tools used in the book. And, while the essayists who rely on them provide excellent explanations, I thought it best to set them out clearly and uniformly in an appendix so that interested students could develop further intuitions about the way they worked.

    In the end, my greatest hope is that Contemplating Courts has as long an academic life as Congress Reconsidered, now in its fifth edition. The contributors and I have tried to produce a book that will accomplish that end. Please let me know where we have succeeded and where we have not so that any necessary adjustments can be made in future editions.

    In editing this volume, I have accrued many, many debts. Now is the time to pay off at least some of them. First and foremost, I thank the people at CQ Press. This is the fourth book I have published with them, and I will keep coming back as long as they will have me. As far as I'm concerned, they run the most professional shop in the business, never sacrificing integrity for profit. Brenda Carter, CQ Press acquisitions editor, deserves special recognition. Despite her job title, Brenda did more than “acquire” this volume; she guided it through every stage of the process and offered many constructive suggestions. She was never shy about letting me know when and where I had gone astray—the most fundamentally important task an editor can perform. I am also grateful to Nancy Lammers, a director at Congressional Quarterly, who commissioned the wonderful cover art, among other things. Our copy editor, Joanne Ainsworth, too, deserves kudos. Joanne faced the daunting task of making sense out of seventeen chapters, written by scholars with significantly different theoretical, methodological, and substantive concerns. And she came through splendidly. I know I speak for all the contributors in acknowledging her efforts on our behalf. Finally, to Ann O'Malley I offer my sincere thanks for considerably smoothing the production process.

    This brings me to the essayists, who all worked hard to produce their chapters. Without their outstanding contributions this book would not have been as strong, or even possible. I am especially indebted to Lawrence Baum, Gregory A. Caldeira, Joseph F. Kobylka, Lynn Mather, and Thomas G. Walker—all of whom provided valuable moral support and concrete suggestions as the project progressed. Jeffrey A. Segal was, as always, a constant source of inspiration. He never begged off debating even the most minor of points, and more often than not he forced me to reconsider my position. Throughout this project, indeed throughout my entire professional life, he has been my biggest fan and my harshest critic. I am grateful for both.

    My third set of debts is to the members of the Department of Political Science at Washington University. I can hardly imagine a better set of colleagues. They are intellectually engaging and congenial—an unbeatable combination. It is hard to know how to thank two of them, in particular. As chair of the department, John Sprague has supported all my efforts with more force than I could possibly deserve. As a colleague with an interest in public law issues, Jack Knight has done much to reshape my thinking about judicial politics; he is always ready to read the roughest of drafts and listen to even my most half-baked ideas.

    Finally, a few personal acknowledgments. My parents, Ann and Ken Spole, have supported my career with genuine enthusiasm, as has my husband, Jay (although occasionally with less enthusiasm, especially after long days at the office). My professional life would have been far different had the personal side been any less. Last but surely not least is Harold J. Spaeth, to whom I dedicate this book. What can I say about a man whose work I so admire, whose professional life I seek to emulate? He is one of the true heroes of my generation of scholars, and I am proud to count him among my closest friends. Harold, this one's for you.

    LeeEpstein, Washington University in St. Louis

    Contributors

    Lawrence Baum is professor of political science at Ohio State University. He is the author of American Courts (1994) and The Supreme Court (1995). His research has dealt with issues such as the sources of change in Supreme Court policy and interactions between the Supreme Court and lower courts. He is currently engaged in research on assessment of explanations for the behavior of judges.

    Lauren Bowen is an assistant professor of political science at John Carroll University. She received her Ph.D. from the University of Kentucky in 1992, writing a dissertation entitled, “Attorney Advertising in the Wake of Bates v. State Bar of Arizona (1977): A Study of Judicial Impact.” The major findings of the dissertation are forthcoming in American Politics Quarterly. She is currently researching the impact of sexual harassment decisions by the U.S. Supreme Court on all affected populations.

    Gregory A. Caldeira is a professor of political science at Ohio State University. He received a Ph.D. from Princeton University in 1978. He has published articles on the courts and American politics in the American Political Science Review, the American Journal of Political Science, the Journal of Politics, the Law and Society Review, the British Journal of Political Science, and many other journals. Currently he is engaged in programs of research on the role and effect of organized interests in the formation of the Supreme Court's agenda; the relationship between interest groups and the Senate in the selection of federal judges, especially on the Supreme Court; and public attitudes toward the European Court of Justice and national high courts in the European Union.

    Charles M. Cameron received his Ph.D. from Princeton University and is now assistant professor of political science at Columbia University. A former Brookings Fellow and recipient of grants from the National Science Foundation, his work has appeared in leading journals of political science. He is a specialist in applied formal theory, who, with Jeffrey Segal and Donald Songer, is writing a book on the judicial hierarchy.

    Thomas W. Church is professor and chair of the Political Science Department at the State University of New York, Albany. He received his B.A. from Whitman College and his Ph.D. from Cornell University. His research has focused on the operation of civil and criminal trial courts and, more generally, on issues relating to the intersection of law and public policy. His most recent work is a comparative study of environmental law and policy in the United States and northern Europe.

    Lee Epstein is professor of political science and resident fellow of the Business, Law, and Economics Center at Washington University in Saint Louis, Missouri. She received her Ph.D. from Emory University in 1983. She is the author of Conservatives in Court (1985), and the coauthor of several books, including The Supreme Court and Legal Change (1992) with Joseph F. Kobylka, and Constitutional Law for a Changing America (1995) with Thomas G. Walker. Her articles on the Supreme Court, interest groups, and related topics have appeared in political science and law journals, including the American Political Science Review, the American Journal of Political Science, and the Journal of Politics. Her current research invokes game theory to study judicial decision making.

    Charles H. Franklin is associate professor of political science at the University of Wisconsin, Madison. In addition to work on public opinion and the Supreme Court, he has written on U.S. Senate campaigns and party identification. His articles have appeared in the American Political Science Review and the American Journal of Political Science.

    Leslie Friedman Goldstein is the author of Constitutional Rights of Women (1988), In Defense of the Text (1991), and Contemporary Cases in Women's Rights (1994) and is the editor of Feminist Jurisprudence: The Difference Debate (1992). She is professor of political science at the University of Delaware and former chair of the Law and Courts Section of the American Political Science Association.

    Christine B. Harrington is the director of the Institute for Law and Society and associate professor of politics at New York University. She has published articles on the politics of court reform, dispute processing and alternative dispute resolution, regulatory litigation, legal ideology, constitutive and interpretive sociolegal theory, and the American legal profession. She is the author of Shadow Justice: The Ideology and Institutionalization of Alternatives to Court (1985); the coauthor, with Lief Carter, of Administrative Law and Politics (1991); and the coeditor, with Paul Brace and Gary King, of The Presidency in American Politics (1989), and, with Maureen Cain, of Lawyers in the Postmodern World: Translation and Transgression (1994).

    Joseph F. Kobylka is associate professor of political science at Southern Methodist University. He received his Ph.D. from the University of Minnesota. He is the author of The Politics of Obscenity (1991) and the coauthor of The Supreme Court and Legal Change (1992) and Public Interest Law (1992), both with Lee Epstein. He has also published articles in a variety of law and political science journals. He is finishing a judicial biography of Justice Harry A. Blackmun and beginning a study of the judicial manifestations of the modern controversy surrounding church-state relations.

    Liane C. Kosaki received her Ph.D. from the University of Michigan, Ann Arbor. She is currently visiting assistant professor of political science at Beloit College. She has written numerous papers on public opinion and the Supreme Court and is currently working on a book, with Charles H. Franklin, on the subject.

    Kevin T. McGuire is assistant professor of political science at the University of Minnesota. He received his Ph.D. from Ohio State University. His publications include The Supreme Court Bar: Legal Elites in the Washington Community (1993) and several articles on decision making in the U.S. Supreme Court.

    Lynn Mather is professor of government at Dartmouth College. Her research has focused on trial courts in the United States. She has published Plea Bargaining or Trial? and Empirical Theories about Courts, as well as various journal articles. Her current research addresses the role of lawyers in divorce cases, exploring issues of lawyer-client interaction, gender difference among divorce lawyers, the effect of mediation on lawyers, and the nature of legal negotiation. Professor Mather has served as chair of the Law and Courts Section of the American Political Science Association (1993–1994) and treasurer of the Law and Society Association (1983–1987).

    David C. Nixon is a graduate student in political science at Washington University in St. Louis. He specializes in American politics and political methodology and is currently completing his doctoral dissertation on relations between legislators and bureaucrats.

    Richard L. Pacelle, Jr., is assistant professor of political science at the University of Missouri, St. Louis. He did his graduate work at Ohio State University and is the author of The Transformation of the Supreme Court's Agenda: From the New Deal to the Reagan Administration. His current research is concerned with the notion of issue evolution in the Supreme Court.

    Gerald N. Rosenberg is associate professor in the Department of Political Science and lecturer in law at the University of Chicago. He earned a master's degree in politics and philosophy from Oxford University, a doctor of law degree from the University of Michigan, and a doctorate in political science from Yale University. A member of the Washington, D.C., bar, he is the author of The Hollow Hope: Can Courts Bring about Social Change? (1991).

    Kim Lane Scheppele is Arthur F. Thurnau Associate Professor of Political Science and Public Policy and adjunct associate professor of law at the University of Michigan. She received her doctoral degree in sociology in 1985 from the University of Chicago. She is the author of Legal Secrets: Equality and Efficiency in the Common Law and has written extensively about rape, domestic violence, abortion, and legal theory. She is currently working on a new book about the development of constitutional consciousness in a post-communist society, having spent the 1994–1995 academic year working at the Hungarian Constitutional Court.

    Jeffrey A. Segal is professor of political science at the State University of New York at Stony Brook. He received his doctoral degree from Michigan State University in 1983. His books include The Supreme Court and the Attitudinal Model (1993) with Harold J. Spaeth, and The Supreme Court Compendium (1994) with Lee Epstein, Harold J. Spaeth, and Thomas G. Walker. His articles on the Supreme Court have appeared in the American Political Science Review, the American Journal of Political Science, the Journal of Politics, and elsewhere.

    Donald R. Songer is professor of political science at the University of South Carolina. He received his doctorate from the University of North Carolina in 1975. He is currently directing a project funded by the National Science Foundation to create a multi-user database on the decisions of the U.S. courts of appeals from 1925 to 1988. His work on appellate court decision making and judicial impact has appeared in a number of journals, including the American Political Science Review, the American Journal of Political Science, the Journal of Politics, and Judicature.

    Harold J. Spaeth is professor of political science at Michigan State University. He received his Ph.D. from the University of Cincinnati and his J.D. from the University of Michigan. He is the author or coauthor of more than ten books, including Supreme Court Decision Making (1976) and The Supreme Court and the Attitudinal Model (1993). Professor Spaeth is also principal investigator of the United States Supreme Court Judicial Database.

    Thomas G. Walker is professor of political science at Emory University. He received his Ph.D. from the University of Kentucky. He is the author or coauthor of ten books, including A Court Divided (1988), which won the prestigious V. O. Key, Jr., Award for the best book on southern politics. Currently he is engaged, with Lee Epstein and William Dixon, in a study of U.S. Supreme Court decision making in times of international crises.

    Daniel S. Ward is assistant professor of political science at Rice University. He received his Ph.D. from New York University in 1989. His research has focused on political institutions and legislative behavior. Among his publications are articles in the Journal of Politics, Legislative Studies Quarterly, Comparative Politics, and Party Politics.

    John R. Wright is professor of political science at George Washington University. His research on interest groups and various aspects of American politics has been published in the American Political Science Review, the American Journal of Political Science, the Journal of Politics, and other specialized journals.

  • Reference Material

    Appendix A: Regression and Pooled Cross-Sectional Time Series

    DavidC.Nixon

    Many of the chapters in this book examine the relationships among some plausibly interconnected variables. Often, the authors seek to explain one particular variable. This is called the dependent variable. Independent variables are those that are thought to cause variation in the dependent variable. Consider the relationship, depicted in Figure A-1, between a dependent variable (Y)—the number of cases filed in U.S. Courts of Appeals—and an explanatory variable (X)—time (fiscal year). Note that for each observation in this set of data, the values of X and Y define a unique point. So, for example, the number of cases filed in 1915 is 1,452; in 1916, 1,518; and so forth. Also observe that a positive relationship exists between X and Y. That is, higher values of Y are associated with higher values of X, while lower values of Y are associated with lower values of X. Here, this means that the number of cases filed has increased over time.

    It is one thing to say that two variables are associated, as is true of the variables cases filed and time. But it is quite another matter to talk about the strength of the relationship between a dependent and an independent variable, a desirable undertaking in social scientific research. That is why scholars invoke regression analysis: it provides a robust technique for estimating the strength of the relationship between variables.

    The mechanics of linear regression are relatively straightforward.1 In its simplest form, it begins with the assumption that the relationship between an independent and dependent variable is a straight line that can be expressed in this way:

    where Y is the dependent variable (in our example, the number of cases filed), coefficient a is the height of the line (called an intercept, or constant), coefficient b is the steepness of the line (called the slope), and X is the independent variable (here, time). Various computer-driven statistical software packages can be used to obtain estimates of the coefficients for a and b.2 For the example shown in Figure A-1, a regression indicates that Y is linearly related to X in the following way,3

    Figure A-1 Caseload of the U.S. Court of Appeals, 1915–1936

    This tells us that the number of case filings increased by about 120 each year. In addition, these estimates can be used to generate a prediction about the number of cases filed in any given year. The predicted value of the dependent variable for a given observation is the value of the independent variable multiplied by its coefficient, plus the intercept. Thus, in Figure A-1, an observation with a value of 3 (in our case that would be the third year, 1917) is predicted to have a value of 1,271. Or,

    Interestingly, as this number makes clear, we cannot perfectly predict the number of cases filed based solely on knowledge of what year it is; the actual number of cases filed in 1917 was 1,446, not 1,271. The line fitted to the data in Figure A-1 reinforces this conclusion because not all the observations fall on it. What this means is that the relationship between time and cases filed is not exact.

    What would an exact fit look like? Consider Figure A-2, a display of a hypothetical set of caseload data. The regression estimates for these data are:

    This means that case filings increase by 100 with each passing year and that the predicted value for year 1 (1915) would be 1,000; for year 2, 1,100; and so forth. But note the difference between these data and those displayed in Figure A-1: Y is an exact linear function of X. If X increases one unit in value then Y increases exactly 100 units; no observations sit off the regression line. In other words, if the year is known, the number of case filings can be perfectly predicted.

    Unfortunately, as Lewis-Beck (1980, 10) makes clear, very few relationships in the social sciences are this exact. More tend to look like Figure A-1 than Figure A-2. Accordingly, it would be better to rewrite equation A-1 in this way:

    where e represents the existence of error. It captures the fact that, in most of our work, X will not perfectly predict Y, just as time does not perfectly predict case filings. The smaller the errors, the more accurate the predictions, or the more fully one can claim to have “explained” the dependent variable.

    Multiple Regression

    What causes error? Why doesn't every value of Y fall exactly on the line depicted in Figure A-1? Answers to these questions abound. But one that readily comes to mind in our illustration is this: as Christine B. Harrington and Daniel Ward suggest in Chapter 9, we would not expect “time” to be the sole cause of variation in the dependent variable. Other factors, such as increases in population and in the number of judges, probably affect case filings, as well. The same holds for almost all political phenomena: they are best explained by more than one independent variable. To understand why, consider all, or at least some, of the factors affecting a voter's choice in an election. A bivariate model (that is, one that includes only one independent variable, as in our use of time to predict caseload) would take into account only one explanatory variable, say, party identification. In a multivariate model (one that includes more than one independent variable) many other explanatory factors are considered, such as a person's gender, education, race, or preference on specific policy items (for example, abortion). In this instance, as in most in the social sciences, it is clear that a multivariate model would provide a more realistic picture of the voting decision.

    Figure A-2 Hypothetical Caseload Data Showing Exact Linear Relationship between X and Y

    Happily, regression with one independent variable (bivariate), as in the previous example, is easily generalized to multiple independent variables (multivariate). Each coefficient in a multivariate regression indicates the expected change in the dependent variable for a hypothetical one-unit change in the independent variable, holding all other variables constant. For example, Harrington and Ward estimate that the effect of per capita income on private civil appeals is −0.746 (see Table 9-2). Each additional dollar of per capita income in a circuit reduces the predicted number of appeals per 100,000 population by −0.746. Taking the average circuit population of 21,000,000 as our baseline, a circuit with a per capita income of $20,000 is predicted to have 157 fewer appeals than a circuit with a per capita income of $21,000, all else being equal (–0.746 × 1 × 210 = 156.6).

    Cross-Sectional versus Longitudinal Data

    As Harrington and Ward note, data may be either cross-sectional or longitudinal For longitudinal data, an identical unit (say, a single appellate court) is observed at multiple time points. (Figure A-1 provides an example of longitudinal data.) The effect of an explanatory variable, as indicated by the regression coefficient for that variable, is the predicted change in the dependent variable resulting from a hypothetical increase of one unit of the explanatory variable, holding all other explanatory variables constant. This is the more traditional understanding of what is normally considered an effect.

    For cross-sectional data, multiple units (say all U.S. Courts of Appeals) are observed at a single moment. Rather than explaining changes in the dependent variable over time, regression with cross-sectional data explains differences in the dependent variable between observational units. The effect of a variable, as indicated by the regression coefficient, is the change in the prediction of the dependent variable for a hypothetical change of one unit in the explanatory variable at that time.

    The distinction between longitudinal and cross-sectional effects may seem subtle, but it is crucial when both cross-sectional and longitudinal data are combined, as they are in Chapter 9. This is called a pooled cross-sectional design.

    Consider a hypothetical data set consisting of three observational units observed at three time points each, as in Figure A-3. Each unit is indicated by a unique symbol. There are three observations for each unit, representing three different time points.

    What effect should we infer in this example? The cross-sectional effect is a positive one. Units with higher values for the explanatory variable are associated with units with higher values for the dependent variable. The longitudinal effect is a negative one, however. For each observational unit, increases in the explanatory variable are associated with decreases in the dependent variable over time. This hypothetical example illustrates that two logically separate effects are present when longitudinal and cross-sectional observations are pooled together. Of course, more subtle confounding scenarios are possible. The cross-sectional and longitudinal effects may be in the same direction but may differ in magnitude. Separating the effects is crucial to establish a valid description of the relationship between the independent and dependent variables.

    The most common approach to this problem is regression with dummy variables. Dummy, or dichotomous, variables are typically coded as 1 (the characteristic is observed, the event occurs) or 0 (the characteristic is not observed, the event does not occur). In addition to the explanatory variables of interest, dummy variables for all but one of the time points or all but one of the observational units are included in the regression. Harrington and Ward adopt this tactic by including forty-five dummy variables (one each for the years 1945–1990) in the regression. This controls for variation over time and guarantees that the regression-estimated effects of the explanatory variables are strictly cross-sectional in nature. The alternative method is to include ten dummy variables (one each for ten of the circuits) in the regression. This technique would eliminate all cross-sectional effects, guaranteeing that the regression-estimated effects are strictly longitudinal in nature. Obviously, Harrington and Ward are more interested in the former approach because they are interested in variation between circuits and not variation over time.

    Figure A-3 Hypothetical Pooled Cross-Sectional Data Set

    Regression with dummy variables controls for variation over time or variation across units. As implemented in Harrington and Ward, the technique removes all time-specific variation by, roughly speaking, averaging over time for each circuit. The analysis of interest, explaining differences between circuits, may then proceed. Of course, this technique may obscure what the data can tell us, for it eliminates all longitudinal information. But the technique's virtue is that the inferences about effects are no longer plagued by the possibility of confounding factors. We can have confidence that we are properly estimating the cross-sectional effect of the independent variables.

    Notes

    1. I adapt this discussion from Lewis-Beck 1980, which provides an excellent and straightforward introduction to regression analysis.

    2. I used SYSTAT to estimate the coefficients depicted in equation A-2. Many other statistical packages are available to estimate a linear regression.

    3. A regression chooses the slope for a line by minimizing the sum of the squared vertical differences between the data points and the line implied by the regression estimate. Hence, linear regression is also referred to as ordinary least-squares regression.

    4. To simplify presentation I used a counter to represent time, rather than year, so that 1915 = 1; 1916 = 2; and so forth.

    Appendix B: Probit and Logit

    DavidC.Nixon

    A great many political variables are discrete: an event either happens or it doesn't; we either observe a characteristic or we don't. Probit and logit models are common statistical tools for analyzing such discrete (or dichotomous) dependent variables—outcomes that can take on only one of two possible values.1 In Chapter 4, for example, Kevin T. McGuire uses the probit model to predict whether a petitioner will be successful in the Supreme Court or not. In Chapter 10, Jeffrey A. Segal, Donald R. Songer, and Charles M. Cameron invoke the logit model to determine the circumstances under which the Supreme Court will uphold or strike down a search and seizure. Charles H. Franklin and Liane C. Kosaki apply the logit model in Chapter 15 to explain individuals’ awareness of particular Court decisions. Finally, Lauren Bowen, in Chapter 16, uses logit to discover whether or not attorneys advertise their services. To put it another way, all four of these chapters are seeking to predict dichotomous dependent variables: petitioner success (they win or they don't); court outcomes (liberal or conservative); public awareness of Court decisions (they are aware or not); and attorney behavior (they advertise or they don't).

    It is easiest to understand and interpret the probit and logit models by focusing on the probabilities of observing these discrete dependent variables. That is, what are the chances of observing one outcome rather than the other? Suppose that the probability, which may be represented as πi, of observing the characteristic in question is somewhere between zero and one for case i. In McGuire's context, this is the equivalent of saying that the probability of a petitioner's success is πi. Instead of presuming that each individual or, in McGuire's case, petitioner has the same probability of success, we have some notions about the kinds of petitioners that are likely to succeed and the kinds that are likely to fail in a given case. A good social scientist specifies plausible and interesting conditions that affect the probability of observing a particular value of the dependent variable. McGuire uses the status of the lawyers on a case to predict success, with the perfectly straightforward hypothesis that more experienced lawyers are more successful. Segal, Songer, and Cameron use the facts of a case to predict whether or not a search will be upheld. And Bowen suggests that attorneys who favor advertising are more likely to advertise than those who do not. These explanatory variables (also called independent variables) are used to test whether the conditions they indicate increase or decrease the probability of observing the dependent variable to a statistically significant degree.2 For McGuire, then, the expectation is that as the independent variable (attorney status) increases (that is, as attorneys achieve higher status), the dependent variable (the chances of Court success) increases too.

    Independent variables are significant to varying degrees in predicting the dependent variable. Logit and probit models employ a numerical technique, maximum likelihood, to derive estimates of coefficients for each of the explanatory variables. These coefficients, which may be represented as β, indicate the direction and strength of the relationship between the independent and dependent variables. If a coefficient is positive, then larger values of that independent variable are associated with a higher probability of observing a 1 for the dependent variable. Bowen, for example, finds that as attorney's attitudes toward advertising become more positive, their probability of advertising increases. The positive coefficients for attitudes in her table support that assertion. McGuire and Segal, Songer, and Cameron find support for their main hypotheses as well.

    Regardless of the sign of β, smaller values of β result in flatter curves and less responsiveness between the independent variable and the dependent variable. If β is zero, then there is virtually no difference (in predicted probability of observing a 1 for the dependent variable) between observations with high values for the independent variable and those with low values. If a coefficient is large enough, we may legitimately conclude that the independent variable is a significant aid in predicting the dependent variable.

    The relationship between an explanatory variable and the probability of observing the characteristic indicated by the dependent variable is illustrated in Figure B-1, for three possible values of β. To see how this works, consider the analysis in the chapter by Segal, Songer, and Cameron, in which the odds that a search will be upheld by the Supreme Court are examined. The estimated coefficient for probable cause is 0.94 (see Table 10-5). This corresponds to the positive curve in Figure B-1. At maximum effect, if X increases from 0 to 1 (which is to say, if the authorities meet the requirements to show probable cause in their search), the probability that the search will be upheld increases from approximately 38 percent to 62 percent. As a second example, McGuire's estimated coefficient for party status in model 1 is 0.05. The curve for that coefficient is not illustrated in Figure B-1. We know, however, that the curve will be flatter than those in the figure because the coefficient is nearer to zero. It turns out that at its maximum effect, as attorney status increases from 1 to 10 (which is to say, as status increases from the lowest to the highest), the probability of success before the Supreme Court increases from approximately 44 percent to 56 percent.

    The McGuire example illustrates a problem: when do we declare that a relatively minor influence (such as party status) is insignificant? The effect of that variable at its maximum, increases the odds of winning by a mere 10 percent. The data in McGuire's Table 4-5indicate that this variable is not significant at the .05 level for four of the five models estimated. One way to think about significance of effects is to realize that estimates for β are always imperfect. Indeed, one should view an estimate for β as a “best guess,” which could be expected to vary within some range just by chance. A simple misprint of the data in a primary source, or the accidental omission of an observation in the records could conceivably alter the statistical results. If the expected range of the coefficient is very large, then we have less confidence that the estimate we obtained is a good one. And if that range includes zero, then we have some reason to question whether β is different from zero. Perhaps β really is zero (the independent variable is completely unrelated to the dependent variable), but mere chance has produced the estimate we observe. That is the logic behind the statistical tests on practically every multivariate model in social science.

    Figure B-1 The Effect of Explanatory Variables

    Statistical analysis allows us to state that we have 95 percent confidence that the real value for β lies within 1.96 standard errors of our estimate. So if β is more than two standard deviations larger (or smaller) than zero, we are 95 percent confident that the independent variable is related to the dependent variable (β is nonzero). To revisit one of our running examples, McGuire's model 1 estimates β for party status as 0.05 with a standard error of 0.02. We can declare with 95 percent confidence that the true value for β lies between 0.01 and 0.09. This range may seem fairly large. The important point is that it does not include zero. We are 95 percent confident that party status does help to predict success in the Court. Yet the coefficient for that variable does not lie more than 1.96 standard errors from zero for McGuire's models 2 through 5. Overall, we should conclude that party status has a minor role at most and probably does not have any effect at all on success before the Supreme Court.

    Notes

    1. Dichotomous variables, also called dummy variables, are typically coded as 1 (the characteristic is observed, the event occurs) or 0 (the characteristic is not observed, the event does not occur). To analyze such variables, as I note above, scholars often invoke logit (the logistic specification) and probit (the normal curve specification) models. For more information on the difference between the two, see Aldrich and Nelson 1984.

    2. When more than one explanatory, or independent, variable is included in a model, it is called a multivariate model. Most political phenomena are best modeled through multivariate techniques. To understand why, consider the factors affecting a voter's choice in the ballot box. A bivariate model would take into account only one explanatory variable, say, party identification. In a multivariate model, many other explanatory factors are considered, such as a person's gender, education, race, or preference on specific policy items (such as abortion).

    Appendix C: Conducting Research on Law and Courts: Sources of Data

    In what follows, we provide information on data sources that students might find useful in their research. Two words of clarification are in order. The first is that we aimed at something short of comprehensiveness, choosing instead to describe sources that are unique or are readily available and accessible. Second, we occasionally mention the archive of the Inter-university Consortium for Political and Social Research (ICPSR) at Ann Arbor, Michigan. The ICPSR's archive is a major repository for databases collected by social scientists. ICPSR disseminates these databases (along with accompanying documentation) free of charge to faculty, staff, and students of colleges and universities that are members of the consortium; individuals at nonmember schools can gain access to the data for a fee. For more details, contact your school's ICPSR representative or write to: ICPSR, The University of Michigan, Institute for Social Research, P.O. Box 1248, Ann Arbor, MI 48106.

    Actors in the Legal System

    Information about state court judges is available from a number of sources. The Book of the States (1992) lists the plans invoked by states to select their judges; Hoffman's The American Bench (various years) identifies current state court judges, as does Want's Federal-State Court Directory (various years). State Court Caseload Statistics (Court Statistics Project, various years) contains data on court caseloads, the number of authorized judgeships per state, and the structure of state court systems. Some of this information is also available in Epstein et al. 1994, Tables 10–18 through 10–25, along with data on the U.S. Supreme Court's treatment of state court decisions (Table 10–26).

    Many volumes contain information on federal court judges. The U.S. Senate Committee on the Judiciary keeps records of federal judicial nomination proceedings; Mersky and Jacobstein (various years) have compiled official congressional hearings and reports of U.S. Supreme Court nominations since 1916. Tables 4–10 through 4–14 in Epstein et al. 1994 also include information on Supreme Court nominees, such as Senate votes and interest group participation. In 1958, the Senate Judiciary Committee produced a useful report containing the names of judges who served on the U.S. circuit courts from 1801 through 1958. More recent information can be gleaned from Brownson's 1993 Judicial Staff Directory (1993), Want's Federal-State Court Directory (various years), Hoffman's The American Bench (various years), and Judges of the United States (1983). These volumes identify all current members of the federal judiciary and provide some biographical information. Data on the number of authorized judgeships and on caseloads can be found in the annual reports of the director, Administrative Office of the U.S. Courts (various years). The Statistical Abstract of the United States (prepared by the U.S. Bureau of the Census) and Epstein et al. 1994 (Tables 10-4 through 10-6, 10–12) also contain caseload data. Reams and Haworth (1978) and Reams (1994) have compiled virtually all congressional hearings and reports on matters pertaining to the federal courts. Finally, the ICPSR-archived database Survey of Judges on the Role of Courts in American Society (no. 7824) contains the results of a questionnaire put to federal and state court judges in five districts. The data include information about the amount of time judges spend on certain kinds of cases, their suggestions for dispute resolution, and their demographic characteristics.

    Martindale-Hubbell Law Directory (various years) and The American Bar (various years) list the names of attorneys practicing in the United States. Who's Who in American Law contains brief profiles of prominent lawyers, judges, and professors. The American Bar Foundation has produced a series of statistical profiles of the U.S. legal profession (see especially Curran et al. 1986). It also has published a report on legal education in the United States (American Bar Association 1991). Several ICPSR-archived studies are worthy of note. John P. Heinz and Edward O. Laumann's “Chicago Lawyers Survey, 1975” (no. 8218) contains information on the social organization of the Chicago Bar; Ronald L. Hirsch's “National Survey of Lawyers’ Career Satisfaction, 1984” (no. 8975) holds data designed to assess career satisfaction among young lawyers, such as their job descriptions, educational background, and psychological characteristics. The U.S. Department of Justice, Bureau of Justice Statistics (BJS), “National Prosecutors Survey, 1990,” also archived at the ICPSR (no. 9579), contains the results of a survey of chief state prosecuting attorneys about the handling of felony cases within their districts.

    Information on interest groups that litigate is available from several sources. Phase II of the United States Supreme Court Judicial Database contains the following information on amicus curiae participation in the U.S. Supreme Court case: total number of amicus curiae participants in a given case, the solicitor general's role, citation to amici in the Court's opinions, and the names and positions taken by amici.1 Tables 7–20 through 7–22 in Epstein et al. 1994 provide summary information on the participation of groups during certain periods of the Court's history, as does Lawrence 1990 (Appendix C), which also gives the win-loss records of key organizations. O'Connor and Epstein 1989, the Council for Public Interest Law 1976, and the Foundation for Public Affairs 1988 house descriptions of many public interest law groups. Finally, Kurland and Casper (various years) have compiled the briefs of the parties and amici curiae filed in landmark constitutional cases.

    Trial Courts

    State court caseload data are available in State Court Caseload Statistics; federal court data can be found in the annual reports of the director, Administrative Office of the United States Courts. The Statistical Abstract of the United States contains a good deal of longitudinal data on the caseloads of federal district courts, including the disposition of certain kinds of criminal disputes. Hoffman's The American Bench (various years) lists the names of current judges, as does Want's Federal-State Court Directory (various years). And Goldman (1965, 1991, 1993) provides a wealth of data on the background characteristics of U.S. district court judges.

    Sources of data on crime and criminal justice in the United States include the many reports of the BJS: Sourcebook of Criminal Justice Statistics, Criminal Victimization in the United States, Prisoners in State and Federal Institutions, and National Survey of Courts, to name just a few.2Crime in the United States, an annual publication of the Federal Bureau of Investigation (FBI), includes data on crimes reported by state and local authorities.3 The Statistical Abstract of the United States contains key tables included in these reports, such as crime rates by state, homicide victims by race and sex, and federal and state prisoner populations. Several ICPSR-archived studies are also worthy of note. They include Thomas W. Church's “Assessing Local Legal Culture” (no. 7808), which looks at local legal culture in four criminal courts (see Chapter 6 of this volume) and Nardulli, Eisenstein, and Flemming's “Comparing Court Case Processing in Nine Courts, 1979–1980” (no. 8621), which contains information on the characteristics of officials involved in the processing of court cases.4

    Civil litigation is explored by Herbert M. Kritzer, David M. Trubek, William L. F. Felstiner, Joel B. Grossman, and Austin Sarat in Civil Litigation in the United States, 1977–1979 (ICPSR no. 7994). The database consists of information (such as negotiation proceedings and relations between clients and lawyers) on a sample of disputes processed in the United States between 1977 and 1979. In a related project (Survey of Households in Five Judicial Districts in the United States: A Civil Litigation Project, 1977–1979; ICPSR no. 9743), David M. Trubek et al. surveyed households involved in lawsuits. The database includes demographic characteristics and information about the disputes. Allan G. Lind et al. (ICPSR no. 9699) gathered data on tort litigants in three state courts in 1989 and 1990. And C. K. Rowland's Federal District Court Civil Decisions, 1981–1987: Detroit, Houston, and Kansas City (ICPSR no. 9367) contains information on unpublished civil cases filed in three district courts, including the date of termination, the nature of the decision, and whether a monetary award was granted.

    Lower Appellate Courts

    For information on judges and caseloads, see State Court Caseload Statistics; the annual reports of the director, Administrative Office of the U.S. Courts (various years); and Epstein et al. 1994, Tables 10-2 through 10-6. Donald R. Songer is in the process of amassing an important database on decisions made in U.S. courts of appeals, which includes information on case issues, the votes of judges, and amicus curiae participants, among many other things. The database will be archived with the ICPSR in 1996. Wheeler and Harrison (1989) have produced a useful volume detailing the history of the federal circuit courts; some of their data are summarized in Epstein et al. 1994, Table 10-1. A 1958 report of the Senate Judiciary Committee contains the names of judges who served on the U.S. circuit courts from 1801 through 1958. The 1993 Judicial Staff Directory (Brownson 1993), Want's Federal-State Court Directory (various years), The American Bench (Hoffman, various years), and Judges of the United States (1983) identify all current members of the federal judiciary. And Goldman (1965, 1991, 1993) provides information on the background characteristics of U.S. courts of appeals judges.5

    The U.S. Supreme Court

    Epstein et al. 1994 contains data and other information on the following dimensions of Court activity: institutional development, review process, opinions and decisions, judicial backgrounds, voting patterns, and impact. Witt (1990) summarizes the holdings of landmark Court cases and provides brief biographies of the justices. And John R. Schmidhauser's ICPSR database (no. 4720) houses data on justices who served between 1789 and 1958. More comprehensive biographical information is available in Cushman (1993) and Friedman and Israel (1969–1978). Martin and Goehlert 1990 is an annotated biography of scholarly writings on the Court. The Constitution of the United States of America (prepared by the Congressional Research Service, Library of Congress) lists state and federal acts overturned by the Supreme Court, as well as Court decisions overruled by subsequent decisions. Harold J. Spaeth's United States Supreme Court Judicial Database, 1953-Current Terms (ICPSR no. 9422), provides a wealth of data beginning with the Warren Court through to the present.6 Among the many attributes of Court decisions coded by Spaeth are the names of the courts making the original decision, the identities of the parties to the cases, the policy context of a case, and the votes of each justice. Spaeth and Jan Palmer are now expanding the database to include the conference votes cast by justices of the Supreme Court under Fred M. Vinson (1946–1952 terms) and Earl Warren (1953–1968 terms) (see also Palmer 1990, which lists the conference votes in cases decided between the 1946 and 1953 terms).

    The Influence of Courts

    The Gallup Poll, the Harris Survey, and the National Opinion Research Center are sources for data on public opinion and the judiciary, especially the U.S. Supreme Court. Chapter 8 of Epstein et al. 1994 contains many tables summarizing public views of the Supreme Court and issues related to the legal system. Questions reviewed include “How knowledgeable is the public about the Court?” and “To what extent does the public support the Court's resolution of specific controversial issues?” The results of a survey, done under the auspices of the BJS, on national attitudes toward courts and justice are reported in “Public Image of Courts, 1977” (ICPSR nos. 7703, 7704). No. 7703 is a survey of the general public; no. 7704 is a survey of judges, lawyers, and community leaders.

    Data on the impact of court decisions on certain public policy questions are available in Chapter 9 of Epstein et al. 1994. Abortion, capital punishment, school desegregation, voter registration, and reapportionment are examples of the issues covered. Becker 1969, Becker and Feeley 1973, and Johnson and Canon 1984 contain information on the impact and implementation of many landmark decisions. Rosenberg 1991 also has a good deal of data on salient Court cases, especially Brown v. Board of Education (1954) and Roe v. Wade (1973).

    Notes

    1. These data will soon be deposited at the ICPSR.

    2. Many of the studies from the BJS are archived with the ICPSR, including “Capital Punishment in the United States” (nos. 9210, 9337, 9507), “Census of State Felony Courts” (no. 8667), “Commercial Victimization Surveys” (no. 8002), and “Historic Statistics on Prisoners in State and Federal Institutions” (no. 8912).

    3. Again, many of the FBI's studies are archived with the ICPSR, including its uniform crime reports (nos. 9252, 9226, 9163).

    4. The ICPSR archive contains many other databases pertaining to matters of criminal justice. Interested readers should consult the ICPSR Guide to Resources and Services.

    5. Along the same lines, Gerard Gryski, Gary Zuk, and Deborah Barrow have amassed an important database of the background characteristics and retirement patterns of lower federal court judges, Multi-User Database on the Attributes of U.S. Courts of Appeals Judges, 1891–1992, which will soon be archived with the ICPSR.

    6. Spaeth is in the process of backdating the database to include the Vinson Court era.

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    Table of Cases

    • Aguilar v. Felton, 473 U.S. 402 (1985)
    • Akron v. Akron Center for Reproductive Health Services, 462 U.S. 416 (1983)
    • Alamo Federation v. Secretary of Labor, 471 U.S. 290 (1985)
    • Arizona v. Fulminante, 499 U.S. 279 (1991)
    • Arrington v. Wilks, 490 U.S. 755 (1990)
    • Ashwander v. TVA, 297 U.S. 288 (1936)
    • Astroline v. Shurberg, 497 U.S. 547 (1990)
    • Baker v. Carr, 369 U.S. 186 (1962)
    • Bank of the United States v. Deveaux, 5 Cranch (9 U.S.) 61 (1809)
    • Bates v. State Bar of Arizona, 433 U.S. 350 (1977)
    • Beal v. Doe, 432 U.S. 438 (1977)
    • Bigelow v. Virginia, 421 U.S. 809 (1975)
    • Board of Education v. Allen, 392 U.S. 396 (1968)
    • Booth v. Maryland, 428 U.S. 496 (1987)
    • Bowen v. Kendrick, 487 U.S. 589 (1988)
    • Bowers v. Hardwick, 478 U.S.186 (1986)
    • Brandenburg v. Ohio, 395 U.S. 444 (1969)
    • Bray v. Alexandria Women's Health Clinic, 113 S.Ct. 753 (1993)
    • Brown v. Board of Education, 347 U.S. 483 (1954)
    • Brown v. Board of Education II, 349 U.S. 294 (1955)
    • Caminetti v. United States, 242 U.S. 470 (1917)
    • Church of the Holy Trinity v. United States, 143 U.S. 457 (1892)
    • Cipollone v. Liggett Group, 593 F.Supp. 1146 (D.N.J. 1984); 893 F.2d 541 (1992); 112 S.Ct. 2608 (1992)
    • Cleveland v. United States, 329 U.S. 14 (1946)
    • Committee for Public Education and Religious Liberty v. Nyquist, 413 U.S. 756 (1973)
    • Committee for Public Education and Religious Liberty v. Regan, 444 U.S. 646 (1980)
    • Cooper v. Aaron, 358 U.S. 1 (1958)
    • Corp. of Presiding Bishop v. Amos, 483 U.S. 327 (1988)
    • County of Allegheny v. ACLU, 492 U.S. 573 (1989)
    • CPERL. See Committee for Public Education and Religious Liberty.
    • Craig v. Boren, 429 U.S. 190 (1976)
    • Daly v. Derrick, 281 Cal.Rptr. 709 (Ct.App. 1991)
    • Demarest v. Manspeaker, 498 U.S. 184 (1991)
    • Doe v. Bolton, 410 U.S. 179 (1973)
    • Dombrowski v. Pfister, 380 U.S. 479 (1965)
    • Edwards v. Aguillard, 482 U.S. 578 (1987)
    • Eisenstadt v. Baird, 405 U.S. 438 (1972)
    • Employment Division, Department of Human Resources of Oregon v. Smith, 494 U.S. 872 (1990)
    • Engel v. Vitale, 370 U.S. 421 (1962)
    • Epperson v. Arkansas, 393 U.S. 97 (1968)
    • Everson v. Board of Education, 330 U.S. 1 (1947)
    • Firefighters v. Cleveland, 478 U.S. 501 (1986)
    • Firefighters v. Stotts, 467 U.S. 561 (1984)
    • Flast v. Cohen, 392 U.S. 83 (1968)
    • Freytag v. Commissioner of Internal Revenue, 501 U.S. 868 (1991)
    • Frothingham v. Mellon, 262 U.S. 447 (1923)
    • Fullilove v. Klutznick, 448 U.S. 448 (1980)
    • Furman v. Georgia, 408 U.S. 238 (1972)
    • Garcia v. San Antonio Metropolitan Transit Authority, 469 U.S. 578 (1985)
    • Georgia v. Stanton, 6 Wall. (73 U.S.) 50 (1868)
    • Gideon v. Wainwright, 372 U.S. 335 (1963)
    • Glidden Co. v. Zdanok, 370 U.S. 530 (1962)
    • Gollust v. Mendell, 501 U.S. 115 (1991)
    • Grand Rapids v. Ball, 473 U.S. 373 (1985)
    • Gregg v. Georgia, 428 U.S. 153 (1976)
    • Griswold v. Connecticut, 381 U.S. 479 (1965)
    • Harper v. Virginia State Board of Elections, 383 U.S. 663 (1966)
    • Harris v. McRae, 448 U.S. 297 (1980)
    • Hernandez v. Commissioner of Internal Revenue, 490 U.S. 680 (1989)
    • Humphrey's Executor v. United States, 295 U.S. 602 (1935)
    • In re Primus, 436 U.S. 412 (1978)
    • In re R.M.J., 455 U.S. 191 (1982)
    • In the Matter of Baby M, 525 A.2d 1128 (N.J.Super. Ch. 1987); 537 A.2d 1127 (N.J. 1988)
    • Johnson v. Transportation Agency, 480 U.S. 616 (1987)
    • Kaiser v. Weber, 443 U.S. 193 (1979)
    • Kiryas Joel Village School District v. Grumett, 114 S.Ct. 2481 (1994)
    • Korematsu v. United States, 323 U.S. 214 (1944)
    • Labine v. Vincent, 401 U.S. 532 (1971)
    • Lamb's Chapel v. Center Moriches Union Free School District, 113 S.Ct. 2141 (1993)
    • Larkin v. Grendel's Den, 459 U.S. 116 (1982)
    • Larson v. Valente, 456 U.S. 228 (1982)
    • Lee v. Weisman, 112 S.Ct. 2649 (1992)
    • Lemon v. Kurtzman, 403 U.S. 602 (1971)
    • Levitt v. Committee for Public Education and Religious Liberty, 413 U.S. 472 (1973)
    • Levy v. Louisiana, 391 U.S. 68 (1968)
    • Lynch v. Donnelly, 465 U.S. 668 (1984)
    • McCardle, Ex parte, 6 Wall. (73 U.S.) 318 (1868)
    • McCardle, Ex parte, 7 Wall. (74 U.S.) 506 (1869)
    • McCollum v. Board of Education, 333 U.S. 203 (1948)
    • McGowan v. Maryland, 366 U.S. 420 (1961)
    • Madsen v. Women's Health Center, 114 S.Ct. 2516 (1994)
    • Maher v. Roe, 432 U.S. 464 (1977)
    • Mapp v. Ohio, 367 U.S. 643 (1961)
    • Marbury v. Madison, 5 U.S. (1 Cranch) 137 (1803)
    • Marsh v. Chambers, 463 U.S. 783 (1983)
    • Martin v. Wilks, 490 U.S. 755 (1989)
    • Maryland v. Craig, 497 U.S. 836 (1990)
    • Meek v. Pittenger, 421 U.S. 349 (1975)
    • Memoirs of a Woman of Pleasure v. Massachusetts [The Fanny Hill case], 383 U.S. 413 (1966)
    • Memphis Fire Dept. v. Stotts, 467 U.S. 561 (1984)
    • Meritor Savings Bank v. Vinson, 477 U.S. 57 (1985)
    • Metal Workers v. EEOC, 478 U.S. 421 (1986)
    • Metro Broadcasting v. FCC, 497 U.S. 547 (1990)
    • Miller v. California, 413 U.S. 15 (1973)
    • Milligan, Ex parte, 4 Wall. (71 U.S.) 2 (1866)
    • Miranda v. Arizona, 384 U.S. 436 (1966)
    • Mississippi v. Johnson, 4 Wall. (71 U.S.) 475 (1867)
    • Mississippi v. Stanton, 6 Wall. (6 U.S.) 50 (1868)
    • Mortensen v. United States, 322 U.S. 369 (1944)
    • Mueller v. Allen, 463 U.S. 388 (1983)
    • Myers v. United States, 272 U.S. 52 (1926)
    • National League of Cities v. Usery, 426 U.S. 833 (1976)
    • New York Times v. Sullivan, 376 U.S. 254 (1964)
    • Ohralik v. Ohio State Bar Association, 436 U.S. 447 (1978)
    • Payne v. Tennessee, 501 U.S. 808 (1991)
    • Personnel Board v. Wilks, 490 U.S. 755 (1990)
    • Planned Parenthood of Central Missouri v. Danforth, 428 U.S. 52 (1976)
    • Planned Parenthood of Southeastern Pennsylvania v. Casey, 112 S.Ct. 2791 (1992)
    • Red Jacket (United Mine Workers v. Red Jacket Consolidated Coal and Coke Company, 18 F.2d 839 (1927))
    • Reed v. Shepard, 939 F.2d 484 (1991)
    • Regents v. Bakke, 438 U.S. 265 (1978)
    • Reynolds v. Sims, 377 U.S. 533 (1964)
    • Richmond v. Croson Co., 488 U.S. 469 (1989)
    • Roe v. Wade, 410 U.S. 113 (1973)
    • Roemer v. Maryland, 426 U.S. 736 (1976)
    • Roth v. United States, 354 U.S. 476 (1957)
    • Rust v. Sullivan, 500 U.S. 173 (1991)
    • Sable Communications v. FCC, 492 U.S. 115 (1989)
    • Schenck v. United States, 249 U.S. 47 (1919)
    • School District of Abington Township v. Schempp, 374 U.S. 203 (1963)
    • Scott v. Sandford, 60 U.S. (19 Howard) 393 (1857)
    • Shapero v. Kentucky State Bar Association, 486 U.S. 466 (1988)
    • Sherbert v. Verner, 374 U.S. 398 (1963)
    • Simmons v. State, 504 N.E.2d 575 (Ind. 1987)
    • South Carolina v. Gathers, 490 U.S. 805 (1989)
    • Stanford v. Kentucky, 492 U.S. 361 (1989)
    • State v. Frost, 577 A.2d 1282 (N.J.Super.Ct.App.Div. 1990)
    • Steelworkers v. Weber, 443 U.S. 193 (1979)
    • Stone v. Graham, 449 U.S. 39 (1980)
    • Strauder v. West Virginia, 100 U.S. 303 (1880)
    • Sullivan v. Stroop, 496 U.S. 478 (1990)
    • Swaggart Ministries v. California, 493 U.S. 378 (1990)
    • Texas Monthly v. Bullock, 489 U.S. 1 (1989)
    • Texas v. Johnson, 491 U.S. 397 (1989)
    • Thornburgh v. American College of Obstetricians and Gynecologists, 476 U.S. 747 (1986)
    • Thorton v. Caldor, 472 U.S. 703 (1985)
    • Tilton v. Richardson, 403 U.S. 672 (1971)
    • Toibb v. Radloff, 501 U.S. 157 (1991)
    • Twining v. New Jersey, 211 U.S. 78 (1908)
    • United States v. Darby Lumber, 312 U.S. 100 (1941)
    • United Mine Workers v. Red Jacket Consolidated Coal and Coke Company, 18 F.2d 839 (1927)
    • United States v. Carotene Products Co., 304 U.S. 144 (1938)
    • United States v. Paradise, 480 U.S. 149 (1987)
    • United States v. Vuitch, 402 U.S. 62 (1971)
    • United States v. Weber, 443 U.S. 193 (1979)
    • Valley Forge Christian College v. Americans United, 454 U.S. 464 (1982)
    • Virginia v. Bobbitt, Prince William County Circuit Court (Va.) CR 33821 (1993)
    • Virginia State Board of Pharmacy v. Virginia Citizens Consumer Council, 425 U.S. 748 (1976)
    • Wallace v. Jaffree, 472 U.S. 38 (1985)
    • Walz v. Tax Commissioner of the City of New York, 397 U.S. 664 (1970)
    • Webster v. Reproductive Health Services, 492 U.S. 490 (1989)
    • West Coast Hotel v. Parrish, 300 U.S. 379 (1937)
    • Westside Community Schools v. Mergens, 496 U.S. 226 (1990)
    • Widmar v. Vincent, 454 U.S. 263 (1981)
    • Windsor v. Pan American Airways, 744 F.2d. 1187 (1984)
    • Wirtz v. Glass Bottle Blowers Association Local 153, 389 U.S. 463 (1968)
    • Wisconsin Public Intervenor v. Mortier, 501 U.S. 597 (1991)
    • Witters v. Washington, 474 U.S. 481 (1986)
    • Wolman v. Walter, 433 U. S. 229 (1977)
    • Wygant v. Jackson Board of Education, 476 U.S. 267 (1986)
    • Yerger, Ex parte, 8 Wall. (75 U.S.) 85 (1869)
    • Young v. American Mini Theatres, 427 U.S. 50 (1976)
    • Yukica v. Leland, Grafton County Superior Court (N.H.) No. 85-E-191 (1985)
    • Zauderer v. Office of Disciplinary Counsel of the Supreme Court of Ohio, 471 U.S. 626 (1985)
    • Zobrest v. Catalina Foothills School District, 113 S.Ct. 2462 (1993)
    • Zorach v. Clausen, 343 U.S. 306 (1952)

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