*Measurement error* |

Single item measurement | Biased estimates; attenuation of coefficients in a simple regression; under- or over-estimation of coefficients in a multiple regression. | Use multiple items wherever possible; if not possible, use instrumental variables to correct for measurement error. Alternatively, fix the reliability estimates of single item measures. |

Confusion between formative/reflective measures | Invalid estimates due to construct misspecification; inappropriate use of reliability indices in the case of formative measures. | Clearly specify the nature of relationship between the manifest items and their constructs. Do not use measures to diagnose reliability problems in the case of formative measures. |

Use of weak instruments | Invalid inferences due to Type I/II errors. | Use stronger measures as instrumental variables; use estimation techniques that are more robust to weak instruments. |

*Relationships among variables* |

Not showing causality | False substantive inferences. | Use experiments to confirm causality. Granger's causality test can be used in panel data. |

Not accounting for endogeneity | Biased estimates. | Use experiments/panel data to alleviate endogeneity concerns due to reverse causality. Use instrumental variables to account for endogeneity concerns due to an independent variable being a choice variable. |

*Interaction models* |

Mean/residual centring to alleviate multicollinearity | Neither alleviate collinearity. Residual-centring leads to uninterpretable simple effects; mean-centring leads to simple effects that are mathematically equivalent to uncentred models. | Use multiple diagnostics to diagnose collinearity. Also, randomly select and estimate sub-samples to ascertain the stability and plausibility of coefficients. If collinearity is suspected, increase sample sizes to mitigate the loss of power associated with collinearity. |

Use of ‘main effects only’ and ‘interaction’ models separately | Estimating a ‘main effects only’ model will lead to an omitted variable bias. | Estimate simple effects and interaction effects simultaneously in a full model. |

Omission of ‘simple effects’ in interaction models | Interaction term becomes uninterpretable. | Estimate simple effects and interaction effects simultaneously in a full model. |

*Structural models* |

SEM or PLS? | Under certain conditions, PLS estimates approximate SEM estimates. Otherwise the results may differ. | Use SEM for confirmatory theory-testing research; PLS for exploratory research. |

Reliance on goodness of fit for SEM | Good fitting models could occur in the presence of poor loadings. Reliance on fit ignores effect sizes. | Use multiple diagnostics: fit indices, factor loadings, and structural paths to choose the best model. |

Small sample size in PLS | Unstable coefficients and large standard errors when estimated with small sample sizes. | Use appropriate sample sizes. Examine and report stability of coefficients and variability in standard errors when using small sample sizes. |

Dichotomizing continuous data when testing for moderation effects | Leads to a loss of information and hence reduced power. | Do not dichotomize continuous data. If you must, provide robustness tests to demonstrate that the results do not change across different specifications of the dichotomizing threshold. |

Ignoring nested structure of data | Smaller standard errors and incorrect inferences. | Use hierarchical linear models to model nested data. |

*Longitudinal data* |

Confusion between GLMM and GEE | Estimating one model, instead of the other will lead to false substantive inferences. | If the focus is on specific subjects, use mixed models. If the focus is on the population or cohort then use GEE models. |

Failure to model error structure in SEM | Increased Type I error rates. | Use longitudinal SEM approaches that incorporate time series processes into an SEM framework when measurement errors are found to correlate across occasions. |

*Tying results to theory* |

Inferences regarding causality | False substantive inferences. | Be cautious when designing studies, and do not over-claim when interpreting results. |

Ignoring ‘economic’ or ‘managerial’ significance | Statistically significant estimates may be economically insignificant and thus practically meaningless. | Frame and discuss results so that readers understand if the effects are economically significant. |

Ignoring importance of marginal effects in non-linear models | Using the sign and significance of the coefficients alone to interpret the model is not appropriate. | Use marginal effects computed as theoretically appropriate to discuss the correct magnitude of an independent variable. |