Explaining Variance in Breastfeeding Intentions and Behaviors Among a Cohort of Midwest Mothers Using a Theory of Planned Behavior-Based Structural Model

Abstract Background The Theory of Planned Behavior (TPB) has guided the investigation of breastfeeding since the 1980’s, incorporating the major constructs of attitudes, subjective norms/normative beliefs, perceived behavioral control, and intentions. The purpose of this research study was to define a TPB-based structural latent variable model so as to explain variance in breastfeeding intentions and behaviors among a cohort of Midwest breastfeeding mothers. Methods The longitudinal descriptive study utilized questionnaire data collected from a convenience sample of 100 women with low-risk pregnancies with the intention to breastfeed at three separate time points (> 30 weeks antepartum, 10 and 60 days postpartum). Data were coded and analyzed using IBM SPSS, SAS and the lavaan package in R. Results Participants were predominantly White (94%, n=94), married (95%, n=95), college-educated (96%, n=96), and had previous breastfeeding experience (75%, n=75). The majority gave birth vaginally (79%, n=75). Varimax analysis revealed a plurality of factors within each domain. Attempts to fit a structural model, including both hierarchical and bi-factor latent variables, failed, revealing a lack of statistical significance and poor fit statistics. Conclusion(s): These findings illustrate the importance of using methods that fit the phenomena explained. Contributors to poor model fit may include outdated tools lacking cultural relevance, a change in social norms, or a failure to capture the possible influence of social media and formula marketing on breastfeeding behaviors. The null finding is a significant finding, indicating the need to revisit and refine the operationalization and conceptual underpinnings of the TPB through qualitative methods such as exploring the lived experiences of breastfeeding women in the Midwest region.

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Information Matrices in Latent-Variable Models

Journal of Educational Statistics ◽

10.3102/10769986014004335 ◽

1989 ◽

Vol 14 (4) ◽

pp. 335-350 ◽

Cited By ~ 12

Author(s):

Robert J. Mislevy ◽

Kathleen M. Sheehan

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Information Matrix ◽

Missing Information ◽

Variable Model ◽

Related Information ◽

Expected Information ◽

The Difference ◽

Information Matrices ◽

Practical Implications

The Fisher, or expected, information matrix for the parameters in a latent-variable model is bounded from above by the information that would be obtained if the values of the latent variables could also be observed. The difference between this upper bound and the information in the observed data is the “missing information.” This paper explicates the structure of the expected information matrix and related information matrices, and characterizes the degree to which missing information can be recovered by exploiting collateral variables for respondents. The results are illustrated in the context of item response theory models, and practical implications are discussed.

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An Investigation of Entrepreneurial Intention Among University Students Using the Theory of Planned Behavior and Parents' Occupation

Advances in Business Strategy and Competitive Advantage - Entrepreneurial Development and Innovation in Family Businesses and SMEs ◽

10.4018/978-1-7998-3648-3.ch009 ◽

2020 ◽

pp. 149-166

Author(s):

Panagiotis A. Tsaknis ◽

Alexandros G. Sahinidis

Keyword(s):

Theory Of Planned Behavior ◽

University Students ◽

Planned Behavior ◽

Latent Variables ◽

Behavioral Control ◽

Entrepreneurial Intention ◽

Subjective Norms ◽

Entrepreneurial Intentions ◽

Perceived Behavioral Control ◽

Policy Makers

The purpose of this paper is to investigate the entrepreneurial intention of university students using the Theory of Planned Behavior (TPB) and parents' occupation. A questionnaire based survey was employed for the data collection. A total of 1244 students participated in the survey. The sample was a convenience one given that the resources available were limited. The size of the sample allows us to proceed with reliable statistical analyses and produce valid conclusions. The findings of our research showed that perceived behavioral control, subjective norms, attitude and parents' occupation are important variables influencing entrepreneurial intention. The variable that affects entrepreneurial intention the most is attitude. The findings led support to the theory and the hypotheses proposed. These findings indicate that the recommended model can be used to explain a large part of variation in entrepreneurial intention. This study, contributes to the entrepreneurial intention literature providing empirical evidence to help formulate policies encouraging university students' entrepreneurship practices, attracting the interest of both educators and policy makers. This evidence will become increasingly important, as research in the field of entrepreneurship continues to place models of entrepreneurial intentions at the center of our understanding of the entrepreneurial process. Future studies could investigate the validity of the findings reported here, in different contexts using more variables, not included in this study and inquire the potential existence of latent variables which may be confounding the relationships discussed in this paper.

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Investigating the mechanisms of theory of planned behavior on Cyberbullying among Thai adolescents

Journal of Health Research ◽

10.1108/jhr-02-2019-0033 ◽

2019 ◽

Vol 34 (1) ◽

pp. 42-55

Author(s):

Naruemon Auemaneekul ◽

Arpaporn Powwattana ◽

Emwadee Kiatsiri ◽

Nanthana Thananowan

Keyword(s):

Theory Of Planned Behavior ◽

Family Violence ◽

Planned Behavior ◽

Self Esteem ◽

School Level ◽

Equation Modeling ◽

Content Type ◽

Thai Adolescents ◽

And Behaviors ◽

Cyberbullying Policy

Purpose The purpose of this paper is to examine the etiological model of cyberbullying behaviors among Thai adolescents, testing the hypothesis that the constructs of theory of planned behavior (TPB), including self-esteem, will influence and have impact on cyberbullying intention and behaviors. Design/methodology/approach Structural equation modeling (SEM) was used to analyze the data. Self-administered questionnaires were used among multi-stage stratified random samples from secondary schools in the Bangkok. The sample size consisting of 354 subjects included those who were victims (44.7 percent), perpetrators (33.1 percent) and witness (67.8 percent). Findings The SEM showed subjective norm (SN) to be the most direct influential factor of cyberbullying intention and behaviors, followed by attitude toward cyberbullying (Intention β=0.31, 0.24; p=0.01, Behavior β=0.09, 0.07; p=0.012 and 0.05, respectively). However, the SEM revealed that all variables from TPB including self-esteem in the equation can explain the variation scores of intention and cyberbullying behaviors at 54 and 67 percent levels (adjusted R2=0.54 and 0.67), respectively. The SEM showed that model modification indices indicate a good fit to the data (χ2=0.00, df=0, p>0.05, CMIN/df=0, GFI=1, AGFI=1, CFI=1 and RMSEA=0). Research limitations/implications The experiences or witness of family violence and support at school level, which is supposed to mitigate the bullying problems, were neglected from this study. Practical implications The preventive measures for cyberbullying behaviors among adolescents should involve activities fostering self-esteem, developing proper attitude and SN to prevent cyberbullying. The initiatives and developed school supportive system for adolescents to understand how to control themselves when engaging in social network are imperative. However, for future research, family violence witness and attempt to lure the cyberbullying victims into offline meeting should be explored more. Social implications TPB and the use of social media should be taken into account for planning and designing appropriate intervention to reduce and eliminate cyberbullying among all stakeholders in both public and private sectors in the area of health and educational institutes in order to endeavor and to advocate the anti-cyberbullying policy in Thailand. Originality/value TPB and self-esteem explained a substantial portion of and more modest but significant amount of variance in cyberbullying intention and behaviors. However, SN and attitude toward cyberbullying which was found to be most influential factors could be the useful information for designing intervention toward cyberbullying prevention for Thai adolescents and advocate implementing the anti-cyberbullying policy in Thailand.

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Adoption of mobile banking services

International Journal of Bank Marketing ◽

10.1108/ijbm-08-2018-0200 ◽

2019 ◽

Vol 37 (5) ◽

pp. 1165-1189 ◽

Cited By ~ 8

Author(s):

Apostolos Giovanis ◽

Pinelopi Athanasopoulou ◽

Costas Assimakopoulos ◽

Christos Sarmaniotis

Keyword(s):

Theory Of Planned Behavior ◽

Technology Acceptance ◽

Planned Behavior ◽

Structural Equation ◽

Goodness Of Fit ◽

Statistical Significance ◽

Explanatory Power ◽

Equation Modeling ◽

Mobile Banking ◽

Content Type

PurposeThe purpose of this paper is to investigate which of four well-established theoretical models (i.e. technology acceptance model, theory of planned behavior, unified theory of acceptance and use of technology, decomposed theory of planned behavior (DTPB)) best explains potential users’ behavioral intentions to adopt mobile banking (MB) services.Design/methodology/approachDrawing on data from 931 potential users in Greece, the structural equation modeling method was used to examine and compare the four models in goodness-of-fit, explanatory power and statistical significance of path coefficients.FindingsResults indicate that the best model is an extension of the DTPB with perceived risk (PR). Customers’ attitude, determined by three rationally-evaluated MB attributes (usefulness, easiness and compatibility), is the main driver of consumers’ intentions to adopt MB services. Additionally, consumers’ perceptions of availability of knowledge, resources and opportunities necessary for using the service, and the pressure of interpersonal and external social contexts toward the use of MB are the other two, less important, adoption drivers. Finally, PR negatively affects attitude formation and inhibits willingness to use MB services.Practical implicationsFindings can help marketers of financial institutions to select the more parsimonious model to develop appropriate marketing strategies to increase adoption rates of MB services.Originality/valueThis is the first study that compares the performance of four well-known innovation adoption models to explain consumers’ behavior in the MB context.

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A Flow-Based Deep Latent Variable Model for Speech Spectrogram Modeling and Enhancement

10.36227/techrxiv.12375284 ◽

2020 ◽

Author(s):

Aditya Arie Nugraha ◽

Kouhei Sekiguchi ◽

Kazuyoshi Yoshii

Keyword(s):

Speech Enhancement ◽

Latent Variables ◽

Latent Variable ◽

Generative Models ◽

Latent Variable Model ◽

Variable Model ◽

Variational Autoencoder ◽

Latent Representations ◽

Low Dimensional ◽

Better Than

This paper describes a deep latent variable model of speech power spectrograms and its application to semi-supervised speech enhancement with a deep speech prior. By integrating two major deep generative models, a variational autoencoder (VAE) and a normalizing flow (NF), in a mutually-beneficial manner, we formulate a flexible latent variable model called the NF-VAE that can extract low-dimensional latent representations from high-dimensional observations, akin to the VAE, and does not need to explicitly represent the distribution of the observations, akin to the NF. In this paper, we consider a variant of NF called the generative flow (GF a.k.a. Glow) and formulate a latent variable model called the GF-VAE. We experimentally show that the proposed GF-VAE is better than the standard VAE at capturing fine-structured harmonics of speech spectrograms, especially in the high-frequency range. A similar finding is also obtained when the GF-VAE and the VAE are used to generate speech spectrograms from latent variables randomly sampled from the standard Gaussian distribution. Lastly, when these models are used as speech priors for statistical multichannel speech enhancement, the GF-VAE outperforms the VAE and the GF.

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Latent Variable GIMME Using Model Implied Instrumental Variables (MIIVs)

10.31234/osf.io/kuedz ◽

2019 ◽

Author(s):

Kathleen Gates ◽

Kenneth Bollen ◽

Zachary F. Fisher

Keyword(s):

Instrumental Variables ◽

Latent Variables ◽

Latent Variable ◽

Dynamic Models ◽

Self Report ◽

Data Driven ◽

Multiple Model ◽

Data Types ◽

Variable Model ◽

Latent Constructs

Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals’ processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data. For this reason, researchers typically gather multiple indicators of the same latent construct and use methods, such as factor analysis, to arrive at scores from these indices. In addition to accurately measuring individuals, researchers also need to find the model that best describes the relations among the latent constructs. Most currently available data-driven searches do not include latent variables. We present an approach that builds from the strong foundations of Group Iterative Multiple Model Estimation (GIMME), the idiographic filter, and model implied instrumental variables with two-stage least squares estimation (MIIV-2SLS) to provide researchers with the option to include latent variables in their data-driven model searches. The resulting approach is called Latent Variable GIMME (LV-GIMME). GIMME is utilized for the data-driven search for relations that exist among latent variables. Unlike other approaches such as the idiographic filter, LV-GIMME does not require that the latent variable model to be constant across individuals. This requirement is loosened by utilizing MIIV-2SLS for estimation. Simulated data studies demonstrate that the method can reliably detect relations among latent constructs, and that latent constructs provide more power to detect effects than using observed variables directly. We use empirical data examples drawn from functional MRI and daily self-report data.

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Application of theory of planned behavior to study online booking behavior

International Journal of Data and Network Science ◽

10.5267/j.ijdns.2021.5.010 ◽

2021 ◽

pp. 331-340 ◽

Cited By ~ 1

Author(s):

Yuli Christina ◽

Ni Nyoman Kerti Yasa

Keyword(s):

Theory Of Planned Behavior ◽

Planned Behavior ◽

Latent Variables ◽

Behavioral Control ◽

Ease Of Use ◽

Partial Least Square ◽

Least Square ◽

The Internet ◽

Perceived Behavioral Control ◽

Online Travel

The development of the internet has influenced the development of the world economy. Various buying and selling transactions that previously could only be done face-to-face, have now developed into transactions via the internet known as e-business or e-commerce. The hotel room online booking system was created to make it easier for consumers to book rooms 24 hours a day. With the availability of the online booking feature, consumers can access hotel information in detail and more transparently, besides that, consumers can also see reviews which can be used as their consideration in choosing hotels and planning holidays. Traveloka's significant development as an Indonesian online travel agent unicorn plays an important role in accelerating the growth rate of the online travel ecosystem, especially for the domestic market. There are many factors that must be examined in finding information, placing orders, and purchasing online. Therefore, this research is focused on online booking behavior. This study aims to determine the influence between variables based on Theory of Planned Behavior, which consists of attitude toward the online booking behavior, subjective norm, perceived behavioral control, online booking intention and online booking behavior at Traveloka. Data was collected from 133 respondents of domestic tourists who have made online bookings at Traveloka. Data were analyzed using Partial Least Square (PLS) statistics with the Smart PLS 3.0 M3 program to determine the complexity of the relationship between latent variables and their indicators. The results of this study indicate that attitude toward the behavior and subjective norms have a positive and significant effect on online booking intention. Meanwhile, perceived behavioral control has no significant effect on online booking intention. Another finding is that online booking intention and perceived behavioral control are known to have a positive and significant effect on online booking behavior. Traveloka management and marketers are also expected to be able to use the results of this research to evaluate and take corrective action on aspects that are deemed inadequate and manage the ease of use of the application to increase online booking intentions through the Traveloka application.

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Choosing the number of factors in independent factor analysis model

Advances in Methodology and Statistics ◽

10.51936/rctv6030 ◽

2005 ◽

Vol 2 (2) ◽

Author(s):

Cinzia Viroli

Keyword(s):

Factor Analysis ◽

Latent Variables ◽

Latent Variable ◽

Information Criteria ◽

Ratio Test ◽

Independent Factor ◽

Analysis Model ◽

Variable Model ◽

Factor Analysis Model ◽

Independent Factor Analysis

Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as a way to model a set of observed variables through linear combinations of hidden independent ones plus a noise term. Despite the peculiarity of its origin the method can be framed within the latent variable model domain and some parallels with the ordinary factor analysis can be drawn. If no prior information on the latent structure is available a relevant issue concerns the correct specification of the model. In this work some methods to detect the number of significant latent variables are investigated. Moreover, since the method defines a probability density function for the latent variables by mixtures of gaussians, the correct number of mixture components must also be determined. This issue will be treated according to two main approaches. The first one amounts to carry out a likelihood ratio test. The other one is based on a penalized form of the likelihood, that leads to the so called information criteria. Some simulations and empirical results on real data sets are finally presented.

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Effect of Hybrid Resources on Management Outcomes Micro-Hydro Power Plants in Remote Areas

International Journal of Design & Nature and Ecodynamics ◽

10.18280/ijdne.150611 ◽

2020 ◽

Vol 15 (6) ◽

pp. 855-864

Author(s):

Muhammad Yusan Naim ◽

Henny Pramoedyo ◽

Nuddin Harahab ◽

Syarifuddin Nodjeng ◽

Sudirman Syam

Keyword(s):

Latent Variables ◽

Latent Variable ◽

Power Plants ◽

Structural Model ◽

Electrical Power ◽

Hydro Power ◽

Remote Areas ◽

Manifest Variable ◽

Hydropower Plants ◽

Hydro Power Plants

The effect of developing hybrid resources on the management outcomes of micro-hydropower plants in remote areas has been studied and analyzed. The hybrid resource is a combination of two energy sources, such as water and solar energy, that operate together in meeting the needs of electrical power in Ambava Village, Tinondo Sub-district, East-Kolaka Regency, Southeast Sulawesi Province. This study has used a management model describing the relationship and influence of latent variables and their manifestation variables. Here, Confirmatory Factor Analysis (CFA) based Common-Pool-Resources (CPR) is the proper method of testing the structural model used. The results show that the Critics-Ratio (CR) and Standard Loading Factor (SLF) have fulfilled the expected value. The direct influence of the variable exogenous hybrid resources to the endogenous variable outcome of 0.213 has fulfilled the Gold of Fit criteria. Then, the direct impact of the most dominant latent variable is the operating dimension of the resource. At the same time, the indirect effect on the manifest variable is the increase in electricity reserve. Furthermore, the most dominant indirect impact of the hybrid resources latent variable is the benefit and cost dimensions, while the most dominant manifest variable is people's welfare savings.

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