Formative Measurement Models in Covariance Structure Analysis: Specification and Identification

Many researchers seem to be unsure about how to specify formative measurement models in software programs like LISREL or AMOS and to establish identification of the corresponding structural equation model. In order to make identification easier, a new, mainly graphically oriented approach is presented for a specific class of recursive models with formative indicators. Using this procedure it is shown that some models have erroneously been considered underidentified. Furthermore, it is shown that specifying formative indicators as exogenous variables rises serious conceptual and substantial issues in the case that the formative construct is truly endogenous (i. e. influenced by more remote causes). An empirical study on the effects and causes of brand competence illustrates this point.Formative Indicators; Latent Variables; Covariance Structure Analysis; Identification

Integrating latent variables in discrete choice models – How higher-order values and attitudes determine consumer choice

Integrated choice and latent variable (ICLV) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, to date applications of ICLV models in marketing are still rare. The present study on travel mode choice clearly demonstrates the value of ICLV models to enhance understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonisms as well as attitudes such as a desire for flexibility impact on travel mode choice. Further, we can show that it is possible to estimate ICLV models with the widely available structural equation modeling package Mplus. This finding is likely to encourage wider usage of this appealing model class in the marketing field.Hybrid choice models; Mode choice; Values; Value-attitude hierarchy; Mplus

Formative Measurement Models in Covariance Structure Analysis

Many researchers seem to be unsure about how to specify formative measurement models in software programs like LISREL or AMOS and to establish identification of the corresponding structural equation model. In order to make identification easier, a new, mainly graphically orientedapproach is presented for a specific class of recursive models with formativeindicators. Using this procedure it is shown that some models have erroneously beenconsidered underidentified. Furthermore, it is shown that specifying formative indicators asexogenous variables rises serious conceptual and substantial issues in the case that theformative construct is truly endogenous (i. e. influenced by more remote causes). Anempirical study on the effects and causes of brand competence illustrates this point

Gruppenvergleiche bei hypothetischen Konstrukten – Die Prüfung der Übereinstimmung von Messmodellen mit der Strukturgleichungsmethodik

Comparing groups with respect to hypothetical constructs requires that the measurement models are equal across groups. Otherwise conclusions drawn from the observed indicators regarding differences at the latent level (mean differences, differences in the structural relations) might be severly distorted. This article provides a state of the art on how to apply multi-group confirmatory factor analysis to assess measurement invariance. The required steps in the analysis of the observed indicator means and variances/covariances are described, placing special emphasis on how to identify noninvariant indicators. The procedure is demonstrated considering the construct brand strength (“Brand Potential Index”, BPI®) introduced by GfK Market Research as an example.Measurement invariance, Partial metric invariance, Multi-group confirmatory factor analysis, Brand strength

Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice

Values lie at the heart of an individual's belief system, serving as prototypes from which attitudes and behaviors are subsequently manufactured. Attitudes and behaviors may evolve over time, but values represent a set of more enduring beliefs. This study examines the influence of values on travel mode choice behavior. It is argued that personal values influence individual attitudes towards different alternative attributes, which in turn impact modal choices. Using data from a sample of 519 German commuters drawn from a consumer panel, the study estimates an integrated choice and latent variable model of travel mode choice that allows for hierarchical relationships between the latent variables and flexible substitution patterns across the modal alternatives. Results from the empirical application support the value-attitude-behavior hierarchical model of cognition, and provide insights to planners and policy-makers on how better to sell public transit as a means of travel

Reproducibility and validity of a diet quality index for children assessed using a FFQ

The diet quality index (DQI) for preschool children is a new index developed to reflect compliance with four main food-based dietary guidelines for preschool children in Flanders. The present study investigates: (1) the validity of this index by comparing DQI scores for preschool children with nutrient intakes, both of which were derived from 3d estimated diet records; (2) the reproducibility of the DQI for preschoolers based on a parentally reported forty-seven-item FFQ DQI, which was repeated after 5 weeks; (3) the relative validity of the FFQ DQI with 3d record DQI scores as reference. The study sample included 510 and 58 preschoolers (2-5-6.5 years) for validity and reproducibility analyses, respectively. Increasing 3d record DQI scores were associated with decreasing consumption of added sugars, and increasing intakes of fibre, water, Ca and many micronutrients. Mean FFQ DQI test-retest scores were not significantly different: 72 (so 11) v. 71 (Si) 10) (P-=0-218) out of a maximum of 100. Mean 3d record DQI score (66 (so 10)) was significantly lower than mean FFQ DQI (71 (so 10); P<0.001). The reproducibility correlation was 0.88. Pearsons correlation (adjusted for within-person variability) between FFQ and 3d record DQI scores was 0.82. Cross-classification analysis of the FFQ and 3d record DQI classified 60% of the subjects in the same category and 3% in extreme tertiles. Cross-classification of repeated administrations classified 62% of the subjects in the same category and 3% in extreme categories. The FFQ-based DQI approach compared well with the 3d record approach, and it can be used to determine diet quality among preschoolers

Integrating latent variables in discrete choice models

Integrated choice and latent variable (ICLV) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, to date applications of ICLV models in marketing are still rare. The present study on travel mode choice clearly demonstrates the value of ICLV models to enhance understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonisms as well as attitudes such as a desire for flexibility impact on travel mode choice. Further, we can show that it is possible to estimate ICLV models with the widely available structural equation modeling package Mplus. This finding is likely to encourage wider usage of this appealing model class in the marketing field

PLS Path Modeling

After years of stagnancy, PLS path modeling has recently attracted renewed interest from applied researchers in marketing. At the same time, the availability of software alternatives to Lohmöller´s LVPLS package has considerably increased (PLS-Graph, PLS-GUI, SPAD-PLS, SmartPLS). To help the user to make an informed decision, the existing programs are reviewed; their strengths and weaknesses are identified. Furthermore, analyzing simulated data reveals that the signs of weights/factor loadings and path coefficients can vary considerably across the different programs. Thus, applied researchers should treat the interpretation of their results with caution. Compared to programs for analysis of covariance structure models (LISREL approach), PLS path modeling software is on equal footing regarding ease of use, but clearly lags behind in terms of methodological capabilities