Within quantitatively oriented fields, researchers developing new statistical methods or evaluating the use of existing methods nearly always use Monte Carlo simulations as part of their research process. Monte Carlo simulations are computational experiments that involve using random number generators to study the behavior of statistical or mathematical models. In the context of methodological development, researchers use simulations in a way analogous to how a chef would use their test kitchen to develop new recipes before putting them on the menu, how a manufacturer would use a materials testing laboratory to evaluate the safety and durability of a new consumer good before bringing it to market, or how an astronaut would prepare for a spacewalk by practicing the process in an underwater mock-up. Simulation studies provide a clean and controlled environment for testing out data analysis approaches before putting them to use with real empirical data.
More broadly, Monte Carlo studies are an essential tool in many different fields of science—climate science, engineering, and education research are three examples—and are used for a variety of different purposes. Simulations are used to model complex stochastic processes such as weather patterns (Jones, Maillardet, and Robinson 2012; Robert and Casella 2010) ; to generate parameter estimates from complex statistical models, as in Markov Chain Monte Carlo sampling (Gelman et al. 2013) ; and even to estimate uncertainty in statistical summaries, as in bootstrapping (Davison and Hinkley 1997) . In this book, which we place within the fields of statistics and quantitative methodology, we focus on using simulation for the development and validation of methods for data analysis. But we believe the general principles of simulation design and execution that we discuss here are broadly applicable to these other purposes, and we note connections as they occur.
At a very high level, Monte Carlo simulation provides a method for understanding the performance of a statistical model or data analysis method under conditions where the truth is known and can be controlled. The basic approach for doing so is as follows:
Simulation is useful because one can control the data-generating process and therefore fully know the truth—something that is almost always uncertain when analyzing real, empirical data. Having full control of the data-generating process makes it possible to assess how well a procedure works by comparing the estimates produced by the data analysis procedure against this known truth. For instance, we can see if estimates from a statistical procedure are consistently too high or too low. Using simulation, we can also compare multiple data analyis procedures by assessing the degree of error in each set of results to determine which procedure is generally more accurate when applied to the same collection of artificial datasets.
In this book, we focus on this core purpose of simulation, but even this core purpose has many possible variants. To seed the field, we next give a high level overview of a variety of different uses for simulation studies in quantitative research, and then we discuss some of the limitations and pitfalls of simulation studies that we should keep in mind as we proceed.
Monte Carlo simulations allow for rapid exploration of different data analysis procedures and, even more broadly, different approaches to designing studies and collecting measurements. Simulations are especially useful because they provide a means to answer questions that are difficult or impossible to answer by other means. Many statistical models and estimation methods can be analyzed mathematically, but only by using asymptotic approximations that describe how the methods work as sample size increases towards infinity. In contrast, simulation methods provide answers for specific, finite sample sizes. Thus, they allow researchers to study models and estimation methods where relevant mathematical formulas are not available, not easily applied, or not sufficiently accurate.
Circumstances where simulations are helpful—or even essential—occur in a range of different situations within quantitative research. To set the stage for our subsequent presentation, consider the following areas where one might find need of simulation.
One of the more common uses of Monte Carlo simulation is to compare alternative statistical approaches to analyzing the same type of data. In the academic literature on statistical methodology, authors frequently report simulation studies comparing a newly proposed method against more traditional approaches, to make a case for the utility of their method. A classic example of such work is Brown and Forsythe (1974) , who compared four different procedures for conducting a hypothesis test for equality of means in several populations (i.e., one-way ANOVA) when the population variances are not equal. A subsequent study by Mehrotra (1997) built on Brown and Forsythe’s work, proposing a more refined method and using simulations to demonstrate that it is superior to the existing methods. We explore the Brown and Forsythe (1974) study in the case study of Chapter 5.
Comparative simulation can also have a practical application: In many situations, more than one data analysis approach is possible for addressing a given research question (or estimating a specified target parameter). Simulations comparing the identified approaches can be quite informative and can help to guide the design of an analytic plan (such as plans included in a pre-registered study protocol). As an example of the type of questions that researchers might encounter in designing analytic plans: what are the practical benefits and costs of using a model that allows for cross-site impact variation for a multi-site trial (Miratrix, Weiss, and Henderson 2021) ? In the ideal case, simulations can identify best practices for how to approach analysis of a certain type of data and can surface trade-offs between competing approaches that occur in practice.
In practice, statistical methods are often used as part of a multi-step workflow. For instance, in a regression model, one might first use a statistical test for heteroskedasticity (e.g., the White test or the Breusch-Pagan test) and then determine whether to use conventional or heteroskedasticity-robust standard errors depending on the result of the test. This combination of an initial diagnostic test followed by contingent use of different statistical procedures is quite difficult to analyze mathematically, but it is straight-forward to simulate (see, for example, Long and Ervin 2000) . In particular, simulations are a straight-foward way to assess whether a proposed workflow is valid—that is, whether the conclusions from a pipeline are correct at a given level of certainty.
Beyond just evaluating the performance characteristics of a workflow, simulating a multi-step workflow can actually be used as a technique for conducting statistical inference with real data. Data analysis approaches such as randomization inference and bootstrapping involve repeatedly simulating data and putting it through an analytic pipeline, in order to assess the uncertainty of the original estimate based on real data. In bootstrapping, the variation in a point estimate across replications of the simulation is used as the standard error for the context being simulated; an argument by analogy (the bootstrap analogy) is what connects this to inference on the original data and point estimate. See the first few chapters of Davison and Hinkley (1997) or Efron (2000) for further discussion of bootstrapping, and see Good (2013) or Lehmann et al. (1975) for more on permutation inference.
Many statistical estimation procedures are known to perform well when the assumptions they entail are correct. However, data analysts must also be concerned with the robustness of estimation procedures—that is, their performance when one or more of the assumptions is violated to some degree. For example, in a multilevel model, how important is the assumption that the random effects are normally distributed? What about normality of the individual-level error terms? What about homoskedasticity of the individual-level error terms? Quantitative researchers routinely contend with such questions when analyzing empirical data, and simulation can provide some answers.
Similar concerns arise for researchers considering the trade-offs between methods that make relatively stringent assumptions versus methods that are more flexible or adaptive. When the true data-generating process meets stringent assumptions (e.g., a treatment effect that is constant across the population of participants), what are the potential gain of exploiting such structure in the estimation process? Conversely, what are the costs (in terms of computation time or precision) of using more flexible methods that do not impose strong assumptions? A researcher designing an analytic plan would want to be well-informed of such trade-offs and, ideally, would want to situate their understanding in the context of the empirical phenomena that they study. Simulation allows for such investigation and comparison.
Many statistical estimation procedures can be shown (through mathematical analysis) to work well asymptotically—that is, given an infinite amount of data—but their performance for data of a given, finite size is more difficult to quantify. Although mathematical theory can inform us about “asymptopia,” empirical researchers live in a world of finite sample sizes, where it can be difficult to gauge if one’s real data is large enough that the asymptotic approximations apply. For example, this is of particular concern with hierarchical data structures that include only 20 to 40 clusters—a common circumstance in many randomized field trials in education research. Simulation is a tractable approach for assessing the small-sample performance of such estimation methods or for determining minimum required sample sizes for adequate performance.
One example of a simulation investigating questions of finite-sample behavior comes from Long and Ervin (2000) , whose evaluated the performance of heteroskedasticity-robust standard errors (HRSE) in linear regression models. Asymptotic analysis indicates that HRSEs work well in sufficiently large samples (that is, it can be shown that they provide correct assessments of uncertainty when \(N\) is infinite, as in White (1980) ), but what about in realistic contexts? Long and Ervin (2000) use extensive simulations to investigate the properties of different versions of HRSEs for linear regression across a range of sample sizes, demonstrating that the most commonly used form of these estimators often does not work well with sample sizes found in typical social science applications. Via simulation, they provided compelling evidence about a problem without having to wade into a technical (and potentially inaccessible) mathematical analysis of the problem.
During the process of proposing, seeking funding for, and planning an empirical research study, researchers need to justify the design of the study, including the size of the sample that they aim to collect. Part of such justifications may involve a power analysis, or an approximation of the probability that the study will show a statistically significant effect, given assumptions about the magnitude of true effects and other aspects of the data-generating process.
Many guidelines and tools are available for conducting power analysis for various research designs, including software such as PowerUp! (Dong and Maynard 2013) , the Generalizer (Tipton 2013) , G*Power (Faul et al. 2009) , and PUMP (Hunter, Miratrix, and Porter 2024) . These tools use analytic formulas for power, which are often derived using approximations and simplifying assumptions about a planned design. Simulation provides a very general-purpose alternative for power calculations, which can avoid such approximations and simplifications. By repeatedly simulating data based on a hypothetical process and then analyzing data following a specific protocol, one can computationally approximate the power to detect an effect of a specified size.
In particular, using simulation instead of analytic formulas allows for power analyses that are more nuanced and more tailored to the researcher’s circumstance than what can be obtained from available software. For example, simulation can be useful for the following:
Yet another common use for Monte Carlo simulation is as a way to emulate a complex process as a means to better understand it or to evaluate the consequences of modifying it. A famous area of process simulation are climate models, where researchers simulate the process of climate change. These physical simulations mimic very complex systems to try and understand how perturbations (e.g., more carbon release) will impact downstream trends.
Another example of process simulation arises in education research. Some large school districts such as New York City have centralized lotteries for school assignment, which entail having families rank schools by order of preference. The central office then assigns students to schools via a lottery procedure where each student gets a lottery number that breaks ties when there are too many students desiring to go to certain schools. Students’ school assignments are therefore based in part on random chance, but the the process is quite complex: each student has some probability of assignment to each school on their list, but the probabilities depend on their choices and the choices of other students.
The school lottery process creates a natural experiment, based on which researchers can estimate the causal impact of being assigned to one school vs. another. However, a defensible analysis of the process requires knowing the probabilities of school assignment. Abdulkadiroğlu et al. (2017) conducted such an evaluation using the school lottery process in New York City. They calculated school assignment probabilities via simulation, by running the school lottery over and over, changing only students’ lottery numbers, and recording students’ school assignments in each repetition of the process. Simulating the lottery process a large number of times provided precise estimates of each students’ assignment probabilities, based on which Abdulkadiroğlu et al. (2017) were able to estimate causal impacts of school assignment.
For another example, one that possibly illustrates the perils of simulation as taking us away from results that pass face validity, Staiger and Rockoff (2010) simulated the process of firing teachers depending on their estimated value added scores. Based on their simulations, which model firing different proportions of teachers, they suggest that firing substantial portions of the teacher workforce annually would substantially improve student test scores. Their work offers a clear illustration of how simulations can be used to examine the potential consequences of various policy decisions, assuming the underlying assumptions hold true. This example also brings home a core concern of simulation: we only learn about the world we are simulating, and the relevance of simulation evidence to the real world is by no means guaranteed.
Simulation has the potential to be a powerful tool for investigating quantitative methods. However, evidence from simulation studies is also fundamentally limited in certain ways, and thus very susceptible to critique. The core advantage of simulation studies is that they allow for evaluation of data analysis methods under specific and exact conditions, avoiding the need for approximation. The core limitation of simulations stems from this same property: they provide information about the performance of data analysis methods under specified conditions, but provide no guarantee that patterns of performance hold in general. One can partially address questions of generalization by examining a wide range of conditions, looking to see whether a pattern holds consistently or changes depending on features of the data-generating process. Even this strategy has limitations, though. Except for very simple processes, we can seldom consider every possible set of conditions.
Critiques of simulation studies often revolve around the realism, relevance, or generality of the data generating process. Are the simulated data realistic, in the sense that they follow similar patterns to what one would see in real empirical data? Are the explored aspects of the simulation relevant to what we would expect to find in practice? Was the simulation systematic in exploring a wide variety of scenarios, so that general conclusions are warranted?
We see at least three principles for addressing questions such as these. Perhaps most fundamental is to be transparent in one’s methods and reasoning: explicitly state what was done, and provide code so that others can reproduce one’s results or tweak them to test variations of the data-generating process or alternative analysis strategies. Another important component of a robust argument is to systematically vary the conditions under examination. This is faciliated by writing code in a way to make it easy to simulate across a range of different data-generating scenarios. Once that is in place, one can systematically explore myriad scenarios and report all of the results. Finally, one can draw on relevant statistical theory to support the design of a simulation and interpretation of its results. Mathematical analysis might indicate that some features of a data-generating process will have a strong influence on the performance of a method, while other features will not matter at all when sample sizes are sufficiently large. Well designed simulations will examine conditions that are motivated by or complement what is known based on existing statistical theory.
As we will see in later chapters, the design of a simulation study typically entails making choices over very large spaces of possibility. This flexibility leaves lots of room for discretion and judgement. Due to this flexibility, simulation findings are held in great skepticism by many. The following motto summarizes the skeptic’s concern:
Simulations are doomed to succeed.
Simulations are alluring: once a simulation framework is set up, it is easy to tweak and adjust. It is natural for us all to continue to do this until the simulation works “as it should.” If our goal is to show something that we already believe is correct (e.g., that our fancy new estimation procedure is better than existing methods), we will eventually find a way to align our simulation with our intuition. 1 This is, simply put, a version of fishing. To counteract this possibility, we believe it is critical to push ourselves to design scenarios where things do not work as expected. An aspiration of an architect of a simulation study should be to explore the boundary conditions that separate where preferred methods work and where they break or fail.
Thus far, we have focused on using Monte Carlo simulations for methodological research. If you do not identify as a methodologist, you may question whether there is any benefit to learning how to do simulations if you are never going to conduct methodological research studies or use simulation to aid in planning an empirical study. We think the answer to this is an emphatic YES, for at least two reasons.
First, in order to do any sort of quantitative data analysis, you will need to make decisions about what methods to use. Across fields, existing guidance about data analysis practice is almost certainly informed by simulation research of some form, whether well-designed and thorough or haphazard and poorly reasoned. Consequently, having a high-level understanding of the logic and limitations of simulation will help you to be a critical consumer of methods research, even if you do not intend to conduct methods research of your own.
Second, we believe conducting simulations deepens one’s understanding of the logic of statistical modeling and statistical inference. Learning a new statistical model (such as generalized linear mixed models) or analytic technique (such as multiple imputation by chained equations) requires taking in a lot of detailed information, from the assumptions of the model to the interpretation of parameter estimates to the best practices for estimation and what to do if some part of the process goes off. To thoroughly digest all these details, we have found it invaluable to simulate data based on the model under consideration. This usually requires translating mathematical notation into computer code, an exercise which makes the components of the model more tangible than just a jumble of Greek letters. The simulated data is then available for inspection, summarizing, graphing, and further calculation, all of which can aid interpretation. Furthermore, the process of simulating yields a dataset which can then be used to practice implementing the analysis procedure and interpreting the results. We have found that building a habit of simulating is a highly effective way to learn new models and methods, worthwhile even if one has no intention of carrying out methodological research. We might even go so far as to argue that whatever you might think, you don’t really understand a statistical model until you’ve done a simulation of it.
This book leans heavily into the “spiral curriculum” idea, where we introduce a concept, then revisit it in more depth later on. In particular, we will kick off with a tiny simulation, provide a high-level overview of the simulation process, and then dive into the details of each step in subsequent chapters. We have many case studies throughout the book; these are designed to make the topics under discussion salient, and are also designed to provide chunks of code that you can copy and use for your own purposes.
We divided the book into several parts. Part I (which you are reading now) lays out our case for learning simulation, introduces some guidance on programming, and presents an initial, very simple simulation to set the stage for later discussion of design principles. Part II lays out the core components (generating artificial data, bundling analytic procedures, running a simulation, analyzing the simulation results) for simulating a single scenario. It then presents some more involved case studies that illustrate the principles of modular, tidy simulation design. Part III moves to multifactor simulations, meaning simulations that look at more than one scenario or context. Multifactor simulation is the heart of good simulation design: by evaluating or comparing estimation procedures across multiple scenarios we can begin to understand general properties of these procedures.
The book closes with two final parts. Part IV digs into a few common technical challenges encountered when programming simulations, including reproducibility, parallel computing, and error handling. Part V covers three extensions to specialized forms of simulation: simulating to calculate power, simulating within a potential outcomes framework for causal inference, and the parametric bootstrap. The specialized applications underscore how simulation can be used to answer a wide variety of questions across a range of contexts, thus connecting back to the broader purposes discussed above. The book also includes appendices with some coding tidbits and further resources as well.
Abdulkadiroğlu, Atila, Joshua D Angrist, Yusuke Narita, and Parag A Pathak. 2017. “Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation.” Econometrica 85 (5): 1373–1432.
Brown, Morton B., and Alan B. Forsythe. 1974. “The Small Sample Behavior of Some Statistics Which Test the Equality of Several Means .” Technometrics 16 (1): 129–32. https://doi.org/10.1080/00401706.1974.10489158.
Davison, A. C., and D. V. Hinkley. 1997. Bootstrap Methods and Their Applications. Cambridge: Cambridge University Press.
Dong, Nianbo, and Rebecca Maynard. 2013. “ PowerUp ! : A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies .” Journal of Research on Educational Effectiveness 6 (1): 24–67. https://doi.org/10.1080/19345747.2012.673143.
Efron, Bradley. 2000. “The Bootstrap and Modern Statistics.” Journal of the American Statistical Association 95 (452): 1293–96. https://doi.org/10.2307/2669773.
Faul, Franz, Edgar Erdfelder, Axel Buchner, and Albert-Georg Lang. 2009. “Statistical Power Analyses Using G * Power 3.1: Tests for Correlation and Regression Analyses.” Behavior Research Methods 41 (4): 1149–60. https://doi.org/10.3758/BRM.41.4.1149.
Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. 2013. Bayesian Data Analysis . 0th ed. Chapman and Hall/CRC . https://doi.org/10.1201/b16018.
Good, Phillip. 2013. Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Springer Science & Business Media.
Hunter, Kristen B., Luke Miratrix, and Kristin Porter. 2024. “PUMP: Estimating Power, Minimum Detectable Effect Size, and Sample Size When Adjusting for Multiple Outcomes in Multi-Level Experiments.” Journal of Statistical Software 108 (6): 1–43. https://doi.org/10.18637/jss.v108.i06.
Jones, Owen, Robert Maillardet, and Andrew Robinson. 2012. Introduction to Scientific Programming and Simulation Using R . New York: Chapman and Hall/CRC . https://doi.org/10.1201/9781420068740.
Lehmann, Erich Leo et al. 1975. “Statistical Methods Based on Ranks.” Nonparametrics. San Francisco, CA, Holden-Day 2.
Long, J. Scott, and Laurie H. Ervin. 2000. “Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model.” The American Statistician 54 (3): 217–24. https://doi.org/10.1080/00031305.2000.10474549.
Mehrotra, Devan V. 1997. “Improving the Brown-Forsythe Solution to the Generalized Behrens-Fisher Problem.” Communications in Statistics - Simulation and Computation 26 (3): 1139–45. https://doi.org/10.1080/03610919708813431.
Miratrix, Luke W, Michael J Weiss, and Brit Henderson. 2021. “An Applied Researcher’s Guide to Estimating Effects from Multisite Individually Randomized Trials: Estimands, Estimators, and Estimates.” Journal of Research on Educational Effectiveness 14 (1): 270–308.
Robert, Christian, and George Casella. 2010. Introducing Monte Carlo Methods with R . New York, NY: Springer. https://doi.org/10.1007/978-1-4419-1576-4.
Staiger, Douglas O, and Jonah E Rockoff. 2010. “Searching for Effective Teachers with Imperfect Information.” Journal of Economic Perspectives 24 (3): 97–118.
Tipton, Elizabeth. 2013. “Stratified Sampling Using Cluster Analysis: A Sample Selection Strategy for Improved Generalizations from Experiments.” Evaluation Review 37 (2): 109–39. https://doi.org/10.1177/0193841X13516324.
White, Halbert. 1980. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica 48 (4): 817–38.