Relationships and Hypotheses in Social Science Research (original) (raw)
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2023
This book has been written by researchers who share the common trait of admiration for the research process. The aim of the book is to support the academicians and students who do research in social sciences, for gaining competence, skills, and knowledge on statistical analysis, interpretation, and reporting. For this purpose, attention was paid to using plain, simple, and understandable language as much as possible in the writing of the book and to supporting the examples with real research data. The book is short and concise in its design. An effort has been made to focus only on the basic concepts in the book. The book aims to introduce undergraduate and graduate students to the scientific research process and to be a companion in the analysis process. Readers can do the analysis they need and report the results by simply selecting the topic they are interested in, without examining the entire book. In addition, the examples given about the topics in the book were first explained theoretically and then solved with SPSS and AMOS package programs. Unlike textbooks, this book aims to present different methodological applications about not only “research methods” (experimental data collection and analysis), but the whole “research process” from start to finish. In this respect, it shows that there may be more than one alternative on the way to the goal. We would like to express that the success of the effort to produce this book has come from the continued support and encouragement we receive from many academics. You can obtain the SPSS data required to follow the analyzes in the book and to do it simultaneously with the book from www.indataanalysis.com Your opinions are valuable to us. lsurucu@yahoo.com
Use of Structural Equation Modeling in Social Science Research
Asian Social Science, 2015
A researcher mostly needs some statistical technique for the interpretation of the data at hand. This choice depends on the nature of the data and the researcher's own understanding and preferences of the available techniques. Structural Equation Modeling (SEM) is one among those techniques. The purpose of the present study is to present some basic aspects this powerful interdependence technique with and analysis of the most common issues of SEM. This paper will present a case as to how SEM excels other statistical techniques. Literature reveals that SEM is one of the most favored statistical techniques among the social science researchers and has been found to be better than other multivariate techniques including multiple regression analysis in examining series of dependence relationships simultaneously. However, it has been felt that the use of SEM in social research is equal to naught. Side by side there hardly exists any published review that systematically describes and critique the use of SEM. The present research is an endeavor to fill that gap. The study contributes to literature on SEM specifically and provides more holistic view of SEM for researchers to use SEM more effectively.
Mediation and Moderation Analysis from the Perspective of Behavioral Science
Mediation and moderation are theories for understanding causal relationships. The purpose of this article is to make researchers aware the difference between mediator and moderator. Research that combines mediation and moderation are common in either basic or applied behavioral research. Usually, this kind of research is structured in terms of mediated moderation or moderated mediation. Unfortunately, many researchers use the terms mediator and moderator interchangeably; not realizing the difference between the two. Therefore, in this article, the differences between a mediator and a moderator are outlined. This article described conceptual basis, research model, data analysis and construct validity issues that are necessary for making inferences in mediation and/or moderation analysis. Then, this article defines the mediating variable and how it differs from moderating variable. Next, longitudinal mediation model, model with moderators as well as mediators, and causal inference for mediation models are described. An empirical illustration is provided using structural equation modeling (SEM) techniques. Statistical methods to analyze mediation and moderation as well as newer techniques are described. Future directions using mediators and moderators with more elaborate models such as moderated mediation and mediation moderation are discussed conceptually.
Testing the Mediation Effect Using Covariance Based Structural Equation Modeling with Amos
2014
I. INTRODUCTION Structural Equation Modeling (SEM) is the one of the prominent method to fulfill the requirement of the necessary for most of the researchers nowadays. This method is performed to overcome the limitation of the previous method whereby are old version that initially are false assumption. According to (Afthanorhan, 2013) this application is the integrating of regression analysis and exploratory factor analysis to ascertain scholar provide surveys in a factual assumption. For an example, some of the scholars often use the computation of mean for each variable to analyze their empirical research and of course totally violate the assumption in which the mean of error should be zero. In the nature of social science, the type of mediation effect is able to let the scholars identify the strength of each mediator variables and competent to capture an attention of scholars to implement particular method for their empirical study. In other words, type of mediator has become enjoyed for some researchers nowadays since this skill probable to expand the contribution of the research paper to present a good knowledge to the readers from a variety of fields and countries across the whole region. The founder namely Cohen (1998) allegation the strength of mediator variable is relies on correlation of coefficient or square multiple correlation (R 2) in the model developed. A square multiple correlation is exist once this variable has been exerted by other variables whereby independent or exogenous variables. In particular, the result provided in mediator variable comes upon the independent variable has a causal effect on the particular variables. In the accordance of Daniel Soper (2010), square multiple correlations (R 2) higher than 0.80 consider high total variation. In addition, there are three types concerning on testing mediated effect beginning by Aronian (1944) followed of Goodman (1960) and has been improve by Sobel test (1982). All of these types use the z-score or z-test to indicate the significant level for their theory. Apparently, the researchers currently interest to perform their mediated effect on the Sobel test that has been supposed a best and precise according to discover of decline error associated with product distribution problem. In furthers, this work paper practice of the volunteerism subject to execute the testing mediating effect using Sobel test whereby comprise of five variables including of three mediator variables. The three mediators variables is emanating from the discovery of previous empirical research wherein these particular variables is conformity to take account the double explanation in such events. Unambiguously, three mediators variable presume Benefits, Challenges and Barrier. Dingle (2001) ordains the Governments is better informed about the people who volunteer, it is likely to become more aware of how policy legislation it introduces can affect, both directly and indirectly , people giving of their time. Of this report prove to justify Benefits factor should be treat as a mediator variable due competency to elucidate two vital roles in one time event. Moreover, the same author which expertise on this area of Dingle (2001) also describe three factors that challenges volunteering which can be indirectly among people to involve the volunteerism program and eventually this particular variable should be implement for the required research. In the accordance of Marlene Wilson (1976) and Eva Schindler-Rainman (1987) explores the barrier is the early Abstract: Nowadays, most of the researchers prefer to perform their research using Structural Equation Modeling (SEM). Further, this application has been extended to enhance the powerful and momentous of the empirical study in order to let the scholars build probe in deeper. Previously, the introduction to mediator variable in statistical analysis already ensure the scholars to provide their research to enlighten concerning on selected variables besides to create a new phenomenon for researchers. However, most of them are rare to take into account on the type of mediator effect once complete the final stage of analysis. In reality, this application manages to determine the strength of mediator variable in analysis by following step by step approach suggested. Hence, this paper intends to illuminate the conditions of type mediator effect whereby Barrier, Benefit, and Challenges factors as well as steps to perform it on Sobel test prevail. The findings suggest Benefits factor to be a partially mediator effect whereas Barrier and Challenges factors to be nonmediation effect.
In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.