<b>LMest</b>: An <i>R</i> Package for Latent Markov Models for Longitudinal Categorical Data

Bartolucci, Francesco; Pandolfi, Silvia; Pennoni, Fulvia

doi:10.18637/jss.v081.i04

Cited by 75 publications

(52 citation statements)

References 28 publications

(46 reference statements)

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“…This is also in line with the LTA by Edelsbrunner et al (2018). The LMest package (Bartolucci et al, 2017) was used to conduct LTA with the present data using LM models. This package is available in R (R Core Team, 2019).…”

Section: Analysessupporting

confidence: 72%

“…Note that one participant can be assigned to multiple classes, but often, one class is dominant (over 75% assignment). This analysis takes into account effects per time point and over time when calculating class assignment and transition probabilities (Bartolucci et al, 2017).…”

Section: Analysesmentioning

confidence: 99%

“…Thus, LTA was conducted with condition as a covariate of transitions only. To determine the number of classes, the fit of models with two to seven classes was investigated using random initialization of the expectation-maximization algorithm, and when the optimal number of classes was found, a deterministic initialization was used (Bartolucci et al, 2017). In a first analysis, two classes appeared to be a result of outliers, as these classes were on either end of the scale (very low accuracy and very high accuracy).…”

Section: Latent Transition Analysismentioning

confidence: 99%

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Inquiry-Based Learning and Conceptual Change in Balance Beam Understanding

Graaf

2020

Front. Psychol.

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Inquiry-based learning has the potential to foster conceptual change, but whether it can induce an advancement in strategy use is not yet known. Such an advancement seems plausible, because conceptual change can be reflected in the use of new strategies. Whether inquiry-based learning leads to advancement in strategy use can be tested with strategy-based tests, such as the balance beam test. Distinct strategies have been proposed and identified for this test. Therefore, the present study compared response patterns on the balance beam test before and after an inquiry-based lesson. The experimental condition completed a digital inquiry-based lesson about the balance beam (n = 113), and the control condition completed a similar inquiry-based lesson but investigated a different topic (n = 44). The participants were aged 8-13 years old and were unfamiliar with the law of moments. The balance beam test (pretest and posttest) consisted of 25 items. Overall accuracy in solving balance beam problems improved after the inquiry-based lesson in the balance beam (BB) condition but not in the control condition. Classes, identified with latent transition analysis (LTA), appeared to be globally in line with previously identified strategies in the balance beam test. Condition was entered as a covariate in the LTA to identify which changes in strategy use could be attributed to the experimental intervention. First, changes from pretest to posttest were found, which supported that a change in strategy use occurred in some children. Second, there were more improvements in the BB condition, and these improvements indicated larger gains compared to the control condition. This means that in science education, it is important to map out prior knowledge and its effect on learning paths. Overall, results suggested that conceptual change could be measured as a change in strategy use and modeled with LTA to reveal that 26% of the children showed conceptual change after a single inquiry-based lesson.

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Section: Analysessupporting

confidence: 72%

Section: Analysesmentioning

confidence: 99%

Section: Latent Transition Analysismentioning

confidence: 99%

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Inquiry-Based Learning and Conceptual Change in Balance Beam Understanding

Graaf

2020

Front. Psychol.

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“…For LM models, a special version of the EM algorithm with a computationally more efficient implementation of the E step may be used. This algorithm is referred to as the Baum-Welch or forward-backward algorithm (Bartolucci et al, 2010;Baum, Petrie, Soules, & Weiss 1970;Vermunt et al, 2008).…”

Section: The Lm Modelsmentioning

confidence: 99%

“…Moreover, the LM model allows transitions between these states from one timepoint to another; that is, the state membership of respondents can change during the period of observation. The LM model finds its application, for example, in educational sciences to study how the interests of students in certain subjects changes over time (Vermunt, Langeheine, & Bockenholt, 1999) and in medical sciences to study the change in health behavior of patients suffering from certain diseases (Bartolucci, Farcomeni & Pennoni, 2010). Various examples of applications in social, behavioral, and health sciences are presented in the textbooks by Bartolucci et al (2013) and Collins and Lanza (2010).…”

mentioning

confidence: 99%

Power Analysis for the Likelihood-Ratio Test in Latent Markov Models: Shortcutting the Bootstrap p-Value-Based Method

Gudicha

Schmittmann

Tekle

et al. 2016

Multivariate Behavioral Research

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The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences

2020

Self Cite

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The Italian market of sparkling wines has undergone a strong expansion driven by what can be defined as the "Prosecco phenomenon." It has extended consumption reaching new and more complex segments with a wide offer of appellations, brands, and prices. We aim to evaluate the Italian market of sparkling wines to figure out the competitive associations among the major brands. We propose two different analyses to disentangle distinctive groups of brands. First, using the information on scanner purchases of sparkling wines recorded by a consumer panel over a 2-year period, and appropriate specifications of the latent class model, we cluster homogeneous groups of winery brands for the product attributes they propose to the market. Then, we analyze consumers' brand preferences from a dynamic perspective by employing a hidden Markov model to identify segments of brands perceived as similar. These results shed light on loyalty behavior and its evolution over time in the market.

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LMest: An R Package for Latent Markov Models for Longitudinal Categorical Data

Cited by 75 publications

References 28 publications

Inquiry-Based Learning and Conceptual Change in Balance Beam Understanding

Inquiry-Based Learning and Conceptual Change in Balance Beam Understanding

Power Analysis for the Likelihood-Ratio Test in Latent Markov Models: Shortcutting the Bootstrap p-Value-Based Method

The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences

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