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Missing data are common in psychological and educational research. With the improvement in computing technology in recent decades, more researchers have begun developing missing data techniques. In their research, they often conduct Monte Carlo simulation studies to compare the performances of different missing data techniques. During such simulation studies, researchers must generate missing data in the simulated dataset by deciding which data values to delete. However, in the current literature, there are limited guidelines on how to generate missing data for simulation studies. Our paper is one of the first that examines ways of generating missing data for simulation studies. I emphasize the importance of specifying missing data rules which are statistical models for generating missing data. I begin the paper by reviewing the types of missing data mechanisms and missing data patterns. I then explain how to specify missing data rules to generate missing data with different mechanisms and patterns. I emphasize the advantages and disadvantages of using different missing data rules and algorithms to generate missing data for simulation studies. Next, I discuss other important aspects of simulation studies involving missing data. I end the paper by offering recommendations for generating missing data for simulation studies.

Zhang and Savalei proposed an alternative scale format to the Likert format, called the Expanded format. In this format, response options are presented in complete sentences, which can reduce acquiescence bias and method effects. The goal of the current study was to compare the psychometric properties of the Rosenberg Self-Esteem Scale (RSES) in the Expanded format and in two other alternative formats, relative to several versions of the traditional Likert format. We conducted two studies to compare the psychometric properties of the RSES across the different formats. We found that compared with the Likert format, the alternative formats tend to have a unidimensional factor structure, less response inconsistency, and comparable validity. In addition, we found that the Expanded format resulted in the best factor structure among the three alternative formats. Researchers should consider the Expanded format, especially when creating short psychological scales such as the RSES.

Today's mature virtual simulation technology should be effectively combined with college education to give full play to better educational advantages. The history teaching supported by modern information technology paves the way for the rational distribution of educational resources and the movement of centralized users to fragmented learning time, which brings infinite possibilities for the development of education. A more liberal education model will inevitably advocate higher requirements of users for self-discipline, so it is difficult to ensure the teaching quality in relevant practice. The rapid development of virtual simulation technology improves the possibility of successful history teaching reform, and the combination of virtual simulation technology and history teaching will effectively promote the great changes and outstanding development of the whole education industry. The combination of virtual technology and history teaching can make students better integrate into the teaching situation, improve learning efficiency, and effectively achieve the educational purpose.

Salt–alkaline stress is one of the most stressful occurrences, causing negative effects on plant development and agricultural yield. Identifying and utilizing genes that affect alkaline tolerance is an excellent approach to accelerate breeding processes and meet the needs for remediating saline–alkaline soil. Here, we employed a mapping population of 176 recombinant inbred lines (RILs) produced from a cross between alkali-tolerant Longdao5 and alkali-sensitive Zhongyouzao8 to identify the quantitative trait loci (QTLs) determining alkali tolerance at the seedling stage. For the evaluation of alkali tolerance, the recovered seedling's average alkali tolerance index (ATI), root number (RN), root length (RL), seedling dry weight (SW), root dry weight (RW), and seedling height (SH) were assessed, together with their relative alkaline damage rate. Under alkaline stress, the ATI was substantially negative connected with the root number, seedling height, seedling dry weight, and root dry weight; however, it was considerably positive correlated with the relative alkaline damage rate of the root number and root dry weight. A total of 13 QTLs for the root number, root length, seedling height, seedling dry weight, root dry weight, and alkali tolerance index under alkaline stress were identified, which were distributed across chromosomes 1, 2, 3, 4, 5, 7, and 8. All of these QTLs formed two QTL clusters for alkali tolerance on chromosome 5 and chromosome 7, designated AT5 and AT7, respectively. Nine QTLs were identified for the relative alkaline damage rate of the root number, root length, seedling height, seedling dry weight, and root dry weight under alkali stress. These QTLs were located on chromosome 2, 4, 6, 7, 8, 9, and 12. In conclusion, these findings further strengthen our knowledge about rice's genetic mechanisms for alkaline tolerance. This research offers clues to accelerate breeding programs for new alkaline-tolerance rice varieties. [ABSTRACT FROM AUTHOR]

Tillage practices are of critical importance in maintaining soil quality on cropland and for food production, with rice cultivation representing a significant portion of the world's food production and greenhouse gas (GHG) emissions. While numerous studies have examined the effects of reduced and no-tillage on soil GHG emissions and rice yields, the impact of adopting a rotational approach to tillage practices on the rice cultivation cycle remains uncertain. In this study, we conducted a four-year (2017–2020) field experiment in a single rice-growing area in Northeast China with the aim of investigating the effects of different tillage practices on GHG emissions from paddy fields and rice yields under full straw return conditions. We set up three experimental treatments: rotary tillage, plowing, and rotational tillage (i.e., a combination of one year of plowing and one year of rotary tillage). The results showed that averaged across all treatments, average methane (CH4, 302.6 ± 51.1 kg ha−1) and nitrous oxide (N2O, 0.86 ± 0.361 kg ha−1) emissions and rice yield (9.0 ± 0.9 t ha−1) did not exhibit significant inter-annual variability during the entire experimental period and were comparable to the average for the region. The ranking of GHG emissions during the rice-growing season was as follows: rotary tillage > plowing > rotational tillage. Across the experimental period, CH4 and N2O emissions were 9.1% and 8.5% lower in the plowing treatment and 21.2% and 13.1% lower in the rotational tillage treatment compared to the rotary tillage treatment. During the experimental period, there was no significant effect of tillage treatments on rice yield. This reduction in emissions may be attributed to changes in soil penetration resistance. In the rotational and plowing treatments, soil penetration resistance was in a range more adapted to rice growth and GHG emissions reduction compared to the rotary tillage treatment. The yield-scale GHG emission intensity was reduced by 12.7% and 26.1% in the plowing and rotational tillage treatments, respectively, in comparison to the rotary tillage treatment. This suggests that rotational tillage is a management practice that can achieve greenhouse gas emission reductions in paddy fields and stabilize or possibly increase rice yields. Consequently, the results demonstrated that a rotational alternation of multiple tillage practices is a synergistic strategy for achieving low carbon and high yield in rice in the cold rice-growing region of Northeast China. [ABSTRACT FROM AUTHOR]

Wilson et al. (2022) compared the Likert response format to an alternative format, which they called the Guttman response format. Using a Rasch modeling approach, they found that the Guttman response format had better properties relative to the Likert response format. We agree with their analyses and conclusions. However, they have failed to mention many existing articles that have sought to overcome the disadvantages of the Likert format through the use of an alternative format. For example, the so-called "Guttman response format" is essentially the same as the Expanded format, which was proposed by Zhang and Savalei (2016) as a way to overcome the disadvantages of the Likert format. Similar alternative formats have been investigated since the 1960s. In this short response article, we provide a review of several alternative formats, explaining in detail the key characteristics of all the alternative formats that are designed to overcome the problems with the Likert format. In a study by Wilson et al. (2022), they compared the commonly used Likert response format in psychological scales to an alternative format known as the Guttman response format. Using a statistical method called Rasch modeling, they found that the Guttman format outperformed the Likert format. While we agree with their conclusion, it is worth noting that there are other articles aiming to improve the Likert format through the use of an alternative format, which Wilson et al. (2022) did not mention. For instance, the Guttman format is essentially the same as the Expanded format proposed by Zhang and Savalei (2016). Similar alternative formats have been investigated since the 1960s. In this short response article, we provide a review of these alternative formats, explaining the key characteristics of all the alternative formats that are designed to overcome the problems of the Likert format. [ABSTRACT FROM AUTHOR]

Many psychological scales written in the Likert format include reverse worded (RW) items in order to control acquiescence bias. However, studies have shown that RW items often contaminate the factor structure of the scale by creating one or more method factors. The present study examines an alternative scale format, called the Expanded format, which replaces each response option in the Likert scale with a full sentence. We hypothesized that this format would result in a cleaner factor structure as compared with the Likert format. We tested this hypothesis on three popular psychological scales: the Rosenberg Self-Esteem scale, the Conscientiousness subscale of the Big Five Inventory, and the Beck Depression Inventory II. Scales in both formats showed comparable reliabilities. However, scales in the Expanded format had better (i.e., lower and more theoretically defensible) dimensionalities than scales in the Likert format, as assessed by both exploratory factor analyses and confirmatory factor analyses. We encourage further study and wider use of the Expanded format, particularly when a scale's dimensionality is of theoretical interest.

Normal theory full-information maximum likelihood (FIML) is a common estimation technique for incomplete data in structural equation modeling (SEM). However, it is not commonly known that approximate fit indices (AFIs) can be distorted, relative to their complete data counterparts, when FIML is used to handle missing data. In this article, we show that two most popular AFIs, the root-mean-square error of approximation (RMSEA) and the comparative fit index (CFI) often approach different population values under FIML estimation when missing data are present. By deriving the FIML fit function for incomplete data and showing that it is different from the usual maximum likelihood (ML) fit function for complete data, we provide a mathematical explanation for this phenomenon. We also present several analytic examples as well as the results of two large sample simulation studies to illustrate how AFIs change with missing data. [ABSTRACT FROM AUTHOR]