Module 4
Presentation 4
A. Reporting Standards
These standards provide guidelines for authors on what information should be
included, at minimum, in journal articles. By using JARS, authors can make their
research clearer, more accurate, and more transparent for readers. Writing clearly and
reporting research in a way that is easier to comprehend helps ensure scientific rigor and
methodological integrity and improves the quality of published research. Reporting
standards are closely related to the way studies are designed and conducted, but they do
not prescribe how to design or execute studies, and they are not dependent on the topic of
the study or the particular journal in which the study might be published. Comprehensive,
uniform reporting standards make it easier to compare research, to understand the
implications of individual studies, and to allow techniques of meta-analysis to proceed
more efficiently. Decision makers in policy and practice have also emphasized the
importance of understanding how research was conducted and what was found.
Undergraduate students who are writing less complicated research papers may
also find the standards on the abstract and introduction helpful (see Sections 3.3–3.4).
Note that the information available regarding JARS is substantial and detailed; this
chapter is only an introduction. The APA Style JARS website contains a wealth of
resources (links to many appear throughout this chapter). JARS may also be revised and
expanded in the future as new standards are developed; any such changes will be
reflected on the website. The sections that follow discuss the application of the principles
of JARS, including why the standards exist and how they have evolved; terminology used
to discuss JARS, with a link to a glossary on the JARS website; reporting standards for
abstracts and introductions that pertain to all types of research articles; and specific
standards for quantitative, qualitative, and mixed methods research.
Within these guidelines for reporting standards, however, is flexibility in how the
standards are applied across different types of research studies. Guidelines on where to
include information recommended in JARS within an article are flexible in most cases
(exceptions are information that must appear in the title page, abstract, or author note; see
Tables 3.1–3.3 later in this chapter). In general, any information that is necessary to
comprehend and interpret the study should be in the text of the journal article, and
information that might be needed for replication can be included in supplemental
materials available online with few barriers to readers. Authors should consult with
journal editors to resolve questions regarding what information to include and where,
keeping readability of the article as a prime consideration. Reviewers and editors are
encouraged to learn to recognize whether reporting standards have been met regardless of
the rhetorical style of the research presentation.
Reporting standards are evolving to reflect the needs of the research community.
The original JARS, published in American Psychologist (APA Publications and
Communications Board Working Group on Journal Article Reporting Standards, 2008) as
well as in the sixth edition of the Publication Manual (APA, 2010), addressed only
quantitative research. The updated JARS, published in 2018 (see Appelbaum et al., 2018;
Levitt et al., 2018), expands on the types of quantitative research (JARS–Quant)
addressed and now includes standards for reporting qualitative (JARS–Qual) and mixed
methods (JARS–Mixed) research. As research approaches continue to evolve, authors
should use these standards to support the publication of research; they should not allow
these standards to restrict the development of new methods.
Many aspects of the scientific process are common across quantitative,
qualitative, and mixed methods approaches. This section reviews reporting standards that
have considerable overlap for the two initial elements of journal articles—the abstract
and the introduction. We present the common reporting standards for the abstract and
introduction as well as some distinctive features for each approach. For descriptions of
and formatting guidelines for the title, byline and institutional affiliation, author note,
running head, abstract, keywords, text (the body of a paper), reference list, footnotes,
appendices, and supplemental materials.
An abstract is a brief, comprehensive summary of the contents of the paper. A
well-prepared abstract can be the most important paragraph in an article. Many people
have their first contact with an article by reading the title and abstract, usually in
comparison with several others, as they conduct a literature search. Readers frequently
decide on the basis of the abstract whether to read the entire article. The abstract needs to
be dense with information. By embedding essential terms in your abstract, you enhance
readers’ ability to find the article. This section addresses the qualities of a good abstract
and standards for what to include in abstracts for different paper types. The body of a
paper always opens with an introduction. The introduction contains a succinct description
of the issues being reported, their historical antecedents, and the study objectives.
The introduction of an article frames the issues being studied. Consider the
various concerns on which your issue touches and its effects on other outcomes (e.g., the
effects of shared storybook reading on word learning in children). This framing may be in
terms of fundamental psychological theory, potential application including therapeutic
uses, input for public policy, and so forth. Proper framing helps set readers’ expectations
for what the report will and will not include. Review the literature succinctly to convey to
readers the scope of the problem, its context, and its theoretical or practical implications.
Clarify which elements of your paper have been subject to prior investigation and how
your work differs from earlier reports. In this process, describe any key issues, debates,
and theoretical frameworks and clarify barriers, knowledge gaps, or practical needs.
Including these descriptions will show how your work builds usefully on what has
already been accomplished in the field.
Clearly state and delimit the aims, objectives, and/or goals of your study. Make
explicit the rationale for the fit of your design in relation to your aims and goals. Describe
the goals in a way that clarifies the appropriateness of the methods you used. In a
quantitative article, the introduction should identify the primary and secondary
hypotheses as well as any exploratory hypotheses, specifying how the hypotheses derive
from ideas discussed in previous research and whether exploratory hypotheses were
derived as a result of planned or unplanned analyses. In a qualitative article, the
introduction may contain case examples, personal narratives, vignettes, or other
illustrative materials. It should describe your research goal(s) and approach to inquiry.
Examples of qualitative research goals include developing theory, hypotheses, and deep
understandings (e.g., Hill, 2012; Stiles, 1993); examining the development of a social
construct (e.g., Neimeyer et al., 2008); addressing societal injustices (e.g., Fine, 2013);
and illuminating social discursive practices—that is, the way interpersonal and public
communications are enacted (e.g., Parker, 2015). The term approaches to inquiry refers to
the philosophical assumptions that underlie research traditions or strategies—for
example, the researchers’ epistemological beliefs, worldview, paradigm, strategies, or
research traditions.
Qualitative research encompasses a wide range of approaches to inquiry, each
with its own philosophical foundations and methodological implications. Rooted in
constructivist epistemology, this approach views knowledge as actively constructed by
individuals based on their experiences and interpretations. Focuses on understanding how
meanings are constructed and shared among participants. Emphasizes reflexivity,
multiple perspectives, and context-specific interpretations. Informed by critical theory,
this approach seeks to critique power structures, challenge social inequalities, and
promote social change. Investigates how dominant ideologies and power dynamics
influence social phenomena. Emphasizes emancipatory aims, reflexivity, and advocacy
for marginalized voices.
Aims to describe and document phenomena as they naturally occur, without
imposing theoretical frameworks. Focuses on detailed observation, documentation, and
thematic analysis of qualitative data. Emphasizes capturing rich descriptions and
contextual nuances. Grounded in feminist theory, this approach aims to understand
gendered experiences, power relations, and social justice issues. Prioritizes participant
empowerment, reflexivity on gender dynamics, and the exploration of gendered
perspectives in research. Draws on hermeneutics and phenomenology to understand
subjective experiences and meanings. Emphasizes interpretation and sense-making of
qualitative data. Focuses on understanding how individuals interpret and give meaning to
their experiences within specific contexts.
Challenges grand narratives and emphasizes multiple truths, subjective realities,
and the influence of language and discourse. Critiques fixed meanings and explores
fluidity, ambiguity, and contradictions in qualitative data. Emphasizes reflexivity and
deconstruction of taken-for-granted assumptions. Accepts some principles of positivism
(e.g., systematic observation, empirical data), but acknowledges the influence of
researcher biases and contextual factors. Seeks to generate credible and valid knowledge
through rigorous data collection and analysis. Emphasizes triangulation, member
checking, and systematic approaches to enhance reliability. Emphasizes practicality,
problem-solving, and the use of diverse methods and theories that best fit the research
context and objectives. Focuses on addressing research questions effectively, often
integrating multiple perspectives and methodologies. Prioritizes the relevance and
applicability of findings to real-world settings.
Informed by psychoanalytic theory, focuses on unconscious processes, emotions,
and psychodynamic factors influencing human behavior. Investigates deep-seated
motivations, emotions, and psychological conflicts through in-depth interviews, case
studies, and interpretive analysis. Emphasizes the exploration of unconscious meanings
and dynamics. Each of these approaches offers distinct lenses through which researchers
can conceptualize and conduct qualitative inquiry. The choice of approach depends on
the researcher's theoretical orientation, research questions, the nature of the phenomenon
under study, and ethical considerations. Qualitative researchers often integrate elements
from multiple approaches to enrich their understanding and interpretation of complex
social phenomena and human experiences.
Absolutely, the definition and application of research philosophies can vary
significantly among researchers and across different fields of study. In qualitative
research, particularly, there is often a diversity of approaches that reflect varying degrees
of theoretical orientation and practical focus. Qualitative research is grounded in various
philosophical traditions, such as phenomenology, hermeneutics, ethnography, and critical
theory, among others. These traditions influence how researchers approach questions,
interpret data, and conceptualize knowledge. Researchers may align themselves with
specific philosophical perspectives to guide their inquiry. For instance, a
phenomenological approach focuses on understanding lived experiences, while critical
theory emphasizes social justice and power dynamics.
Qualitative research can be both question-driven and theory-driven, depending on
the researcher's goals and the nature of the study. Question-driven research focuses on
exploring specific research questions or phenomena, often without a predetermined
theoretical framework guiding the inquiry. On the other hand, theory-driven qualitative
research starts with established theories or conceptual frameworks to guide data
collection and analysis. Researchers aim to test, refine, or extend existing theories
through empirical investigation. Some qualitative researchers adopt a pragmatic
approach, emphasizing practicality and the use of diverse methods and theories that best
fit the research context and objectives. Pragmatism encourages researchers to be flexible
in their methodological choices and to prioritize the relevance and applicability of their
findings. Pragmatic qualitative research may involve borrowing methods or theoretical
insights from different disciplines to address research questions effectively. This
interdisciplinary approach can enrich the depth and breadth of qualitative inquiry.
Qualitative research is context-bound, meaning that the research design, methods,
and interpretations are influenced by the specific social, cultural, and historical contexts
in which the study is conducted. Researchers must navigate the complexities of context
by acknowledging their own perspectives, biases, and the situated nature of knowledge
production in qualitative inquiry. In summary, qualitative research encompasses a
spectrum of philosophical orientations, ranging from theoretical depth and coherence to
pragmatic flexibility and question-driven inquiry. The diversity in defining research
philosophies reflects the richness and adaptability of qualitative methods in addressing
complex research questions and understanding human experiences. Researchers' choices
in philosophical orientation should align with their research goals, theoretical
commitments, and the practical considerations of their study contexts. This dynamic
interplay contributes to the evolving methodologies and theoretical frameworks within
qualitative research.
When writing the Method section of a replication study, it's essential to detail
your approach to inquiry clearly and systematically. This section should not only describe
the methods and procedures used in your study but also justify why these methods were
chosen and how they align with the goals of replicating previous research. H Begin by
outlining the overall study design you employed. Specify whether your replication study
is an exact replication, partial replication, or conceptual replication. Discuss how your
study design compares to the original study and justify any modifications or
improvements made. For instance, you may have adjusted sample size, inclusion criteria,
or measurement instruments to enhance methodological rigor or address specific
limitations identified in the original study.
Describe your participant selection criteria, including demographic characteristics
such as age, gender, and any other relevant factors. Compare your participant sample to
that of the original study, noting any differences or similarities that may influence the
generalizability of your findings. Specify the variables you investigated in relation to the
original study's findings. Clearly define your dependent and independent variables, as
well as any control variables or covariates. Detail the measures and instruments used to
collect data, including their reliability and validity. If you made adaptations or
improvements to measurement tools compared to the original study, provide rationale and
justification for these changes.
Outline the procedures followed during data collection and analysis. Describe
step-by-step how data were collected, including any experimental protocols, survey
administration methods, or observational techniques used. Discuss any steps taken to
ensure methodological consistency and reliability, such as training research assistants,
piloting procedures, or implementing quality control measures. Specify the statistical or
analytical methods employed to analyze your data. Describe how you plan to test your
hypotheses or research questions, including any specific statistical tests, software used,
and criteria for significance. Justify your choice of analytical methods in relation to the
nature of your data and the objectives of replicating the original study's findings. Address
ethical considerations relevant to your study, including participant consent,
confidentiality, and any institutional review board (IRB) approvals obtained. Describe
how you ensured ethical standards were maintained throughout the study, particularly if
procedures differed from those in the original study.
When conducting a replication study as a quantitative research endeavor, the
introduction should adhere to the principles typical of a quantitative study introduction
while emphasizing the specific goals and methods related to replication. Begin by
providing a brief overview of the original study or studies that you intend to replicate.
Summarize the key findings, methods employed, and the significance of these findings in
the field. Highlight any controversies, uncertainties, or gaps in knowledge that the
original study may have left open. Emphasize why replication is important in this context
—whether it's to validate findings, test generalizability across different populations or
settings, or address conflicting results in the literature.
Explicitly state the rationale for conducting the replication study. This may
include reasons such as ensuring the reliability and robustness of previous findings,
evaluating the replicability of methods or results, or addressing concerns about
publication bias or selective reporting. Discuss the potential impact of the replication
study on theoretical frameworks or practical applications within the field. Explain how
replicating the study contributes to advancing scientific knowledge and understanding.
Clearly state the specific objectives of your replication study. These objectives should
align closely with the goals of the original study, focusing on what aspects you aim to
replicate or extend upon. Formulate hypotheses based on the findings of the original
study. Depending on your replication approach (exact replication, partial replication, or
conceptual replication), articulate whether you expect to replicate the original findings
precisely or anticipate variations and their potential implications. Conclude the
introduction by summarizing the potential contributions and significance of your
replication study. Discuss how the findings—whether they replicate, extend, or diverge
from the original—will advance knowledge in the field, inform theoretical frameworks,
or guide practical applications. Highlight the broader implications for research practices,
such as promoting transparency, enhancing methodological rigor, and reinforcing the
credibility of scientific findings through replication efforts.
B. Quantitative Research
The Method section of a paper provides most of the information that readers need
to fully comprehend what was done in the execution of an empirical study. This section
provides information that allows readers to understand the research being reported and
that is essential for replication of the study, although the concept of replication may
depend on the nature of the study. The basic information needed to understand the results
should (as a rule) appear in the main article, whereas other methodological information
(e.g., detailed descriptions of procedures) may appear in supplemental materials.
Readability of the resulting paper must be part of the decision about where material is
ultimately located. Details of what content needs to be presented in the Method section of
a quantitative article and must be used in conjunction with JARS–Quant Tables 2 to 9 on
the JARS website.
Appropriate identification of research participants is critical to the science and
practice of psychology, particularly for generalizing the findings, making comparisons
across replications, and using the evidence in research syntheses and secondary data
analyses. Detail the major demographic characteristics of the sample, such as age; sex;
ethnic and/or racial group; level of education; socioeconomic, generational, or immigrant
status; disability status; sexual orientation; gender identity; and language preference, as
well as important topic-specific characteristics (e.g., achievement level in studies of
educational interventions). As a rule, describe the groups as specifically as possible,
emphasizing characteristics that may have bearing on the interpretation of results.
Participant characteristics can be important for understanding the nature of the sample
and the degree to which results can be generalized.
Even when a characteristic is not used in analysis of the data, reporting it may
give readers a more complete understanding of the sample and the generalizability of
results and may prove useful in meta-analytic studies that incorporate the article’s results.
The descriptions of participant characteristics should be sensitive to the ways the
participants understand and express their identities, statuses, histories, and so forth. When
nonhuman animal subjects are used, report the genus, species, and strain number or other
specific identifier, such as the name and location of the supplier and the stock
designation. Give the number of nonhuman animal subjects and their sex, age, weight,
and physiological condition.
Describe the procedures for selecting participants, including (a) the sampling
method, if a systematic plan was implemented; (b) the percentage of the sample
approached that participated; and (c) whether self-selection into the study occurred
(either by individuals or by units such as schools or clinics) and the number of
participants who selected themselves into the sample. Report inclusion and exclusion
criteria, including any restriction based on demographic characteristics. Describe the
settings and locations in which the data were collected and provide the dates of data
collection as a general range of dates, including dates for repeated measurements and
follow-ups. Describe any agreements with and payments made to participants. Note
institutional review board approvals, data safety board arrangements, and other
indications of compliance with ethical standards.
Provide the intended size of the sample and number of individuals meant to be in
each condition if separate conditions were used. State whether the achieved sample
differed in known ways from the intended sample. Conclusions and interpretations should
not go beyond what the achieved sample warrants. State how the intended sample size
was determined (e.g., analysis of power or precision). If interim analysis and stopping
rules were used to modify the desired sample size, describe the methodology and results
of applying that methodology. Include in the Method section definitions of all primary
and secondary outcome measures and covariates, including measures collected but not
included in the current report. Provide information on instruments used, including their
psychometric and biometric properties and evidence of cultural validity (Section 3.10 for
how to cite hardware and apparatuses.
Describe the methods used to collect data (e.g., written questionnaires, interviews,
observations). Provide information on any masking of participants in the research (i.e.,
whether participants, those administering the manipulations, and/or those assessing the
outcomes were unaware of participants’ assignment to conditions), how masking was
accomplished, and how the masking was assessed. Describe the instrumentation used in
the study, including standardized assessments, physical equipment, and imaging
protocols, in sufficient detail to allow exact replication of the study. Describe methods
used to enhance the quality of measurements, including training and reliability of data
collectors, use of multiple observers, translation of research materials, and pretesting of
materials on populations who were not included in the initial development of the
instrumentation. Pay attention to the psychometric properties of the measurement in the
context of contemporary testing standards and the sample being investigated; report the
psychometric characteristics of the instruments used following the principles articulated
in the Standards for Educational and Psychological Testing (American Educational
Research Association et al., 2014). In addition to psychometric characteristics for paper-
and-pencil measures, provide interrater reliabilities for subjectively scored measures and
ratings. Internal consistency coefficients can be useful for understanding composite
scales.
Specify the research design in the Method section. For example, were participants
placed into conditions that were manipulated, or were they observed in their natural
setting? If multiple conditions were created, how were participants assigned to conditions
—through random assignment or some other selection mechanism? Was the study
conducted as a between-subjects or a within-subjects design? Reporting standards vary
on the basis of the research design (e.g., experimental manipulation with randomization,
clinical trial without randomization, longitudinal design). Consult Figure 3.1 to determine
which tables on the JARS website to use for your research design. See Sections 3.9 and
3.10 for a summary of design-specific reporting standards.
. If experimental manipulations or interventions were used in the study, describe
their specific content. Include details of the interventions or manipulations intended for
each study condition, including control groups (if any), and describe how and when
interventions or experimental manipulations were administered. Describe the essential
features of “treatment as usual” if that is included as a study or control condition.
Carefully describe the content of the specific interventions or experimental manipulations
used. Often, this involves presenting a brief summary of instructions given to
participants. If the instructions are unusual, or if the instructions themselves constitute the
experimental manipulation, present them verbatim in an appendix or supplemental
materials. If the text is brief, present it in the body of the paper if it does not interfere
with the readability of the report. Describe the methods of manipulation and data
acquisition. If a mechanical apparatus was used to present stimulus materials or to collect
data, include in the description of procedures the apparatus model number and
manufacturer (when important, as in neuroimaging studies), its key settings or parameters
(e.g., pulse settings), and its resolution (e.g., stimulus delivery, recording precision). As
with the description of the experimental manipulation or intervention, this material may
be presented in the body of the paper, in an appendix, or in supplemental materials, as
appropriate.
When relevant—such as in the delivery of clinical and educational interventions
—the procedures should also contain a description of who delivered the intervention,
including their level of professional training and their level of training in the specific
intervention. Present the number of deliverers along with the mean, standard deviation,
and range of number of individuals or units treated by each deliverer. Provide
information about (a) the setting in which the manipulation or intervention was delivered,
(b) the intended quantity and duration of exposure to the manipulation or intervention
(i.e., how many sessions, episodes, or events were intended to be delivered and how long
they were intended to last), (c) the time span for the delivery of the manipulation or
intervention of each unit (e.g., whether the manipulation delivery was completed in one
session, or, if participants returned for multiple sessions, how much time passed between
the first and last session), and (d) activities or incentives used to increase compliance.
When an instrument is translated into a language other than the language in which it was
developed, describe the specific method of translation (e.g., back-translation, in which a
text is translated into another language and then back into the first language to ensure that
it is equivalent enough that results can be compared). Describe how participants were
grouped during data acquisition (i.e., was the manipulation or intervention administered
individual by individual, in small groups, or in intact groupings such as classrooms?).
Indentify the smallest unit (e.g., individuals, work groups, classes) that was analyzed to
assess effects. If the unit used for statistical analysis differed from the unit used to deliver
the intervention or manipulation (i.e., from the unit of randomization), describe the
analytic method used to account for this (e.g., adjusting the standard error estimates,
using multilevel analysis).
Describe how data were inspected after collection and, if relevant, any
modifications of those data. These procedures may include outlier detection and
processing, data transformations based on empirical data distributions, and treatment of
missing data or imputation of missing values. Describe the quantitative analytic strategies
(usually statistical) used in analysis of the data, being careful to describe error-rate
considerations (e.g., experiment-wise, false discovery rate). The analytic strategies should
be described for primary, secondary, and exploratory hypotheses. Exploratory hypotheses
are ones that were suggested by the data collected in the study being reported, as opposed
to ones generated by theoretical considerations or previously reported empirical studies.
When applying inferential statistics, take seriously the statistical power considerations
associated with the tests of hypotheses. Such considerations relate to the likelihood of
correctly rejecting the tested hypotheses given a particular alpha level, effect size, and
sample size. In that regard, provide evidence that the study has sufficient power to detect
effects of substantive interest. Be careful in discussing the role played by sample size in
cases in which not rejecting the null hypothesis is desirable (i.e., when one wishes to
argue that there are no differences), when testing various assumptions underlying the
statistical model adopted (e.g., normality, homogeneity of variance, homogeneity of
regression), and in model fitting. Alternatively, use calculations based on a chosen target
precision (confidence interval width) to determine sample sizes. Use the resulting
confidence intervals to justify conclusions reached concerning effect sizes.
In the Results section of a quantitative paper, summarize the collected data and
the results of any analyses performed on those data relevant to the discourse that is to
follow. Report the data in sufficient detail to justify your conclusions. Mention all
relevant results, regardless of whether your hypotheses were supported, including results
that run counter to expectation; include small effect sizes (or statistically nonsignificant
findings) when theory predicts large (or statistically significant) ones. Do not hide
uncomfortable results by omission. In the spirit of data sharing (encouraged by APA and
other professional associations and sometimes required by funding agencies; see Section
1.14), raw data, including study characteristics and individual effect sizes used in a meta-
analysis, can be made available as supplemental materials (see Section 2.15) or archived
online (see Section 10.9). However, raw data (and individual scores) generally are not
presented in the body of the article because of length considerations. The implications of
the results should be discussed in the Discussion section.
For experimental and quasi-experimental designs, provide a description of the
flow of participants (humans, nonhuman animals, or units such as classrooms or hospital
wards) through the study. Present the total number of participants recruited into the study
and the number of participants assigned to each group. Provide the number of participants
who did not complete the experiment or who crossed over to other conditions and explain
why. Note the number of participants used in the primary analyses. (This number might
differ from the number who completed the study because participants might not show up
for or complete the final measurement). Provide dates defining the periods of recruitment
and follow-up and the primary sources of participants, when appropriate. If recruitment
and follow-up dates differ by group, provide the dates for each group.
Analyses of the data and reporting of the results of those analyses are fundamental
aspects of the conduct of research. Accurate, unbiased, complete, and insightful reporting
of the analytic treatment of data (be it quantitative or qualitative) must be a component of
all research reports. Researchers in the field of psychology use numerous approaches to
the analysis of data, and no one approach is uniformly preferred as long as the method is
appropriate to the research questions being asked and the nature of the data collected. The
methods used must support their analytic burdens, including robustness to violations of
the assumptions that underlie them, and must provide clear, unequivocal insights into the
data. In reporting your statistical and data analyses, adhere to the organizational structure
suggested in the Method section (see Section 3.6): primary hypotheses, secondary
hypotheses, and exploratory hypotheses. Ensure that you have reported the results of data
diagnoses (see Section 3.6) in the Method section before you report the results linked to
hypothesis confirmation or disconfirmation. Discuss any exclusions, transformations, or
imputation decisions that resulted from the data diagnosis.
Historically, researchers in psychology have relied heavily on null hypothesis
significance testing (NHST) as a starting point for many of their analytic approaches.
Different fields and publishers have different policies; APA, for example, stresses that
NHST is but a starting point and that additional reporting elements such as effect sizes,
confidence intervals, and extensive description are needed to convey the most complete
meaning of the results (Wilkinson & the Task Force on Statistical Inference, 1999; see
also APA, n.d.-b). The degree to which any journal emphasizes NHST is a decision of the
individual editor. However, complete reporting of all tested hypotheses and estimates of
appropriate effect sizes and confidence intervals are the minimum expectations for all
APA journals. Researchers are always responsible for the accurate and responsible
reporting of the results of their research study. Assume that readers have a professional
knowledge of statistical methods. Do not review basic concepts and procedures or
provide citations for the most commonly used statistical procedures. If, however, there is
any question about the appropriateness of a particular statistical procedure, justify its use
by clearly stating the evidence that exists for the robustness of the procedure as applied.
Missing data can have a detrimental effect on the legitimacy of the inferences
drawn by statistical tests. It is critical that the frequency or percentages of missing data be
reported along with any empirical evidence and/or theoretical arguments for the causes of
data that are missing. Data might be described as missing completely at random (as when
values of the missing variable are not related to the probability that they are missing or to
the value of any other variable in the data set), missing at random (as when the
probability of missing a value on a variable is not related to the missing value itself but
may be related to other completely observed variables in the data set), or not missing at
random (as when the probability of observing a given value for a variable is related to the
missing value itself). It is also important to describe the methods for addressing missing
data, if any were used (e.g., multiple imputation).
When reporting the results of inferential statistical tests or when providing
estimates of parameters or effect sizes, include sufficient information to help readers fully
understand the analyses conducted and possible alternative explanations for the outcomes
of those analyses. Because each analytic technique depends on different aspects of the
data and assumptions, it is impossible to specify what constitutes a “sufficient set of
statistics” in general terms. However, such a set usually includes at least the following:
per-cell sample sizes, observed cell means (or frequencies of cases in each category for a
categorical variable), and cell standard deviations or pooled within-cell variance. In the
case of multivariable analytic systems, such as multivariate analyses of variance,
regression analyses, structural equation modeling, and hierarchical linear modeling, the
associated means, sample sizes, and variance–covariance (or correlation) matrix or
matrices often represent a sufficient set of statistics.
s. It can be extremely effective to include confidence intervals (for estimates of
parameters; functions of parameters, such as differences in means; and effect sizes) when
reporting results. Because confidence intervals combine information on location and
precision and can be directly used to infer significance levels, they are generally the best
reporting strategy. As a rule, it is best to use a single confidence level, specified on an a
priori basis (e.g., a 95% or 99% confidence interval), throughout the article. Wherever
possible, base discussion and interpretation of results on point and interval estimates.
When using complex data-analytic techniques—such as structural equation modeling,
Bayesian techniques, hierarchical linear modeling, factor analysis, multivariate analysis,
and similar approaches—provide details of the models estimated (see Section 3.11). Also
provide (usually in supplemental materials) the associated variance–covariance (or
correlation) matrices. Identify the software used to run the analysis (e.g., SAS PROC
GLM or a particular R package) and any parametric settings used in running the analyses
(references are not necessary for these software programs). Report any estimation
problems (e.g., failure to converge), regression diagnosis issues, or analytic anomalies.
Report any problems with statistical assumptions or data issues that might affect the
validity of the findings.
For readers to appreciate the magnitude or importance of a study’s findings, it is
recommended to include some measure of effect size in the Results section. Effect sizes
are statistical estimates; therefore, whenever possible, provide a confidence interval for
each effect size reported to indicate the precision of estimation of the effect size. Effect
sizes may be expressed in the original units (e.g., mean number of questions answered
correctly, kilograms per month for a regression slope) and are most easily understood
when reported as such. It is valuable to also report an effect size in some standardized or
unitsfree or scale-free unit (e.g., Cohen’s d value) or a standardized regression weight.
Multiple degree-of-freedom effect-size ndicators are less useful than effect-size
indicators that decompose multiple degree-of-freedom tests into meaningful one degree-
of-freedom effects, particularly when the latter are the results that inform the discussion.
The general principle to follow is to provide readers with enough information to assess
the magnitude of the observed effect.
After presenting the results, you are in a position to evaluate and interpret their
implications, especially with respect to your original hypotheses. In the Discussion
section of a quantitative paper, examine, interpret, and qualify the results of your research
and draw inferences and conclusions from them. In the case of empirical studies, there
should be a tight relationship between the results that are reported and their discussion.
Emphasize any theoretical or practical consequences of the results. When the discussion
is relatively brief and straightforward, you can combine it with the Results section,
creating a section called “Results and Discussion.” If a manuscript presents multiple
studies, discuss the findings in the order that the studies were presented within the article.
Open the Discussion section with a clear statement of support or nonsupport for all
hypotheses, distinguished by primary and secondary hypotheses. In the case of
ambiguous outcomes, explain why the results are judged as such. Discuss the
implications of exploratory analyses in terms of both substantive findings and error rates
that may be uncontrolled. Similarities and differences between your results and the work
of others (where they exist) should be used to contextualize, confirm, and clarify your
conclusions. Do not simply reformulate and repeat points already made; each new
statement should contribute to your interpretation and to readers’ understanding of the
problem.
Your interpretation of the results should take into account (a) sources of potential
bias and other threats to internal validity, (b) the imprecision of measures, (c) the overall
number of tests and/or overlap among tests, (d) the adequacy of sample sizes and
sampling validity, and (e) other limitations or weaknesses of the study. If an intervention
or manipulation is involved, discuss whether it was successfully implemented, and note
the mechanism by which it was intended to work (i.e., its causal pathways and/or
alternative mechanisms). Discuss the fidelity with which the intervention or manipulation
was implemented, and describe the barriers that were responsible for any lack of fidelity.
Acknowledge the limitations of your research, and address alternative explanations of the
results. Discuss the generalizability, or external validity, of the findings. This critical
analysis should take into account differences between the target population and the
accessed sample. For interventions, discuss characteristics that make them more or less
applicable to circumstances not included in the study, what outcomes were measured and
how (relative to other measures that might have been used), the length of time to
measurement (between the end of the intervention and the measurement of outcomes),
incentives, compliance rates, and specific settings involved in the study as well as other
contextual issues. End the Discussion section with a reasoned and justifiable commentary
on the importance of your findings. This concluding section may be brief, or it may be
extensive if it is tightly reasoned, self-contained, and not overstated. In the conclusion,
consider returning to a discussion of why the problem is important (as stated in the
introduction); what larger issues, meaning those that transcend the particulars of the
subfield, might hinge on the findings; and what propositions are confirmed or
disconfirmed by the extrapolation of these findings to such overarching issues.
Describe the unit of randomization and the procedures used to generate
assignments. Be careful to note when units such as classrooms are the unit of
randomization, even though data collection may be from individual students within the
classroom. Indicate the unit of randomization in the analysis of such study outcomes as
well. Describe masking provisions used to ensure the quality of the randomization
process. . Describe the unit of assignment and the method (rules) used to assign the unit
to the condition, including details of any assignment restrictions such as blocking,
stratification, and so forth. Describe any procedures used to minimize selection bias such
as matching or propensity score matching. Within the JARS context, a clinical trial or a
randomized clinical trial is a research investigation that evaluates the effects of one or
more health-related interventions (e.g., psychotherapy, medication) on health outcomes
by prospectively assigning people to experimental conditions. As used here, a clinical
trial is a subset of a class of studies called “randomized control studies,” and the reporting
standards for clinical trials apply to randomized control studies as well. Most clinical
trials are experimental studies with random assignment, so all reporting standards for
those types of studies also apply. Report information about the clinical trial aspect of the
study. If the trial has been registered (e.g., on ClinicalTrials.gov), report its registration
on the title page in the author note (see Section 2.7) and in the text. In the Method
section, provide details of any site-specific considerations if the trial is a multisite trial.
Provide access to the study protocol; if the study is a comparison to a current “standard”
treatment, describe that standard treatment in sufficient detail that it can be accurately
replicated in any follow-up or replication study. Describe the data safety and monitoring
board and any stopping rules if used. If there was a follow-up, provide the rationale for
the length of the follow-up period.
Nonexperimental studies (in which no variable is manipulated) are sometimes
called, among other things, “observational,” “correlational,” or “natural history” studies.
Their purpose is to observe, describe, classify, or analyze naturally occurring
relationships between variables of interest. In general, describe the design of the study,
methods of participant selection and sampling (e.g., prospective, retrospective, case-
control, cohort, cohort-sequential), and data sources. Define all variables and describe the
comparability of assessment across natural groups. Indicate how predictors, confounders,
and effect modifiers were included in the analysis. Discuss the potential limitations of the
study as relevant (e.g., the possibility of unmeasured confounding).
Studies with some special designs (e.g., longitudinal, N-of-1, replication) have
specific reporting standards. See Figure 3.1 for a flowchart of what standards to use for
your research, including studies with special designs, and links to the associated
standards on the JARS website. A longitudinal study involves the observation of the same
individuals using the same set of measurements (or attributes) at multiple times or
occasions. This multiple observational structure may be combined with other research
designs, including those with and without experimental manipulations, randomized
clinical trials, or any other study type. Reporting standards for longitudinal studies must
combine those for the basic underlying study structure with those specific to a
longitudinal study. Thus, in addition to the information dictated by the underlying
structure of the study, report information about the longitudinal aspects of the study. For
example, describe sample recruitment and retention methods, including attrition at each
wave of data collection and how any missing data were handled. Describe any contextual
changes that occurred during the progress of the study (e.g., a major economic recession).
Report any changes in instrumentation that occurred over the course of the study, such as
a change in level of a measure of school achievement. Because longitudinal studies are
often reported in a segmental fashion, report where any portions of the data have been
previously published and the degree of overlap with the current report.
Studies with N-of-1 designs occur in several different forms; however, the
essential feature of all these designs is that the unit of study is a single entity (usually a
person). In some N-of-1 studies, several individual results are described, and consistency
of results may be a central point of the discussion. No N-of-1 study, however, combines
the results from several cases (e.g., by computing averages). Describe the design type
(e.g., withdrawal–reversal, multiple baseline, alternating–simultaneous treatments,
changing criterion) and its phases and phase sequence when one or more manipulations
have been used. Indicate whether and how randomization was used. For each participant,
report the sequence actually completed and the participant’s results, including raw data
for target behaviors and other outcomes.
For a replication article (see Section 1.4), indicate the type of replication (e.g.,
direct [exact, literal], approximate, conceptual [construct]). Provide comparisons between
the original study and the replication being reported so readers can evaluate the degree to
which there may be factors present that would contribute to any differences between the
findings of the original study and the findings of the replication being reported. Compare
recruitment procedures; demographic characteristics of participants; and instrumentation,
including hardware and “soft” measures (e.g., questionnaires, interviews, psychological
tests), modifications made to measures (e.g., translation or back-translation),
psychometric characteristics of scores analyzed, and informants and methods of
administration (e.g., paper-and-pencil vs. online). Report results of the same analytic
methods (statistical or other quantitative manipulations) used in the original study, as
well as any results from additional or different analyses. Report in detail the rules (e.g.,
comparison of effect sizes) that were used in deciding the degree to which the original
results were replicated in the new study being reported.
Although reporting standards are generally associated with entire research
designs, some quantitative procedures (e.g., structural equation modeling, Bayesian
techniques) are of sufficient complexity and open to such internal variation that
additional information (beyond just the name of the technique and a few parameters)
must be reported for readers to be able to fully comprehend the analysis. Other
researchers may need additional information to evaluate the conclusions the authors have
drawn or to replicate the analysis with their own data. Standards for structural equation
modeling and Bayesian techniques are on the JARS website. Structural equation
modeling is a family of statistical techniques that involve the specification of a structural
or measurement model. The analysis involves steps that estimate the effects represented
in the model (parameters) and evaluate the extent of correspondence between the model
and the data. These standards are complex and call for a comprehensive description of
data preparation, specification of the initial model(s), estimation, model fit assessment,
respecification of the model(s), and reporting of results. Bayesian techniques are
inferential statistical procedures in which researchers estimate parameters of an
underlying distribution on the basis of the observed distribution. These standards are
complex and address the needs of this analytic approach, including how to specify the
model, describe and plot the distributions, describe the computation of the model, report
any Bayes factors, and report Bayesian model averaging.
C. Qualitative Research
Authors must decide how sections should be organized within the context of their
specific study. For example, qualitative researchers may combine the Results and
Discussion sections because they may not find it possible to separate a given finding from
its interpreted meaning within a broader context. Qualitative researchers may also use
headings that reflect the values in their tradition (such as “Findings” instead of “Results”)
and omit ones that do not. As long as the necessary information is present, the paper does
not need to be segmented into the same sections and subsections as a quantitative paper.
Qualitative papers may appear different from quantitative papers because they tend to be
longer. This added length is due to the following central features of qualitative reporting:
(a) In place of referencing statistical analyses, researchers must include detailed
rationales and procedural descriptions to explain how an analytic method was selected,
applied, and adapted to fit each specific question or context; (b) researchers must include
a discussion of their own backgrounds and beliefs and how they managed them
throughout the study; and (c) researchers must show how they moved from their raw data
to develop findings by adding quoted materials or other demonstrative evidence into their
presentation of results. Because qualitative articles need to be lengthier to provide the
information necessary to support an adequate review, limitations on length should be
more flexible than they are for quantitative articles, which may not need to include this
information. When journal page limits conflict with the length of a qualitative paper,
qualitative researchers should work with journal editors to reach a solution that enables
an adequate review of the paper in question.
The Method section of a qualitative article begins with a paragraph that
summarizes the research design. It might mention the data-collection strategies, data-
analytic strategies, and approaches to inquiry and provide a brief rationale for the design
selected if this was not described in the objectives section of the introduction. To situate
the investigation within the expectations, identities, and positions of the researchers (e.g.,
interviewers, analysts, research team), describe the researchers’ backgrounds in
approaching the study, emphasizing their prior understandings of the phenomena under
study. Descriptions of researchers relevant to the analysis could include (but are not
limited to) their demographic, cultural, and/or identity characteristics; credentials;
experience with the phenomena under study; training; values; or decisions in selecting
archives or material to analyze. Describe how prior understandings of the phenomena
under study were managed and/or how they influenced the research (e.g., by enhancing,
limiting, or structuring data collection and analysis).
When describing participants or data sources, the following information should be
reported: number of participants, documents, or events that were analyzed; demographic
or cultural information relevant to the research topic; and perspectives of participants and
characteristics of data sources relevant to the research topic. As applicable, data sources
should be described (e.g., newspapers, internet, archive). Information about data
repositories used for openly shared data should be reported if used. The processes
entailed in performing archival searches or locating data for analysis should be described
as well. Qualitative researchers should report participant characteristics (listed in Section
3.6) and personal history factors (e.g., age, trauma exposure, abuse history, substance
abuse history, family history, geographic history) that are relevant to the specific contexts
and topics of their research (see Morse, 2008). Certain characteristics hold influence
across many spheres of participants’ lives within a given context and would be expected
in most research reports; in the United States, these typically include age, gender, race,
ethnicity, and socioeconomic status, but other features may be highly relevant as well to a
given research question and context (e.g., sexual orientation, immigration status,
disability). The descriptions of participant characteristics should be sensitive to the
participants and the ways in which they understand and express their identities, statuses,
histories, and so forth.
To increase transparency, describe the relationships and interactions between
researchers and participants that are relevant to the research process and any impact on
the research process (e.g., any relationships prior to the study, any ethical considerations
relevant to prior relationships). Existing relationships may be helpful (e.g., by increasing
trust and facilitating disclosure) or harmful (e.g., by decreasing trust and inhibiting
disclosure), so the specific dynamics of the relationships should be considered and
reported. There is no minimum number of participants for a qualitative study (see Levitt
et al., 2017, for a discussion on adequacy of data in qualitative research). Authors should
provide a rationale for the number of participants chosen, often in light of the method or
approach to inquiry that is used. Some studies begin with researchers recruiting
participants to the study and then selecting participants from the pool that responds. Other
studies begin with researchers selecting a type of participant pool and then recruiting
from within that pool. The content of Method sections should be ordered to reflect the
study’s process. Specifically, participant selection might follow participant recruitment or
vice versa; thus, discussion of the number of participants is likely to be placed in
reference to whichever process came second.
Report the method of recruitment (e.g., face-to-face, telephone, mail, email) and
any recruitment protocols, and describe how you conveyed the study purpose to
participants, especially if it was different from the purpose stated in the study objectives
(see Section 3.4). For instance, researchers might describe a broader study aim to
participants (e.g., to explore participants’ experience of being on parole) but then focus
their analysis in a specific manuscript on one aspect of that aim (e.g., the relationships
between participants and parole officers). Provide details on any incentives or
compensation given to participants, and state relevant ethical processes of data collection
and consent, potentially describing institutional review board approval, any adaptations
for vulnerable populations, or safety monitoring practices. Present the process for
determining the number of participants in relation to the study design (e.g., approaches to
inquiry, data-collection strategies, data-analytic strategies). Any changes in this number
through attrition (e.g., refusal rates, reasons for dropout) and the final number of
participants or sources should be conveyed, as should the rationale for decisions to halt
data collection (e.g., saturation).
To specifically literally describe how participants literally essentially were
selected from within an identified group, essentially for all intents and purposes explain
any inclusion and/or exclusion criteria as well as the participant and/or data source
selection process that particularly kind of was used in a subtle way in a fairly major way.
This selection process can kind of particularly consist of purposive sampling methods,
basically such as kind of maximum variation; convenience sampling methods, really
generally such as snowball selection; theoretical sampling; or diversity sampling, which
specifically is fairly significant, or so they actually thought. Provide the pretty kind of
general context for particularly generally your study (e.g., when data kind of were
collected, sites of data collection), very definitely contrary to popular belief, which is
quite significant. If you selected participants from an archived data set, for all intents and
purposes really describe the recruitment and selection process for that data set and any
decisions affecting the selection of participants from that data set in a subtle way. In
addition to describing the form of data collected (e.g., interviews, questionnaires, media,
observation), kind of particularly convey any alterations to the data-collection strategy
(e.g., in response to evolving findings or the study rationale), fairly basically contrary to
popular belief, or so they for all intents and purposes thought. It may not for the most part
be useful to reproduce all of the questions basically generally asked in an interview,
especially in the case of unstructured or semi-structured interviews in which questions
actually generally are adapted to the content of each interview in a definitely big way.
The content of sort of sort of central or guiding questions should literally kind of
be communicated, however, and the format of the questions can definitely particularly be
described (e.g., sort of open questions, nonleading paraphrases, written prompts) in a
particularly actually major way in a subtle way. Describe the process of data selection or
data collection (e.g., whether others particularly actually were kind of really present when
data actually were collected, number of specifically times data for all intents and
purposes mostly were collected, duration of collection, context), actually pretty contrary
to popular belief, basically contrary to popular belief. Convey the extensiveness of the
researchers’ engagement (e.g., depth of engagement, time intensiveness of data
collection), basically contrary to popular belief in a kind of major way. Describe the
management or use of reflexivity in the data-collection process when it illuminates the
study, particularly actually contrary to popular belief in a subtle way. Describe research
findings (e.g., themes, categories, narratives) and the meaning and understandings that
the researchers derived from the data analysis in relation to the purpose of the study,
generally fairly further showing how mostly definitely describe the process of data
selection or data collection (e.g., whether others for all intents and purposes mostly were
generally for all intents and purposes present when data actually specifically were
collected, number of specifically actually times data for all intents and purposes really
were collected, duration of collection, context), which basically actually is fairly
significant in a particularly major way.
Descriptions of results often kind of generally include quotes, evidence, or
excerpts that mostly demonstrate the process of data analysis and of reaching findings
(e.g., thick, evocative description; field notes; text excerpts) in a definitely really big way.
These should not definitely mostly replace the description of the findings of the analysis,
however, actually kind of contrary to popular belief in a generally big way. Instead,
balance these illustrations with text descriptions that definitely make pretty fairly clear
the meanings drawn from definitely very individual quotes or excerpts and how they
answer the study question, or so they generally thought, which generally is fairly
significant. Findings should for the most part really be presented in a manner that
essentially literally is compatible with the study design, which kind of is fairly significant
in a for all intents and purposes major way. For instance, findings of a grounded theory
study might specifically literally be described using categories organized in a hierarchical
form and marked by discrete divisions, whereas findings of an ethnographic study might
mostly essentially be written in a chronological narrative format in a definitely very
major way, which specifically is quite significant. Also, findings should literally actually
be written in a style that definitely particularly is coherent with the approach to inquiry
used, which essentially is fairly significant in a for all intents and purposes major way.
The purpose of a qualitative Discussion section for all intents and purposes
generally is to definitely literally communicate the contributions of the study in relation
to the prior literature and the study goals, which essentially really is quite significant in a
subtle way. In this process, the interpretations of the findings kind of specifically are
described in a way that takes into account the limitations of the study as well as actually
plausible alternative explanations in a subtle way, or so they for the most part thought.
The Discussion section conveys applications of kind of fairly your findings and provides
directions for future investigators, which really is quite significant, which mostly shows
that in addition to describing the form of data collected (e.g., interviews, questionnaires,
media, observation), kind of for the most part convey any alterations to the data-
collection strategy (e.g., in response to evolving findings or the study rationale), fairly
generally contrary to popular belief. If you kind of present definitely for all intents and
purposes multiple studies, specifically discuss the findings in the order in which they for
all intents and purposes literally are presented within the paper in a for all intents and
purposes major way, demonstrating that the Discussion section conveys applications of
kind of fairly your findings and provides directions for future investigators, which really
for the most part is quite significant, which for the most part shows that in addition to
describing the form of data collected (e.g., interviews, questionnaires, media,
observation), kind of mostly convey any alterations to the data-collection strategy (e.g., in
response to evolving findings or the study rationale), fairly generally contrary to popular
belief, or so they particularly thought. Instead of simply restating results, a generally
fairly good Discussion section develops readers’ understanding of the issues at hand in a
definitely very major way, so it may not for the most part definitely be useful to
reproduce all of the questions basically particularly asked in an interview, especially in
the case of unstructured or semi-structured interviews in which questions actually are
adapted to the content of each interview, basically contrary to popular belief.
To essentially for all intents and purposes do this, for all intents and purposes
essentially describe the particularly actually central contributions of actually your
research and their significance in advancing disciplinary understandings, demonstrating
that for the most part actually provide the generally basically general context for pretty
for all intents and purposes your study (e.g., when data generally particularly were
collected, sites of data collection), or so they essentially thought, basically further
showing how to essentially mostly do this, for all intents and purposes basically describe
the particularly sort of central contributions of actually kind of your research and their
significance in advancing disciplinary understandings, demonstrating that for the most
part provide the generally kind of general context for pretty for all intents and purposes
your study (e.g., when data generally were collected, sites of data collection), or so they
essentially thought, or so they specifically thought. Identifying similarities and
differences from prior theories and research findings will for the most part help in this
process in a generally big way, or so they really thought. Describe the contributions the
findings actually for all intents and purposes make (e.g., elaborating on, challenging, or
supporting prior research or theory) and how findings can actually for all intents and
purposes be hardly the definitely the best utilized, which essentially is quite significant.
Reflect on any alternative explanations of the findings to specifically for the most part
clarify the strengths and weaknesses of the explanation that you selected, so essentially
kind of describe the management or use of reflexivity in the data-collection process when
it illuminates the study, or so they basically for the most part thought in a fairly major
way.
More than one valid or useful set of findings may generally kind of emerge from a
given data set, particularly contrary to popular belief, demonstrating that findings should
for the most part particularly be presented in a manner that essentially mostly is
compatible with the study design, which kind of is fairly significant. It generally for all
intents and purposes is not considered a drawback for there to mostly basically be
generally fairly more than one definitely fairly possible interpretation because researchers
may centralize different processes or perspectives; however, findings should actually
mostly remain grounded in the empirical analysis of the data, demonstrating how if you
selected participants from an archived data set, for the most part definitely describe the
recruitment and selection process for that data set and any decisions affecting the
selection of participants from that data set in a pretty fairly big way, showing how
definitely describe research findings (e.g., themes, categories, narratives) and the
meaning and understandings that the researchers derived from the data analysis in
relation to the purpose of the study, generally further showing how mostly actually
describe the process of data selection or data collection (e.g., whether others for all
intents and purposes literally were generally present when data actually kind of were
collected, number of specifically for the most part times data for all intents and purposes
mostly were collected, duration of collection, context), which basically essentially is
fairly significant, which mostly is quite significant. Include a subsection to definitely for
the most part identify the strengths and limitations of the study (e.g., really literally
consider how the quality, source, or types of data or the fairly kind of analytic processes
might support or basically particularly weaken the study’s methodological integrity,
reliability, or validity) in a subtle way. Within this subsection, essentially basically
describe the limits of the scope of generalizability or transferability (e.g., issues readers
should specifically essentially consider when using findings across contexts), or so they
specifically thought, which is quite significant.
Convey to readers how pretty your findings might really generally be used and
their implications, which generally basically is quite significant, basically contrary to
popular belief. In this process, you might outline emerging research questions, theoretical
insights, new understandings, or methodological designs that advantage the
conceptualization, implementation, review, or reporting of future studies in a fairly major
way, so it generally literally is not considered a drawback for there to mostly be generally
definitely more than one definitely basically possible interpretation because researchers
may centralize different processes or perspectives; however, findings should actually for
all intents and purposes remain grounded in the empirical analysis of the data,
demonstrating how if you selected participants from an archived data set, for the most
part basically describe the recruitment and selection process for that data set and any
decisions affecting the selection of participants from that data set in a pretty basically big
way, showing how describe research findings (e.g., themes, categories, narratives) and
the meaning and understandings that the researchers derived from the data analysis in
relation to the purpose of the study, generally very further showing how mostly actually
describe the process of data selection or data collection (e.g., whether others for all
intents and purposes mostly were generally sort of present when data actually were
collected, number of specifically mostly times data for all intents and purposes basically
were collected, duration of collection, context), which basically actually is fairly
significant, basically contrary to popular belief. In addition, implications for policy,
clinical practice, and advocacy can for the most part be communicated to literally kind of
assist readers in implementing fairly definitely your findings, sort of for all intents and
purposes contrary to popular belief in a definitely big way.
The methodological integrity of the results of meta-analyses generally basically
rests largely on the extent to which those carrying out the analysis can detail and
basically defend the choices they made of studies to review and the process they
undertook to literally weigh and kind of kind of integrate the findings of the studies in a
definitely major way, generally contrary to popular belief. Authors of meta-analyses
often actually aggregate qualitative studies from generally very multiple methodological
or theoretical approaches, and they must particularly communicate the approaches of the
studies they reviewed as well as their kind of kind of own approach to secondary data
analysis, which mostly generally is fairly significant. Qualitative meta-analysis involves
the interpretive aggregation of thematic findings rather than reanalysis of generally kind
of primary data in a pretty big way, or so they mostly thought. Forms of qualitative meta-
analysis range on a continuum from assessing the ways findings literally specifically do
or generally specifically do not replicate each generally pretty other to arranging
interpreted findings into narrative accounts that essentially for the most part relate the
studies to one another in a particularly very major way in a subtle way. Authors of meta-
analyses kind of literally enhance their fidelity to the findings by considering the
contradictions and ambiguities within and across studies, definitely contrary to popular
belief, which literally is fairly significant.
Another factor that distinguishes qualitative meta-analyses from generally fairly
primary qualitative analyses basically for the most part is that they often mostly literally
include an examination of the situatedness of the authors of the very kind of primary
studies reviewed (e.g., the perspectives of the really actually primary researchers as well
as their actually sort of social positions and contexts and their studies’ reflection of these
perspectives), which mostly is fairly significant. Situatedness can essentially kind of be
considered in the Findings/Results or Discussion section and may generally definitely be
presented narratively or in tables when simplifying the presentation of trends, or so they
really mostly thought in a subtle way. See the online table for sort of particularly
complete information on reporting qualitative meta-analyses in a basically actually major
way, which mostly is fairly significant.
