Encyclopedia of Sociology Vol

a true, underlying model that generated the observations we are studying and a proposed model that will be tested against data. Quantitative analysis begins, then, with some theoretical understanding of the properties of groups of social objects; this understanding leads to the specification of a model of the interaction of these properties, after which observations of these properties on a sample of the objects are collected. The performance of the model is then evaluated to determine to what degree the model truly describes the underlying process.

The measurement of a property of a social object is called a variable. Variables can be either fixed or random. Fixed variables are those determined by the investigator; they usually occur in experiments and will not be of concern in this chapter. All other variables are random. The random nature of these variables is the unavoidable consequence of two things; first, the fact that our observations are samples, that is, groups of instances of social objects drawn from a population (that is, a very large number of possible instances to be observed); second, the fact that our theories and data collection are often unable to account for all the relevant variables affecting the variables included in the analysis. Probability theory in social models, or, equivalently, random variables in social models, will derive from these two subtopics: sampling and the specification of residual or excluded variables in the models.

A certain philosophical difference of opinion arises among probability theorists about the nature of the true source of the randomness in nature. One group argues that these features are inherent in reality, and another argues they are simply the consequence of ignorance. The primary modeling tool of the former group is the stochastic process (Chung 1974), while that of the latter is the Bayesian statistical model (de Finetti 1974).

MAIN CONCEPTS

Probability is a name assigned to the relative frequency of an event in an event space, that is, a set of possible events. For example, we might define the event space as the two sides of a single coin labeled heads (H) and tails (T): {T, H}. The actual outcome of a coin flip is a random variable, X, say, and the probability of the outcome H is P(X = H). The probability distribution function (or PDF)

assigns a quantity to this probability. By definition, for a fair coin P(X = H) = .5. Since the event space is composed of only two events, then P(X = T) + P(X = H) = 1, that is, one or the other event occurs for certain, and P(X = T) = 1 - P(X = H) = .5. Thus the probability of T is equal to the probability of H and the coin is fair.

In general we assign numbers to the events in our event space, allowing us to use mathematical language to describe the probabilities. For example, the event space of the number of people arriving at a bank’s automatic teller machine (ATM) is {0, 1, 2, . . .} over a given time interval ∆t. Given certain assumptions, such as that the arrival time of each person is independent of anyone else’s, we can derive a theoretical PDF. For a given time interval ∆t, the probability of the number of people X can be shown to be

P (X = k | ∆t ) = (λ∆t )ke −(λ∆t ) k!

where λ is the mean rate of people arriving at the ATM over the time interval ∆t, and k = 0,1,2, . . .

Suppose from bank records we are able to determine that 100 people per hour complete a transaction at a particular ATM during normal working hours. For ∆t equal to one minute or 1/60 an hour, the PDF is

For some selected values of k we have

If we assume that each person spends about a minute at the ATM, we should expect one or more people standing in line behind someone at the

ATM about 50 percent of the time since the probability of two or more people arriving during a one minute interval is

P (X >= 2) = 1 − P (X = 0) − P (X = 1) = .4963

The event spaces for the examples above are discrete, but continuous event spaces are also widely used. A common PDF for continuous event spaces is the normal distribution:

where µ and σ2, commonly called the mean and variance respectively, are parameters of the distribution, and χ is a real number greater than minus infinity and less than plus infinity. The normal PDF is the most widely used distribution in social models, first because it had advantageous mathematical properties and second because its specification in many cases can be justified on the basis of the central limit theorem (Hogg and Tanis 1977, p. 155).

Other important concepts in probability theory are the cumulative distribution function (or CDF), joint distributions (distributions involving more than one variable), and conditional distributions. The CDF is the probability of X being less than or equal to x, that is, Pr(X < x). An accessible introduction to probability may be found in Hogg and Tanis (1977).

SAMPLING

In physics all protons behave similarly. To determine their properties, any given instance of a proton will do. Social objects, on the other hand, tend to be complex, and their properties can vary considerably from instance to instance. It is not possible to draw conclusions about all instances of a social object from a given one in the same manner we might from single instance of a proton. Given equivalent circumstances, we cannot expect everyone to respond the same way to a question about their attitudes toward political issues or to behave the same way when presented with a set of options.

For example, suppose we wish to determine the extent to which a person’s education affects his

or her attitudes towards abortion. Let Ai represent a measurement of the attitude of some person, labeled the ith person, scored 0 if they are opposed to abortion or 1 if not. Let Bi be the measurement of the person’s education, scored 0 for less than high school, 1 for high school but no college, or 2 for at least some college.

Given measurements on a sample of people, we would find that they would be distributed in some fashion across all the six possible categories of the two variables. Dividing the number that fall into each category by the total number in the sample would give us estimates of the empirical distribution for the probabilities: PR(A = 0, B = 1), PR(A = 0, B = 2), and so on. We might also model this distribution. For example, an important type of model is the loglinear model (Goodman 1972; Haberman 1979; Agresti 1990), which models the log of the probability:

log PR(A = i, B = j ) = λiA + λBj + λijAB

where λAi, λBj and λABij are parameters (actually sets of parameters). In this model the λABij parameters represent the associations between A and B, and an estimate of these quantities might have important implications for a theory.

Given a sample distribution, computing an estimate of λABij is straightforward (Bishop, Fienberg, and Holland 1975). It is important, however, to realize that such an estimate is itself a random variable, that is, we can expect the estimate to vary with every sample of observations we produce. If the sample is properly selected, in particular if it is a simple random sample in which each person has an equal chance of being included in the sample, it can be shown that the estimates of λABij have, in large samples at least, a normal distribution (Haberman 1973). Our estimates, then, are themselves parameters of a distribution, usually the means of a normal distribution. It follows that the fundamental parameters upon which a theory will depend can never be directly observed and that we must infer its true value from sample data.

All research on social objects is unavoidably research on samples of observations. Therefore all such research will necessarily entail at the very least a probabilistic sampling model, and the conclusions drawn will require properly conceived statistical inference.

MODELS WITH EXCLUDED VARIABLES

The Regression Model. The most well-known and widely used statistical model is the regression model. It is a simple linear hypersurface model with an added feature: a disturbance term, which represents the effects on the dependent variable of variables that have not been measured. To the extent that the claims or implications of a theory may be put into linear form, or at least transformed into linear form, the parameters (or regression coefficients) may be estimated and statistical inference drawn by making some reasonably benign assumptions about the behavior of the variables that have been excluded from measurement. The key assumption is that the excluded variables are uncorrelated with included variables. The failure of this assumption gives rise to spurious effects; that is, parameters may be underor overestimated, and this results in faulty conclusions. The statistical inference also requires a homogeneity of variance of the disturbance variables, called homoscedasticity. The variation of the excluded variables must be the same across the range of the independent variables. This is not a critical assumption, however, because the consequence of the violation of this assumption is inefficiency rather than bias, as in the case of the spurious effects. Moreover, the underlying process generating the heteroscedasticity may be specified, which would yield efficient estimates, or a modified inference may be computed, based on revised estimates of the variances of the distribution of the parameter estimates (White 1980).

For example, a simple regression of income, say, on years of education may be described, yi = b0 + bixi + εi, where yi and xi are observations on income and years of education, respectively, of the ith person b0 and b1 are regression coefficients, and εi is the disturbance term. Estimates of b0 and b1 may be found (without making any assumptions about the functional form of the distribution of εi by using perhaps the most celebrated theorem in statistics, the Gauss-Markov theorem, and they are usually called ordinary least squares estimates.

If we gather the observations into matrices, we can rewrite the regression equation as functions of matrices: Y = XB + E, where Y is an N x 1 vector of observations on the dependent variable. X an N x K matrix of observations on K independent variables, B a K x 1 vector of regression coefficients,

and E an N x 1 vector of disturbances. With this notation the estimates in B may be described Bˆ = (X’X)-1 X’Y, where; the ‘‘^’’ over the B emphasizes that they are estimates of the parameters.

Our observations are samples, and since our estimates of B will vary from sample to sample, it follows that these estimates will themselves be random variables. Appealing again to the GaussMarkov theorem, it is possible to show that the ordinary least squares estimates have a normal distribution with variance-covariance matrix equal to VarCov( ) = σ2ε (X’X)-1 X’Y, where σ2ε is estimated by σ2ε = (Y - XB)’(Y- XB)/(N - K - 1).

Other models. The regression model in the previous section is a ‘‘single equation’’ model, that is, it contains one dependent variable. A generalization of the regression model incorporates multiple dependent variables. This model may be represented in matrix notation as BY = ΓX + Z, where Y is an N x L matrix of L endogenous variables (i.e., variables that are dependent in at least one equation), B is an L x L matrix of coefficients relating endogenous variables among themselves, X is an N x K matrix of K exogenous variables (i.e., variables that are never dependent), Γ is an L x K matrix of coefficients relating the exogenous variables to the endogenous variables, and Z is an N x L matrix of disturbances. Techniques have been developed to produce estimates and statistical inference for these kinds of models (Judge et al. 1982; Fox 1984).

Measurement error is another kind of excluded variable, and models have been developed to incorporate them into the regression and simultaneous equation models. One method for handling measurement error is to use multiple measures of an underlying latent variable (Bollen 1989; Jöreskog and Sörbom 1988). A model that incorporates both measurement error and excluded variable disturbances may be described in the following way:

where Y and X are our observations on the endogenous and exogenous variables respectively, Λy, and Λz are coefficient matrices relating the underlying variables to the observed variables, η and ξ are the

latent endogenous and exogenous variables respectively, B and Γ are coefficient matrices relating the latent variables among themselves εy and εz are the measurement error disturbances, and ζ is the excluded variable disturbance.

This model incorporates three sources of randomness, measurement error disturbance, excluded variable disturbance, and sampling error. Models of the future may contain a fourth source of randomness: a structural disturbance in the coefficients. These latter models are called random coefficient models and are a special case of the most general kind of probabilistic model called the mixture model (Judge et al. 1982; Everitt 1984).

The models described to this point have been linear. Linearity can be a useful approximation that renders the problem tractable. Nonlinearity may be an important aspect of a theoretical specification, however, and methods to incorporate nonlinearity in large-scale models have been developed (Amemiya 1985). It also appears to be the fact that most social measures are not continuous, real variables, which is what is assumed by the regression and simultaneous models described above. Thus, much work is now being devoted to the development of models that may be used with measures that are limited in a variety of ways— they are categorical, ordinal, truncated, or censored, for example (Muthén 1984; Maddala 1983). Limited variable methods also include methods for handling variations on the simple random method of sampling.

Probability theory has had a profound effect on the modeling of social processes. It has helped solve the sampling problem, permitted the specification of models with excluded variables, and provided a method for handling measurement.

REFERENCES

Agresti, A. 1990 Categorical Data Analysis. New York:

John Wiley.

Amemiya, T. 1985 Advanced Econometrics. Cambridge,

Mass.: Harvard University Press.

Bishop, Y. M. M., S. E. Fienberg, and P. W. Holland 1975

Discrete Multivariate Analysis: Theory and Practice. Cambridge, Mass.: MIT Press.

Bollen, K. A. 1989 Structural Equations with Latent Variables. New York: John Wiley.

Chung, K. L. 1974 Elementary Probability Theory with Stochastic Processes. Berlin: Springer-Verlag.

de Finetti, B. 1974 Theory of Probability, 2 vols. New York: John Wiley.

Everitt, B. S. 1984 An Introduction to Latent Variable Models. London: Chapman and Hall.

Fox, J. 1984 Linear Statistical Models and Related Methods. New York: John Wiley.

Goodman, L. A. 1972 ‘‘A General Model for the Analysis of Surveys.’’ American Journal of Sociology 37:28–46.

Haberman, S. J. 1973 ‘‘Loglinear Models for Frequency Data: Sufficient Statistics and Likelihood Equations.’’

Annals of Mathematical Statistics 1:617–632.

——— 1979 Analysis of Qualitative Data, vol. 2, New Developments. Orlando, Fla.: Academic.

Hogg, Robert V., and Elliot A. Tanis 1977 Probability and Statistical Inference. New York: Macmillan.

Jöreskog, K. G., and Dag Sörbom 1988 LISREL VII. Chicago: SPSS.

Judge, George G., R. Carter Hill, William Griffiths, Helmut Lutkepohl, and Tsoung-Chao Lee 1982 Introduction to the Theory and Practice of Econometrics. New York: John Wiley.

Maddala, G. 1983 Limited-Dependent and Qualitative Vari-

ables in Econometrics. Cambridge, England: Cambridge

University Press.

Muthén, B. 1984 ‘‘A General Structural Equation Model with Dichotomous, Ordered Categorical, and Continuous Latent Variable Indicators.’’ Psychometrika 49:115–132.

Tuma, Nancy Brandon, and Michael T. Hannan 1984

Social Dynamics: Models and Methods. Orlando, Fla.:

Academic.

White, H. 1980 ‘‘A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.’’ Econometrica 48:817–838.

RONALD SCHOENBERG

PROBATION AND PAROLE

The criminal justice system is the primary institution responsible for the formal social control of criminal deviance. Those who violate the criminal law are subject to a variety of sanctions, ranging from the reprimand of a police officer to execution by hanging. Most offenders are not apprehended, and among those who are arrested many are not prosecuted nor convicted of a crime. For offenders who are found guilty, either by trial or

more often by negotiated guilty plea, the sentence handed down by the court typically mandates correctional supervision, usually either some form of probation or incarceration with early release to some form of parole.

Even though probation and parole have been integral components of corrections since the nineteenth century, the differences between them are not always clear. Both are postconviction alternatives to incarceration that include supervision in the community by a probation or parole officer, who, depending on the jurisdiction, may be the same person. They are conditional releases to the community that are contingent on compliance with stipulated conditions, which if violated, may lead to revocation. Many probation and parole programs are similar (e.g., intensive supervision) or they share clientele. Last, as alternatives to incarceration, both are less expensive, less punitive, and probably more effective strategies of crime control.

The major difference between probation and parole is that probationers are sentenced directly to community supervision without being incarcerated, while parolees serve part of their sentence incarcerated before they are released to parole. Parole is a conditional release from confinement, whereas probation is a conditional suspension of a sentence to confinement. In both cases, a new crime or technical violation of conditions may lead to enhanced restrictions or incarceration. A general definition of probation is the conditional supervised release of a convicted offender into the community in lieu of incarceration (Allen et al. 1985). Parole is the conditional supervised release of an incarcerated offender into the community after serving part of the sentence in confinement (Clear and Cole 1986).

PROBATION

Before informal probation was created in Boston in 1841 by philanthropist John Augustus, and the first statewide probation law was enacted in Massachusetts in 1978, convicted offenders were typically fined or imprisoned, often serving their full sentence. Probation was instituted as an alternative to incarceration at a time when jail and prison overcrowding became a critical management and humanitarian issue. Probation was considered a front-end sentencing solution to overcrowding,

intended specifically for less serious, first-time, and juvenile offenders amenable to ‘‘rehabilitation.’’

Over the years, rehabilitation has remained the primary goal of probation, and to this end, probation facilitates behavioral reform in a variety of ways. First, the often negative practical and symbolic consequences of the stigma of being an ‘‘ex-con’’ are neutralized. As less notorious and visible ‘‘probationers,’’ the label will have less deleterious effects on the rehabilitative process. Second, the contaminating effects of imprisonment are avoided. This is particularly important for the less experienced offender, who may learn more about crime from more sophisticated, and sometimes predatory, inmates. The ‘‘pains of imprisonment’’ also produce anger, resentment, hostility, cynicism, and many other dysfunctional attitudes and feelings that make it more difficult to reform. Third, probation supports the existing social integration of the offender in the free community of noncriminals, including family, neighbors, employers and coworkers, friends, teachers and classmates, and others who are critical to the informal social control of crime. The offender released from incarceration will have the more difficult task of ‘‘reintegration.’’ Fourth, the rehabilitative programs and services available to probationers are generally less coercive and more varied, flexible, and effective than those provided for prisoners. Fifth, the implied trust in leaving an offender in the community to demonstrate the ability to conform reinforces a positive attribution of self and expectations of appropriate behavior. Probation is more likely than incarceration to contribute to a self-fulfilling prophecy of rehabilitation.

Secondary goals of probation are more punitive. Probation is a penal sanction by virtue of the restrictions placed on the freedom of the offender. The conditions range from very lenient (e.g., weekly phone contact with a probation officer) to very punitive (e.g., twenty-four-hour home confinement), depending on the nature of the offense and offender characteristics. The goal of crime control can also be addressed by enhanced monitoring of probationers’ compliance with the terms of probation, particularly their whereabouts. This can be accomplished by increasing the number and duration of meaningful (namely, face-to-face) contacts between probationer and probation officer, in the department’s office, at home, at work,

in a residential or nonresidential program facility, or anywhere else in the community. More comprehensive control is possible with electronic monitoring devices; for example, transmitter anklets can verify the location of a probationer within, or outside of, a stipulated free movement area. The goal of deterrence is served to the extent that rehabilitation and punishment succeed in preventing probationers from committing more crimes and returning to the criminal justice system. Finally, justice is achieved when probation is the appropriate sentence for the offense and offender, and its application is equitable and uniform across race, sex, and socioeconomic status categories (McAnany et al. 1984).

The decision to grant probation is the product of a complex organization of legal actors, sentencing procedures, decision criteria, and system capacity. The decision may be initiated by the prosecutor, who negotiates a guilty plea in exchange for a recommendation to the judge that the defendant be sentenced to probation. Or it may await conviction at trial. In either case, a presentence investigation report prepared by a probation officer may support the recommendation by providing background information on the offender and an assessment of the public risks and prospects for probation success.

There are intense organizational pressures to minimize the number of trials and to divert convicted offenders from incarceration: There are huge case backlogs in the courts (Meeker and Pontell 1985) and tremendous overcrowding in jails and prisons, as evidenced by the almost forty states in 1988 that were under court order to reduce inmate populations in order to meet a variety of correctional standards (Petersilia 1987). Incredibly, it has been estimated that more than 90 percent of convictions for felonies are the result of negotiated guilty pleas (McDonald 1985), and a high percentage of those receive probation since, by state, from 25 to 70 percent of convicted felons are sentenced to probation (Petersilia 1985). It is clearly in the personal and organizational interests of the defendant, prosecutor, judge, and even the jailer and prison superintendent to support ‘‘copping’’ a plea for probation.

Whether the conviction is negotiated or decided at trial, the judge sentences the offender, within

the constraints imposed by the sentencing model and guidelines used in the jurisdiction. Most states use indeterminate sentencing, where judges have substantial discretion in rendering sentences and parole authorities are responsible for release decisions of incarcerated offenders. The trend is toward determinate sentencing, where judges and parole boards have much less influence on sentence and release decisions. In both models, probation is a widely used sentencing option, especially for less serious offenders but even for many serious offenders: Nationally, as high as 20 percent of violent offenders receive probation, including 13 percent of defendants convicted of rape and 9 percent of those convicted of homicide (Lisefski and Manson 1988).

Despite the confluence of the trend toward determinate sentencing, more pervasive justice model practices in corrections, and increased public pressure to be more punitive with criminals, there are relatively more offenders on probation than incarcerated or on parole than there were two decades ago (Petersilia et al. 1985). More serious offenders are being incarcerated for more fixed sentences, but the concomitant institutional overcrowding has produced a greater utilization of probation, as well as a variety of types of probation designed to meet the needs of both less serious and middle-range offenders, who in the past would have been more likely to be incarcerated. In addition to ‘‘standard probation,’’ characterized by assignment to a probation officer with a caseload of as many as two hundred probationers and nominal contact, supervision, and rehabilitative services, a whole range of ‘‘intermediate sanctions’’ has been created that includes programs that are typically more punitive, restrictive, intensive, and effective than standard probation (Petersilia 1987; Morris and Tonry 1990). Judges now have a diversity of probation alternatives at sentencing: intensive supervised probation, home confinement, electronic monitoring, residential centers (halfway houses), and split sentences (jail/ probation). These alternatives are often combined and coupled with other probation conditions that require restitution to victims, employment or education, payment of program costs, random urinalysis, specialized treatment or classes (e.g., Alcoholics Anonymous), or community service. Many of the intermediate sanctions are also used in the supervision of parolees.

PAROLE

Like probation, parole in the United States was created to relieve the serious overcrowding problem in prisons at the beginning of the nineteenth century. Years before informal probation began, some prison wardens and correctional administrators were releasing prisoners before their full sentence was served. They were either released outright, much as if they had received a pardon, or monitored informally by the police. Based on the European correctional innovations of ‘‘good time’’ and ‘‘ticket of leave,’’ formal parole emerged toward the end of the nineteenth century, with the first indeterminate sentencing law passed in 1876 in New York (Champion 1990).

Until Maine abolished parole in 1976, all states had indeterminate sentencing and parole authorities. In general, within these systems a prisoner earns good time by productive participation in institutional programs and good conduct. The accumulated good time is subtracted from the sentence to determine the time incarcerated. This decision is typically made by a parole board, which is often a group of political appointees from a variety of occupations and constituencies. The offender is then released (or awarded a leave) to the supervision of a parole officer. If an offender does not commit a new crime or violate the conditions upon which release to parole is contingent, he or she can complete the remainder of the sentence as a parolee, to the time of discharge and freedom.

The goals of parole are anchored in indeterminate sentencing and the tenets of the rehabilitative ideal. It is assumed that offenders are amenable to reformation, through both the rehabilitation provided by the prison’s treatment, educational, and vocational programs and the reintegration back into the free world facilitated by the transitional programs and services of parole. These twin primary goals of ‘‘rehabilitation’’ and ‘‘reintegration’’ drive the decisions and actions of the parole system. Parole is granted when the prisoner is considered ready for release, based on behavior during confinement, the extent to which rehabilitation is evident, and the apparent risk to public safety. In practice, many offenders spend a relatively small proportion of their sentence incarcerated; for instance, a convicted murderer with a life sentence

may ‘‘do hard time’’ for as few as, say, ten years and serve the rest of the sentence on parole. Parole is revoked, or modified, when there are indications that reintegration is in jeopardy or unlikely, owing to violation of parole conditions. A new crime, in particular, but even a technical violation may be sufficient for the parole board to reincarcerate a parolee. The parole board also has the discretionary authority to set dates for parole hearings, fix minimum terms and release dates, determine good time credits, and specify parole conditions and requirements.

Like probation, the secondary goals of parole include punishment, crime control, and deterrence. After all, parole is a part of the penal sanction defined by the sentence to imprisonment, and, depending on the type of parole and stipulated conditions, the parole experience can be very restrictive and quasi-custodial. Effective rehabilitation, supervision, and monitoring of parolees should also produce deterrent effects—the combination of reformation and punishment should prevent future criminal conduct among parolees.

Unfortunately, by the mid-1970s, evidence had accumulated that suggested that parole was not an especially effective crime control strategy (Martinson 1974), and the shift away from the rehabilitative ideal to a more punitive justice philosophy (von Hirsch 1976) began in earnest. About one-third of states have returned to some form of determinate sentencing, and more are likely to follow. The discretionary power of judges in rendering sentences and of parole boards in implementing them has been abridged, in order to make decisions more rational and fair by linking them to the severity of the offense rather than the characteristics of the offender. Offenders are now more likely to be serving sentences in confinement; since 1980, the rate of incarceration has increased by 76 percent (Bureau of Justice Statistics 1989). They are also less likely to be placed on parole by a paroling authority; between 1977 and 1987, releases from imprisonment decided by parole boards dropped from about 70 percent to 40 percent of all releases, while mandatory releases increased from roughly 5 percent to 30 percent of the total (Hester 1988). With determinate sentencing, many states simply do not have paroling authorities or parole supervision in the community.

These changes have also affected the types of parole that are still available in a majority of states. Although many parole and probation programs are similar, the usually more serious offenses and criminal histories of parolees and the shift toward more punitive correctional systems have led to a hardening of the conditions of (cf. U.S. Sentencing Commission 1987), less utilization of ‘‘standard parole,’’ and a greater emphasis on protecting public safety by extending custody from the institution to the community. The goals of rehabilitation and reintegration have become less important, while crime control and deterrence have become more important. Consequently, there is greater reliance on transitional programs that maximize monitoring and supervision, while providing opportunities and services (e.g., employment, school, counseling, drug treatment) intended to facilitate reentry into the community and desistance from crime during parole and, ultimately, after discharge from correctional supervision. These programs are more intensive and custodial, often involving residential placement in a halfway house, intensive parole supervision, or home confinement with electronic monitoring. From these community bases, parolees may participate in work or school releases, home furloughs, counseling, religious services, and a variety of other reintegrative activities.

Although parole is not used to the extent that it was before the advent of determinate sentencing, and there are those who believe that it should be abolished in all states, some research suggests that determinate sentencing is no more a panacea than prior correctional reforms. There may be a leveling of sentencing disparities, and more offenders are being incarcerated. But they, on the average, are doing less time and, after release, may be as likely to be reconvicted and reincarcerated (Covey and Mande 1985).

RESEARCH

While the research evidence on the efficacy of determinate sentencing may be sketchy, there are many studies of other issues in probation and parole that have produced more substantive results. Social scientific research on probation and parole has tended to revolve around a set of related issues that are common to both: program effectiveness, recidivism, and classification and

prediction. The overriding empirical and policy question is ‘‘What works?’’ Attempts to address the question vary in rigor and quality, and the answers are neither direct nor simple.

There are innumerable studies of program effectiveness, most of them not producing useful, much less compelling, evidence of program success or failure. For example, many studies conclude that a probation program is successful because 30 percent of participants recidivate or that a parole program is successful because 40 percent of participants recidivate. There are serious problems with those kinds of studies. First, they do not compare the program being evaluated with others, either with other probation or parole programs, or across correctional alternatives (e.g., release, probation, incarceration, and parole).

Second, without comparison groups, one can only evaluate program effectiveness in relation to some standard of success. But preordained acceptable levels of recidivism are determined normatively, not empirically. Normative criteria of success cannot be applied uniformly across the incredible variation in probation and parole programs. For instance, some probation programs, because of the very low risk participants, should probably generate recidivism rates that are closer to 5 percent than 30 percent.

Third, recidivism is often not defined or measured adequately. Generically, recidivism has come to mean ‘‘reoffending,’’ particularly by offenders who have had contact with the criminal justice system, as measured typically by rearrest, reconviction, reincarceration, or some variation or combination thereof. But what does a probationer or parolee relapse to, and what is the most appropriate and accurate measure? The answers are complicated by the fact that probation and parole can be revoked if an offender commits a crime that becomes known to criminal justice authorities or by a noncriminal violation of release conditions (a ‘‘technical violation’’). Paradoxically, practically all studies ignore the substantial amount of successful criminal behavior that remains hidden from officials, but many use both revocation criteria as measures of recidivism. Which measure is used can dramatically affect judgments about program effectiveness. Evaluations of three intensive probation supervision programs show that technical violations, as compared to new crimes, account for

the majority of revocations. The revocation rates due to technical violations for these programs were 56 percent, 70 percent, and 85 percent, respectively. However, if the technical violations are not counted in the recidivism rates, the rates drop to 7 percent, 8 percent, and 5 percent, respectively (Petersilia 1987). Depending on program objectives, recidivism, no matter how measured, may not be the only or most appropriate criterion of program effectiveness. What may also be useful is assessment of relative costs and savings, impacts on jail and prison overcrowding, effects on public perceptions of safety, performance at school or in the workplace, changes in offenders’ attitudes and self-concept, and so on.

More rigorous studies utilize comparison groups in order to assess the relative effectiveness of different correctional strategies. A typical study compares the recidivism rates of various combinations of offenders on probation, incarcerated, and on parole, and concludes that probationers are least likely and ex-prisoners are most likely to recidivate. Of course, one would predict those results based on the differences between the groups in their risk to recidivate. The selection biases of the court place the least serious, low-risk offenders on probation and the most serious, high-risk offenders in institutions. The observed differences in recidivism do not reflect the relative effectiveness of the programs, but the original differences in the recidivism risks of the comparison groups.

Some studies attempt to produce more comparable groups by using more objective probation and parole prediction instruments to classify and then compare offenders by level of recidivism risk across programs. That is, they try improve comparability by ‘‘matching’’ offenders within the different program groups. For example, a study of the relative effectiveness of standard probation, intensive supervision probation, and incarceration with parole classified offenders within each group into low-, medium-, high-, and maximumrisk levels. Comparisons of recidivism, measured by rearrest, reconviction, and reincarceration, across the three program alternatives for each of the four categories of offenders (namely, least likely to most likely to recidivate) show that parole is least effective in preventing recidivism at all levels of recidivism risk. The differences between standard and intensive probation are not as consistent: no matter how recidivism is measured,

the rate is higher among intensive supervision probationers who are lowand high-risk offenders; among medium-risk offenders, it varies by the measure of recidivism; and for maximum-risk probationers, there seems to be little difference in the effectiveness of standard or intensive supervision, except for the somewhat higher reincarceration rate among intensive supervision probationers (Erwin 1986).

The equivocal findings of this and many similar studies reflect the difficulty in predicting recidivism risk with any degree of accuracy. The most comprehensive and statistically sophisticated techniques (e.g., cluster analyses, linear models, complex contingency tables) are not much more accurate than bivariate tabular procedures developed seventy years ago by Ernest Burgess. And no technique is able to predict recidivism with higher than 70 percent accuracy, with most slightly better than chance (Blumstein et al. 1986). Therefore, it is virtually impossible to make groups comparable on the basis of recidivism risk, or any other prediction criteria, which compromises the validity of the findings regarding differential program effectiveness.

The mixed results probably also reflect the paradox of intensive supervision programs in general: Increasing supervision and monitoring may increase, rather than decrease, the probability of recidivism. The offender is at greater risk to recidivate, simply because there is a better chance that unacceptable conduct will be observed. However, depending on the declared program goals, this may indicate success rather than failure: If intensive supervision of probation and parole are intended to enhance crime control and public safety, rather than to rehabilitate, higher rates of recidivism may demonstrate program effectiveness (Gottfredson and Gottfredson 1988).

Research on probation and parole effectiveness cannot produce compelling findings from studies that depend on comparisons of typically noncomparable groups. What is necessary are ‘‘equivalent’’ groups that are created through random assignment within experiments. Unfortunately, experimental designs are usually more expensive and more difficult to implement and complete in natural settings. Consequently, they are extremely rare in research on probation and parole. For instance, there are more than one hundred

studies of the effectiveness of intensive probation supervision, but none have an experimental design with random assignment to program conditions (Petersilia 1987). There are some current efforts to implement studies of probation and parole that have experimental designs, but if the objective is to produce valid and useful knowledge on ‘‘what works,’’ there must be a greater commitment on the part of the criminal justice system, funding agencies, and the social science research community.

The field of probation and parole research and study will continue to be important for social scientists. While there is no indication that the description and analysis presented here is changing at this time, the concern with the issues is active. Some more recent writings add to the character of the presentation here, and are well worth reviewing for persons who wish to pursue the development of the field. (See Albonetti and Hepburn 1997; Heilbrun 1999; Geerken and Hayes 1993; Gendreau et al. 1994; McCorkle and Crank 1992; and Turner et al. 1992.)

(SEE ALSO: Court Systems of the United States; Criminal Sanctions; Criminology; Social Control)

Geerken, Michael, and Hennessey Hayes 1993 ‘‘Probation and Parole: Public Risk and the Future of Incarceration Alternatives.’’ Criminology 31(4):549–564.

Gendreau, Paul, Francis Cullen, and James Bonta 1994 ‘‘Intensive Rehabilitation Supervision: The Next Generation in Community Corrections?’’ Federal Probation 58(1):72–78.

Gottfredson, Michael, and Don M. Gottfredson 1988

Decision Making in Criminal Justice. New York: Plenum.

Heilbrun, Alfred 1999 ‘‘Recommending Probation and Parole.’’ In Allen Hess and Irving Weiner, eds., The Handbook of Forensic Psychology. New York: John Wiley.

Hester, Thomas 1988 Probation and Parole, 1987. Washington, D.C.: U.S. Department of Justice.

Lisefski, Edward, and Donald Manson 1988 Tracking

Offenders. Washington, D.C.: U.S. Department of

Justice.

McAnany, Patrick D., Doug Thomson, and David Fogel (eds.) 1984 Probation and Justice: Reconsideration of a Mission. Cambridge, Mass.: Oelgeschlager, Gunn and Hain.

McCorkle, Richard, and John Crank 1997 ‘‘Meet the New Boss: Institutional Change and Loose Coupling in Parole and Probation.’’ American Journal of Criminal Justice 21(1):1–26.

McDonald, William F. 1985 Plea Bargaining: Critical Issues and Common Practices. Washington, D.C.: U.S. Department of Justice.

REFERENCES

Albonetti, Celesta, and John Hepburn 1997 ‘‘Probation Revocation: A Proportional Hazards Model of the Conditioning Effects of Social Disadvantage.’’ Social Problems 44(1):124–138.

Allen, Harry E., Chris Eskridge, Edward Latessa, and Gennaro Vito 1985 Probation and Parole in America. New York: Free Press.

Blumstein, Alfred, Jacqueline Cohen, Jeffrey Roth, and Christy Visher (eds.) 1986 Criminal Careers and ‘‘Career Criminals.’’ Washington, D.C.: National Academy Press.

Bureau of Justice Statistics 1989 Prisoners in 1988 (bulletin). Washington, D.C.: U.S. Department of Justice.

Champion, Dean J. 1990 Probation and Parole in the United States. Columbus, Ohio: Merrill.

Clear, Todd, and George F. Cole 1986 American Corrections. Belmont, Calif.: Brooks/Cole.

Covey, Herbert C., and Mary Mande 1985 ‘‘Determinate Sentencing in Colorado.’’ Justice Quarterly 2:259–270.

Erwin, Billie S. 1986 ‘‘Turning Up the Heat on Probationers in Georgia.’’ Federal Probation 50:17–24.

Martinson, Robert 1974 ‘‘What Works? Questions and Answers about Prison Reform.’’ Public Interest 35:22–54.

Meeker, James, and Henry M. Pontell 1985 ‘‘Court Caseloads, Plea Bargains, and Criminal Sanctions: The Effects of Section 17 P.C. in California.’’ Criminology 23:119–143.

Morris, Norval, and Michael Tonry 1990 Between Prison and Probation: Intermediate Punishments in a Rational Sentencing System. New York: Oxford.

Petersilia, Joan 1985 Probation and Felony Offenders. Washington, D.C.: U.S. Department of Justice.

——— 1987 Expanding Options for Criminal Sentencing. Santa Monica, Calif.: Rand.

———, Susan Turner, James Kahan, and Joyce Peterson 1985 Granting Felons Probation: Public Risks and Alternatives. Santa Monica, Calif.: Rand.

Quinn, James, and John Holman. ‘‘Electronic Monitoring and Family Control in Probation and Parole.’’

Journal of Offender Rehabilitation 17(3–4):77–87.

Turner, Susan, Joan Petersilia, and Elizabeth Deschenes 1992 ‘‘Evaluating Intensive Supervision Probation/ Parole (ISP) for Drug Offenders.’’ Crime and Delinquency 38(4):539–556.