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Steps in Research Process (Partial Least Square of Structural Equation Modeling (PLS-SEM) A Review)
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Data Analysis is the process of methodically applying statistical and logical methods to describe and explain, condense, recap, and evaluate data. Data analysis refers to the process of developing answers to research questions through the examination and clarification of data. The very basic steps in the analysis process are to recognize problems, determine the availability of appropriate data, decide on which methods are suitable for answering the research questions, apply the methods and estimate, summarize and discuss the results. The design and analytical path of any research program should have a specific methodological direction based on its research objective and framework. This study is a review, analysing data from studies that utilize a quantitative method, follow a survey method design and apply Partial Least Square of Structural Equation Modeling (PLS-SEM). The quantitative approach is also known as a traditional, positivism, experimental or empiricist research approach. This study aims to review all the steps that need to be carried out before applying SEM using SPSS and after this, all the steps of SEM need to be reported.
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... Reductions commonly precede structural equation modeling (SEM) for multivariate resilience to food insecurity data (Alinovi et al., 2008). PLS-PM is a viable alternative to SEM for non-normal, small sample sizes (Benitez et al., 2020;Chin, 1998;McIntosh et al., 2014;Samani, 2016;M. Tenenhaus, 2010). ...
... Wold (1982Wold ( , 1985 developed partial least squares path modeling (PLS-PM). PLS-PM can act as an alternative to a structural equation modeling (SEM) (Garson, 2016;Romano & Palumbo, 2021;Samani, 2016;M. Tenenhaus, 2008;Vinzi, Chin, et al., 2010), which Alinovi et al. (2008) recommended for modeling resilience to food insecurity. ...
... My multistage approach culminated in PLS-PM, which is considered an alternative to SEM for non-normal data and small sample sizes, as was the case in this study (Crocetta et al., 2021;Romano & Palumbo, 2021;Vinzi, Chin, et al., 2010). Samani (2016) breaks-down well the differences between SEM and PLS-PM. Much like principal component analyses, PLS-PM is preferred "because latent variables are calculated as a weighted sum of their indicators," (Sanchez, 2013, p. 39) (Romano & Palumbo, 2021;Tubadji & Pelzel, 2015). ...
Indigenous West African farmers are among the most climate change threatened globally. Food insecurity is prevalent in West Africa because ecological, social, political, and economic instabilities, and globalization worsen climate pressures. In this study, I collaborated with the community of Bikotiba (bih-CO-ti-buh), Togo, to understand their household agricultural food access, one aspect of resilience to food insecurity. I adopted a feminist approach of reflexivity, radical vulnerability, and radical empathy, combined with decolonizing principles, to argue that there could be an ethical way for well-trained Western researchers to engage Indigenous communities, if negotiated carefully. Together, Indigenous Research Assistants and I developed and conducted semi-structured interviews in the local language, Bassari, with 56% of the heads of households in Bikotiba, and led community meetings with the demographics of men, women, and students. We learned that maize production in Bikotiba is threatened by climatic, political, and environmental changes, making maize subsistence a glaring leverage point in the community’s food security, in addition to the social-political-economic and human rights injustices keeping rural farmers impoverished in Togo. This study demonstrates the cross-cultural possibilities to advance food systems research with Indigenous communities if Western scholars foster feminist decolonizing principles. This research is only possible if supported by communities like Bikotiba, and this study provides compelling insights on the possibilities when communities support research.
... As a result, in order to reply to the polls. For this study, a self-administered questionnaire was used for data collection (Samani, 2016). Questionnaires were distributed using a combination of face-to-face as well as online social media and messaging platforms. ...
Alumni engagement is considered one of the key strategies for education institutes to stay competitive and resourceful. Students after graduation can enable their institutes to tap a number of resources through their networks, thus helping with marketing, increasing employability, donations, and mentorship opportunities. Though universities have started capitalizing extensively on alumni as a resource, little is known about the factors influencing alumni engagement in the Malaysian universities context. To address the issue, this study tried to look into the factors affecting alumni engagement by using the School of Business and Economics, Universiti Putra Malaysia as a case study. A quantitative research design was used to collect data from the respondents. The results showed that student experience, external environment, and networking opportunities have a significant and positive effect on alumni engagement. The study has significant theoretical and practical implications and warrants the need for future studies.
... In general data analysis is the process of methodically applying the statistical and logical methods to describe and explain, condense, recap, and evaluate data (Sanaz, 2016). It concentrated on the process of developing answers to research questions through the examination and clarification of data and it start with recognizing research problems, determine the availability of appropriate data, decide on which methods are suitable for answering the research questions, apply the methods, summarize and discuss the results. ...
... The population was used to determine the sample size, which was 387 people. According to (Samani, 2016), a non-response rate of 30 percent should be factored into the sample size calculation to account for possible bias. So, the study's total sample size was increased to 503 SMEs operating in Lagos State by including the non-response rate allowance of 30% (116). ...
Environment scrutiny has shown that family businesses are the common form of business in Nigeria and their going concern has generated academic debate and public discourse worldwide. Yet, the features orchestrating continuity in these family businesses are different and geographically diverse. One of the major issues to which this can be attributed to is the uncommonness of adequate succession planning practices. The objective of this paper is to examine the effect of succession planning on family business continuity in Lagos State, Nigeria. A survey research design was adopted for this paper with 503 selected SMEs operating in Lagos State as the target population. Taro Yamane sample size method was adopted and data was collected through the use of structured questionnaire adapted and validated for the study. The Cronbach’s alpha coefficient for the questionnaire items ranges between 0.850 and 0.775. The questionnaire response rate was 93.20%. The gathered data were analyzed using descriptive and inferential (Pearson product moment correlation and regression analysis) statistics. The analysis of the data revealed that succession planning had a significant effect on family business continuity (R=0.738, R2 =0.545, Adj. R2 = 0.538, F (78.821) =1.95); p<0.05). This paper concluded that, succession planning is pertinent to the enhancement of continuity in family businesses in Lagos State. The paper therefore, recommend that for family businesses to continue to perform and achieve continuity, succession planning must be put into strategic plan of the organization so that employees have adequate leadership competence and mentoring culture.
... It is mainly applied in the PLS-SEM and Covariance-Based Structural Equation Modeling (CB-SEM), etc. The PLS-SEM methid has better predictive accuracy when compared to CB-SEM, which focuses on parameter assessment [30]. In exploratory research, if little about the relationship among the variables is known, PLS-SEM, not CB-SEM, should be considered [31]. ...
Shrub layer diversity is an essential component of the forest ecosystem diversity, that contributes significantly to structuring the community and maintaining diversity, especially in plantation forests. In previous studies, researchers have reported the strong relationship among various factors (i.e., soil composition, mean annual temperature, etc.) and shrub diversity. However, how these factors jointly influence shrub diversity and which factors could be considered the key factors is still unknown. In this study, we attempted to quantify the effect among environmental factors, soil factors and forest stand factors on shrub diversity. Twenty-seven variables were selected from 57 Chinese pine plantation plots in Huanglong Mountain, Yanan City, Shaanxi Province, China. The path models showed that latent variable of soil properties is the main effective factor of latent variable of shrub diversity (directly effect, path coefficient = 0.344) and the latent variable of site conditions is another effective factor of latent variable of shrub diversity (indirectly effect, path coefficient = 0.177); Besides, the latent variable of site conditions and forest properties directly affect the latent variable of soil properties (path coefficient = 0.514 and 0.326, respectively). Among the latent variable of soil properties, soil water content (SWC) has the biggest weight of 0.666, which indicated the most significant contribution of SWC to latent variables of shrub diversity. Total nitrogen, weighted 0.375, and total phosphorus, weighted 0.308, are also important factors and make a similar contribution to latent variable of shrub diversity. Soil organic matter (SOM) has a minimal impact (lowest weight, 0.059); among the objective variables of site condition, altitude contributes the most and is followed by litter thickness, weighted at 0.722 and 0.448, respectively. Furthermore, among all the variables affecting the latent variable of forest properties, forest age is recognized as the maximum impactor of soil property change, which weighted −0.941; and is followed by forest stock volume and diameter at breast height (DBH), weighted 0.795 and 0.788, respectively. The crowding index (C) has the lowest weight (−0.235) and demonstrated that spatial distribution and crowding of trees have minimal impact on the latent variable of Soil properties. diversity Overall, our study provides new insights into quantifying the relationships among different driving factors that potentially play a significant role in determining shrub layer diversity within the plantation forest.
Provides a nontechnical introduction to the partial least squares (PLS) approach. As a logical base for comparison, the PLS approach for structural path estimation is contrasted to the covariance-based approach. In so doing, a set of considerations are then provided with the goal of helping the reader understand the conditions under which it might be reasonable or even more appropriate to employ this technique. This chapter builds up from various simple 2 latent variable models to a more complex one. The formal PLS model is provided along with a discussion of the properties of its estimates. An empirical example is provided as a basis for highlighting the various analytic considerations when using PLS and the set of tests that one can employ is assessing the validity of a PLS-based model. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Previous research on buyer behavior in high technology markets has focused on specific outcomes (product adoption or rejection) as opposed to buyers’ underlying processes. The authors identify key dimensions of high technology markets and draw on organization theory and information economics to develop hypotheses about their effects on organizational buyers’ search behavior. They also present propositions about how certain aspects of a firm's present buying situation may influence its perceptions of the characteristics of a given market. An empirical test of the propositions in a sample of computer workstation purchases shows good support for the hypothesized relationships.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
A critical element in the evolution of a fundamental body of knowledge in marketing, as well as for improved marketing practice, is the development of better measures of the variables with which marketers work. In this article an approach is outlined by which this goal can be achieved and portions of the approach are illustrated in terms of a job satisfaction measure.
