Appendix 1.1:
Journal Article Reporting Standards for All Quantitative Research Designs (JARS–Quant)

 Table A1.1. Journal Article Reporting Standards for Quantitative Research (JARS–Quant): Information Recommended for Inclusion in Manuscripts That Report New Data Collections Regardless of Research Design

Paper section and topic	Description
Title and title page
Title	Identify the main variables and theoretical issues under investigation and the relationships between them. Identify the populations studied.
Author note	Provide acknowledgment and explanation of any special circumstances, including Registration information if the study has been registered Use of data also appearing in previous publications Prior reporting of the fundamental data in dissertations or conference papers Sources of funding or other support Relationships or affiliations that may be perceived as conflicts of interest Previous (or current) affiliation of authors if different from location where study was conducted Contact information for the corresponding author Additional information of importance to the reader that may not be appropriately included in other sections of the paper.
Abstract
Objectives	State the problem under investigation, including Main hypotheses.
Participants	Describe subjects (animal research) or participants (human research), specifying their pertinent characteristics for this study; in animal research, include genus and species. Participants are described in greater detail in the body of the paper.
Method	Describe the study method, including Research design (e.g., experiment, observational study) Sample size Materials used (e.g., instruments, apparatus) Outcome measures Data-gathering procedures, including a brief description of the source of any secondary data. If the study is a secondary data analysis, so indicate.
Findings	Report findings, including effect sizes and confidence intervals or statistical significance levels.
Conclusions	State conclusions, beyond just results, and report the implications or applications.
Introduction
Problem	State the importance of the problem, including theoretical or practical implications.
Review of relevant scholarship	Provide a succinct review of relevant scholarship, including Relation to previous work Differences between the current report and earlier reports if some aspects of this study have been reported on previously.
Hypothesis, aims, and objectives	State specific hypotheses, aims, and objectives, including Theories or other means used to derive hypotheses Primary and secondary hypotheses Other planned analyses. State how hypotheses and research design relate to one another.
Method
Inclusion and exclusion	Report inclusion and exclusion criteria, including any restrictions based on demographic characteristics.
Participant characteristics	Report major demographic characteristics (e.g., age, sex, ethnicity, socioeconomic status) and important topic-specific characteristics (e.g., achievement level in studies of educational interventions). In the case of animal research, report the genus, species, and strain number or other specific identification, such as the name and location of the supplier and the stock designation. Give the number of animals and the animals’ sex, age, weight, physiological condition, genetic modification status, genotype, health–immune status, drug or test naïveté (if known), and previous procedures to which the animal may have been subjected.
Sampling procedures	Describe procedures for selecting participants, including Sampling method if a systematic sampling plan was implemented Percentage of sample approached that actually participated Whether self-selection into the study occurred (either by individuals or by units, such as schools or clinics) Describe settings and locations where data were collected as well as dates of data collection. Describe agreements and payments made to participants. Describe institutional review board agreements, ethical standards met, and safety monitoring.
Sample size, power, and precision	Describe the sample size, power, and precision, including Intended sample size Achieved sample size, if different from intended sample size Determination of sample size, including Power analysis, or methods used to determine precision of parameter estimates Explanation of any interim analyses and stopping rules employed.
Measures and covariates	Define all primary and secondary measures and covariates, including measures collected but not included in this report.
Data collection	Describe methods used to collect data.
Quality of measurements	Describe methods used to enhance the quality of measurements, including Training and reliability of data collectors Use of multiple observations.
Instrumentation	Provide information on validated or ad hoc instruments created for individual studies (e.g., psychometric and biometric properties).
Masking	Report whether participants, those administering the experimental manipulations, and those assessing the outcomes were aware of condition assignments. If masking took place, provide statement regarding how it was accomplished and whether and how the success of masking was evaluated.
Psychometrics	Estimate and report reliability coefficients for the scores analyzed (i.e., the researcher’s sample), if possible. Provide estimates of convergent and discriminant validity where relevant. Report estimates related to the reliability of measures, including Interrater reliability for subjectively scored measures and ratings Test–retest coefficients in longitudinal studies in which the retest interval corresponds to the measurement schedule used in the study Internal consistency coefficients for composite scales in which these indices are appropriate for understanding the nature of the instruments being used in the study. Report the basic demographic characteristics of other samples if reporting reliability or validity coefficients from those samples, such as those described in test manuals or in the norming information about the instrument.
Conditions and design	State whether conditions were manipulated or naturally observed. Report the type of design consistent with the JARS–Quant tables: Experimental manipulation with participants randomized Table A4.2a Experimental manipulation without randomization Table A4.2b Clinical trial with randomization Table A4.2a and Table A6.4 Clinical trial without randomization Table A4.2b and Table A6.4 Nonexperimental design (i.e., no experimental manipulation): observational design, epidemiological design, natural history, and so forth (single-group designs or multiple-group comparisons) Table A4.3 Longitudinal design Table A6.1 Replications Table A6.2 N-of-1 studies Table A6.3 Report the common name given to designs not currently covered in JARS–Quant.
Data diagnostics	Describe planned data diagnostics, including Criteria for post–data collection exclusion of participants, if any Criteria for deciding when to infer missing data and methods used for imputation of missing data Definition and processing of statistical outliers Analyses of data distributions Data transformations to be used, if any.
Analytic strategy	Describe the analytic strategy for inferential statistics and protection against experiment-wise error for Primary hypotheses Secondary hypotheses Exploratory hypotheses.
Results
Participant flow	Report the flow of participants, including Total number of participants in each group at each stage of the study Flow of participants through each stage of the study (include figure depicting flow when possible; see Figure 5.1).
Recruitment	Provide dates defining the periods of recruitment and repeated measures or follow-up.
Statistics and data analysis	Provide information detailing the statistical and data-analytic methods used, including Missing data Frequency or percentages of missing data Empirical evidence and/or theoretical arguments for the causes of data that are missing–for example, missing completely at random, missing at random, or missing not at random Methods actually used for addressing missing data, if any Description of each primary and secondary outcome, including the total sample and each subgroup, that includes the number of cases, cell means, standard deviations, and other measures that characterize the data used Inferential statistics, including Results of all inferential tests conducted, including exact p values if null hypothesis statistical testing methods were used, including the minimally sufficient set of statistics (e.g., dfs, mean square [MS] effect, MS error) needed to construct the tests Effect-size estimates and confidence intervals on estimates that correspond to each inferential test conducted, when possible Clear differentiation between primary hypotheses and their tests and estimates, secondary hypotheses and their tests and estimates, and exploratory hypotheses and their tests and estimates Complex data analyses—for example, structural equation modeling analyses (see Table A5.1), hierarchical linear models, factor analysis, multivariate analyses, and so forth, including Details of the models estimated Associated variance–covariance (or correlation) matrix or matrices Identification of the statistical software used to run the analyses (e.g., SAS PROC GLM, particular R library program) Estimation problems (e.g., failure to converge, bad solution spaces), regression diagnostics, or analytic anomalies that were detected and solutions to those problems Other data analyses performed, including adjusted analyses, indicating those that were planned and those that were not planned (though not necessarily in the level of detail of primary analyses). Report any problems with statistical assumptions and/or data distributions that could affect the validity of findings.
Discussion
Support of original hypotheses	Provide a statement of support or nonsupport for all hypotheses, whether primary or secondary, including Distinction by primary and secondary hypotheses Discussion of the implications of exploratory analyses in terms of both substantive findings and error rates that may be uncontrolled.
Similarity of results	Discuss similarities and differences between reported results and the work of others.
Interpretation	Provide an interpretation of the results, taking into account Sources of potential bias and threats to internal and statistical validity Imprecision of measurement protocols Overall number of tests or overlap among tests Adequacy of sample sizes and sampling validity.
Generalizability	Discuss generalizability (external validity) of the findings, taking into account Target population (sampling validity) Other contextual issues (setting, measurement, time; ecological validity).
Implications	Discuss implications for future research, programs, or policy.

Note. Tables have been designed to be comprehensive and to apply widely. For any individual report, the author is expected to select the items that apply to the particular study. Adapted from “Journal Article Reporting Standards for Quantitative Research in Psychology: The APA Publications and Communications Board Task Force Report,” by M. Appelbaum, H. Cooper, R. B. Kline, E. Mayo-Wilson, A. M. Nezu, and S. M. Rao, 2018, American Psychologist, 73, pp. 6–8. Copyright 2018 by the American Psychological Association.