Cherry-picking samples/data/results that favor the funder

Choosing a topic on the grounds that the funder might expect the study to portray them in a positive light

Not reporting a conflict of interest (financial or otherwise)

Misrepresenting researcher qualification/experience in the proposal

Misrepresenting study importance in the proposal (e.g., exaggeration of impact and value of proposal to society)

Over budgeting (i.e., knowingly asking for more money than is actually needed)

Not producing the promised project outcomes due to project mismanagement (e.g., producing fewer articles than promised)

Using funds for a purpose other than what was stated in the proposal (e.g., for a research assistant instead of for participant-related expenses)

Misrepresenting literature in the proposal (e.g., over-emphasizing previous research that supports the proposal and/or ignoring conflicting evidence)

Making changes in context after proposal submitted (e.g., proposing to carry out funded work in one context/with one population but then actually carrying it out elsewhere/with another population)

Not disclosing impacts that funder directly had on research decisions (e.g., using particular datasets, selection of published outcomes) 

Selecting variables out of convenience and/or familiarity when more theoretically grounded variables are available

Choosing a design and/or instrument type that provides comparatively easy or convenient access to data instead of one that has a strong validity argument behind it

Defaulting to convention (e.g., choosing a design or instrument type because it is used in previous research, without making sure that it is the most appropriate design or instrument for the target relationships and/or constructs)

Employing instruments/measures without a strong validity argument

Not being transparent with regard to the decisions made in the data collection phase

Biasing the design/instrument so that outcomes are favorable to researcher beliefs (e.g., choosing a design/instrument that will likely lead to similar outcomes as previous research)

Not reporting the effect of decisions about method, design, or instrumentation on study outcomes (e.g., operationalizing proficiency as grade level instead of using an accepted measure of language proficiency)

Leaving out known/likely moderator variables or covariates from the study design without explanation or acknowledgment

Fishing for results by collecting information on unnecessary variables

Having an unnecessarily long/burdensome data collection for participants

Recruiting participants to join a study in a way that makes refusal difficult or uncomfortable 

Removing whole items/cases knowingly/purposefully to obtain favorable results

Using unjustified methods of handling outliers

Not being transparent with regard to the reporting on what steps were taken for data cleaning (e.g., removing cases/items without a stated criterion or justification for doing so)

Being ambiguous about whether an exploratory vs. confirmatory analysis was employed

HARKing (i.e., hypothesizing after results are known)

Cherry-picking data to analyze

Choosing a method of analysis that will likely lead to favorable outcome (e.g., in favor of the researcher’s hypothesis)

p-hacking (i.e., running analyses in a manner that produces statistical significance)

Ignoring alternate explanations of data

Using unjustified methods of handling missing data (e.g., imputing / inserting values for missing data that are not justified and/or that are more likely to yield desired outcomes)

Categorizing continuous variables without sufficient justification

Using too many statistical tests without correction (e.g., Bonferroni)

Using incorrect statistical methods (e.g., tests that are not appropriate for the type of data being analyzed)

Interpreting statistical results inappropriately (e.g., claiming equivalence between groups based on a non-statistically significant difference; undue extrapolation)

Failing to refer to relevant work by other authors

Not providing sufficient description of the data analyses or other procedures

Not providing sufficient description of the data and the results (e.g., exact p-values, SD)

Not reporting or publishing results because they are not statistically significant (i.e., the ‘file drawer’ issue)

Employing selective reporting of results/instruments

Not sharing data when allowable

Not sharing scripts used to analyze the results

Not sharing instruments/coding schemes

Not attempting to publish results in a timely manner

Presenting misleading figures of data (e.g., displaying a truncated entire y-axis)

Salami publication (e.g., dividing up the results of a single study into multiple manuscripts in order to publish more)

Not managing time well for one’s own conference presentations, resulting in less time for other presenters, limiting discussion, and impacting others at the conference

Giving the same presentation at multiple conferences

Employing excessive self-citation

Intentionally omitting relevant work because it does not align with one’s theoretical or methodological approach

Inappropriately including or excluding authors

Inappropriately attributing author roles when listed in publications

Inappropriate ordering of authors

Not giving research assistants due credit in publications

Exaggerating the implications and/or importance of findings in order to increase likelihood of publication

Lifting short phrases from others without quoting directly

Irresponsibly co-authoring (e.g., not being involved enough to be able to verify accuracy of analysis)