It was found that the percentage of drivers using seat belts increased in the experimental community but remained stable or declined slightly in the comparison community (Piquero and Piquero, 2002).
An example of the before-and-after design would be the analysis of the impact of the Massachusetts Bartley-Fox gun law. This law carried a one-year minimum prison sentence for the unlicensed carrying of firearms. Their early evaluation showed a decrease in gun-related assaults, robberies, and homicides, but was offset by increases in non-gun assaults and robberies using other weapons (Piquero and Piquero, 2002).
Cross-sectional designs involve studies of one group at one point in time. Consequently, they offer a quick look at or snapshot of the phenomena that is being studied. Classically, they refer to a representative sample of the group and therefore allow researchers to simplify their findings. Cross-sectional research designs saturate criminology and criminal justice research. Hirschi's famous study of causes of delinquency utilized a cross-sectional design in which he asked male respondents a series of questions related to involvement in delinquent activities and emotional ties to social bonds (Piquero and Piquero, 2002).
There are two frequently used longitudinal research designs, panel and cohort studies. Both look at the same group over a period of time and are usually concerned with assessing within- and between-group change. Panel studies pursue the same group or sample over time, while cohort studies look at more specific populations as they change over time. Panel studies classically interview the same set of people at two or more periods of time. For example, the National Crime Victimization Survey (NCVS) arbitrarily selects a certain number of households from across the United States and interviews a member from each a sequence of seven times at six-month intervals. Cohort studies follow individuals or specific cohorts as they change over time. One model example of a cohort study was conducted by Marvin Wolfgang and his colleagues in Philadelphia. The authors followed the criminal records of all boys born in Philadelphia in 1945 through the age of eighteen. Similarly, Tracy, Wolfgang and Figlio tracked the criminal history of males and females born in Philadelphia in 1958 (Piquero and Piquero, 2002).
Time-series designs classically involve variations of multiple observations of the same group over time or at consecutive points in time. Normally, they analyze a single variable such as the crime rate at consecutive time periods, and are particularly useful for studies of the impact of new laws or social programs. An example of a time-series design would be to look at the murder rate in the United States over the last twenty years or to evaluate the murder rate of the United States and Canada over the same period of time (Piquero and Piquero, 2002).
An interrupted time-series design looks at a single variable at consecutive time periods with measures taken before some form of interruption and other observations taken after the intervention. An example of an interrupted time-series design can be found in Spelman and Eck (1987). These authors looked at the number of larcenies from automobiles in Newport News, Virginia. The intervention in this study was a problem-oriented policing program that was made up of special tracking and investigation of crime events. The results showed that the amount of larcenies dropped considerably right away after the intervention took place and remained drastically small for over one year after the intervention. In another interrupted time series study, D'Alessio and Stolzenberg investigated the impact of Minnesota sentencing guidelines on jail incarceration. They found that the onset of the sentencing guidelines increased judicial use of the jail sanction beyond the effect of preexisting trends (Piquero and Piquero, 2002).
Although time-series designs are particularly useful in studying trends over time and how such trends are inclined by some sort of intervention, researchers should be aware of one key feature of time-series designs - the inability to control for all potential unauthentic effects. A researchers may be studying the effect on robberies of a mandatory convenience store law that necessitates stores to have at least two clerks working during hours of operation and after looking at the number of robberies before and after the law took effect, the researcher observed that the number of robberies considerably decreased after the law was put into place. Consequently, the researcher maintained that the law led to the reduction in the number of robberies committed and concluded that the law should be comprehensive to other locales. Nevertheless, what the researcher may have failed to think about was the recent capture of two offenders who were committing almost all convenience store robberies, and who just happened to be caught about the same time the law took effect. In sum, researchers need to be careful in making sure...
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