PROTOCOL: Interview and interrogation methods and their effects on true and false confessions: An update and extension

Abstract

This is the protocol for a Campbell systematic review. The objective is to assess the effects of interrogation approach on confession outcomes for criminal (mock) suspects.

Full text

DOI: 10.1002/cl2.1314
PROTOCOLS
PROTOCOL: Interview and interrogation methods and their
effects on true and false confessions: An update and extension
Mary Catlin
1
|David B. Wilson
1
|Allison D. Redlich
1
|Talley Bettens
1
|
Christian A. Meissner
2
|Sujeeta Bhatt
3
|Susan Brandon
4
1
Criminology, Law and Society, George Mason University, Fairfax, Virginia, USA
2
Department of Psychology, Iowa State University, Ames, Iowa, USA
3
Institute for Defense Analyses, Alexandria, Virginia, USA
4
SyncScience, New York, New York, USA
Correspondence
Mary Catlin, Criminology, Law and Society,
George Mason University, Fairfax, VA, USA.
Email: mcatlin@gmu.edu
Funding information
High‐Value Detainee Interrogation Group, USA
Abstract
This is the protocol for a Campbell systematic review. The objective is to assess the
effects of interrogation approach on confession outcomes for criminal (mock)
suspects.
1|BACKGROUND
1.1 |Description of the problem
In 1999, Victoria Bell Banks convinced law enforcement to release
her from custody after claiming to be pregnant. Later, when the
Choctaw County Sheriff came looking for a baby, Victoria claimed
she had a miscarriage. The sheriff was suspicious and eventually
began questioning Victoria's estranged husband about the infant.
After several days of intense interrogation, Medell Banks falsely
confessed to the child being born alive and buried near his property.
It was not until medical doctors examined Victoria and determined
that she was physically incapable of being pregnant that Medell was
exonerated (NRE, 2021). It feels surprising that someone would
confess to a crime they did not commit (Leo, 2009), let alone a crime
that never took place. Researchers, however, have long recognized
the problematic nature of false confessions (e.g., Münsterberg, 1908).
False confessions pose several problems for the criminal legal
system (e.g., Kassin, 2014; Scherr et al., 2020). First, it is very difficult
to distinguish between true and false confessions. For example, one
set of researchers asked a group of prisoners to tape themselves
providing a true confession to a crime they had committed and been
convicted of and a second false confession to a crime that the
researchers had fabricated. These tapes were then presented to both
college students and trained police officers who were asked to
identify whether the confession they were viewing was true or false.
Unfortunately, while the police officers claimed to be more confident
in their judgments about the veracity of the confessions, they were
no more accurate than the college students and both groups overall
performed poorly (Kassin et al., 2005). Confessions—whether true or
false—are so powerful that they tend to persuade juries and even
judges more than any other piece of evidence. Mock jury studies
have found that confessions are seen as more inculpatory than any
other form of evidence (e.g., Kassin & Neumann, 1997) and that even
when individuals are informed that a confession was coerced and
should legally be disregarded, the confession still influences their guilt
decisions (Kassin & Sukel, 1997).
Even if the confession were deemed inadmissible and completely
disregarded by the legal actors asked to make a final determination of
guilt, the confession could still impact perceptions of the case
through other evidence. Specifically, research examining forensic
confirmation biases (see Kassin et al., 2013) has found that knowing a
confession has been obtained can impact forensic experts charged
with determining whether fingerprints match (Kukucka &
Campbell Systematic Reviews. 2023;19:e1314. wileyonlinelibrary.com/journal/cl2
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https://doi.org/10.1002/cl2.1314
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© 2023 The Authors. Campbell Systematic Reviews published by John Wiley & Sons Ltd on behalf of The Campbell Collaboration.

Kassin, 2014) or even the conclusions of DNA tests (Dror &
Hampikian, 2011). The undue influence of confessions on other
evidence creates two problems: (1) legal actors could be influenced
by a (false) confession even if they do not realize it and (2) law
enforcement conducting the investigation are unintentionally
increasing their own confirmation biases. Specifically, once a suspect
confessesions and the other available evidence begins lining up with
that same suspect, the chances become reduced that law enforce-
ment will use limited resources to continue pursuing other potential
leads. The snowball effect that a false confession can create is part of
the framework discussed in the Cumulative Disadvantage Framework
(Scherr et al., 2020). Once a false confession occurs, several cognitive
processes activate that can culminate in a wrongful conviction. First,
when an innocent suspect confessesions, law enforcement is less
motivated to track down other leads and may turn their attention
almost exclusively to the suspect who confessed. That confession
then taints other forensic evidence making the innocent suspect
appear even more guilty. If the innocent suspect does not plead guilty
first, at trial it is unlikely that jurors will be able to forget the
confession evidence once it is introduced, even if instructed to
ignore. Regardless, in both plea and trial settings, the confession will
serve as the single most powerful piece of evidence. It comes as no
surprise then that individuals end up wrongfully convicted because of
a false confession (Scherr et al., 2020).
In fact, of the over 3000 exonerations, estimates from the NRE
suggest that 12% of all known wrongful convictions involved a false
confession (NRE, 2020)—like Medell Banks. Beyond the false
confession, wrongful convictions also mean that the true perpetrator
has not been identified. In fact, in one estimate, researchers suggest
that an additional 41,000 crimes a year are committed by the true
perpetrators in cases where someone has been wrongfully convicted
(Norris et al., 2020). Thus, wrongful convictions can produce two
harms: an innocent individual is punished for a crime they did not
commit, and a guilty offender remains unpunished and often at large
in the community (see Norris et al., 2020). A key driver of wrongful
convictions is false confessions, which almost exclusively occur
during interrogations, and have been shown to be influenced by the
interrogation method used. Taking stock of the evidence on the
relative performance of the two dominant interrogation methods (i.e.,
accusatory and information gathering) can inform police department
policies and training regarding interrogation methods and the broader
public debate around police reforms.
1.2 |Description of the intervention
False confessions are the product of an interrogation process, and
the method by which an interrogation is conducted likely affects both
the rate of truthful confessions and false confessions. An optimal
interrogation method will maximize the former and minimize the
latter. While there are endless variations in how an interrogation may
be conducted, the two general styles are an accusatory approach and
an information‐gathering approach (Kelly et al., 2013). The proposed
meta‐analysis will focus on accusatorial and information‐gathering
approaches to interrogations, briefly described in the following
paragraphs, as they are the most popular approaches.
First, the Reid technique is often referred to as the exemplar of
an accusatorial approach. This most common interrogation manual
taught to law enforcement in the United States starts with an
assumption of guilt and relies on law enforcement's ability to detect
deception—typically through nonverbal behavior like avoiding eye
contact. The goal of the interrogation is to obtain a confession from a
suspect (Inbau et al., 2013), focusing on minimization (e.g., sugges-
tions of leniency, justifications, and rationalizations) and maximization
(e.g., refusing to accept denials, exaggerating the severity of the
situation, exaggerating and/or fabricating evidence) tactics (Kassin
and McNall, 1991). For a more complete discussion of accusatorial
tactics, see Kelly et al., 2013.
Second, just as Reid is the examplar of an accusatorial approach,
PEACE has become the primary example of an information‐gathering
approach. Facing concerns over false confessions and their possible
connection to an accusatory interrogation method, Great Britain
passed the Police and Criminal Evidence Act (PACE) of 1984. The goal
of PACE was to move away from accusatory interrogation techniques
that closely resembled those used in the United States. In contrast, the
PACE system's goal for the interview is to gather information through
techniques such as rapport building, open‐ended questioning, and
encouraging suspects to speak freely (Bull & Milne, 2004). Many other
countries, such as Australia, New Zealand, and Norway have adopted
this information‐gathering approach. Stemming from PACE, a new
standard of investigative interviewing was formed and has since
become the standard alternative to accusatorial interrogation ap-
proaches: PEACE. PEACE is the structure of all interviews—regardless
of the interviewee's status in the investigation—including the Planning
and Preparation of the interview, Engaging the interviewee and
Explaining the ground rules of the interview process, obtaining an
Account, Closure of the interview, and Evaluation of the interview
process (see Milne et al., 2007 for an overview). See Gabbert et al.
(2021) for a systematic review of rapport techniques. Further, there is
research conducted by the High Value Detainee Group (HIG) that has
led to an additional model of rapport‐based information gathering (see
Meissner et al., in press).
This alternative approach to accusatorial interrogations raises the
question of whether these information‐gathering techniques would
reduce false confessions without reducing true confessions.
1.3 |How the intervention might work
The interrogation is the setting of most confessions, with researchers
suggesting that the interrogation approach could be directly
responsible for false confessions (Ofshe and Leo, 1997). Specifically,
two factors could influence whether an interrogation will result in a
false confession from an innocent person: (1) if interrogators enter an
interrogation with an a priori assumption of the suspect's guilt and (2)
how rapport is used during the interrogation. Assumptions of guilt
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can be problematic because interrogators who believe the (innocent)
suspect is guilty are more likely to induce a false confession because
they are more likely to use coercive interrogation tactics (Narchet
et al., 2011). Theoretically, then, interrogation approaches that
encourage assumptions of guilt will be more likely to induce false
confessions, while interrogation approaches that avoid assumptions
of guilt should minimize false confessions. According to the
Cumulative Disadvantage Framework (CDF; Scherr et al., 2020), false
confessions are a natural consequence of accusatorial interrogations
(Mortimer & Shepherd, 1999). More specifically, the CDF framework
argues that the assumption of guilt triggers a series of actions that
can lead to a false confession because of confirmation biases. In turn,
a false confession can influence the interpretation of forensic
evidence by investigators, suggesting even more “inculpatory”
evidence than existed before the false confession (e.g., Kassin
et al., 2013). From there, the chances of innocents accepting a plea
deal or being convicted at trial increase dramatically (e.g., Appleby &
Kassin, 2016; Leo, 2009). Even if exonerated, the false confession
holds severe consequences for exonerees attempting to reenter
society (e.g., Kukucka & Evelo, 2019).
In contrast, the information‐gathering approach is argued to
reduce false confessions by avoiding presumptions of guilt.
Information‐gathering approaches, like PEACE, do not start with an
assumption of guilt. Theoretically, by changing the goal of an
interview from obtaining confessions to information, these tech-
niques avoid the harmful actions associated with the confirmation
bias in interrogation settings (e.g., CDF; Scherr et al., 2020).
Assumptions—or lack thereof—of guilt could also influence the
use of rapport during interrogations. Rapport, broadly defined, is the
connection established between the interviewee and the interviewer.
Rapport tactics are typically intentional behaviors employed by
interviewers to encourage information disclosure from interviewees.
These tactics can be verbal, para‐verbal, or non‐verbal, though the
most common tactic in the literature is active listening. Importantly,
information‐gathering approaches recognize that rapport is not a
static characteristic and can change over the course of an interaction
(Alison & Alison, 2017). In contrast, the Reid technique only discusses
rapport at the start of an interview phase (Inbau et al., 2013). This
approach is problematic because rapport can, and likely will, wane as
more accusatorial approaches are introduced to the relationship.
Furthermore, by itself, rapport can be used as a form of minimization
by falsely leading interviewees to believe the interviewer is working
in their best interest (David et al., 2017). By addressing rapport
throughout the interview, information‐gathering approaches should
enhance interviewee cooperation, which in turn should produce more
reliable information (Vanderhallen & Vervaeke, 2014). In fact,
research has shown that interviewers who are able to establish and
maintain rapport are more likely to obtain favorable outcomes (Walsh
& Bull, 2012). When interviewers use rapport tactics, especially those
aimed at aligning the interviewee with the interviewer, interviewees
are more likely to perceive rapport. The perceived rapport increases
cooperation, which in turn increases the amount of information
disclosed by the interviewee (Brimbal et al., 2019,2021; Dianiska
et al., 2021). Thus, because information‐gathering approaches both
avoid assumptions of guilt and actively build rapport throughout
interrogations, this approach should result in fewer false confessions
than accusatorial approaches that do neither.
1.4 |Why it is important to do this review
Meissner and colleagues (Meissner et al., 2012) conducted a
Campbell systematic review and meta‐analysis investigating rates of
true and false confessions across information‐gathering, accusatorial,
and direct questioning (i.e., control) interrogation techniques across
both field and experimental (laboratory) studies. The synthesis of the
field‐based studies found that both information‐gathering and
accusatorial interrogation techniques were more likely to elicit a
confession when compared to direct questioning. These findings are
limited, however, because field studies cannot establish ground truth
(actual guilt or innocence). As such, estimated rates of false versus
true confessions are not possible, only the overall effectiveness of
the method at getting a confession (regardless of its reliability). In
contrast, the findings from Meissner et al.'s (2012) systematic review
of the experimental studies suggested that while both information‐
gathering and accusatorial approaches increased the number of
confessions as compared to control conditions (i.e., direct question-
ing), accusatorial approaches also increased the number of false
confessions when contrasted directly with information‐gathering
approaches. The authors cautioned strong claims regarding the
comparison of the two approaches because the analysis was based
on small sample sizes and they urged future researchers to more
explicitly compare information‐gathering and accusatorial techniques
(Meissner et al., 2012).
This review is a partial update of Meissner and colleagues'
(Meissner et al., 2012) review, focusing solely on the experimental
studies. It is important to focus on the experimental studies as only
experimental studies can speak to the diagnosticity (i.e., the ability to
maximize true confessions while minimizing false confessions) of
interrogation approaches. By their nature, experimental studies
cannot exactly mirror interrogations that happen in the real world.
The tactics and scripts used in experimental studies, however, are
based on real‐world interrogation manuals (e.g., Inbau et al., 2013),
induce meaningful physiological and psychological changes in the
participants accused (e.g., Guyll et al., 2019; Normile & Scherr, 2018),
and focus on the same underlying psychological processes that exist
in real interrogations. Thus, it is reasonable to use experimental
studies as analogues for real‐world practice to benefit from the
implications the associated diagnosticity information can have on
policy and practice. The results of the proposed partial update will
not only be able to speak to whether the legal system should
continue relying on accusatorial techniques but will also be able to
speak to the growth of accusatorial tactics across contexts.
Specifically, the Reid corporation has expanded their practices
beyond the interrogation room to other settings such as high
schools, Child Protective Service offices, and so on. The results of
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this study will provide policymakers and practitioners with evidence‐
based research to indicate whether accusatorial approaches produce
an increase in false confessions.
To our knowledge, there have been three other meta‐analyses
looking at interview and interrogation research in the time since 2012.
Themorerecenteffortusedmeta‐analytic approaches to examine the
effect of rapport‐building and support tactics on children's disclosure in
forensic interviews (Lavoie et al., 2021). The focus on child witnesses,
however, limits the applicability of these results to the proposed update
(which focuses solely on suspects). The second meta‐analysis is more
pertinent to our efforts as it examined the prevalence of false confessions
across experimental paradigms. Results indicated that typing task studies
(i.e., the alt‐key paradigm) were the most likely to result in false
confessions regardless of typing speed. Furthermore, compared to all
other tactics, false evidence ploys (i.e., lying or bluffing to suspects about
evidence) were more likely to result in false confessions (Stewart
et al., 2016). The last meta‐analysis examined the social, cognitive, and
affective factors associated with true and false confessions obtained
using the cheating paradigm. Results demonstrated that false confessions
were associated with perceptions of the consequences of confessing and
perceptions of the interrogation context (Houston et al., 2014). None of
these meta‐analyses, however, looked at the impact of interrogation
approaches on suspect false confessions. Therefore, updating Meissner
and colleagues' (Meissner et al., 2012)reviewisimportant,paying
particular attention to experimental paradigm and accusatorial tactics. We
have conducted a pilot search based on the proposed methodology, and
can confirm there exist sufficient numbers of new studies (~8) to warrant
an updated review. These additional lab‐based studies conducted since
2010 and advances in the methods of meta‐analysis over the past
decade, such as the development of network meta‐analysis and
associated software implementations, will extend prior synthesis work
in this area.
2|OBJECTIVES
The current study is a partial update and extension of Meissner and
colleagues' (2012) prior Campbell systematic review titled Interview
and Interrogation Methods and their Effects on True and False
Confessions, focusing solely on experimental or laboratory‐based
studies. Our objective is to assess the effects of interrogation
approach on confession outcomes for criminal (mock) suspects.
To address our objective, a series of meta‐analyses will be
conducted, contrasting accusatorial, information‐gathering, and
direct questioning interrogation approaches on their ability to elicit
true and false confessions. Like its predecessor, the proposed meta‐
analysis will focus on (mock) suspects as the population of interest,
interview style as the intervention, and the diagnosticity (i.e., ability
to increase true confessions while minimizing the number of false
confessions) of the interview styles as the indicator of effectiveness.
Furthermore, to expand on past work, the current effort also
proposes a meta‐analysis contrasting two accusatorial tactics:
minimization and maximization (including false evidence ploys). To
accomplish this goal, a network meta‐analysis will be conducted to
compare not only macro‐level interrogation styles but how different
approaches within the accusatorial approach (i.e., minimization and
maximization) compare to each other and to other schools of
interrogation techniques.
3|METHODS
3.1 |Criteria for considering studies for this review
All of the criteria for study inclusion are based on the original review
by Meissner and colleagues (Meissner et al., 2012). However, we
have noted where we plan to make departures from those original
criteria.
3.1.1 |Types of studies
For this update, we will include only experimental studies, regardless
of publication status, that randomly assign mock subjects (i.e., not real
criminal justice suspects in field studies) to two (or more) interroga-
tion (interview) conditions. The experimental manipulation must
include the random assignment of an accusatorial or information‐
gathering interrogation technique. The two techniques can be
compared with each other, compared to a control interrogation
technique (e.g., direct questioning), or for studies with only
accusatorial techniques, include some contrast of minimization,
maximization, and control tactics. Participants (mock suspects—see
below) can be entirely aware of the nature of the study (e.g., some
studies challenge participants to “get away”with an act of wrong-
doing) or can be deceived to various degrees (e.g., some studies lead
participants to believe they are facing academic consequences for the
supposed wrongdoing). Any experimental paradigm is eligible (e.g.,
both cheating and alt‐key paradigms will be included) and studies can
include more than one manipulated factor. However, manipulated
factors not pertinent to our review will only be reported with the
description of study characteristics, not analyzed.
The prior review also included field‐based observational studies.
These will be excluded from this review as our objective focuses on
the reliability of confessions, which is not possible to assess with field
studies (where ground truth remains unknown).
3.1.2 |Types of participants
Participants will be mock suspects who were accused of some
wrongdoing. Studies that include victims or witnesses of wrongdoing,
however, will not be eligible. Thus, only the data relevant to mock
suspects will be considered if a study population comprises more
than mock suspects. However, the type of mock‐suspect will not be
limited by race, age, ethnicity, gender, or any other demographic
characteristics.
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3.1.3 |Types of interventions
For the purposes of this partial update, an interview or interroga-
tion method is (1) an intentional use of one or more (2) established
interrogation tactics used to (3) induce a confession. An intentional
use means that the intervention is part of the experimental
manipulations (see Types of Studies) and that at least part of the
interrogation tactic was scripted. In other words, studies where
mock experimenters were allowed complete freedom in how they
attempted to get a confession will not be eligible. By established
interrogation tactic, we mean those tactics that have been
associated with either an accusatorial or information‐gathering
approach. For example, false evidenceploysareassociatedwith
accusatorial methods and are included in accusatorial interrogation
manuals (Inbau et al., 2013). When not identified by name, an
accusatorial technique can be identified by their goal: to obtain a
confession, typically modeled on the Reid technique (see Inbau
et al., 2001). Accusatorial techniques also include the use of
minimization and maximization tactics, which themselves encom-
pass a wide range of more specific tactics (see Kelly et al., 2015 for
an overview). Conversely, information‐gathering techniques can
be identified by their goal to seek information, an example being
thePEACEmodel(seeMilne&Bull,1999). Information‐gathering
techniques often use cognitive interview and rapport‐building
tactics. Any tactic identified by Gabbert et al. (2021)wouldbe
eligible. Direct questioning or control techniques can involve
elements of the other two techniques, but the main advance of
control techniques are short, declarative sentences/questions that
are goal‐oriented. Finally, to determine that a tactics' purpose is to
induce a confession, the tactic must be introduced before the
request for a confession.
3.1.4 |Types of outcome measures
Primary outcomes
Eligible studies will include either true or false (or both) confession
rates as the dependent variable. Depending on the study design
(e.g., some studies include exclusively innocent participants), the
study must report either the number of true confessions (i.e.,
confessions provided by guilty participants), false confessions (i.e.,
confessions provided by innocent participants), or both. The
ground truth of the confession (i.e., true vs. false confession) is
the priority for the coding stage. If a study contains both primary
confession and secondary confession information, only the primary
confession data will be considered. Primary confessions are those
provided by the supposed wrongdoer and are the outcome of
interest. Secondary confessions are typically defined as either an
individual admitting they witnessed an act of wrongdoing, which
could make them complicit because of their lack of action (e.g.,
Swanner et al., 2010), or an individual conveying that the supposed
wrongdoer confessed to them (e.g., Wetmore et al., 2014). In
either case, the individual is not the supposed wrongdoer and will
not be considered in our analyses.
Secondary outcomes
There are no secondary outcomes for this review.
3.1.5 |Duration of follow‐up
This is not relevant for the review. The dependent variable is
measured during the single laboratory session with the subject.
3.1.6 |Types of settings
There is no proposed geographic limitation to study location.
However, for pragmatic reasons, only studies published in English
will be considered.
3.2 |Search methods for identification of studies
3.2.1 |Electronic searches
The listed databases and keywords are heavily influenced by the
meta‐analysis being replicated (Meissner et al., 2012). The
keyword combinations and filters, however, were generated by
the first author of the current effort. We will search across titles,
abstracts, author‐supplied keywords, and indexing terms when
possible. All searches will be limited to those results available in
English. We will not restrict our search or eligibility criteria by the
date of publication as all relevant studies, even those captured in
the original effort, will be included in our analyses. The following
databases, organized by publisher platforms, are proposed for the
search process:
ProQuest
1. Australia & New Zealand Database
2. Criminal Justice Database
3. ERIC
4. ProQuest Dissertations & Theses Global
5. Psychology Database
6. Social Science Database
7. Sociological Abstracts
8. Sociology Database
9. UK & Ireland Database
EBSCO
1. APA PsycExtra
2. APA PsycInfo
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3. Criminal Justice Abstracts
4. National Criminal Justice Reference Services Abstracts
5. Psychology and Behavioral Sciences Collection
Web of Science
1. Conference Proceedings Index: Social Sciences & Humanities
2. Social Science Citation Index
Other
1. CINCH: Australian Criminology Database (Informit)
2. Google
3. Google Scholar
The following keywords will be used in the systematic search (*
denotes that the word could have various endings; for example,
interrogat* could be interrogation or interrogator):
1. Interrogat*
2. Information (gathering)
3. Inquisitorial
4. Interview*
5. Suspect*
6. Confess*
7. ‘Cognitive interview*’
8. ‘Conversation management’
9. ‘Ethical interview*’
10. Disclos*
11. ‘Strategic evidence’
12. Accusat*
13. ‘Deception detection’
14. PEACE
15. PACE
16. Adversar*
17. Miranda
18. Coerc*
19. Entrap*
20. ‘Due process of law’
21. Reid
22. Minimi?*
23. Maximiz*
24. Random*
25. Control
26. Comparison
27. Experiment*
28. RCT
29. Manipulat*
30. Lab*
31. Factorial
32. Guilt*
33. Innocen*
34. Responsib*
35. Commit*
36. Effect
The keywords will be condensed to the following search logic
for databases that allow for advanced Boolean logic: (interrogat*
OR information OR inquisitorial OR interview* OR accusat* OR
“deception detection”OR PEACE OR PACE OR adversar* OR
REID OR minimi?* OR maximiz* OR “cognitive interview*”OR
“conversation management”OR “ethical interview*”OR “strategic
evidence”OR miranda OR coerc* OR entrap* OR responsib* OR
commit*) AND (random* OR control OR comparison OR experi-
ment* OR RCT OR manipulat* OR lab* OR factorial OR effect)
AND (confess* OR disclos*) AND (suspect* OR guilt* OR
innocen*). See Supporting Information: Appendix 1for the full
search conducted in ProQuest.
3.2.2 |Searching other resources
Once the digital search has been completed, all studies deemed
eligible for inclusion will be searched for relevant literature. Each
eligible study will also be used to conduct forward citation searching
in Google Scholar. Furthermore, we have several resources to
complement the search, including:
1. Meissner, C. A., Redlich, A. D., Bhatt, S., & Brandon,
S. (2012). Interview and interrogation methods and their
effects on true and false confessions. Campbell Systematic
Reviews.doi:10.4073/csr.2012.13 [Lists approximately 80
references]
2. Madon, S., More, C., & Ditchfield, R. (2019). Interrogations and
confessions. In N. Brewer & B. Douglass (Eds.) Psychological
science and the law. New York: Guilford Press. [Lists approximately
75 references]
3. Kassin, S. M., Drizin, S., Grisso, T., Gudjonsson, G., Leo, R. A., &
Redlich, A. D. (2010). APLS Approved White Paper, Police‐
induced confessions: Risk factors and recommendations. Law
and Human Behavior.doi:10.1007/s10979-009-9188-6.[Lists
approximately 300 references]
4. Stewart, J. M., Woody, W. D., Pulos, S. (2016). The prevalence of
false confessions in experimental laboratory simulations: A meta‐
analysis. Behavioral Sciences & the Law, 36,12–31. [Lists
approximately 100 references]
5. High‐Value Detainee Interrogation Group. (2016). Interroga-
tion: A review of the Science. [Lists approximately 780
references]
Finally, the reviewers have many well‐established contacts
with researchers studying interviewing and interrogation here in
the United States and abroad. In Table 1,wehavestartedalistof
possible researchers to contact. We will reach out to known and
unknown contacts for unpublished or “in press”studies to possibly
include.
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3.3 |Data collection and analysis
3.3.1 |Description of methods used in primary
research
The typical study will randomly assign mock suspects (volunteers,
typicallycollege/universitystudents)tooneoftwoormore
experimental conditions representing different interrogation
methods. The two most common experimental paradigms are
referred to as the cheating paradigm (e.g., Russano et al., 2005)
and the alt‐key paradigm (e.g., Kassin & Kiechel, 1996). In the
cheating paradigm, interrogation methods are typically crossed
with guilt status. Guilt status is manipulated by a confederate
who either does or does not induce participants to help them
solve an independent logic problem. In the alt‐key paradigm, all
participants are typically innocent as the computer is pro-
grammed to crash regardless of participant action. Regardless
of guilt, all participants will be accused of some wrongdoing.
These mock suspects can be entirely aware of the nature of the
study (e.g., some studies challenge participants to “get away”with
an act of wrongdoing), but are typically deceived to various
degrees (e.g., some studies lead participants to believe they are
facing academic consequences for the supposed wrongdoing). At
the point of accusation, studies typically manipulate the interro-
gation method through experimenter scripts before asking for a
signed confession from participants (see Stewart et al., 2016 for
an overview of typical experimental confession studies).
Based on our knowledge of the field, we do not anticipate eligible
studies will include non‐standard designs. If, however, a non‐standard
design is discovered, a member of our research team is an expert in
meta‐analysis and works closely with the Campbell Collaboration and
they will be responsible for calculating effect sizes from non‐standard
designs.
3.3.2 |Selection of studies
Two independent coders will screen the titles and abstracts, using
Abstrackr (Abstrackr.cebm.brown.edu), of all studies identified in the
digital search for potential eligibility. Each coder will screen 100% of
the titles and abstracts and inter‐rater reliability will be evaluated by
percent agreement. The independent coders will be trained by the
first author by going over the pre‐registered protocol, practicing
screening a subset together, and then going over a pilot round of
abstracts screened independently. Furthermore, the coders and first
author will meet on a weekly basis to discuss any confusions or
disagreements as they arise. All studies deemed potentially eligible
(i.e., both raters answered ‘yes’or ‘maybe’when asked if the study
meets all eligibility criteria) will then be accessed in their full form
against the eligibility criteria. This second round of screening will also
be conducted by two independent coders who will determine final
eligibility. A single individual will scan the reference lists of eligible
studies and of the secondary search sources, such as prior reviews.
TABLE 1 Initial list of potential individuals to contact.
Name Affiliation
Iris Blandón‐Gitlin California State University, Fullerton, FL, USA
Randy Borum University of South Florida, FL, USA
Joseph Buckley John Reid and Associates, IL, USA
Stephanie Cardenas Williams College, MA, USA
Julie Cherryman University of Portsmouth, United Kingdom
Mark Costanzo Claremont Graduate School, CA, USA
Brian Cutler Ontario Tech University, Canada
David Dixon University of New South Wales, Australia
Steven Drizin Northwestern School of Law, IL, USA
Jacqueline Evans Florida International University, FL, USA
Ronald Fisher Florida International University, FL, USA
Par Anders Granhag Goteborg University, Sweden
Max Guyll Iowa State University, IA, USA
Maria Hartwig John Jay College of Criminal Justice, NY, USA
Lorraine Hope University of Portsmouth, United Kingdom
Saul Kassin John Jay College of Criminal Justice, NY, USA
Mark Kebbell Griffith University
Christopher Kelly St. Joseph's University, PA, USA
Steven Kleinman MacDill Air Force Base, FL, USA
Gunther Kohnken University of Kiel, Germany
Jeff Kukucka Towson University, MD, USA
Michael Lamb University of Cambridge, United Kingdom
Amy Leach Ontario Tech University, Canada
Richard Leo University of San Francisco School of Law,
CA, USA
Stephanie Madon Iowa State University, IA, USA
Samantha Mann University of Portsmouth, United Kingdom
Jaume Masip University of Salamanca, Spain
Rebecca Milne University of Portsmouth, United Kingdom
Amelia Mindthoff Iowa State University, IA, USA
Fadia Narchet University of New Haven, CT, USA
Christopher Normile Allegheny College, PA, USA
Jennifer Perillo Indiana University of Pennsylvania, PA, USA
Melissa Russano Roger Williams University, RI, USA
Kyle Scherr Central Michigan University, MI, USA
Laura Smalarz Arizona State University, AZ, USA
Brent Snook Memorial University of Newfoundland,
Canada
Leif Stromwall University of Gothenburg, Sweden
Paul Taylor Lancaster University, United Kingdom
Aldert Vrij University of Portsmouth, United Kingdom
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3.3.3 |Data extraction and management
All studies deemed eligible for inclusion will be coded for key
variables (e.g., effect size information) and study characteristics (e.g.,
publication type) by two independent coders. Discrepancies will be
resolved through discussion, and when consensus cannot be reached,
one of the lead reviewers will make the final decision.
Coders will be trained by the lead reviewers in steps: (1) coders
will verbally walk through the code sheet for discussion and
clarification, (2) the lead reviewers will demonstrate how to code
an article in its entirety, and (3) the coders will practice on a small
subset of articles for review and feedback from the lead reviewers.
This iterative process will continue throughout the coding process
with regular meetings to discuss coding issues.
Coders are likely to be volunteers from Dr. Redlich's research
team, some of whom have prior experience with meta‐analyses.
Generally, coding will include four hierarchical data levels: a study
level, an experimental condition level, an outcome level, and an effect
size level. Using LibreOffice, we will create a database that allows for
the one‐to‐many hierarchical nature of our coding protocol (e.g., one
study could include several experimental conditions, measure more
than one outcome, and have several effect sizes).
•Study level variables will include static information (e.g., publica-
tion type, publication year, geographic location). As such, there will
be one record per study at this level of coding.
•Experimental condition level coding will be conducted for each
relevant group of the research design. Thus, there will be one record
for each eligible experimental condition within a study. For example, if
a study included a factor with three levels of interrogation techniques
(i.e., accusatorial vs. information‐gathering vs. direct questioning), three
condition coding sheets will be completed to capture each group.
Information specific to each condition will be coded at this level, such
as sample size and interrogation method.
•The outcome level will code information specific to each eligible
outcome measure. Thus, there will be one record per outcome. In
addition to indicating the outcome construct, coded items will
capture whether the variable includes all participants, innocent
participants, guilty participants, or some other grouping.
•The effect size level will code all necessary statistical information to
calculate a logged odds ratio (L
OR
) and its variance for each outcome.
As such, these will be one record per coded effect size. Coders will be
instructed to identify the most detailed numerical data available when
coding for effect size information (see Supporting Information:
Appendix 2for the full coding sheet). When eligible studies do not
report all necessary data, we will make a good faith effort to contact
authors to obtain the necessary information.
A subset of these studies will be used to test the initial coding
protocol. That is, the coding protocol will be tested for usability/clarity
and utility in capturing relevant information from each study. This initial
testing phase of the coding protocol will also provide an additional
training opportunity for the coders. We anticipate that the initial coding
will result in refinements to the coding protocol to ensure consistent
coding across coders and alignment between coding options and study
characteristics.
3.3.4 |Assessment of risk of bias in included studies
We will assess the risk of bias of the included studies through a
combination of unique coding items developed by us specifically for
this research literature and items that were adapted from the
Cochrane risk‐of‐bias tool for randomized trials (Higgins et al., 2019).
The language of the latter were modified to better fit the
characteristics of studies eligible for this review. We excluded items
that were not relevant to this literature. The specific items are in the
coding protocol (see Supporting Information: Appendix 2).
More specifically, the risk‐of‐bias items address the following
methodological issues: random assignment to both the interrogation
technique and guilt conditions, treatment of violations to the
randomization process (e.g., participants assigned to the guilty
condition who refused to cheat), level of deception employed in
the study, treatment of participants suspicious of the true purpose of
the study, and whether mock interrogators were blind to the guilt
status of mock suspects. To address the confession outcome, coders
will document any missingness of confession outcomes, including
selective reporting of confession outcomes. See Supporting Informa-
tion: Appendix 2for the coding protocol.
We will provide a table of these items for each coded study in the
final report. Furthermore, we will investigate the potential for bias by
examining the relationship between each bias item and effect sizes in a
moderator analysis. Potential sources of bias and the associated
moderator analyses will inform our interpretation of the findings.
3.3.5 |Measures of experimental effect
We will use the odds ratio as the effect size index. The outcomes are
dichotomous and we are interested in comparing pairs of experi-
mental conditions. Thus, the data can be represented as a 2 × 2
contingency table.
3.3.6 |Unit of analysis issues
The unit of analysis will be the individual study participant. We do not
anticipate any complex issues around the unit‐of‐analysis or unit‐of‐
assignment (also the participant) among the eligible designs.
3.3.7 |Criteria for determination of independent
findings
There are only two eligible outcomes for this review: true
confession and false confession. However, because these studies
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may have any number of experimental conditions, numerous
effect sizes may be possible for each outcome. For example, if a
study has three conditions (accusatory, information‐gathering,
and direct questioning), there are three possible pairings of these
conditions and as such three possible effect sizes for each
outcome. We will code all possible effect‐size combinations but
maintain independence at the analysis stage in two ways. The
first is simply to perform separate meta‐analyses for each
contrast of interest, such as an analysis comparing accusatory
to information‐gathering methods. If a study has two accusatory
conditions, these will be collapsed for such an analysis. The
second is to perform a network meta‐analysis that takes
advantage of the network of comparisons provided by these
studies.
Given the nature of the research designs in this area and
based on the previous work done on this topic (i.e., Meissner
et al., 2012), we do not anticipate that any study will employ
repeated measurement of an outcome of interest. Thus,
we do not anticipate outcomes from multiple timepoints to be
problematic.
3.3.8 |Dealing with missing data
We will contact authors to request missing effect‐size
data. Any study that meets all eligibility criteria but for
which we are unable to compute an effect size and are
unable to get the needed data from the authors will be
identified and discussed in the manuscript. Missing descriptive
information regarding a study's methods will simply be noted and
reported.
3.3.9 |Assessment of heterogeneity
We will assess heterogeneity using the Q‐test and the I
2
statistic.
3.3.10 |Assessment of reporting biases
Given the nature of these studies, selective outcome reporting is
unlikely. It is common practice for some researchers to only
measure one of the two outcomes of interest. However, we note
as part of our risk‐of‐bias tool if there is any indication in a coded
manuscript that one of the two outcomes of interests was
measured but not reported. The greater risk in this literature is
publication bias, though we will attempt to mitigate this risk by
purposefully seeking unpublished work as well as published
manuscripts. When there are at least 10 effect sizes for a given
analysis, publication bias will be assessed. We will do so in three
ways: (1) a visual inspection of the funnel plot; (2) a trim‐and‐fill
analysis, including reporting the adjusted effect size estimate; and
(3) an Egger's regression test.
3.3.11 |Data synthesis
Data synthesis will be conducted via random‐effects meta‐analysis
based on the logged odds ratio. The models will be estimated using
the restricted maximum likelihood (REML) estimator of τ
2
. The basic
meta‐analyses will be run using the metafor package by Viechtbauer
(Viechtbauer, 2010). As stated above, separate models will be
estimated for each conceptually relevant pairing of interview style.
We will also conduct a network meta‐analysis. A network meta‐
analysis extends a traditional meta‐analysis by examining all available
comparisons (both direct and indirect) in a network. A network meta‐
analysis is particularly suited to our goal as it will allow us to directly
compare the relative effectiveness of interrogation techniques.
Within this network, we will differentiate variations on the interview
method, such as maximization and minimization accusatory ap-
proaches. This separation is important to investigate as some studies
have found that minimization tactics increase confessions (Guyll
et al., 2019; Normile & Scherr, 2018), while others find no differences
in direct‐questioning and minimization tactics (Woestehoff, 2016).
Following suggestions from the Campbell methods brief on network
meta‐analysis, we will present a network diagram, information on
inconsistency factors, a league table, and ranking of each interroga-
tion approach through rankograms and cumulative ranking plots (see
Wilson et al., 2016). The network meta‐analyses will be conducted in
R using the netmeta package (Rücker et al., 2021).
3.3.12 |Subgroup analysis and investigation
of heterogeneity
We will perform categorical moderator analyses on the cheating
paradigm versus the alt‐key paradigm. This will be performed using
the metafor package by Viechtbauer (Viechtbauer, 2010) and the
analog‐to‐the‐ANOVA analytic framework. In metafor, this is
accomplished via the rma.uni() function combined with the predict()
function. This approach assumes a common τ
2
across subgroups, an
assumption that seems reasonable for these studies. Likewise, we will
conduct moderator analyses using each risk of bias item to determine
how potential bias will influence the interpretation of our results.
3.3.13 |Sensitivity analysis
We will be analyzing effect sizes in two ways. First, using traditional
meta‐analytic methods to examine each experimental condition
pairing of interest. Second, using network meta‐analysis. These two
approaches are complementary and provide a form of sensitivity
analysis of each method.
3.3.14 |Treatment of qualitative research
Qualitative research will not be considered as part of this review.
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3.3.15 |Summary of findings and assessment of the
certainty of the evidence
We will provide a Summary of Findings table with the results of the
meta‐analyses. We will not, however, use GRADE or a GRADE‐like
system as we do not believe that it is appropriate for this review. The
focus here is not an assessment of whether a treatment is effective or
ineffective. Rather, we are trying to establish the relative performance
of different interview methods in eliciting reliable versus unreliable
confessions. The confidence intervals around the mean effect sizes are
the first line of information on the certainty of the evidence. The
second line of information on the certainty of the evidence is
methodological weaknesses identified via our risk‐of‐bias assessment.
The overall results will be interpreted within the context of any
weaknesses identified, particularly if they are prevalent across studies.
ACKNOWLEDGMENTS
We would like to acknowledge the reviewers of the original
Campbell project—Christian A. Meissner, Allison Redlich, Sujeeta
Bhatt, and Susan Brandon (Meissner et al., 2012)—and the authors
(including those individuals already mentioned) of the subsequent
publication—Stephen Michael, Jacqueline Evans, and Catherine
Camilletti (Meissner et al., 2014)—for laying the foundation which
the current review rests upon.
CONTRIBUTIONS OF AUTHORS
Mary Catlin is an advanced graduate student being advised by
Professor Redlich. She has conducted a pilot test of the updated
meta‐analysis outlined above as part of a class assignment for a meta‐
analysis course taught by Professor Wilson, who is an expert in meta‐
analysis. Professor Wilson has written one of the most widely used
texts on meta‐analysis (Lipsey & Wilson, 2001), serves as the
methods editor for the Crime and Justice Group for the Campbell
Collaboration, is an associate editor for Research Synthesis Methods,
and was awarded the Frederick Mosteller Award for Distinctive
Contributions to Systematic Reviewing. Furthermore, Professor
Wilson has produced several systematic reviews within the field of
criminology (e.g., Wilson et al., 2018,2019) and has written on the
utility of network meta‐analyses with recommendations for Campbell
reviews specifically (Wilson et al., 2016).
Both Professors Redlich and Meissner are content experts
and conducted the original review. Professor Redlich has
reviewed the literature on US police and military interrogations,
most notably as part of the editing team for two volumes
outlining investigative interviewing and interrogation interna-
tionally (Walsh et al., 2016a,2016). Furthermore, Professor
Redlich was involved in the American Psychology‐Law Society's
scientific review committee's “white paper”on police interroga-
tions and false confessions (see Kassin et al., 2010). Professor
Meissner has evaluated the deception detection and interview-
ing/interrogation literatures, including conducting several meta‐
analyses in this area (Meissner & Kassin, 2002; Meissner
et al., 2017; Snook et al., 2021). He has also co‐organized a
conference sponsored by the American Psychological Association
on investigative interviewing. This conference developed into a
co‐edited volume entitled, Interrogations and confessions: Cur-
rent research, practice, and policy recommendations, which was
published by the American Psychological Association (Lassiter
and Meissner, 2010). Furthermore, Professor Meissner was
awarded the Excellence in Research Award from the International
Investigative Interviewing Research Group, has been awarded a 3
year research grant from the US Department of Justice to
investigate the state of the interrogation literature and the
dissemination of scientific knowledge in that area.
Talley Bettens is a graduate student being supervised by
Professor Redlich. She has assisted Professor Redlich with another
meta‐analysis, currently under review for publication, that required
many of the same tasks/skills that will be asked of her for the current
endeavor.
Finally, both Sujeeta Bhatt and Susan Brandon contributed to
the original review. Furthermore, Drs. Bhatt and Brandon served
as program managers for research at the High‐Value Detainee
Interrogation Group (Federal Bureau of Investigation). Under
their leadership, a decade of research was conducted on best
practices in interviewing and interrogation. In this context, Drs.
Bhatt and Brandon have contributed significantly to the develop-
ment of the research literature on science‐based approaches
to investigative interviewing (Brandon&Meissner,inpress;
Brandon et al., 2019).
•Content: Mary Catlin and Allison Redlich
•Systematic review methods: Mary Catlin and David B. Wilson
•Statistical analysis: Mary Catlin and David B. Wilson
•Information retrieval: Mary Catlin and Christian A. Meissner
•Screening and coding: Mary Catlin and Talley Bettens
•Drafting of reports: Mary Catlin, Allison Redlich, David B. Wilson,
and Christian A. Meissner
•Editing of reports: all authors
DECLARATIONS OF INTEREST
None of the reviewers have financial conflicts of interest. Drs.
Redlich, Meissner, Bhatt, and Brandon were authors on the original
review which this update is based on. Furthermore, Professor Wilson
was the Campbell editor who oversaw the original review.
PRELIMINARY TIMEFRAME
Searches for published and
unpublished studies:
September 1, 2021–October
31, 2022
Pilot testing and coding: January 1, 2022–May 1, 2022
Analyses: September 1–January 1, 2023
Preparation of final report: November 15, 2022–March
15, 2023
Dissemination and publication: March 16–May 30, 2023
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PLANS FOR UPDATING THIS REVIEW
The review will be updated every 5–10 years. These efforts will
primarily be led by reviewers Meissner and Redlich, or their students.
SOURCES OF SUPPORT
Internal sources
•No sources of support provided
External sources
•High‐Value Detainee Interrogation Group, USA
HIG is a three‐agency entity (FBI, CIA, DOD) dedicated to
evidence‐based interrogation practices. They fund researchers to test
and identify effective interrogation approaches. To that end, HIG—
through Iowa State University—is funding the current project in
exchange for a report (submitted September 2022) outlining which
interrogation approaches are most effective at maximizing true
confessions and minimizing false confessions.
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SUPPORTING INFORMATION
Additional supporting information can be found online in the
Supporting Information section at the end of this article.
How to cite this article: Catlin, M., Wilson, D. B., Redlich, A.
D., Bettens, T., Meissner, C. A., Bhatt, S., & Brandon, S. (2023).
PROTOCOL: Interview and interrogation methods and their
effects on true and false confessions: An update and
extension. Campbell Systematic Reviews, 19, e1314.
https://doi.org/10.1002/cl2.1314
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