Evaluating Reasonableness Tests for Longitudinal Measurement Invariance in Structural Equation Modeling using Confirmatory Factor Analysis

Researchers are typically interested in comparing groups of people and/or comparing people across time. If researchers are to conclude differences are due to group dynamics or time, we must establish that the measure(s) we are using are actually invariant across groups or time. Some statistical methods (ANOVA and regression) make this assumption without direct evaluation. Conducting analyses in the Structural Equation Modeling (SEM) framework using Confirmatory Factor Analysis (CFA) is one way the assumption of measurement invariance can be evaluated directly. Many researchers have studied multiple group invariance and current invariance testing recommendations are based on multiple group studies and simulations. There is a lack of literature on testing invariance in longitudinal designs. Current guidelines recommend researchers apply the same guidelines from multiple group to longitudinal designs. Longitudinal designs are more complicated and may need different recommendations. The current study evaluates measurement invariance in longitudinal CFA in order to ascertain if the current guidelines based off the multiple group case are acceptable when applied to the longitudinal framework

A Novel Item-Allocation Procedure for the Three-Form Planned Missing Data Design

We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The random item allocation (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale\u27s items across the X-, A-, B-, and C-blocks when allocating the items in a three-form design. Direct empirical support for the performance of our method is only available for scales containing at least 12 items, so we also propose a modified approach for use with scales containing fewer than 12 items. We also discuss the limitations of our procedure and several nuances for researchers to consider when implementing three-form PMDDs using our method. The RIA procedure allows researchers to implement statistically sound three-form planned missing data designs without the need for expert knowledge or results from prior statistical modeling. The RIA procedure can be used to construct both “paper-and-pencil” questionnaires and questionnaires administered through online survey software. The RIA procedure is a simple framework to aid in designing three-form PMDDs; implementing the RIA method does not require any specialized software or technical expertise

Getting beyond the null: Statistical modeling as an alternative framework for inference in developmental science

We describe statistical modeling as a powerful alternative to null hypothesis significance testing (NHST). Modeling supports statistical inference in a fundamentally different way from NHST which can better serve developmental researchers. Modeling requires researchers to fully articulate their beliefs about the processes under study and to communicate that understanding through the structure of a probabilistic model before testing specific hypotheses. Research hypotheses are assessed through estimated parameters of the model and by conducting model comparisons. We conclude the paper with a series of worked examples that highlight the merits of the statistical modeling approach as a tool for scientific inference

Maximizing data quality and shortening survey time: Three-form planned missing data survey design

Simulation studies have shown the three-form planned missing data design efficiently collects high quality data while reducing participant burden. This methodology is rarely used in sport and exercise psychology. Therefore, we conducted a re-sampling study with existing sport and exercise psychology survey data to test how three-form planned missing data survey design implemented with different item distribution approaches effect constructs’ internal measurement structure and validity. Results supported the efficacy of the three-form planned missing data survey design for cross-sectional data collection. Sample sizes of at least 300 (i.e., 100 per form) are recommended for having unbiased parameter estimates. It is also recommended items be distributed across survey forms to have representation of each facet of a construct on every form, and that a select few of these items be included across all survey forms. Further guidelines for three-form surveys based upon the results of this resampling study are provided

Maximizing Data Quality and Shortening Survey Time: Three-Form Planned Missing Data Survey Design

Simulation studies have shown the three-form planned missing data design efficiently collects high quality data while reducing participant burden. This methodology is rarely used in sport and exercise psychology. Therefore, we conducted a re-sampling study with existing sport and exercise psychology survey data to test how three-form planned missing data survey design implemented with different item distribution approaches effect constructs’ internal measurement structure and validity. Results supported the efficacy of the three-form planned missing data survey design for cross-sectional data collection. Sample sizes of at least 300 (i.e., 100 per form) are recommended for having unbiased parameter estimates. It is also recommended items be distributed across survey forms to have representation of each facet of a construct on every form, and that a select few of these items be included across all survey forms. Further guidelines for three-form surveys based upon the results of this resampling study are provided

A novel item-allocation procedure for the three-form planned missing data design

We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The random item allocation (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale's items across the X-, A-, B-, and C-blocks when allocating the items in a three-form design. Direct empirical support for the performance of our method is only available for scales containing at least 12 items, so we also propose a modified approach for use with scales containing fewer than 12 items. We also discuss the limitations of our procedure and several nuances for researchers to consider when implementing three-form PMDDs using our method

The influence of nurse manager competency on practice environment, missed nursing care, and patient care quality: A cross-sectional study of nurse managers in U.S. hospitals

AIMS: Identify and examine drivers of nurse manager competency and high-quality practice environments. BACKGROUND: Nurse managers are a key predictor of positive professional practice environments, which are, in turn, associated with nurse, patient, and organisational outcomes. However, little work has examined the factors that contribute to nurse manager competency. METHODS: Nurse managers completed online surveys, which were matched to unit-level aggregate data of their subordinate direct care nurses' responses on the National Database of Nursing Quality Indicators. This resulted in a final sample of 541 nurse managers across 47 U.S. hospitals. Multilevel path analysis was utilized to assess a model of the antecedents and consequences of nurse manager competency. RESULTS: Nurse manager competency and practice environments were predictive of missed nursing care and nurse-reported quality of care. Nurse manager experience was found to have twice the effect on competency as advanced education. CONCLUSIONS: Nurse manager competency and its downstream effects are achieved through nurse manager experience and advanced education. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse manager competency yields better practice environments and nursing care. Considering the influence of experience, careful attention should be paid to the competency development process of more novice nurse managers

A novel item-allocation procedure for the three-form planned missing data design

We propose a new method of constructing questionnaire forms in the three-form planned missing data design (PMDD). The random item allocation (RIA) procedure that we propose promises to dramatically simplify the process of implementing three-form PMDDs without compromising statistical performance. Our method is a stochastic approximation to the currently recommended approach of deterministically spreading a scale's items across the X-, A-, B-, and C-blocks when allocating the items in a three-form design. Direct empirical support for the performance of our method is only available for scales containing at least 12 items, so we also propose a modified approach for use with scales containing fewer than 12 items. We also discuss the limitations of our procedure and several nuances for researchers to consider when implementing three-form PMDDs using our method

The Self-Determination Inventory–Student Report: Confirming the factor structure of a new measure

The Self-Determination Inventory–Student Report (SDI-SR) was developed to address the need in the field for new, theoretically aligned measures of self-determination. The purpose of this study was to establish the most robust and efficient set of items to assess the self-determination of adolescents with and without disabilities on the SDI-SR. Confirmatory factor analysis (CFA), using mean and covariance structures, was used to evaluate the factor structure of the SDI-SR to inform decisions on scale reduction. The items were tested across 20 groups generated by crossing disability (i.e., no disability, learning disability, intellectual disability, autism spectrum disorders, and other health impairment) and race/ethnicity (i.e., White, Black, Hispanic, and Other) groups. A robust set of 21 items that align closely with their associated constructs were identified. These 21 items showed strong measurement properties, including measurement invariance at the item level across the 20 groups. Implications for future research and practice are discussed

The Self-Determination Inventory–Student Report:Confirming the factor structure of a new measure

The Self-Determination Inventory–Student Report (SDI-SR) was developed to address the need in the field for new, theoretically aligned measures of self-determination. The purpose of this study was to establish the most robust and efficient set of items to assess the self-determination of adolescents with and without disabilities on the SDI-SR. Confirmatory factor analysis (CFA), using mean and covariance structures, was used to evaluate the factor structure of the SDI-SR to inform decisions on scale reduction. The items were tested across 20 groups generated by crossing disability (i.e., no disability, learning disability, intellectual disability, autism spectrum disorders, and other health impairment) and race/ethnicity (i.e., White, Black, Hispanic, and Other) groups. A robust set of 21 items that align closely with their associated constructs were identified. These 21 items showed strong measurement properties, including measurement invariance at the item level across the 20 groups. Implications for future research and practice are discussed