SummaryQualtrics (L. Qualtrics 2018) allows users to create and disseminate online surveys. It is used by researchers and other analysts to field responses for the purposes of (academic) research. While users can manually download survey responses from Qualtrics, importing this data into R is cumbersome. The R package qualtRics (Ginn 2017) focuses on the retrieval of survey data using the Qualtrics API and aims to reduce the pre-processing steps needed to prepare this data for analysis. Currently, the package is the only package on CRAN that offers such functionality, and is included in the official Qualtrics API documentation (Qualtrics n.d.).The primary goal of the package is to provide a bridge between the Qualtrics user interface (where the survey is designed) and R (where the results can be analyzed) by using as few steps as possible. Users can store their API credentials in a file in an R project root directory that automatically loads when the library is called. This eliminates the need to remember API credentials and prevents the user from accidentally sharing sensitive information if they share their work. The package contains three core functions to retrieve survey data. The first of these functions -getSurveys() -retrieves a data frame containing an overview of surveys to which the user has access. Using a unique survey id, the user can download and import their data using getSurvey(). This function takes care of requesting, downloading and unpacking the data. It is then imported into R with the readSurvey() function. This last function can also be used to import manual data exports and supports both current and legacy data formats.Apart from the above functionality, the package supports the automatic conversion of single-choice multiple choice questions. Using the rich metadata that Qualtrics provides about surveys, it is possible to automatically convert ordinal data to ordered factors. This functionality will be expanded on an ongoing basis to include other variable types.
Spurred in part by the ever-growing number of sensors and web-based methods of collecting data, the use of Intensive Longitudinal Data (ILD) is becoming more common in the social and behavioural sciences. The ILD collected in this field are often hypothesised to be the result of latent states (e.g. behaviour, emotions), and the promise of ILD lies in its ability to capture the dynamics of these states as they unfold in time. In particular, by collecting data for multiple subjects, researchers can observe how such dynamics differ between subjects. The Bayesian Multilevel Hidden Markov Model (mHMM) is a relatively novel model that is suited to model the ILD of this kind while taking into account heterogeneity between subjects. While the mHMM has been applied in a variety of settings, large-scale studies that examine the required sample size for this model are lacking. In this paper, we address this research gap by conducting a simulation study to evaluate the effect of changing (1) the number of subjects, (2) the number of occasions, and (3) the between subjects variability on parameter estimates obtained by the mHMM. We frame this simulation study in the context of sleep research, which consists of multivariate continuous data that displays considerable overlap in the state dependent component distributions. In addition, we generate a set of baseline scenarios with more general data properties. Overall, the number of subjects has the largest effect on model performance. However, the number of occasions is important to adequately model latent state transitions. We discuss how the characteristics of the data influence parameter estimation and provide recommendations to researchers seeking to apply the mHMM to their own data.
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