CRJ 302 Research Methods Module 6- Qualitative MethodsSlide 1

CRJ 302 Research Methods: Module 6- Qualitative Methods
Slide 1: Major Types of Qualitative Methodology
Qualitative research methods are best utilized when researchers
are asking questions about why things happen or why persons
behave a certain way. Observations in qualitative research tend
to occur in a natural setting rather than in a secondary location
such as a laboratory. Often times, observations are
complemented by a researchers own experiences.
Interviewing includes asking participants questions in a formal
or informal manner about their experiences and perspectives of
a given topic. In-depth interviews are those that yield deep
responses after a researcher asks open-ended questions.
Focus groups are an extension of interviews. However, instead
of an interviewer focusing on the responses of a single
respondent, are group of participants are asked questions at the
same time to get their combined perspective on a given topic.
Focus groups are often used when researchers need to interview
hard-to-reach populations.
Lastly, secondary data analysis, especially of archival
documents, is a fourth form of qualitative data analysis. In
secondary data analysis, a researcher will examine charts,
procures, electronic communications, record, newspapers, and
other similar documents to see how information has been
presented to a given population.
Slide 2: Common Features
Most qualitative research consists of researchers using inductive
reasoning to investigate a social phenomenon. As a reminder,
when research is inductive in nature it is explanatory and allows
a researcher to start with an observation of a phenomena and

end with a theory. This indicative process allows qualitative
researchers to focus on processes and phenomena that have been
previously unstudied and unexplained.
Qualitative research primarily utilizes nonprobability sampling,
meaning that samples are not random. Samples for qualitative
research may be selected because they are conveniently located,
because they represent an extreme or underrepresented group, or
because they are relevant to a theoretical phenomenon. So,
while nonprobability samples may not be generalizable, they do
allow qualitative researchers to gather a population that is
representative of the groups that they can study in depth.
Qualitative researchers must always apply reflexivity to their
studies. Reflexivity is the practice of researchers to reflect on
how their own characteristics and presence may shape the
research process and potentially bias any results. Reflexivity
occurs throughout the research process, and qualitative
generally attempt to address any concerns they have that may
influence their projects in a biased way. Reflexivity also
prompts a researcher to consider how their interpretation of any
data gathered through their methodology will be interpreted
based on their stance as an insider or outsider to the studied
population. Sometimes, researchers will move from being an
outsider to being an insider, which may affect the way they
interpret their data. Qualitative researchers must self-reflect
constantly in order for their projects to be objective.
Slide 3: Observation
Field research always consists of direct observations of a
physical or social setting and the behaviors or events that occur
in these settings. Often times field research studies groups,
either those that form formally (such as organizations, like
police officers in a police department) or they may be informal,
such as gangs or friend groups. Based on a researchers
background, the sociopolitical context surrounding the research

question, and any ethical concerns, a researcher must determine
how intensive they want their observation to be.
In general, there are too types of participation that can occur in
too different ways. First, will discuss the differences between
participant and non-participant observation. Participant
observation is when a researcher is involved, in some degree,
with the activity of a group that is being studied. A researcher
may live or work in the setting that they wish to study in order
to gain a greater perspective about the characteristics of the
environment. Sometimes researchers will deliberately take part
in participant observation research in order to prove to a
population that they are trustworthy in order to gain more in-
depth data. When participating in participant observation, a
researcher must constantly practice reflexivity, as their
perspectives may become biased with their continued personal
involvement in a given environment.
Nonparticipant observation is when a researcher observes
activities and settings without participating themselves. This
method of observation allows a researcher to maintain greater
levels of objectivity, but they do run the risk of having more
superficial data.
The two methods that a researcher can use to conduct
observations are by implementing overt or covert observation.
Overt observation means that a researcher has identified
themselves and informed those that they are observing of their
positon as a researcher. In most cases, participant observation
tends to be overt in nature, as to get consent from participants
to perform research a researcher must identify themselves to
participants. However, a limitation of overt participation is
participant reactivity. This means that participants who know a
researcher is present may change their behaviors when they
know that researcher is present which would bias the data
collected.

Covert observation is when a researcher conceals their identity
so that those they are observing are unaware that they are being
observed. This addresses the issue of reactivity because
participants won’t change their behavior if they don’t know they
are being watched. But it may be ethically challenging, as
participants cannot give informed consent. In some instances,
when the researcher is observing behavior in a very public
space, the issue of consent is less serious. However, performing
participant observation in a private area may require researchers
to identify themselves in order for their research to be ethical in
nature.
Slide 4: Steps to conducting field research
After choosing the general topic or question of your research
project, the first step to conducting field research is to identify
the group or setting that you wish to study. As a researcher, you
need to consider various sites or groups to study especially
whether or not they are appropriate for the question you want to
answer, if they are accessible, if there is any risk to yourself or
the participants, and whether or not it is ethical to study said
population. In general, it is considered a good practice in
qualitative researcher to explain why you selected a setting or
group. After selecting the group or setting you will be studying,
you must learn about that setting. This includes learning about
the mannerisms of the group or setting, the physical attributes
and what you may have to change as a researcher to fit in, you
should understand the typical activities of a setting or group as
well. Being prepared with an understanding of what a setting or
group does and why will increase your professionalism as a
researcher.
After selecting a group or setting and learning what you can
about that environment, you have to gain access to said group or
setting. If your looking at a physical location that is public in
nature, this may be easy. But if your studying a private location

such as a domestic violence shelter, or a hard-to-reach
population such as gang members, then it may be much more
difficult to gain participants. Researchers should devel op a
strategy to gain access that will work best for a given
environment. This may mean getting an “in” or a person who
would recommend you to other persons in a given setting or
achieving the acceptance to a locations “gatekeeper”, or a
person in authority whose permission you may need to access a
particular setting. Getting access can take a lot of time, so
researchers should be aware of this limitation before they start
their project and plan accordingly.
During either the same period that a researcher is attempting to
gain access to a setting or population, or directly after, they
must decide on their role and relationship in the project. This is
the stage where a researcher must determine if they will act as a
participant observer or a non-participant. A researcher must also
determine what role they will fulfill in a given environment.
Will they be attached to a specific person in an environment?
Will they spend their time in a specific location? The type of
data a researcher gather may depend on the relationships and
roles that they fill in an environment. After establishing their
role and relationship in their project, a researcher must
determine what they are going to observe or who they are going
to interview. In a qualitative project, a sample will be
determined by a variety of factors. The sampling method,
convenience, accessibility, and sometimes luck will influence
the final sample that a researcher obtains for their project. The
more structured a project is, the more set-in-stone a sample will
be, but the more flexible a researcher is, the less structured the
sample.
The next step in a qualitative research project is to gather the
data. In field research, this consists of taking field notes or
audio recordings. Field notes are detailed, written accounts of
what a researcher observed while in the field. Transcriptions are

essentially the written notes, verbatim, that a researcher
gathered using audio recordings. However, a researcher must be
sure of their environment and whether or not participants have
consented to audio recording before they use this method of
data gathering. Analysis of field notes and audio transcriptions
consists of analytic memo’s. These are the in depth analysis of
all notes and materials that a researcher has gathered over the
course of their research project. When preparing analytic
memos, researchers are looking for patterns and concepts that
emerge throughout their data.
At some point, a researcher must leave the field that they have
been conducting a project in. The time period of a project may
depend on the funding a project has, the time limitations of
either the participants or the researcher, or a natural conclusion
of an event may occur that allows researchers to transition out
of their observed environment. Reflexivity must be practiced at
this stage of research as well, as researchers must consider how
the people or setting they observed or interview will react to
their leaving. In some cases, debriefing may have to occur at the
end of an observation period.
The last step to conducing field research is finalizing the report.
Qualitative reports tend to include both methodologically sound
analysis of patterns and potentially a quantitative analysis of
observed data that has been numerically coded for analysis.
However, qualitative projects also allow the opportunity for
researchers to integrate quotes and statements from their
participants to be integrated into their writing. It is often
considered best practice for researchers to share the final report
or a summarized version of their final analysis to the population
they studied, so that they have a sense of closure form their
experience participating in the project.
Slide 5: Steps to conducting interviews
The first step of conducting interviews is the same as conducing

general field research. A researcher must identify what their
questions are and which group of people might be able to
provide the best answer to those questions. After selecting a
group, a researcher must recruit their participants.
Recruitment of participants for interviews is just as difficult as
recruitment general field observations. You must often gain
access to hard-to-reach populations, and you must also make
sure that they are knowledgeable about your topic of interest
and willing to openly answer your questions and discuss your
topic. To get a group of participants for interviews, you may
have to gain an “in” or address gatekeepers who will provide
you with access to the group you need.
Either while or before gathering participants, a researcher must
develop an interview guide. An interview guide is list of topics
and specific questions that a researcher will ask or address
during an interview. Interview guides are incredibly important
as they will help structure the flow of an interview and make
sure that a researcher addresses all of the topics necessary to
complete data analysis. Just as when making surveys,
researchers must carefully consider the wording of their
questions and make sure that the language will be
understandable to their population of interest. Asking yes/no
questions, followed by asking a participant to expand may help
participants to structure their thoughts and develop concrete
answers. However, having too many open ended questions or
yes/no questions at a given time may make the interview
cumbersome, so make sure to have some variation to keep
conversation flowing. Having a list of “probes” or words and
phrases used to encourage elaboration may also help
participants develop more in-depth answers.
After gaining access to participants and developing an interview
form, the next step is to gather data and conduct the interviews.
When a researcher shows up to an interview, they should have

multiple copies of signed consent forms, multiple sets of the
interview questions, an audio recorder that can be used if the
participant gives permission, paper for notes to be written on,
and a prepared brief explanation of your project and what it will
offer to your participants. After confirming voluntary
participation and asking for permission to use an audio
recorder, the interview can begin. The type of interview you are
conducting will determine how the interview process goes. In
unstructured interviews, the interview is much like a
conversation. Semi-structured interviews, which are the most
commonly used form of interview, allow a researcher to ask
each of the questions that they prepared, but the depth in which
each question will be answered will depend on the participant’s
experience and rapport with the interviewer. Structured
interviews function similarly to a verbal survey, with each
question being asked in the exact format as the interview
schedule, and sometimes allowing only for particular answers.
Even if an interview is being recorded, it is suggested that the
interviewer takes notes of responses and observations that may
occur throughout the interview. This adds context to the
interview during analysis and reassures the participant that you
are paying attention to their responses.
The analysis process for qualitative interview data consists of
transcribing the interviews and than coding them. Coding
consists of sorting data into categories based on patterns and
themes that emerge from the data. In general these themes will
emerge organically through an indicative process, but coding
can also be used to confirm or deny preconceived expectations
about themes in a deductive manner, that were developed based
on theoretical backgrounds of a given topic. The final research
report tends to consist of a rich narrative and description that is
integrated with theoretical analysis. Additionally, it is generally
considered best practices to send a copy or summary of the final
report to the interview participants so that they can see the
product of their participation in the project.

Focus Groups
Focus groups are similar to interviews, in the sense that a
researcher asks a series of questions to participants in hopes of
achieving descriptive and in-depth feedback. However, focus
groups allow a researcher to interview multiple individuals at
once in order to achieve some different goals than can be
achieved in individual one-on-one interviews.
Focus groups consist of groups of 7-10 individuals that tend to
be relatively homogenous in nature being led in a discussion
based on questions asked by a researcher. The researcher will
guide discussions and encourage participation. The goals of
focus groups are varied. They can be used to discover findings
within a group setting, where a researcher can observe how
group dynamics influence responses. Focus groups can also be
used to develop and improve survey instruments, by testing out
responses and opinions. And lastly, they are useful in
identifying a range of opinions on any given issue that the
researcher is addressing. Additionally, performing several focus
groups on the same topic will allow for a consistency check that
will increase the internal validity of the project.
Slide 7: Secondary Data Analysis
Secondary data analysis is when a researcher uses pre-existing
data to answer questions not intended by those who collected
the data. Secondary data analysis can be qualitative or
quantitative in nature depending on the source and type of
secondary data used. For example, most social science data
archives will consist of the data and results of surveys or
official statistics which can be downloaded into statistical
packages to run unique analyses to answer questions not asked
by the original researchers. Alternative, archival data such as
official records, media, and historical documents can be
analyzed qualitatively to create an analysis that is historically
informed or that analyzes social phenomena without addressing

live participants.
Data archives have become popular methods of sharing data and
statistics, often gathered from large populations, with other
researchers. This allows for questions to be asked and types of
statistical analyses to be run that were not or could not be
conducted by the original researchers that gathered the data.
One commonly used data archive is the ICPSR or Inter-
University Consortium for Political and Social Researcher,
which provides hundreds of data sets available for researchers
either to use immediately or after approval. Many governmental
or non-profit agencies also release their data for public use and
analysis. When analyzing secondary data in a quantitative
manner, a researcher will very likely face unique challenges.
For example, a researcher must become incredibly familiar with
the data set and understand how variables were coded in the
past and what measurements were used for specific variables. If
the measurement is invalid, this may cause a problem during
analysis. Secondary data also requires substantial time to clean,
meaning that the original coding is often awkward to work with
and may require a researcher to spend time re-coding and re-
labeling variables so that they are easier to work with or make
more methodological sense.
Content analysis is the secondary data analysis used for
qualitative methods. Alternative to the statistics used for
secondary data analysis in quantitative research, content
analysis is the systematic analysis of the symbolic content
present in materials, which result in a set of coded variables or
categories similar to those developed in interviewing. Content
analysis can be conducted on a wide range of materials
including historical documents, official records, media such as
books, newspapers, videos, speeches, and may more documents.
The information provided by these materials is systematically
analyzed by one or more researcher to create coding schemes
and themes that will then be analyzed. The coding scheme

includes the operationalization and conceptualization of the
variables a researcher finds in their analysis and allows for
multiple researchers or coders to analyze data in the same
manner. For example, if three people are coding newspapers for
various officer involved use of force, and the goal is to describe
how different types of force are presented in media, a coding
scheme would help define how different police uses of force are
to be categorized for analysis (i.e., verbal force, physical force,
use of Taser or pepper sprays, use of gun or other lethal
weapons, ect…). Having these definitions of use of force will
allow all three researchers to code the newspaper articles the
same way.
Slide 8: Qualitative vs. Quantitative Data Analysis
This slide consists of a table that may be useful in differentiated
the purposes and uses of qualitative and quantitative data
analysis. If you are looking for ways to explain why you are
using a certain method of data analysis and collection in your
final project, this table may be useful.
In general, qualitative research focuses on the meanings and
symbols represented in a given data set. These data sets often
include individual people, but may also include documents.
There are rarely predetermined categories available when
conducting qualitative analysis; rather the point of the analysis
is to develop categories for future analysis and use.
Alternatively, quantitative research focuses on explaining or
quantifying social phenomena. Instruments are designed in
order to measure variables in the most objective manner
possible, and analyses are used to draw the most generalizable
conclusions from the data as possible. In order to make these
generalizations possible, large amounts of cases are gathered,
though the amount of information gathered from each case may
be limited.

Both qualitative and quantitative research methods have their
benefits and their drawbacks. A researchers use of a particular
method may be determined by their personal preference or,
more often, by the type of question they are asking. Questions
attempting to understand the meanings behind phenomena or
answer the inquiry of why things occur are best answered using
qualitative methods. However, descriptive analyses and attempts
at understanding the mechanisms of a relationship with the
inquiry of how things occur, may be best answered using
quantitative analysis. Thus, depending on the question a
researcher is asking and the samples and or data sets available
to them will more than likely determine whether or not a
qualitative or quantitative methodology will be applied in a
research project.
Slide 9: Module Wrap-Up
After reading the texts and listening to the lecture prepared for
this module, you should be confident in your ability in
completing the learning objectives from the unit.
You should be able to distinguish the various techniques used to
perform various types of qualitative data analysis. Additionally,
you should be able to evaluate the various ethical issues that
may be involved with qualitative data analysis, especially
involving voluntary consent and deception. Lastly, you should
be able to debate how combining research methods and
measurements may affect causal validity in research projects,
and address the various weaknesses inherent in different
methodologies.
Your fourth assignment will be due at the end of this module,
and will consist of creating a methods section for your final
project this semester. In this assignment, you will be developing
your preliminary research design and methodology for your
final project for the semester. It will need to include a
discussion of the target population and sample for your project,

the method of data collection, and other descriptions related to
your variables or methodologies. Make sure to check blackboard
or the syllabus for further guidelines regarding this assignment
and do not hesitate to post questions in to the interactive
discussion board for feedback from your classmates or
professor.
Slide 1: Intro to Surveys
Surveys are one method of collecting data for research that
includes participants responding to written questions and
prompts. Survey research includes questionnaires, or
instruments containing the questions and measurements in a
self-administered format, the respondent, or person taking the
survey, and a knowledge of the response rate, or the percentage
of persons who completed the survey in your sample.
Because surveys are self-administered, researchers must pay
close attention to the way that questions are developed and
whether or not they are understandable to the respondents who
will be surveyed. Questions on a survey can be either closed or
open-ended. Being open-ended means that a respondent is able
to fill in their own personalized response to a question. Often,
this requires the researcher to ask for specific information or for
a narrative response. Alternatively, close-ended questions are
when respondents choose from a list of possible responses that
were provided by the researcher. Close-ended questions are also
called fixed-choice or forced-choice questions.
Both open-and close-ended questions have advantages and
disadvantages. Close-ended questions allow for a quick
response, consistency, and are easier to statistically analyze, but
may cause results to be biased if a participant is forced to
choose an option that does not fit with their actual opinion or
perspective. Open-ended questions allow for detailed responses,

and reduce biases related to preconceived answers, but must be
thoroughly reviewed before they can be used for analysis.
Slide 2: Principles for writing questions
In order to make your survey as high-quality as possible, you
should always put effort into writing clear and meaningful
questions, avoiding confusing phrasing, minimizing bias,
avoiding making disagreement, and maximizing response
categories while minimizing opportunities for participants to
float.
Essentially, wringing clear and meaningful questions means that
surveys should be generalizable and understandable to many
people. You cannot rephrase a survey question that someone
does not understand because it would bias the results, so always
strive to make sure that your questions are easily interpretable
and your meaning clear. In order to increase this clarity, make
sure to use correct grammar and keep your phrases shorter. If
your survey includes complex ideas, separate them into
different questions and provide definitions when necessary, and
always avoid vagueness. Being more specific will reduce
confusion and bias.
You must also strive to avoid double negatives, double-barreled
questions, and your answers should always be mutually
exclusive and exhaustive. Having double negatives in your
questions will increase the likelihood that a respondent will
misinterpret your question. While professors may use double
negatives on tests to trip-up students, doing so in surveys will
negatively affect your results. An example of a question that
used double negatives is: ‘Do you disagree that there should not
be a death penalty?’ Instead of making this phrasing
complicated, you should instead ask, ‘Do you agree with having
a death penalty?’

Double-barreled questions are those that address two different
topics in the same question. For example, the question, ‘Do you
think the prison system should stop releasing inmates on
weekends and concentrate on rehabilitation?’ is a double
barreled question. Instead of having this as one question, the
researcher should have split it into two questions, the first
asking about weekend releases, and the second asking about
rehabilitation.
In order for your survey questions to be mutually exclusive and
exhaustive, you should always make sure there are no overl aps
in your response categories, and that they cover all options that
a respondent could pick. If you ask the question of how many
times a person has been arrested and your options are 0-1 or 1-
3, then they are not mutually exclusive, because the number one
was included in both categories, and they are not exhaustive, as
a person could have been arrested more than three times. Thus,
a better choice of options would be: 0, 1-3, 4-6, 7 or more.
Slide 3: Types of Questions
Likert questions are often used with declarative statements
rather than with questions. The options available are generally
on a five point scale, ranging from strongly agree to strongly
disagree, although there has been an increased movement to
remove the neutral section. You also have the opportunity to
include not applicable options if you feel that some statements
will not be necessary for some respondents.
Filter questions and skip patterns are used when questions may
only apply to some respondents. It allows the survey to become
quicker for those with whom the measurements do not apply,
and it reduces respondent burden. If your survey is not
electronic with automatic skip patterns, make sure your survey
is clearly labeled in terms of which questions should be skipped
and to which question a respondent should move to if they are

not required to answer a certain section.
Lastly, demographic questions are those that provide a basic
description of your respondents. They include questions that
address age, sex, gender, race/ethnicity, education, income,
religion, employment status, occupation, region of residence,
sexuality, ect… While you do not need to include every
demographic variable in your research, you should include some
in order to make sure your sample is as representative as
possible.
Slide 4: Types of Surveys
Surveys can be either self-administered or administered by the
researcher. Based on the population you are trying to reach and
the number of participants you are attempting to recruit, your
method of surveying may be different. Self-administered
surveys are when respondents fill out their own questionnaires,
and are generally the most economical way to survey large
numbers of people. Electronic surveys are becoming
increasingly used as technology advances, especially as
surveying programs have developed methods of transferring
results straight into statistical packages, however, it is more
difficult to know that the electronic surveys are representative
unless you have a list of emails or other forms of identification
before hand from which you can draw a representative and
random sample.
Researcher-administered surveys may increase the quality of the
data a researcher gathers, however, they may take more time
and resources to procure. Not only does the researcher have to
set aside large amounts of time to complete these forms of
surveying, but they can also be expensive as those
administering the survey require training and may need to travel
to different locations to administer the questionnaires.

In the end, the type of survey that you administer is going to
depend on the population you are trying to reach, your sample
size, the topic you are trying to address, and the time and
resources you have available to complete the project.
this module, you should be confident in your ability to complete
the learning objectives from the unit.
In particular, you should be able to elaborate on how a
questionnaire or interview schedule is the foundation of
surveys, and debate on various survey layouts and the
implications of effective survey design. Consider writing up
your own practice survey questions to get used to avoiding
double negatives, double-barreled questions, and making sure
you can develop questions that are mutually exclusive and
exhaustive.
Your second discussion board assignment will be due at the end
of this module. Remember, you must make your own response
to the question, and complete two further responses to various
classmates. Make sure to check blackboard or the syllabus for
further guidelines regarding this assignment and do not hesitate
to post questions in to the interactive discussion board for
feedback from your classmates or professor.
Slide 1: Why Experiment?
Experiments are research projects where a manipulated
independent variable is followed by measuring a change in a
dependent variable. There must be at least two groups in
experiments that were randomly assigned and treated exactly

alike. If a researcher takes into account association, direction of
influence, and eliminates rival explanations, then there are only
two explanations: the relationship is causal in nature or, the
results occurred by chance. Tests of statistical significance are
used in order to measure the likelihood that a result of an
experiment could have occurred due to chance. Statistical
significance is also known as the t-statistic, or the probability
that differences are due to chance is less than .05. Otherwise
stated, researchers generally accept values of .05 which means
that the probability of a relationship occurring due to chance is
only 5 times out of 100.
Slide 2: Variations in Experiments, Pt. I
While experiments must always include comparison groups,
random assignment, and dependent variable assessment,
experiments can be structured in different ways. The three
variations in experiments discussed here will be posttest only
control group designs, pretest-posttest control group designs,
and factorial designs.
Posttest only control group designs are the most basic
experimental designs. In these experiments, the dependent
variable is measured after the experimental manipulation is
applied. In pretest-posttest control group designs, the dependent
variable is measured both before and after the experimental
manipulation in order to allow for comparisons of the dependent
variable. Researchers can apply multiple posttests at differing
times to see how time affects dependent variable manipulation,
or can measure changes in the dependent variable after making
various manipulations. Lastly, the third manipulation of
experimental design, are factorial designs, which is when two or
more independent variables are manipulated. Factorial designs
allow evidence of impact for each individual factor
manipulated, as well as a measurement of the factors joined
together.

Variations in Experiments, Pt. II
On top of manipulations that researchers can use in
experimental designs, manipulations in the experimental context
can also influence results. For example, laboratory experiments
are those that occur in a controlled environment, which allow
for a more targeted examination of the effects of manipulation,
but are less realistic as there are no intervening variables
present that may exist in the natural environment. Survey-based
experiments are when participants are given different, randomly
assigned versions of survey questions, however in this design
while measurement is usually valid and reliable, the participant
is considering hypothetical scenario’s and questions rather than
actually living the experience which may bias their results.
Field experiments are when experiments are conducted in a
natural setting, which addresses the sterile environment issue of
laboratory experiments, but decreases the ease of examining the
effects of manipulation. Audit studies are a particular type of
field experiments, where matched pairs of participants both
participate in an experiment, but only one of the pair is
randomly assigned the manipulation, thus allowing for
comparison between the pairs.
Slide 4: Variations in Experiments, Pt. III
Quasi-experimental designs are those that resemble
experiments, but do not include random assignment. These
designs are often sued when random assignment is not possible
or feasible in a given population. Quasi-experiments have more
validity than not attempting to apply experimental methods, but
have less validity and explanatory power than pure experimental
studies.
Examples of quasi-experimental designs include: nonequivalent

control group designs which are similar to pretest-posttest
experimental designs, however, the groups are not randomly
assigned and are instead just considered to be similar to one
another, before-and-after designs which is when a group is
followed over time with pretests and posttests implemented
after an intervention or manipulation is applied in order to
measure outcomes, and lastly, ex post facto control groups
designs, which is when an independent variable is already
present or not present in the participant groups, and a dependent
variable is measured, meaning that there is not random
assignment or manipulation.
When developing and designing studies, researchers must be
cognizant of the various benefits and drawbacks of different
methodologies and manipulations in order to make sure their
research is unbiased as possible.
Slide 5: Validity
The validity of a research design is also affected by the
different manipulations applied during experiments. Thus, when
considering a project, you must always consider internal and
external validity, and how to reduce the threats to these
variables.
Internal validity is evidence that rules out the possibility that
factors other than the manipulated independent variable are
responsible for the measured outcome. This is why it is
important to always rule out the possibility of extraneous
variables that need to be considered in an environment before
conducting a study. Threats of internal validity include not
controlling for extraneous variables, selection bias when
participants are not randomly assigned, maturation, or the a
natural psychological or physiological change that takes place
in participants that may change their reactions, and history,
which is when environmental factors other than the

experimental manipulation change a participants reaction.
External validity is the extent to which experimental findings
may be generalized to other settings, measurements,
populations, and time periods. One of the goals of research is
that findings can be applied outside of an individual study, so
considering how to manipulate study designs to increase
external validity is important. External validity can be increased
by making sure results that happen in a laboratory also occur in
a natural setting, by replicating studies, or using a different
sample of participants potentially in different settings to see if
the same results occur, and being thorough with sample
selection.
While it may be impossible to make internal and external
validity perfect, being honest about limitations and doing
everything in your power as a researcher to increase internal
and external validity will increase the impact of your study.
You should be able to examine casual explanations for attitudes,
behaviors, and events and additionally, explain the difference
between causation and correlation. Remember that correlation
does not imply causality. You should be able to justify the value
of experiments and differentiate between the various
manipulations to experiments that may need to be applied when
doing research. Lastly, you should be able to explain the
importance of internal and external validity while identifying
the treats to these validities in experiments and describe the
different methods that overcome threats to validity.

Your third assignment will be due at the end of this module, and
will consist of creating a literature review for your final project
this semester. In this assignment, you should provide some
justification for why your topic is important for further study
and should also identify and review at least 6 of the maj or
studies that have addressed your topic in the past. Make sure to
check blackboard or the syllabus for further guidelines
regarding this assignment and do not hesitate to post questions
in to the interactive discussion board for feedback from your
classmates or professor.
Slide 1: The Research Process
A research design is an overall plan of study that researchers
will use when developing a project and collecting data.
Research designs include four steps: selecting a research topic,
reviewing the current literature and considering theory,
formulating a research question, and then developing a research
design based off of the question you are asking and the method
you want to use in order to collect data.
The first step in the research process is selecting a research
topic. Topic selection may be influenced by various external
factors as discussed in previous modules including academic
motivations, policy motivations, political motivations, and
personal interests. In criminology, often times our research
questions relate to crime, deviance, or victimization. When
doing research, you should always strive to choose a topic that
interests you so that you will not get tired of conducting a
research project. Additionally, the topic should be practice, so
that you can achieve some sort of success while researching.

Once you have chosen a general topic, the second step in the
research process is conducting a literature review which
includes both past studies and books that have been written on
your chosen topic, as well as theoretical explanations that have
been used to explain phenomena within your chosen topic.
When reading prior literature, it is always important to consider
not just the content, but also the methodology that past
researchers have used when developing projects on your chosen
topic. This is important for two reasons: one, you may be able
to find what methodology is best suited for researching your
topic, and two, you may be able to use a different methodology
than what was used in the past to make your project unique.
Conducing a literature review can take a lot of time and energy,
as you are expected to read as much literature as you can, if not
all of the past literature. While you may find a gap in the
literature that provides you with a unique research question, you
may also have to reroute your chosen topic based on past
research. Throughout the research process, you must always be
flexible when considering your topic, questions, and how you
may need to evolve your own project or interests in order to
develop something new and meaningful.
Formulating a research question can happen both before or after
the literature review. It may also evolve throughout the research
process based on what you learn in your literature review.
Essentially, a scientific research question is one that is
answerable through systematic collection and analysis of
verifiable data. Research questions should contribute to
conversations and investigations that are occurring in the social
science field of your choosing; in this case, criminology. A
good research question should be focused and feasible in terms
of managing time and resources.
After you have completed these three steps in the research
process, you must develop your research design. The research
design should detail how you will go about gathering and

analyzing data to answer your research question.
Slide 2: Designing Research
Most research questions, especially in quantitative research, are
searching to establish or explain a relationship between two or
more variables. Before starting to identify these relationships
however, researchers must define the variables that they are
using. First, a variable is a measured concept that may vary
across cases or across time, but is essentially some
characteristic or concept of identification you are attempting to
measure. Variables can include concepts such as age, gender,
and income as well as more abstract characteristics such as self-
control, anger, or attachment. After identifying what variables
will be important in your research, you must consider the units
of analysis.
Units of analysis are the entities such as people, nations and
artifacts that are being studied, compared in terms of variables.
Social scientists, including criminologists, study a variety of
different units of analysis, including individual people and
groups such as families or communities. If a researcher was
studying how whether affects people’s moods, their unit of
analysis would be individuals. Alternatively, if a researcher was
considering whether larger organizations have more
bureaucratic characteristics than smaller organizations, the unit
of analysis would be organizations. Before you start conducting
your research, you should be able to describe the unit of
analysis for your research question.
After determining your unit of analysis, your should start
identifying your variables. As discussed earlier, an independent
variable is one that a researcher will manipulate with the belief
that it will influence or cause a change in a different variable.
The variable expected to change based on the influence of the
independent variable is the dependent variable. You must
always be able to distinguish between your independent and

dependent variables in order to conduct a research project.
There are additional types of variables that may be included in
your projects including extraneous variables, antecedent
variables, intervening variables, and control variables.
Extraneous variables are those that are not part of a
hypothesized relationships and they can be described as
antecedent or intervening. Antecedent variables are those that
occur before the influence of the independent variable on the
dependent variable that may influence their relationship. If I am
considering how the amount of water influences plant growth,
an antecedent variable may be a bug infestation that occurred
prior to the watering that affected the relationship between
watering and plant growth.
An intervening variable is one that might influence the effect of
the independent variable on the dependent variable. Or, in other
terms, it is an effect of the independent variable that causes the
change in the dependent variable, meaning that the independent
variable does not directly cause the dependent variable.
Consider the statistic that hotter weather increases homicides.
When just considering these two variables, the relationship
seems to be direct, however, there may be an intervening
variable mediating the relationship, such as anger. Hot
temperatures tend to increase anger, and then greater levels of
anger may lead to assaults and homicides. Thus, the intervening
variable of anger, which is caused by the high temperatures is
causing the homicides rather then the temperature itself.
Lastly, control variables are those that are held constant to
prevent variation during analysis. Holding variables constant
allows researchers to rule out variables that are not of
immediate interest but that may also explain part of the
relationship being investigated. Some variables commonly
controlled for include gender, race, income, and other
demographic characteristics as well as variables identified as

influencing factors in past research.
As stated earlier, often times research questions are looking to
define relationships between two or more variables. A causal
relationship is one in which a theorized change in one variable
directly produces an change in a second variable. Alternatively,
spurious relationships are when one variable seems to affect a
secondary variable, when in reality the change is caused by an
antecedent or intervening variable. Researchers must consider
the relationships produced in their analysis carefully, as
statistical significance (or the likelihood that the result of a
study, such as a relationship between two variables, could have
occurred) may occur in both spurious and casual relationships.
Slide 3: Conceptualization & Operationalization
Conceptualization is the development and clarification of
concepts and can includes various types of definitions such as
conceptual definitions and operationalization’s. A conceptual
definition is a verbal definition of a concept derived from
theory which directs the search for measures. Operationalization
is the process of identifying empirical indicators and the
procedures for applying them to measure a concept.
Conceptualizations can emerge based on characteristics and
definitions developed in past research. For example, the
definition of social capital has been refined and developed
based on past research considering what social capital is and
how it is gathered. Conceptualization allows researchers to
refine and elaborate on the theoretical foundations of their
research and provide a basis for linking theory to data.
Conceptualization of variables may differ if a researcher is
doing deductive or inductive research. In deductive research,
conceptualization includes translating portions of an abstract
theory into specific variables to be tested, whereas in inductive
research conceptualization is an important part of the proces s

used to make sense of related observations and is often
developed as part of the analysis rather than prior to the
analysis.
Operationalization is when researchers identify ways of
observing variation in order to connect concepts to empirical
observations. Essentially, operationalization is defining the
method that a researcher will use to measure a concept.
Operationalization begins by defining and specifying
dimensions of a concept and then determining methods of
measuring these concepts. Often more than one method of
measurement is used in order to ensure that a concept is being
defined and measured correctly. These definitions of
measurements are called operational definitions.
Slide 4: Levels of Measurement
Levels of measurement tell us what numbers mean when we
compare people or other units into categories. There are four
levels of measurement used in research: nominal, ordinal,
interval, and ratio.
Nominal measurement is a system that classifies information
into two or more categories that are non-numerical. Examples of
variables that are usually nominal include: race, religion, or
gender. Each of these variables have differing, non-numerical
categories that participants in a research project can choose. In
order to be nominal, variables must be exhaustive and mutually
exclusive. By being exhaustive, the measurement requires that a
measure includes all possible values or categories that can be
classified. To be mutually exclusive, the measurement requires
that each case be placed in one and only one category.
Ordinal measurement is when numbers indicate the rank order o
cases on some variable where the numbers assigned indicate
only the order of categories. Survey questions that have the

categories never, sometimes, always are examples of ordinal
ranking.
Interval measurement is when a variable has the same qualities
as ordinal level variables such as ranking, but there is also an
equal distance or interval between the assigned numerical
values. Some examples of interval level variables include
temperature, distance measurements such as miles, feet, or
inches. However, you cannot calculate mathematical differences
in interval level variables because there is no true zero because
the zero point does not signify the absence of the power. For
example, even if it is zero degrees out, that does not mean there
is no temperature.
Ratio level measurements include variables with numerical
values and fixed zeros which makes it possible to
mathematically interpret the variable. For example, when
considering income, you can divide one into the toher to form a
ratio that signifies their comparison to one another, such as
$20,000 being half of $40,000.
Slide 5: Validity and Reliability
Measurement validity is the goodness of fit between an
operational definition of a concept and the actual value of a
concept. Otherwise stated, it considers the question of whether
or not we are actually measuring what we think we are
measuring. Reliability is similar to validity, but measures the
consistency of an operational definition, or does our
measurement always measure the same way. Of these two
concepts, validity is more critical, as reliability can be high
even if validity is low if a researcher is successfully measuring
a concept incorrectly the same way multiple times.
There are multiple forms of validity and reliability assessment.
Reliability assessments include test-retest reliability, internal

consistency, and inter-rater reliability. Test-retest reliability is
a method of establishing reliability which involves testing the
same persons or units on two separate occasions such as
administering a survey on two separate days. However, test-
retest reliability does have limitations, including needing to test
participants or units twice which takes more time, and the
possibility that a person might just re-report their original
answers. Internal consistency avoids these limitations by
measuring the consistency of scores across all items of a
composite scale or measure. If you have multiple questions re-
affirming the same concept, then answers that correspond with
one another on the same scale would thus have high reliability.
Inter-rater reliability is the extent to which different observes or
coders get the same results when analyzing data separately. The
greater the consistency the greater the reliability.
Validity assessments include face validity, content validity,
convergent validity, and construct validity. Face validity is an
assessment where a researcher uses superficial and subjective
assessment of whether or not your study or test measures what it
is supposed to measure. However, because it is dependent on
researcher interpretation, it can be inherently biased. Content
validity is measured using the knowledge of experts who are
familiar with the construct being measured. Similarly to face
validity, this assessment can still be biased depending on the
objectivity of the expert. Convergent validity is based on the
extent to which independent measures of the same concept are
related to one another. Convergent validation is enhanced by
using multiple alternative measures and by using measures
based on different operational methods. Lastly, construct
validity is an assessment based on an accumulation of research
evidence indicating that a measure is related to other variables
as theoretically expected which must often be accumulated
across studies.
Slide 6: General Sampling Concepts

The four most basic concepts of sampling include the target
population, population, sampling frame, and sampling unit. A
target population includes the entire group you want to
generalize your research to. The population are members of a
target population from which your sample is actually selected
from. Sampling frames are lists of members of a population
from which a sample is selected. Lastly, a sample unit is any
single unit sampled from the population.
The goal of sampling is to use a sample of elements of the
population to learn about the entire population. There are two
types of generalizability, sample generalizability is the ability
to generalize from a subset (sample) of a larger population, and
cross-population generalizability is the ability to generalize
from findings about one group, population, or setting to other
groups, populations, or setting. To generalize to populations, a
sample must be as representative as possible, or it has
characteristics similar to the population. A non-representative
sample may contain characteristics which are over or under
represented. A measurement of non-representative samples is
sampling error, or the difference between the characteristics of
a sample population from which it was drawn. Essentially, the
representativeness of a sample can be undermined by
nonresponse or bias.
Slide 7: Probability Sampling
Probability sampling is a method of sampling that allows
researchers to know in advance how likely it is that any element
of a population will be selected for a sample, thus making
populations statistically representative and generalizable to a
whole population. To develop a probability sampling method,
researchers first need to define a target population, develop a
sampling frame, and lastly, calculate the coverage error, or the
error that occurs when the sampling frame does not match the

population. The four major types of probability sampling
includes sampling random sampling, systematic random
sampling, stratified random sampling, and cluster sampling.
Slide 8: Simple and Systematic Random Sampling
Simple random sampling is a probability sampling design in
which every case and every possible combination of cases has
an equal chance of being included. Simple random sampling
requires a complete list of the population, thus making it
difficult to apply simple random sampling frames.
Systematic random sampling starts with a researcher
determining the number of their sample in reference to a
population. After dividing the sample size from the population
size, a researcher gains the sampling interval. From the list of
persons within a population, a researcher takes every nth
element from the list.
Both simple and systematic sampling creates essentially the
same sample, however both also require the population to be
known and a list of persons in said population.
Slide 9: Stratified Random Sampling and Cluster Sampling
When using stratified random sampling, a researcher must
distinguish all elements in a population according to their value
on some characteristic. These characteristics form strata, or
levels of groups based on some given characteristic. There are
two types of stratified random sampling: proportionate and
disproportionate stratified sampling. Proportionate stratified
sampling allows researchers to create strata based on characters
that are the same proportion of the whole population form which
to select a random sample. Disproportionate stratified sampling
is when a sample is taken from equal proportions of an entire
population.

Cluster sampling is used when a researcher doesn’t have a
sampling frame with a definite list of elements, or when it is too
expensive to cover the sampling frame such as hidden
populations or large geographical areas. When using cluster
sampling, you may select inmates from clusters of prisons or
students from clusters of schools.
Slide 10: Nonprobability Sampling
Nonprobability sampling is a method of sampling whereby each
member of a population has unequal probability of selection.
This method is used when a probability sample cannot be
obtained or when a topic of the study includes rare or hard to
access populations. There are four types of nonprobability
sampling methods: availability sampling, quota sampling,
purposive sampling, and snowball sampling.
Slide 11: The Four Types of Nonprobability Sampling
Availability sampling is used by selecting any and all units that
are available for a researcher to access. However, due to the
bias of this sample, it can be difficult to implement. Quota
sampling is intended to overcome this flaw by creating samples
that include quotas of units or participants who have certain
characteristics represented in a population.
Purposive sampling is when each sample element is selected for
a purpose, potentially due to the participants unique position in
the population. Which may involve studying the entire
population of some limited group or a sub-set of populations.
Lastly, snowball sampling is used by getting referrals of further
participants from a known member of a population. Those
referrals then give the researcher more names, thus increasing
the sample size.

Module Wrap-Up
You should be able to evaluate the measurements and
conceptualizations of effective research, including defining and
operationalizing variables. You should be able to distinguish the
differences between reliability and validity and identify the
various methods of calculating both characteristics. Similarly,
you should be able to define and elaborate on sampling error
and its representation between sample characteristics and target
populations. Lastly, you should be able to identify and interpret
sampling methods and when each method should be used.
Your second assignment will be due at the end of this module,
and will consist of identifying and describing the variables you
plan to use in your final project using the characteristics
discussed in this module including operationalization and
defining units of analysis. Make sure to check blackboard or the
syllabus for further guidelines regarding this assignment and do
not hesitate to post questions in to the interactive discussion
board for feedback from your classmates or professor.
EYEWITNESS EVIDENCE 1
EYEWITNESS EVIDENCE 2

Eyewitness Evidence
Eyewitness Evidence
Introduction
Many people put profound faith in eyewitness evidence and
over the years, eyewitness evidence has been regarded as the
“best” kind of evidence (Boyce, Beaudry, & Lindsay, 2017).
Eye witness evidence has a lot of impact in any criminal case
and it can alter the trajectory of the case in immeasurable
amounts. Eyewitness evidence can be used to convict a person,
and this is an example of just how powerful the topic is (Boyce,
Beaudry, & Lindsay, 2017). Due to the faith put in it by many
people despite the common criticism it faces sometimes, it is
therefore important to understand the impacts of its reliability.
Identifying the impacts of reliability in the credibility of eye
witness evidence is essential as it establishes the appropriate
use of eye witness evidence in criminal cases.
Justification For Why This Topic Is Important To Study
It is important to study the reliability of eyewitness evidence
since this type of evidence is critically important to the justice
system. Eyewitness evidence is utilized in the reconstructions of
facts from past events which makes it very important in
criminal trials (Pedzek & O’Brien, 2014). Since eyewitness
evidence itself is critically important to the justice system, the
study of its reliability is essential since challenges to the many
assumptions of the general public and legal system regarding its
accuracy can be put forth. Eyewitness evidence can be used in
either appropriate reinforcing of the judgment in criminal cases
or can also lead to wrongful convictions ( O’ Neill Shermer,
Rose, & Hoffman, 2011). This is why it is important to
understand how reliable eyewitness evidence is before using it.

Eyewitness evidence is regarded as one of the most convincing
forms of evidence in any criminal trial. It is easy to explain why
eyewitness evidence holds so much weight in criminal trials
since people trust their perception and experience. The
statement "I will believe it when I see it" is not just a cliché,
but a statement with an incredibly huge persuasive form of
evidence that can be allowed even in the justice system (Wells,
Memon, & Penrod, 2016). The Criminal justice system aims to
provide justice and one of the forms of doing so is ensuring that
one of the elements used in making judgments, (eyewitness
evidence), is reliable and trustworthy. Studying the reliability
of eyewitness evidence and factors that can influence it is
essential in guiding how to use witness evidence in criminal
trials. A person ending up being convicted of a crime they did
not commit just because eyewitness evidence claimed so and the
evidence itself is not reliable makes the whole process wrong.
The reliability of eyewitness evidence should be tested to
determine its credibility which will inspire its appropriate use
in criminal trials (Wells, Memon, & Penrod, 2016).
Literature Review
Boyce, Beaudry, and Lindsay (2017) explain that before
believing eyewitness evidence, various issues should be
examined. One of the issues is reliability. Boyce, Beaudry, and
Lindsay (2017) explain that the reliability of eyewitness
evidence should be directly proportional to whether the
evidence is believed or not. It is important to differentiate
between inaccurate and accurate witness evidence since various
groups would suffer hardships associated with trials and arrests
in regard to eyewitness evidence. The authors explained that
members of the jury should be taught cues for how to measure
the reliability of eyewitness evidence and how to look for
loopholes in them Boyce, Beaudry, and Lindsay (2017).
The issue of eyewitness evidence should not be merely about its
availability or lack thereof but also its reliability as well
(Pedzek & O’Brien, 2014). Professionals should use the aspect
of reliability of the eyewitness evidence to determine how the

evidence will inform their decisions appropriately. In
determining the reliability of eyewitness evidence, all factors
pertaining to the evidence should be examined. This is because
sometimes information that could be useful is ignored which
affects the reliability of the eyewitness evidence ( O’ Neill
Shermer, Rose, & Hoffman, 2011)ss. The practitioners within
the criminal justice system should find out whether the
eyewitness evidence is reliable or not and discuss the
implications of their findings (Wells, Memon, & Penrod, 2016).
Garrett et al. (2020) explain that the issues of reliability of
eyewitness evidence should be a pressing concern in criminal
scenarios. Garrett et al. (2020) explain that the practitioners
may be blown away by the high confidence in the eyewitness
evidence hence end up disregarding various critical factors of
the information, such as its reliability. The authors did a study
aimed at exploring the relationship between courtroom members
and eyewitness evidence and they discovered that most jurors
give the most weight to the eyewitness evidence itself with no
special focus on its reliability (Garrett et al., 2020). Garrett et
al. (2020) explained that the reliability of eyewitness evidence
has implications for its effectiveness and the legal actions taken
based on it.
Houston et al. (2013) report that legal professionals and jurors
alike are often insensitive to factors affecting eyewitness
evidence such as its reliability. The authors conducted their
research aimed at assessing the extent to which judges
understand factors that may undermine the reliability and
accuracy of eyewitness evidence. Houston et al. (2013) also did
a survey comparing responses from a multiple-choice
questionnaire method and a scenario-based survey to identify
the alterations between the two Houston et al. (2013).
Generally, the judges depicted their high level of acceptance to
the fact that reliability is a key factor in eyewitness evidence
Houston et al. (2013). Additionally, the responses that were
gathered from the multiple-choice format survey produced
evidence that seemed more reliable compared to when the

participants generated their own responses Houston et al.
(2013). The authors concluded that indeed reliability is an
essential factor in eyewitness evidence and recommended
training of legal professionals and jurors to appreciate the
impact of reliability of eyewitness evidence.
Wise, Safer, and Moro (2011) did a survey on 532 U.S. law
enforcement officers to identify how they went about
eyewitness evidence. 449 officers were from departments that
had not implemented eyewitness reforms while 83 officers were
from the department that had implemented eyewitness reforms
Wise, Safer, and Moro (2011). The authors found that officers
from both groups showed little knowledge of how the reliability
of eyewitness evidence is important. Wise, Safer, and Moro
(2011) also report that the manner in which the officers
conducted interviews and identification procedures of
eyewitness evidence hugely impacted the reliability of the
evidence. They reported that officers from reform departments
used more correct line-up procedures of handling eyewitness
evidence than those in non-reform department's, but the two
groups showed no difference in the knowledge of the impact of
reliability and eyewitness evidence (Wise, Safer, & Moro, 2011)
Strengths And Weaknesses
The studies have various strengths such as providing in-depth
analysis of the topics they addressed. The studies also used the
appropriate methods of data collection and analysis which was
essential in the appropriateness of reporting the findings.
Additionally, the studies vividly and comprehensively discussed
the findings and related them to the objective of the studies.
The main weakness that the studies had was the lack of
suggestions and recommendations after their discussions. The
studies focused on how jurors and legal professionals do not
understand the importance of reliability of eyewitness evidence,
but they do not provide suggestions or recommendations for
solving the problem.
Conclusion
As seen from the reviewed literature above, the reliability of

eyewitness evidence is more important than its availability.
Sadly, jurors and legal professionals do not have enough
knowledge on how the reliability of eyewitness evidence could
have various implications in criminal trials. There is need to
address how the reliability of eyewitness evidence relates to its
credibility and how these factors can guide the appropriate use
of eyewitness evidence in criminal trials.
References
Boyce, M., Beaudry, J., & Lindsay, R. C. L. (2017). Belief
of eyewitness identification evidence. Psychological science in
the public interest, 7(2), 45-75.
Garrett, B. L., Liu, A., Kafadar, K., Yaffe, J., & Dodson, C. S.
(2020). Factoring the Role of Eyewitness Evidence in the
Courtroom. Journal of Empirical Legal Studies, 17(3), 556-579.
Houston, K. A., Hope, L., Memon, A., & Don Read, J. (2013).
Expert testimony on eyewitness evidence: In search of common
sense. Behavioral Sciences & the Law, 31(5), 637-651.
National Institute of Justice (US). (2019). Technical Working
Group for Eyewitness Evidence. Eyewitness evidence: A guide

for law enforcement. US Department of Justice, Office of
Justice Programs, National Institute of Justice.
O'Neill Shermer, L., Rose, K. C., & Hoffman, A. (2011).
Perceptions and credibility: Understanding the nuances of
eyewitness testimony. Journal of Contemporary Criminal
Justice, 27(2), 183-203.
Pedzek, K., & O'Brien, M. (2014). Plea bargaining and
appraisals of eyewitness evidence by prosecutors and defense
attorneys. Psychology, Crime & Law, 20(3), 222-241.
Wells, G. L., Memon, A., & Penrod, S. D. (2016). Eyewitness
evidence: Improving its probative value. Psychological science
in the public interest, 7(2), 45-75.
Wise, R. A., Safer, M. A., & Maro, C. M. (2011). What US law
enforcement officers know and believe about eyewitness
factors, eyewitness interviews and identification
procedures. Applied Cognitive Psychology, 25(3), 488-500.

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