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Plea-Bargaining Law: the Impact of Innocence, Trial Penalty, and Conviction

Probability on Plea Outcomes

Article  in  American Journal of Criminal Justice · August 2020

DOI: 10.1007/s12103-020-09564-y

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1 Running Head: PLEA-BARGAINING LAW

ORIGINAL ARTICLE

Plea-bargaining law: The impact of innocence, trial penalty,

and conviction probability on plea outcomes

Miko M. Wilford, Ph.D.1

Gary L. Wells, Ph.D.2

Annabelle Frazier, LMHC, Ph.D.3

1University of Massachusetts Lowell, Department of Psychology, Lowell, MA, USA; ORCiD:

0000-0002-8653-8893

2Iowa State University, Department of Psychology, Ames, IA, USA; ORCiD: N/A

3Southern New Hampshire University, Department of Psychology, Manchester, NH, USA;

ORCiD: 0000-0002-5046-5979

Correspondence concerning this article can be sent to Miko M. Wilford, University of

Massachusetts Lowell, Department of Psychology, 850 Broadway Street, Lowell, MA, USA

01854.

E-mail: [email protected]; Phone: (978) 934-3975

Wilford, M. M., Wells, G. L., & Frazier, A. (2021). Plea-bargaining law: The impact of

innocence, trial penalty, and conviction probability on plea outcomes. American Journal

of Criminal Justice, 46(3), 554-575. https://doi.org/10.1007/s12103-020-09564-y

This is a post-review, pre-copyedited version of the article accepted for publication in the

American Journal of Criminal Justice.

©Southern Criminal Justice Association, 2020. This paper is not the copy of record and may not

replicate the authoritative document published in the Springer journal.

PLEA-BARGAINING LAW 2

Abstract

Despite the prevalence of guilty pleas, we know relatively little about factors that

influence the decision to plead. Replicating and extending Dervan and Edkins’ (2013), we

conducted two experiments to examine the effects of guilt status, trial penalty, and conviction

likelihood on plea outcomes using an adaptation of a high-stakes cheating paradigm. Students

were led to believe that they were participating in a study examining team versus individual

problem solving. Those randomly assigned to a guilty condition were induced to cheat on an

individual problem by a study confederate (in clear violation of the study instructions). All

participants were later accused of cheating in the research study, and were offered the analogue

of a plea deal in an academic context. Across both experiments, guilty participants were

significantly more likely to plead guilty than innocent participants. In Experiment 2, conviction

probability affected plea rates only among the innocent. The trial penalty manipulation had no

significant effect on plea rates. Reasons for pleading guilty differed between the innocent and the

guilty, whereas the plea rejection rationales were similar across the two groups. Overall, this

research highlights several avenues for further research aimed at improving the current system of

pleas to reduce false guilty pleas.

Keywords: behavior, criminal justice system, decision making, law, psychology

PLEA-BARGAINING LAW 3

Plea-bargaining law: The impact of innocence, trial penalty, and conviction probability on plea

outcomes

“… criminal justice today is for the most part a system of pleas,

not a system of trials” (Lafler v. Cooper, 2012, p. 11)

With the above quote, the U.S. Supreme Court acknowledged and underscored a

shocking trend in criminal prosecution. Prior to the 1980s, approximately 20% of federal

criminal convictions were obtained via a courtroom trial process (Oppel, 2011). By 2006, this

number had dropped to 5% (Ross, 2006), and even more recently, the Bureau of Justice Statistics

(2015) documented that only 2.6% of federal criminal convictions were the result of trials.

Instead, as Justice Kennedy wrote for the majority in Lafler v. Cooper (2012), all except a very

small percentage of criminal convictions are obtained within a system of pleas.

Scholars have long-posited that the plea system could lead to false guilty pleas, but a

number of obstacles have made it difficult to observe such cases (Fisher, 2000; Stephens, 2013).

Despite the obstacles, the National Registry of Exonerations (2015) reported that nearly 50% of

the exoneration cases documented in 2015 involved a false guilty plea. In fact, exoneration cases

involving false guilty pleas have been increasing over the last several years (Innocence Project,

2017; National Registry of Exonerations, 2015). This rise might be due to our increased ability to

discover exonerating evidence rather than an increase in false guilty pleas per se, but the fact

remains that guilty pleas do lead to wrongful convictions that often go undetected.

Studying the Decision to Plead

Research on guilty pleas has often relied on two methods: analyzing real-world data (e.g.,

conviction data or self-reports from defendants; Bordens & Basset, 1985) for which ground-truth

regarding guilt status is unknown; or, collecting data via hypothetical vignettes/narratives for

PLEA-BARGAINING LAW 4

which the extent to which people’s decisions accurately reflect what they would do if the

decision involved real personal consequences is questionable (Redlich, Wilford, & Bushway,

2017; Wilford, Shestak, & Wells, 2019). To study plea bargaining in terms of actual behavior

(rather than imagined scenarios), Dervan and Edkins (2013) adapted a paradigm used to study

false confessions—the cheating paradigm (Russano, Meissner, Narchet, & Kassin, 2005).

In their experiments, Dervan and Edkins (2013) told 76 student-participants that they

would be participating in a study comparing individual and team problem-solving. Along with a

partner, each participant was taken into a testing room and given several logic problems to solve.

Unbeknownst to participants, partners were actually confederates instructed to induce some

participants to “cheat” on the individual portion of the logic test (Dervan & Edkins, 2013;

Russano et al., 2005). Later, the experimenter accused all participants of cheating, and offered

each a “plea”: in exchange for an admission of guilt and forfeiture of research credits, student-

participants would avoid facing an “academic review board” to adjudicate their guilt, and a

potential ethics course requirement if found guilty (Dervan & Edkins, 2013).

This modified cheating paradigm offered several apparent advantages over both

hypothetical vignettes and offender self-reports. In addition to enabling experimental assignment

of guilt status (an advantage over self-reports), the paradigm enables researchers to strengthen

several components of validity fundamental to experimental research. That is, because

participants’ experience was real (rather than imagined), the paradigm allows researchers to

improve the psychological realism (Wilson et al., 2010) of plea experiments. Participants were

able to experience the emotional distress of an accusation, the pressure associated with the plea

offer, and the stress of anticipating a real punishment (Dervan & Edkins, 2013). In fact, several

of Dervan & Edkins’ (2013) participants were excluded because they were so distressed they

PLEA-BARGAINING LAW 5

were unable to finish the experiment (Edkins & Dervan, 2012). These emotional reactions offer a

significant enhancement to external validity, which is otherwise limited by the hypothetical

nature of vignettes.

Plea Bargaining Variables

Guilt status. Dervan and Edkins (2013) examined two key predictors: factual guilt status

and trial penalty. Numerous studies have shown that guilty individuals are more likely to accept

a plea than innocent individuals, even when other manipulated variables are kept constant

(Bordens, 1984; Gregory et al., 1978; Redlich & Shteynberg, 2016; Tor, Gazal Ayal, & Garcia,

2010; Wilford & Wells, 2018). Despite the consistency of this finding, the difference in plea

rates between guilty and innocent defendants has varied. In Dervan and Edkins’ (2013) study,

results indicated that participants who cheated (labeled as “guilty”) were 6.38 times more likely

to accept a plea than those who did not cheat (“innocent”).

Trial penalty or plea discount. Dervan and Edkins (2013) also employed a “trial

penalty” manipulation, varying the punishments that participants could expect after conviction at

“trial” if the plea offer was refused. Several analyses of real convictions have supported the

existence of trial penalties (or plea discounts) finding that those convicted at trial received

sentences that were significantly longer (by as much as 30-60%) than the sentences of defendants

in similar cases who entered plea agreements (Bushway & Redlich, 2012; Bushway et al., 2014;

Ulmer & Bradley, 2006; though not all cases appear to result in meaningful plea discounts, see

Abrams, 2011; Frazier, Shockley, Keenan, Wilford, & Gonzales, 2019). Experimental research

(relying primarily on vignettes) has also found a positive relationship between increases in plea

discount and likelihood of plea acceptance (Bordens, 1984; Gregory, Mowen, & Linder, 1978,

Exp. 1; McAllister & Bregman, 1986a; Zimmerman & Hunter, 2018), though the magnitude of

PLEA-BARGAINING LAW 6

the effect sometimes differs between the innocent and the guilty (in studies that also manipulated

guilt status). Other studies have examined the effect of the plea discount on the offers or

recommendations made by attorneys, producing mixed results (Kramer, Wolbransky, &

Heilbrun, 2007; McAllister & Bregman, 1986a; 1986b). Yet, in Dervan and Edkins (2013), the

lenient and harsh trial penalty manipulation had no impact on plea rates among either the

innocent or the guilty.

Conviction probability. Research also finds an effect of conviction probability on plea

decisions. Bordens (1984) found that the guilty were more likely to plead when the probability of

conviction reached 50% and remained constant with further increases, while the same increase

only occurred among the innocent when the probability of conviction reached 90%. In contrast,

Tor et al. (2010) found that the likelihood of pleading among the guilty was highest when

conviction probability was between 50-70% and decreased significantly as the probability

increased beyond 70%; among the innocent, in contrast, likelihood of pleading rose sharply as

conviction probability reached 70% and continued to rise. Other studies have demonstrated a

similar effect of conviction probability on the likelihood of pleading, though the threshold

probability necessary to increase plea acceptance varied across studies (Helm & Reyna, 2017;

Helm et al., 2018). McAllister and Bregman (1986a) manipulated both plea discount and

conviction probability, finding that as both variables increased, so too did the probability of plea

acceptance. More recently, Zimmerman and Hunter (2018) also found an increase in plea

acceptance as conviction likelihood increased.

Individual plea rationales. Setting aside research indicating that individual differences

can also impact plea outcomes (e.g., Kutateladze, Andiloro, & Johnson, 2016; Sommers,

Goldstein, & Baskin, 2014), studies actually measuring real and participant-defendants’

PLEA-BARGAINING LAW 7

individual reasons for accepting pleas produce other interesting and sometimes systematic

patterns. Research relying on individuals’ rationales, whose (real) convictions resulted from

guilty pleas, found prosecutorial pressure (Bordens & Basset, 1985; Malloy, Shulman, &

Cauffman, 2014), expediency (Bordens & Basset, 1985, Redlich, Summers, & Hoover, 2010),

perceived conviction likelihood and subsequent sentence (Albonetti, 1990), indirect pressures

(e.g., family suffering; Bordens & Basset, 1985), and remorse were often reported. In addition,

some self-proclaimed false plea defendants have said they felt their situation was hopeless, or

that they pled to protect someone (Redlich et al., 2010).

In experimental research, few studies have directly examined participant-defendants’

reasons for pleading guilty. Consequently, many of the rationales reported by defendants in field

studies have yet to be observed in experimental research. Yet, one of the few studies that did

evaluate participants’ reasons for pleading guilty in an experiment found that many participants

cited the ease of pleading guilty, which appeared distinct from reasons that related more closely

to plea discount (fear of consequences) or conviction probability (pressure; Wilford & Wells,

2018). Further, recent research has demonstrated the influence of perceived fairness of the plea

offer on the likelihood of pleading (Redlich & Shteynberg, 2016; Tor et al., 2010). Thus, it

seems that those actually faced with the decision to plead guilty perceive being influenced by a

number of factors worthy of additional investigation.

The Current Research

The current research offers a replication and extension of Dervan and Edkins’ (2013)

high-stakes cheating paradigm study. Notably, this will be the first project to manipulate both

trial penalty and conviction probability in a modified cheating paradigm. This is an important

contribution being that the dominant model of plea decision-making, the shadow-of-the-trial

PLEA-BARGAINING LAW 8

model (SoT model), only accounts for predictive effects of these two variables (Bushway &

Redlich, 2012; Bushway, Redlich, & Norris, 2014; Redlich et al., 2017). Yet, many question

whether the SoT model is overly simplistic (Bibas, 2004), and whether it omits other important

predictive variables (e.g., guilt status, Wilford & Khairalla, 2019). Thus, the inclusion of these

two SoT variables along with guilt status will help us to determine how these effects might

compound or interact to influence plea outcomes in a more realistic high-stakes decision-making

paradigm. While our two experiments also used a modified cheating paradigm (like Dervan and

Edkins, 2013), we further adapted the paradigm in several ways.

Omitting the need to confess. An important feature of the modified paradigm was that,

unlike Dervan and Edkins (2013), a confession was not required of participants who accepted the

plea. This is consistent with the fact that Alford and nolo contendere pleas do not require a guilt

admission and are accepted in most jurisdictions (Redlich & Ozdogru, 2009). Alford pleas allow

defendants to maintain their innocence while accepting a plea deal, and nolo contendere (or no

contest) pleas do not require defendants to make any statements as to their guilt or innocence

while accepting a plea offer (Hudson v. United States, 1926; North Carolina v. Alford, 1970).

While Alford and nolo contendere pleas are less common than more traditional or standard guilty

pleas (in which the defendant does admit guilt), they are not rare occurrences. In 2004, a Bureau

of Justice Statistics survey recorded a combined 2,553 (17.6% of those surveyed) Alford and

nolo contendere pleas. Redlich and Ozdogru (2009) used these numbers to estimate that 207,181

state inmates would fall within these categories. Thus, it was important to see whether the

general effects observed by Dervan and Edkins (2013) would be replicated if participants were

not required to provide an explicit confession in order to accept a plea offer. Omitting the need to

confess was additionally important as a recent study, relying on a similar paradigm, has

PLEA-BARGAINING LAW 9

demonstrated that different processes may underlie confession and plea decisions (Wilford &

Wells, 2018).

Penalties and discounts. Dervan and Edkins (2013) did not find a significant difference

between their two trial penalty conditions. Hence, it was important to assess whether the plea

deal offered by Dervan and Edkins (2013) was potentially too lenient (i.e., forfeiting their study

credit) thereby making both potential trial penalties appear harsh in both the lenient and severe

conditions. In the current study, we chose a plea offer analogous to real-world pleas that include

community service; these deals have become more common as a method of diverting offenders

from overcrowded prisons (Subramanian, Moreno, & Broomhead, 2014). Participants were faced

with a choice—agree to work twenty hours in the research lab over the next month (accept the

plea deal) or risk a possible charge of academic dishonesty through the Dean of Students Office

if charged later (reject the plea deal). Thus, accepting the plea in our experiment was costlier (20

hours of work) than Dervan and Edkins (2013; the plea punishment was somewhat similar to

what was offered by Henderson and Levett, 2018, but they did not also manipulate trial penalty).

Further, Dervan and Edkins (2013) manipulated the severity of the trial penalty by

varying the duration of the ethics requirement imposed upon participants if found guilty by the

academic review board (after refusing to plead guilty). Specifically, participants were faced with

a course that would meet for three hours every week for an entire semester (harsh condition), or a

series of three 3-hour seminars (lenient condition). Assuming the average semester is 14 weeks,

the difference between these two punishments is 33 hours. Thus, it is possible that the null effect

of sentence severity emerged because these two punishments were not perceived as significantly

different by the participants who were only exposed to one of the two conditions. In other words,

because the type of punishment (i.e. taking an ethics course, delivered via 3-hour increments)

PLEA-BARGAINING LAW 10

remained the same, the time involved might not have been a particularly salient detail to

participants. Further, while many defendants are faced with quantitative comparisons when

offered a plea deal (e.g., 5 years in prison versus 10 years in prison), they are often faced with

qualitatively different punishments as well (e.g., prison versus probation). In fact, past cases have

indicated that qualitative differences in the punishments one can face when pleading guilty

versus being found guilty at trial can weigh heavily on the mind of a defendant (e.g., North

Carolina v. Alford, 1970). Thus, we chose to employ qualitatively different punishments inspired

by the university’s actual academic sanctions in cases of academic dishonesty.

Conviction probability. Unlike Dervan and Edkins (2013), we also chose to manipulate

the probability of conviction. We manipulated conviction probability for two reasons: 1)

conviction probability is broadly believed to impact plea outcomes (e.g., SoT model), and 2)

conviction probability and trial penalty could be naturally associated being that people have a

tendency to perceive worse outcomes as less likely (thus, we felt it was important to account for

both potential variables). Conviction probability was manipulated by telling participants that

their likelihood of being found guilty of cheating (if they rejected the plea) was either somewhat

likely (25%) or extremely likely (80%). These numbers were chosen to maximize the difference

between the two conditions while also preserving the plausibility of the manipulation.

We started by introducing our newly adapted cheating paradigm to a new sample of

student participants choosing to only manipulate guilt status (Experiment 1). After finding the

new paradigm effective, we added the trial penalty and conviction probability manipulations in

Experiment 2. Hence, Experiment 2 conditions were designed to test for main effects and

interactive effects of the trial penalty, probability of conviction, and guilt status within a high-

stakes paradigm. In both studies, we predicted that although guilty participants would accept

PLEA-BARGAINING LAW 11

plea deals at a higher rate than innocent participants, a substantial number of the innocent

(differing significantly from the ideal proportion of zero) would nonetheless accept plea deals–

we expected this proportion to be consistent with prior literature. In Experiment 2, we predicted

that an increase in conviction probability, as well as an increase in the magnitude of the trial

penalty, would increase plea acceptance (as the shadow-of-the-trial model would predict). But,

we also predicted that the effect of conviction probability and the trial penalty would differ

between the innocent and the guilty such that innocent participants would be more affected by

these variations than guilty participants.

Experiment 1

Method

Participants. One hundred and sixty-five undergraduate students enrolled in introductory

courses at a large American Midwest university participated in this experiment in exchange for

course research credit (97 females and 68 males). The participants averaged 19 years of age with

a range of 18-45 years.

The complexity of this research paradigm resulted in several necessary exclusions.

Twenty-three of the 165 study participants (13.9%) were omitted from all data analyses. Of

these, eight were removed due to suspicion regarding the true purpose of the study. Participants

excluded due to suspicion accurately described one of two possible elimination criteria during

debriefing. The criteria included: 1) the confederate-participant’s involvement with the study, or

2) the study’s purpose as examining how people would react to an accusation and subsequent

deal. An additional five people in the guilty condition had to be excluded for refusing to provide

the confederate with their answer, thereby making them innocent despite their random

assignment to guilt. Four other people were discounted due to early suspension of the study

PLEA-BARGAINING LAW 12

given their evident emotional distress during the accusation process. The remaining six people

were excluded because they: possessed research lab experience (n = 2),1 were non-native English

speakers (n = 2), participated in a similar study (n = 1), or the experimenter made a significant

error (n = 1). The final study sample was N = 142 (71 participants per experimental cell).

Materials. Many of the materials (e.g., problem solving packets, personality

questionnaires, etc.) used in this study were adapted from previous research and can be made

available upon request to the corresponding author.

Procedure. The procedure utilized in this study was adapted from previous research

(Dervan & Edkins, 2013; Russano et al., 2005). Participants were told that the researchers were

interested in examining how people completed problems both individually and in teams. A

confederate posing as another participant waited outside the laboratory with the real participant.

After providing informed consent and completing an initial questionnaire, the experimenter2

provided the confederate and participant five minutes for a rapport-building session.

After the rapport session, the experimenter returned with two packets of individual logic

problems and one packet of team logic problems. Participants were instructed to work together

on the team problems only, and to solve the individual problems alone. Experimenters then left

the room while the problems were solved. Participants randomly assigned to the guilty condition

were induced to cheat by the confederate on the second individual problem—hereafter referred

to as the triangle problem. If the participant resisted the confederate’s initial request for help on

1We chose to exclude everyone with psychological research lab experience due to the increased

likelihood that they would be familiar with studies involving deception. We also felt they would

not have perceived the consequence of working in the lab as negatively as most people. 2Experiment 1 included eight female experimenters. Experiment 2 included twelve

experimenters: nine were female and three were male. All of the experimenters (for both

experiments) underwent extensive training including three ~90-minute sessions supervised by

the lead author.

PLEA-BARGAINING LAW 13

the problem, the confederate would request help up to two more times. The confederates never

asked participants in the innocent condition for help on the individual problems.

After participants completed a personality measure, the experimenter returned, stating

that an issue arose while scoring the logic problems. The experimenter asked the confederate to

exit the room with her; three minutes later, she returned with the confederate and asked the

participant to follow her to a separate room. While alone with the participant, the experimenter

explained that the participant and the confederate had the same wrong answer on the triangle

problem. She stated that such a match is statistically improbable unless the two shared answers

on that problem—a violation of study instructions.

The experimenter told participants that the professor in charge of the study (who was not

present for the session) had been contacted to determine how to proceed. She then revealed that

the suspected conduct could be considered academic dishonesty. Once the seriousness of the

situation had been explained, she stated that the professor wanted the situation to be remedied in

some way. To ensure the participant fully understood the impact of cheating (and the importance

of study instructions), the professor requested that s/he be asked to work in the lab for 20 hours.

The information participants were provided presented them with two basic options:

Option 1: Sign a statement affirming your agreement to work in the lab for 20 hours over

the next four weeks and the accusation will be dropped

Option 2: Refuse to sign the statement and face a possible charge of academic dishonesty

through the Dean of Students Office

The experimenter then handwrote a statement for participants to sign acknowledging

their acceptance of the agreement. The statement said, “I agree to work 20 hours on the Problem

Solving with Personality study by (one month after that day’s date).” If participants did not sign

PLEA-BARGAINING LAW 14

the statement, the experimenter waited and reiterated the request that they sign up to two more

times. The experimenter then exited the room to update the professor.

After the experimenter returned with a final questionnaire, participants were administered

a funnel debriefing in which they were gradually probed for suspicion as the deception in the

study was progressively explained. All participants were provided with on-campus student

counseling services information in case the study caused lingering stress.

Results

Plea outcomes. Similar to Dervan and Edkins (2013), most guilty participants (80.3%)

did accept the plea offer, 95% CI [69.6%, 87.9%]. Moreover, guilty participants accepted the

plea deal at a reliably higher rate than did innocent participants, P1 – P2 = 28.2%, 95% CI

[12.7%, 41.9%]. This finding is consistent with other high-stakes plea studies (Dervan & Edkins,

2013; Henderson & Levett, 2018; Wilford & Wells, 2018). However, innocent participants were

also more likely to accept the plea offer than to reject it (at a rate of 52.1%, 95% CI [40.7%,

63.3%]); this proportion ( P1 – P2 = 52.1%, 95% CI [39.6%, 63.3%]) is also on par with Dervan

and Edkins (2013; 56.4%) despite the fact that the penalty associated with pleading in our study

was more severe.

Plea decision rationales. Participants were asked to report why they chose to reject or

accept the plea deal. Their responses were recorded by a research assistant and coded by two

coders (who were blind to participants’ guilt status) into categories. Categories were

systematically narrowed down by their similarity, resulting in the frequencies described in Table

1. To test whether the pattern of responses differed significantly between the innocent and the

guilty, we conducted two omnibus chi-square analyses. Prior to these tests, categories with

expected count totals of fewer than five had to be collapsed into the “Miscellaneous” category.

PLEA-BARGAINING LAW 15

Thus, the top four reasons participants provided for accepting the plea were preserved, and all

other responses were joined with Miscellaneous responses.

The top four reasons participants provided for accepting the plea included avoiding worse

consequences, perceiving the plea as the best possible option, feeling pressured or trapped, and

taking responsibility for one’s actions (i.e., because of actual guilt). For example, a participant

whose response was placed in the pressure category stated, “It didn’t seem like I had much of a

choice.” Another participant, whose rationale was focused on avoiding worse consequences

stated they accepted the plea offer “... because I didn't want it to go on my academic record and I

just got a scholarship - don't want to lose it since I am struggling paying for school.” In contrast,

a participant who perceived the plea as a favorable option reported, “It [working in the lab] will

fit in better with my schedule.” Miscellaneous responses included comments like, “I don’t

know,” and “I didn’t feel like arguing about it.”

The chi-square analysis indicated that the pattern of these responses differed somewhat

between the innocent and the guilty, although this difference did not reach statistical

significance, X2(4, N =80) = 8.57, p = .073, V = .32. The differences appeared to be driven

primarily by the fact that innocent participants provided far more Miscellaneous responses than

guilty participants. Guilty participants also, unsurprisingly, cited their own guilt more frequently

than innocent participants. For example, one participant stated she accepted the plea, “because I

messed up so I want to make it up.”

Because fewer participants rejected the plea, only three reasons for rejecting were

maintained, and all other response categories were lumped with Miscellaneous answers.

Participants’ Miscellaneous reasons for rejecting the plea included rationales such as “ because

the consequences were way too much for the small problem,” and “because it [the process]

PLEA-BARGAINING LAW 16

wasn’t official.” This analysis provided no indication that the reasons for rejecting a plea differed

between the innocent and the guilty, X2(3, N =47) = 1.14, p = .768, V = .15. The predominant

reason for rejection in both groups was innocence—as in Dervan and Edkins’ (2013)

experiments, a significant proportion of guilty individuals did not label their behavior as

cheating, and consequently, cited their innocence. For instance, a participant who rejected the

plea offer insisted, “I didn't do anything wrong, and I'm willing to argue that.” In sum, it appears

that the innocent and the guilty differed somewhat in their reasons for accepting a plea, but were

similar in their reasons for rejecting a plea.

Discussion

Consistent with Dervan and Edkins’ (2013) findings, the false plea rate in Experiment 1

was so high that the diagnosticity ratio for a guilty plea was just 1.54. The diagnosticity ratio

provides a measure of how much a particular variable is indicative of, in a legal context, guilt. A

diagnosticity ratio of 1.54 essentially means that someone who pleads guilty is only 1.54 times

more likely to actually be guilty than someone who does not plead guilty. In addition to this

relatively low ratio, very few other discernable differences emerged between the innocent and

the guilty across a number of measures. While significantly more guilty participants agreed to

the plea offer, more than half the innocent participants agreed to the same offer, despite us

increasing the penalty associated with pleading guilty. Further, the reasons for accepting a plea

were similar among the innocent and the guilty.

Experiment 2

Experiment 2 was designed to build on the results of Experiment 1. In addition to testing

the replicability of the previous effects, Experiment 2 incorporated manipulations of the trial

penalty, as well as probability of conviction. The predictions were that (a) increasing the

PLEA-BARGAINING LAW 17

perceived probability of conviction at trial should increase plea acceptance, and (b) increasing

the trial penalty should decrease plea acceptance. Moreover, we also predicted that innocent

participants would be more affected by the probability-of-conviction and trial-penalty

manipulations than guilty participants. Thus, we aimed to determine whether the overall plea

acceptance rates observed in Experiment 1 would replicate, and to test whether the innocent and

the guilty are truly motivated by different factors when accepting a plea deal.

Method

Participants. Three hundred and seventy-nine undergraduate students at a large Midwest

university received research credits in exchange for their participation in the study. The study

sample was 61% female. Participants ranged from 18 to 28-years with a mean age of 19 years.

Experiment 2 employed the same exclusion criteria as Experiment 1, with the added

requirement that participants from Experiment 1 could not participate in Experiment 2. Forty

total participants (10.6%) were omitted from subsequent data analyses. Of these, the most

common reason for exclusion was early suspension of a study session due to emotional distress

(n = 16). Nine others were excluded for resisting the confederate’s requests for help, despite their

random assignment to guilt. Six participants were omitted because they were not native English

speakers (an eligibility requirement). Only two participants were excluded due to their suspicion

of the study’s actual purpose (using the same standards as Experiment 1). The remaining

participants were excluded due to: possessing advanced research experience (n = 4),

experimenter error (n = 2), or prior experience in a similar study (n = 1). The final study sample

was N = 339 with a range of 38 to 49 participants per experimental condition (Dervan & Edkins,

2013 recruited 18 to 20 participants per cell).

PLEA-BARGAINING LAW 18

Design. This study employed a 2 (innocent or guilty) x 2 (trial penalty: more lenient or

more strict penalty) x 2 (probability of conviction: somewhat likely or extremely likely)

between-participants design.

Materials. The materials used in this experiment were identical to those in Experiment 1

with the exception of a few additional post-manipulation questions to examine the effects of the

probability and penalty manipulations.

Procedure. The procedure for Experiment 2 differed from Experiment 1 in four ways.

First, the rapport-building session was shortened from five to three minutes to provide the extra

time needed for the additional study manipulations. Second, the authority in charge of arbitrating

the accusation (if the plea was rejected) changed from the Dean of Students Office to the

Department of Psychology’s Human Research Ethics Review board. Similar to Dervan and

Edkins’ (2013) “Academic Review Board,” this board was fictitiously described as having

procedures for determining when students are guilty of cheating in research studies, as well as

possessing the authority to impose sanctions on students found guilty. This change was made due

to some particularly emotional reactions to the possibility of punishment from the Dean of

Students Office—an office that many students associate with serious cases. Third, participants

faced one of two possible consequences if they rejected the plea and were found guilty of

cheating later. Participants in the more lenient penalty condition were told that they may be

required to write a 15-page APA-formatted research paper on the ethics of research. Participants

in the more strict penalty condition were told that they may face indefinite academic probation

and an F on the research portion of their grade (in whatever class to which the research credits

were designated to apply); in most courses, this punishment would lower their overall grade by

5-10%. Within the university code of conduct regarding academic dishonesty (at many academic

PLEA-BARGAINING LAW 19

institutions), completing writing assignments that highlight the misconduct are common

sanctions for those found guilty of academic dishonesty. These sanctions are also typically

considered the least severe, while types of “conduct probation” or academic probation are

considered more serious. Although both punishments are positioned on the less severe side of

available academic sanctions, they were the strictest of the enumerated sanctions that would not

result in further increases in participant distress, and thus, in further exclusion of participants.

Further, using these types of sanctions allowed us to determine the effect of qualitative

differences in punishment (unlike Dervan and Edkins, 2013). The final difference was that

participants were provided with estimates on the likelihood that they would be found guilty of

cheating. Specifically, experimenters reported that the professor estimated, based on his

experience dealing with similar cases, that the odds of being found guilty (probability of

conviction) were either somewhat likely, around 25%, or extremely likely, around 80%. To

better ensure the clarity of this manipulation, participants were provided with both a non-numeric

description of probability (i.e., somewhat or extremely likely), as well as a percentage (25 or

80%).

Results

Manipulation checks. To check the validity of the trial penalty manipulation,

participants were asked, “How severe did you feel the possible consequences of not agreeing to

the statement were?” Responses were measured on a 5-point Likert scale from 1 (Not at all

severe) to 5 (Very severe). Interestingly, participants in the more lenient condition actually

produced a mean rating of 2.64 (SD = 1.29) on a 5-point Likert scale whereas participants in the

more strict condition produced a mean rating of 1.75 (SD = 1.07); this difference was significant;

t(337) = 6.95, M1 – M2 = 0 .89, 95% CI [.64, 1.14], p < .001. Thus, the more strict and more

PLEA-BARGAINING LAW 20

lenient penalty designations were perceived by participants such that the more lenient condition

(15-page paper) was perceived as harsher and the more strict condition (fail research component

and indefinite academic probation) was perceived as more lenient (contradicting the

characterization of these penalties in the university codes of conduct). Because neither penalty

exceeded the midpoint, it appears that participants did not perceive either punishment to be that

severe. Guilt status also seemed to impact perceptions of the trial penalty such that the guilty

perceived the potential penalty as more lenient (M = 2.05, SD = 1.16) than did the innocent (M =

2.32; SD = 1.34); t(337) = 2.00, M1 – M2 = 0 .27, 95% CI [.00, 0.54], p = .047. Thus, perceived

severity of the penalty was colored both by the penalty itself as well as the knowledge that one is

innocent or guilty and, consequently, more or less deserving of punishment.

To ensure that the probability of conviction manipulation was effective, participants were

asked, “Given the evidence in the current situation [if you hadn’t signed the statement], how

likely is it that you would have been charged with cheating and consequently lost your research

privileges [been required to write the 15-page research paper on the ethics of research]?” with

variations depending on the trial penalty condition and whether the participants had agreed to

sign the statement. Responses were made with a Likert-type scale from 1 (extremely unlikely) to

10 (extremely likely). The probability of conviction manipulation did have the expected effect;

overall, participants in the extremely likely condition generally reported higher values for their

perceived probability of conviction (M = 4.31; SD = 3.22) than those in the somewhat likely

condition (M = 3.52; SD = 2.75); t(333) = 2.42, M1 – M2 = 0.79, 95% CI [0.15, 1.43], p = .016.

Guilt and innocence also had an impact on the perceived probability of conviction such that the

guilty reported a higher likelihood of conviction (M = 4.70; SD = 3.08) than the innocent (M =

3.14; SD = 2.74); t(333) = 4.89, M1 – M2 = 1.56, 95% CI [0.93, 2.18], p < .001. This is

PLEA-BARGAINING LAW 21

noteworthy considering that all participants were presented with the same evidence; namely, that

they had produced the same wrong answer on a particular problem.

Plea outcomes. To ensure that all potential statistical interactions were captured, we

performed a logistic regression analysis for which participants’ plea acceptance or rejection was

the outcome of interest, and their guilt status as well as the probability of conviction and trial

penalty were treated as potential modifiers. More specifically, we used a hierarchical binary

logistic regression model in which main effects were entered on the first block, two-way

interactions on the second block, and the three-way interaction was entered on the third and final

block. The presence of higher-order interaction terms renders all lower-order terms conditional

main effects, necessitating the hierarchical structure of effects (see Table 2 for the proportions of

plea acceptance in all eight experimental conditions).

Neither the three-way interaction nor any of the two-way interactions were significant, Bs

< 0.51, SEs > 0.49, Wald’s χ2(1) < 0.88, ps > .35. As in Experiment 1, guilt status had a

significant impact on plea outcomes with guilty participants being more likely to plead than

innocent participants, B = 1.11, SE = 0.25, Wald’s χ2(1) = 19.53, p<.001, eB = 3.04 (95% CI

[1.86, 4.99]). The odds of a plea were 3.04 times more likely from guilty participants (3.89:1)

than from innocent participants (1.38:1).3 In addition, the probability of conviction also

significantly influenced participants’ plea decisions, B = 0.55, SE = 0.25, Wald’s χ2(1) = 5.02, p

= .03, eB = 1.74 (95% CI [1.07, 2.82]). The odds of a plea were 1.74 times greater when the

probability of conviction at trial was high (2.84:1) compared to when the probability of

conviction at trial was low (1.74:1). The impact of the trial penalty on plea outcomes was not

3If the descriptive odds of accepting the plea bargain are used to compute the odds ratio, it will

differ slightly from the value produced by the logistic regression model. This discrepancy is due

to the logistic regression model computing values for which the effects of all other model

predictors are removed.

PLEA-BARGAINING LAW 22

significant, though this could be explained by participants’ unexpected perceptions of the

severity of the two punishments. The plea acceptance rate among the innocent was 58.1%, 95%

CI [50.7%, 65.3%], and among the guilty the plea acceptance rate was 80.2%, 95% CI [73.6%,

85.6%]. These proportions are extremely close to those found in Experiment 1.

Probability of conviction. Although the two-way interaction was not significant, plea

acceptance outcomes produced by the probability-of-conviction manipulation followed a pattern

suggesting that innocent participants were more influenced by this manipulation than the guilty

participants, as predicted (B = -0.47, SE = 0.51, Wald’s χ2(1) = 0.88, p =.35, eB = 0.62, 95% CI

[0.23, 1.68]). Examination of the raw proportions indicates that the likelihood of conviction had

a negligible impact on guilty participants’ willingness to accept the plea with those being told

conviction was somewhat likely (collapsing data from the two trial penalty conditions) accepting

the plea deal 78.2% of the time, and those told that their chances of conviction were very likely

accepting 82.5% of the time, P2 – P1 = 4.3%, 95% CI [-7.9%, 16.2%]. In contrast, the probability

of conviction had a significant impact on the innocents’ propensity to accept the plea deal

(collapsing across the trial penalty conditions) with 49.4% accepting in the somewhat likely

condition and 67.1% accepting in the extremely likely condition; P2 – P1 = 17.6%, 95% CI

[2.9%, 31.3%]. Although this pattern was in line with our predictions, the insignificant

interaction, of course, means that this finding must be interpreted with caution.

The trial penalty. Although the trial penalty manipulation did not reliably impact plea

outcomes, its potential impact was also more apparent among the innocent than the guilty;

innocent: P2 – P1 = -6.6%, 95% CI [-20.8%, 0.8%] versus guilty: P2 – P1 = -2.9%, 95% CI [-

14.8%, 9.4%], respectively. The effect among the innocent, although not significant, appeared to

indicate that participants in the more strict penalty condition were actually more likely to accept

PLEA-BARGAINING LAW 23

the plea than were innocents in the more lenient penalty condition. But, in light of the data

examining perceived severity of the punishments (i.e., that the more lenient penalty was actually

perceived as more severe), this pattern makes sense. Again, while this pattern was in line with

our predictions, the insignificant interaction means that this finding must be interpreted with

caution and warrants additional study.

Participant Rationales. As in Experiment 1, after choosing to accept or reject the plea

deal, participants were asked to explain their choice. Participant responses were again coded into

categories and narrowed down until the final categories emerged. This question produced more

variability than in the previous experiment, which precludes us from making definitive

conclusions (given the limited number of participants in certain cells; see Table 3). However, we

again conducted omnibus chi-square analyses after collapsing any response types with total

expected counts of fewer than five into the Miscellaneous category.

The analysis of reasons for accepting the plea included the four categories identified in

the first experiment, as well as five additional categories (for a total of nine): felt confused or did

not know, seemed like the right thing to do, could not prove innocence, and to conclude the

situation. For instance, one participant whose rationale related to expediency stated they accepted

the offer “To just go along with it,” while another participant lamented his inability to prove his

innocence, stating “there's no sense arguing against system.” Many of these response categories

had been originally observed in Experiment 1, but were collapsed into the Miscellaneous

category due to smaller expected cell counts. Again, chi-square analysis showed that there was a

significant difference in the responses provided by the innocent versus the guilty for choosing to

accept the plea bargain, X2(8, N = 233) = 20.31, p = .009, V = .29. This finding does seem to

indicate that the pattern in Experiment 1 might have achieved significance if additional

PLEA-BARGAINING LAW 24

participants had been included. However, the pattern of this effect differed somewhat from

Experiment 1; instead of producing more variability in their responses, innocent individuals were

more likely to feel trapped into accepting the plea. For instance, one participant observed, “...I

felt there was no other option since she [the experimenter] couldn’t talk to anyone else.”

Nonetheless, as in Experiment 1, guilty participants regularly cited their guilt as a reason

for accepting the plea whereas innocent individuals never provided this reason. The chi-square

analysis examining reasons for rejecting the plea included five response categories. This analysis

was again not significant, X2(4, N = 105) = 6.24, p = .182, V = .24. In sum, as in Experiment 1,

innocent participants provided a different pattern of responses for accepting the plea than guilty

participants, but reasons for rejecting the plea were relatively similar between the two groups.

Discussion

Overall, many of the findings from Experiment 1 were replicated in Experiment 2. First,

an alarming proportion of innocent participants still agreed to accept the plea deal. The overall

rate of false guilty pleas exceeded 50% in both experiments, and the diagnosticity of a guilty plea

failed to exceed 1.5. Second, the pattern of responses for accepting the plea differed significantly

between the innocent and the guilty—this finding replicates the trend from Experiment 1.

Reasons for rejecting the plea, on the other hand, did not differ by guilt status in either

experiment. Although it remains possible that there are some differences underlying innocent

and guilty participants’ decision to reject a plea, the current results suggest that these differences

are not as robust as those that drive the decision to accept a plea.

Experiment 2 also extended the findings of this research by examining the impact of two

new variables. Probability of conviction noticeably impacted the proportion of plea acceptance

among the innocent, although the two-way interaction was not significant, with no discernable

PLEA-BARGAINING LAW 25

impact on the guilty. These findings indicate that the innocent may be more influenced by the

probability of conviction. The effect of the trial penalty, on the other hand, was unclear due to

participants’ unexpected perceptions regarding the severity of the penalties, which contradicted

how these punishments are characterized in university codes of conduct.

General Discussion

Replicating the findings in Dervan and Edkins’ (2013) study, the current research showed

that not only can innocent individuals accept plea deals (also seen in Henderson & Levett, 2018;

Wilford & Wells, 2018), but that they can do so at a rate that renders the outcome (accept versus

reject) one of low diagnosticity. Of course, diagnosticity is also impacted by base rates of guilt

versus innocence, which were likely substantially different in this experiment than in the real

world. Participants’ reasons for accepting the plea did differ between the innocent and the guilty,

but reasons for rejecting the plea did not.

Conviction probability

Although our conviction-probability manipulation might appear overly simplistic, it is

important to note that the extremely-likely condition rate was in the range of real trial conviction

rates (the combined federal-level jury trial and nonjury trial conviction rate in 2012 was 83.2%;

Bureau of Justice Statistics, 2015). Further, providing an estimate for the probability of

conviction did not preclude participants from drawing their own conclusions regarding their

perceived probability of conviction. There are a number of factors, beyond reported probabilities,

that could influence participants’ and defendants’ perceived likelihood of conviction. This

assumption was clearly demonstrated by the significant effect of guilt status on participants’

perceived probability of conviction. Despite being provided an explicit probability figure,

participants’ perceptions of probability were still susceptible to other influences.

PLEA-BARGAINING LAW 26

The current high-stakes experiment produced results that generally resemble those

obtained in vignette studies (e.g., Bordens, 1984). Similarly, Tor et al. (2010) found that

participants asked to assume innocence were less likely to reject pleas as probability of

conviction exceeded 50%; participants asked to assume guilt maintained steady levels of plea

acceptance with little impact of the probability manipulation. In sum, the probability of

conviction appeared to influence plea outcomes, but moreso among the innocent. Further,

innocent individuals reported lower perceived probabilities of conviction than guilty individuals.

The trial penalty

As in Dervan and Edkins (2013), and contrary to expectation, we did not find a reliable

effect of the trial penalty on plea outcomes among the guilty or the innocent. Our manipulation

was a consequence of thorough discussion regarding what punishments could plausibly be

administered after a cheating charge of this nature along with an examination of university codes

of conduct. Yet follow-up analyses revealed that the distinction between our trial penalty

conditions was not as clear as we had anticipated, and perhaps, perceived similarly to the

distinction between imprisonment and community-based corrections among offenders in the real

world. That is, while researchers and policymakers may have believed defendants should seek to

avoid imprisonment, many in fact see imprisonment as a less-severe penalty than prolonged

probation (Crouch, 1993; May & Wood, 2010; Wodahl, Garland, & Schweitzer, 2020).

Similarly, it is possible that our perceptions did not align with the real experience of students,

who despise writing far more than we had predicted. That said, further research should better

assess the impact of the trial penalty and plea discount.

Whereas the complexities with the trial penalty manipulation render its potential impact

unclear, it is clear that further research in this area is needed (Schneider & Zottoli, 2019). With

PLEA-BARGAINING LAW 27

the prevalence of mandatory minimum sentences, discrepancies between the penalties offered

through pleas and the ones threatened at trial can be dramatic (U.S. Sentencing Commission,

2011). Further, our finding that perceptions of severity regarding the trial penalty differ between

the innocent and the guilty unveils the possibility of an even greater innocence effect (Kassin,

2005). In fact, the debate between the plea discount and trial penalty label could depend on the

guilt status of the defendant. Guilty defendants could be more prone to viewing pleas as a

method of securing discounts on the sentence they would otherwise serve; innocent defendants,

on the other hand, could view any increase in the punishment they face as a penalty for rejecting

a plea offer. Thus, increasing trial penalties or plea discounts could have little effect on pleas

among the guilty, but could have a profound impact on the innocent.

Participant-Defendant Rationales

The data indicated that innocent and guilty individuals are driven by similar factors when

rejecting a plea. In contrast, reasons for accepting the plea differed more substantially between

the innocent and the guilty. However, in both experiments, the total number of participants who

accepted the plea significantly outnumbered those who rejected. Regardless, results seem to

indicate that there is more variation in reasons for accepting a plea than there is for rejecting a

plea. This finding is not dissimilar to the literature on true and false confessions, in which

theoretical models on internal accountability and social pressure have been argued to exert

differential influences on guilty and innocent participants (Houston, Meissner, & Evans, 2014),

observations related to the wide variety of motivations for accepting a plea can be classified as

attributable to internal and external pressures as well. Thus, the admission of guilt associated

with a guilty plea may function similarly, in the psychological sense, to a confession statement.

This finding could have interesting implications from a legal reform standpoint.

PLEA-BARGAINING LAW 28

The pattern of responses participants provided for their plea decisions converged well

with interviews of actual defendants who were asked to explain their plea decisions (e.g.,

Bordens & Bassett, 1985; Hussemann & Siegel, 2019), with common explanations including

remorse, sentence-related motivations, expediency, and the likelihood of conviction. In their

conclusion, Bordens and Bassett (1985) argued that all of the factors they identified followed a

central theme—pressure. Hussemann and Siegel (2019) similarly observed pressure as a central

theme in their sample of defendants. Other studies have echoed this sentiment with Viljoen et al.

(2005) and Redlich et al. (2010) also finding pressure to be a significant factor in people’s

decision to plead guilty. In our study, we believe pressure manifested as responses related to an

avoidance of worse consequences, expediency, and the likelihood of conviction, whereby

participants wanted to “escape” the situation by resolving it more quickly, or by agreeing to the

offer so as to avoid what many perceived as a highly-likely conviction. Further, the results from

Experiment 2 indicated that many of the false guilty pleas were driven by participants feeling

“trapped”. These motivations are similar to those described by real-world defendants.

Limitations

The penalties faced by criminal defendants who are offered a plea deal are far harsher,

and qualitatively different than those faced by our participants. Relatedly, the unexpected

findings surrounding the perceived severity of our trial penalty manipulations may highlight

critical differences in the perception of these penalties. Thus, they highlight the importance of

further research measuring how onerous penalties are actually perceived by defendants (i.e.,

while a penalty might be legally classified as relatively worse than another, that does not mean it

is actually perceived to be worse by those who face them). However, the sanctions used in our

study may be somewhat similar to penalties faced by low-level misdemeanor defendants

PLEA-BARGAINING LAW 29

(Marceau & Rudolph, 2012; Petersen, 2019). This application of the cheating paradigm has

additional similarities to misdemeanor pleas in many jurisdictions (e.g., “creative” pleas, speed

of negotiation, and lack of opportunity to consult with counsel; Marceau & Rudolph, 2012)

making this paradigm a fitting parallel for a large percentage of plea cases. Further, the plea deal

offered in this study imposed the harshest penalty in a plea simulation study to-date (20 hours of

work in a research lab).

Furthermore, the timeline for the current research (as in Dervan & Edkins, 2013) was

much shorter than real-world plea procedures, and did not include a bargaining component.

Criminal suspects are typically not offered a plea deal until they have been questioned by law

enforcement and formally charged with a crime. Thus, the amount of time between the formal

accusation and the initial plea offer can vary from days to months. Ethical considerations

precluded us from extending the study timeline. It is, therefore, currently unclear whether

reducing the timeline increased the pressure to plead, and thus, false guilty pleas. However, there

are reasons to believe that reducing the timeline could decrease false guilty pleas. Prolonged pre-

trial detention (due to unaffordable or denied bail) reportedly increases the likelihood of pleading

(Rakoff, 2014; Peterson, 2019). Further, real world cases have also involved short-term plea

deals (e.g., 24-hour only offers; also known as exploding offers) meaning that time pressure can

also be a factor in the real world (Gross, 2015; Zottoli, Daftary-Kapur, Winters & Hogan, 2016).

Finally, important limitations are presented by the sample itself, in both our study, and in

Dervan and Edkins’ (2013) original design. In both, the participants were recruited from

universities with primarily white students. In contrast, in the criminal justice system, the vast

majority of defendants are non-whites (Stevenson, & Mayson, 2018). In fact, Black and Latinex

defendants are over-represented in the U.S. criminal justice system, and their behavior and

PLEA-BARGAINING LAW 30

experience appear to differ in several important ways from that of white defendants (Albonetti,

1990; Kutateladze, Andiloro, & Johnson, 2016). Specifically, these defendants often receive less

favorable plea deals (Kutateladze, Andiloro, & Johnson, 2016), and appear to accept plea offers

less frequently, possibly due to mistrust in the system (Albonetti, 1990). Further, college

participant pools possess less educational and economic diversity than is seen in the criminal

justice system. It is, therefore, possible that a sample more representative of real defendants

would have produced different distributions of plea behavior. Thus, conducting further research

with more diverse samples remains a critically-important endeavor.

Conclusion

In Lafler v. Cooper (2012), the Court ruled that, the ubiquity of plea-bargaining and its

growing role in due process should confer a Constitutional right to defendants who have secured

legal counsel: that their counsel perform effectively during this process. This decision, as Justice

Antonin Scalia wrote for the dissent, “… opens a whole new field of constitutionalized criminal

procedure: plea-bargaining law…” (Lafler v. Cooper, 2012, p. 1 of dissent). Researchers should

ultimately reveal what policies can protect the innocent from plea convictions.

The current research has demonstrated that plea outcomes among the innocent can be

more fluid than plea outcomes among the guilty. Any impact of the probability of conviction and

trial penalty manipulations on the guilty was negligible. In contrast, the probability of conviction

had a noticeable impact on the proportion of innocent pleas. This finding is extremely important

from a policy standpoint. It demonstrates that plea-bargaining reform could significantly reduce

the number of false guilty pleas without a comparable reduction in true guilty pleas. Further

research should continue to demonstrate how “plea-bargaining law” can be written to preserve

the process for the guilty while protecting the innocent.

PLEA-BARGAINING LAW 31

References

Abrams, D. S. (2011). Is pleading really a bargain? Journal of Empirical Legal Studies, 8(1),

200-221. doi: 10.1111/j.1740-1461.2011.01234.x

Albonetti, C. A. (1990). Race and the probability of pleading guilty. Journal of Quantitative

Criminology, 6, 315-334. doi: 10.1007/BF01065413

Bibas, S. (2004). Plea bargaining outside the shadow of a trial. Harvard Law Review, 117(8),

2463-2547. doi: 10.2307/4093404

Bordens, K. S. (1984). The effects of likelihood of conviction, threatened punishment, and

assumed role on mock plea bargaining decisions. Basic and Applied Social Psychology,

5, 59-74. doi: 10.1207/s15324834basp0501_4

Bordens, K. S., & Bassett, J. (1985). The plea bargaining process from the defendant’s

perspective: A field investigation. Basic and Applied Social Psychology, 6, 93-110. doi:

10.1207/s15324834basp_0602_1

Bureau of Justice Statistics. (2015). Federal justice statistics, 2012 - statistical tables (NCJ

Report No. 248470). Washington D.C.: U.S. Department of Justice.

Bushway, S. D., & Redlich, A. D. (2012). Is plea bargaining in the ‘shadow of the trial’ a

mirage? Journal of Quantitative Criminology, 28(3), 437-454. doi: 10.1007/s10940-011-

9147-5

Bushway, S. D., Redlich, A. D., Norris, R. J. (2014). An explicit test of plea bargaining in the

“shadow of the trial.” Criminology, 52(4), 723-754. doi: 10.1111/1745-9125.12054

Crouch, B. M. (1993). Is incarceration really worse? Analysis of offenders' preferences for

prison over probation. Justice Quarterly, 10(1), 67-88.

PLEA-BARGAINING LAW 32

Dervan, L. E., & Edkins, V. A. (2013). The innocent defendant’s dilemma: An innovative

empirical study of plea bargaining’s innocence problem. Journal of Criminal Law and

Criminology, 103, 1-48. doi: 10.2139/ssrn.2071397

Edkins, V. A., & Dervan, L. E. (2012). Pleading innocents: Laboratory evidence of plea

bargaining's innocence problem. Current Research in Social Psychology, 14-21.

https://crisp.org.uiowa.edu/sites/crisp.org.uiowa.edu/files/2020-04/art5.12.13_1.pdf

Fisher, G. (2000). Plea bargaining’s triumph. The Yale Law Journal, 109(5), 857-1086. doi:

10.2307/797483

Frazier, A., Shockley, K., Keenan, J. M., Wilford, M. M., & Gonzales, J. E. (2019). When a plea

is no bargain at all: Comparing sentencing outcomes for Massachusetts defendants in

non-sexual and sexual crimes. Albany Law Review, 82, 101-140.

Gregory, W. L., Mowen, J. C., & Linder, D. E. (1978). Social psychology and plea bargaining:

Applications, methodology, and theory. Journal of Personality and Social Psychology,

36,1521-1530. doi: 10.1037/0022-3514.36.12.1521

Gross, S. R. (2015, July 24). The staggering number of wrongful convictions in America. The

Washington Post. Retrieved from https://www.washingtonpost.com/opinions/the-cost-of-

convicting-the-innocent/2015/07/24/260fc3a2-1aae-11e5-93b7-5eddc056ad8a_story.html

Helm, R. K., Reyna, V. F., Franz, A. A., Novick, R. Z., Dincin, S., & Cort, A. E. (2018).

Limitations on the ability to negotiate justice: attorney perspectives on guilt, innocence,

and legal advice in the current plea system. Psychology, Crime & Law, 1-20. doi:

10.1080/1068316X.2018.1457672

PLEA-BARGAINING LAW 33

Helm, R. K., & Reyna, V. F. (2017). Logical but incompetent plea decisions: A new approach to

plea bargaining grounded in cognitive theory. Psychology, Public Policy, and Law, 23(3),

367. doi: 10.1037/law0000125

Henderson, K. S., & Levett, L. M. (2018). Investigating predictors of true and false guilty pleas.

Law and Human Behavior, 42(5), 427-441. doi: 10.1037/lhb0000297

Houston, K. A., Meissner, C. A., & Evans, J. R. (2014). Psychological processes underlying true

and false confessions. In Investigative interviewing (pp. 19-34). Springer, New York,

NY.

Hussemann, J., & Siegel, J. (2019). Pleading guilty: Indigent defendant perceptions of the plea

process. Tennessee Journal of Law and Policy, 13(2), 459-522.

Innocence Project (2017). Our impact. Retrieved from https://25years.innocenceproject.org/

impact/

Kassin, S. M. (2005). On the psychology of confessions: Does innocence put innocents at risk?

American Psychologist, 60, 215-228. doi: 10.1037/0003-066X.60.3.215

Kramer, G. M., Wolbransky, M., & Heilbrun, K. (2007). Plea bargaining recommendations by

criminal defense attorneys: Evidence strength, potential sentence, and defendant

preference. Behavior Sciences and the Law, 25, 573-585. doi: 10.1002/bsl.759

Kutateladze, B. L., Andiloro, N. R., & Johnson, B. D. (2016). Opening Pandora’s Box: How

does defendant race influence plea bargaining? Justice Quarterly, 33, 398-426.

Lafler v. Cooper, 132 U.S. 1376 (2012).

Malloy, L. C., Shulman, E. P., & Cauffman, E. (2014). Interrogations, confessions, and guilty

pleas among serious adolescent offenders. Law and Human Behavior, 38(2), 181.

PLEA-BARGAINING LAW 34

Marceau, J., & Rudolph, N. (2012). Colorado counsel conundrum: Plea bargaining,

misdemeanors, and the right to counsel. Denver University Law Review, 89(2), 327-367.

May, D. C., & Wood, P. B. (2010). Ranking correctional punishments: Views from offenders,

practitioners, and the public. Carolina Academic Press.

McAllister, H. A., & Bregman, N. J. (1986a). Plea bargaining by defendants: A decision theory

approach. The Journal of Social Psychology, 126(1), 105-110. doi: 10.1080/00224545.

1986.9713576

McAllister, H. A., & Bregman, N. J. (1986b). Plea bargaining by prosecutors and defense

attorneys: A decision theory approach. Journal of Applied Psychology, 71(4), 686-690.

National Registry of Exonerations. (24 November, 2015). Innocents who plead guilty. Retrieved

from http://www.law.umich.edu/special/exoneration/Documents/NRE.Guilty.Plea.

Article1.pdf

North Carolina v. Alford, 400 U.S. 25 (1970).

Oppel, R. A. Jr. (2011, September, 26). Sentencing shift gives new leverage to prosecutors. The

New York Times. A1. Retrieved from http://www.nytimes.com/2011/09/26/us/tough-

sentences-help-prosecutors-push-for-plea-bargains.html?_r=0

Petersen, N. (2019). Low-level, but high speed?: Assessing pretrial detention effects on the

timing and content of misdemeanor versus felony guilty pleas. Justice Quarterly, 36(7),

1314-1335.

Rakoff, J. S. (2014, November 20). Why innocent people plead guilty. The New York Review of

Books, 61(18). Retrieved from http://www.nybooks.com/articles/2014/11/20/why-

innocent-people-plead-guilty/

PLEA-BARGAINING LAW 35

Redlich, A. D., & Ozdogru, A. A. (2009). Alford pleas in the age of innocence. Behavioral

Sciences and the Law, 27, 467-488. doi: 10.1002/bsl.876

Redlich, A. D., & Shteynberg, R. V. (2016). To plead or not to plead: A comparison of juvenile

and adult true and false plea decisions. Law and Human Behavior. doi:

10.1037/lhb0000205

Redlich, A. D., Summers, A., & Hoover, S. (2010). Self-reported false confessions and false

guilty pleas among offenders with mental illness. Law and Human Behavior, 34, 79-90.

doi: 10.1007/s10979-009-9194-8

Redlich, A. D., Wilford, M. M., & Bushway, S. (2017). Understanding guilty pleas through the

lens of social science [Special anniversary issue]. Psychology, Public Policy and Law,

23(4), 458-471. doi: 10.1037/law0000142

Ross, J. E. (2006). The entrenched position of plea bargaining in United States legal practice.

The American Journal of Comparative Law, 54, 717-732. doi: 10.2307/20454559

Russano, M. B., Meissner, C. A., Narchet, F. M., & Kassin, S. M. (2005). Investigating true and

false confessions within a novel and experimental paradigm. Psychological Science,

16(6), 481-486. doi: 10.1111/j.0956-7976.2005.01560.x

Schneider, R. A., & Zottoli, T. M. (2019). Disentangling the effects of plea discount and

potential trial sentence on decisions to plead guilty. Legal and Criminological

Psychology. doi: 10.1111/lcrp.12157

Sommers, I., Goldstein, J., & Baskin, D. (2014). The intersection of victims’ and offenders’ sex

and race/ethnicity on prosecutorial decisions for violent crimes. Justice System Journal,

35(2), 178-204. doi: 10.1080/009826X.2013.869153

PLEA-BARGAINING LAW 36

Stephens, R. (2013). Disparities in postconviction remedies for those who plead guilty and those

convicted at trial: A survey of state statutes and recommendations for reform. The

Journal of Criminal Law and Criminology, 103(1), 309-342.

Stevenson, M., & Mayson, S. (2018). The scale of misdemeanor justice. Boston University Law

Review, 98, 731-777.

Subramanian, R., Moreno, R., & Broomhead, S. (2014). Recalibrating justice: A review of 2013

state sentencing and corrections trends. New York: Vera Institute of Justice, Center on

Sentencing and Corrections.

Tor, A., Gazal-Ayal, O., & Garcia, S. M. (2010). Fairness and the willingness to accept plea

bargain offers. Journal of Empirical Legal Studies, 7, 97-116. doi: 10.1111/j.1740-

1461.2009.01171.x

Ulmer, J. T., & Bradley, M. S. (2006). Variation in trial penalties among serious violent offense.

Criminology, 44(3), 631-670.

U.S. Sentencing Commission (2011). Mandatory minimum penalties in the federal criminal

justice system. Retrieved from http://www.ussc.gov/research/congressional-reports/2011-

report-congress-mandatory-minimum-penalties-federal-criminal-justice-system

Viljoen, J. L., Klaver, J., & Roesch, R. (2005). Legal decisions of preadolescent and adolescent

defendants: Predictors of confessions, pleas, communication with attorneys, and appeals.

Law and Human Behavior, 29(3), 253-277. doi: 10.1007/s10979-005-3613-2

Wilford, M. M., & Khairalla, A. (2019). Innocence and plea bargaining. In V. A. Edkins & A. D.

Redlich (Eds.), A system of pleas: Social science’s contribution to the real justice system

(pp. 132-152). New York, NY: Oxford University Press.

PLEA-BARGAINING LAW 37

Wilford, M. M., Shestak, A., & Wells, G. L. (2019). Plea bargaining. In N. Brewer & A.

Bradfield-Douglass (Eds.), Psychological Science and the Law. Guilford Press.

Wilford, M. M., & Wells, G. L. (2018). Bluffed by the dealer: Distinguishing false pleas from

false confessions [Special section on Guilty Pleas]. Psychology, Public Policy & Law,

24(2), 158-170. doi: 10.1037/law0000165

Wilson, T. D., Aronson, E., & Carlsmith, K. (2010). The art of laboratory experimentation. In S.

T. Fiske, D. T. Gilbert, and G. Lindzey (Eds.), The Handbook of Social Psychology (5th

Ed.), Volume 1 (pp. 51-81). Hoboken, NJ: John Wiley & Sons, Inc.

Wodahl, E. J., Garland, B. E., & Schweitzer, K. (2020). Are jail sanctions more punitive than

community-based punishments? An examination into the perceived severity of alternative

sanctions in community supervision. Criminal Justice Policy Review, 31(5), 696-720.

Zimmerman, D. M., & Hunter, S. (2018). Factors affecting false guilty pleas in a mock plea

bargaining scenario. Legal and Criminological Psychology, 23(1), 53-67. doi:

10.1111/lcrp.12117

Zottoli, T. M., Daftary-Kapur, T., Winters, G. M., & Hogan, C. (2016). Plea discounts, time

pressures, and false-guilty pleas in youth and adults who pleaded guilty to felonies in

New York City. Psychology, Public Policy, and Law, 22(3), 250-259. doi:

10.1037/law0000095

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Table 1

The Reasons Provided for Plea Decisions in Experiment 1

Reasons for Accepting Innocent Guilty Reason for Rejecting Innocent Guilty

Avoiding Worse

Consequences 29.4% (10) 23.9% (11) Innocent 51.6% (16) 43.8% (7)

Best Alternative 23.5% (8) 37.0% (17) No Proof 12.9% (4) 6.3% (1)

Trapped/Pressured 14.7% (5) 10.9% (5) I’ll Fight This 9.7% (3) 12.5% (2)

Guilty 0.0% (0) 13.0% (6) Miscellaneous 25.8% (8) 37.5% (6)

Miscellaneous 32.4% (11) 15.2% (7)

Note: The % frequency of reasons provided for acceptance and rejection of the plea deal among

the guilty versus the innocent research participants. The number (n) of participants providing

each reason appear in parentheses.

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Table 2

Proportion of plea acceptance across all eight experimental conditions in Experiment 2

Innocent 25% Chance of Conviction 80% Chance of Conviction

More

Lenient

Penalty

More Strict

Penalty

More

Lenient

Penalty

More Strict

Penalty

52.3 (23)

[37.9, 66.2]

46.5 (20)

[32.5, 61.1]

70.5 (31)

[55.8, 81.8]

63.4 (26)

[48.1, 76.4]

Guilty 25% Chance of Conviction 80% Chance of Conviction

More

Lenient

Penalty

More Strict

Penalty

More

Lenient

Penalty

More Strict

Penalty

81.6 (31)

[66.6, 90.8]

75.5 (37)

[61.9, 85.4]

82.1 (32)

[67.3, 91.0]

82.9 (34)

[68.7, 91.5]

Note. The frequency appears in parentheses (n); the confidence intervals for each proportion

appear in the second row.

PLEA-BARGAINING LAW 40

Table 3

The Reasons Provided for Plea Decisions in Experiment 2

Note: The % frequency of reasons provided for acceptance and rejection of the plea deal among

the guilty versus the innocent research participants. The number (n) of participants providing

each reason appear in parentheses.

Reasons for Accepting Innocent Guilty Reason for Rejecting Innocent Guilty

Avoiding Worse

Consequences 38.4% (38) 37.3% (50) Innocent 75.0% (54) 54.5% (18)

Best Alternative 24.2% (24) 23.1% (31) Miscellaneous 13.9% (10) 18.2% (6)

Trapped/Pressured 20.2% (20) 10.4% (14) Unfair Punishment 4.2% (3) 9.1% (3)

Confused/Don’t Know 4.0% (4) 4.5% (6) No Proof 4.2% (3) 6.1% (2)

Seemed Right 4.0% (4) 6.7% (9) No Time 2.8% (2) 12.1% (4)

Can’t Prove Innocence 3.0% (3) 1.5% (2)

Conclude Situation 3.0% (3) 2.2% (3)

Guilty 0.0% (0) 13.4% (18)

Miscellaneous 3.0% (3) 0.7% (1)

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