#structural equation modeling

I just finished an initial draft proposal for two studies, each with two hypotheses.  My theory is pretty strong but I’m very much still working on my methods; I may change my predictor variables and I need to elaborate on my analysis methods A LOT.  The thing that kills me about this proposal is that I had to choose hypotheses that require relatively obscure methods that I’ve never used before.  Two of the hypotheses use multilevel structural equation modeling, which isn’t that far outside my knowledge zone since I know multilevel modeling and structural equation modeling, but my sample size will be sup par and the coding for these analyses have the potential to be complicated.  For the other two hypotheses I’m currently proposing to use univariate and multivariate tobit regression since my outcome variables are ratios and there is (theoretical) bottom censoring.  Tobit regression is used in criminology and econometrics but it’s rarely used in public health.  Likewise public health tends to neglect multivariate regression in favor of just running separate models.  I could get at similar concepts to what I’m interested in using different analysis techniques for slightly different hypotheses, but of course I’m stuck on the current hypotheses I have that require ratio outcome variables.  I am not satisfied with multivariate tobit regression for my study one hypotheses.  I want to more examine the relationship between two dependent variables, both of which are ratios, more directly than multivariate analysis may permit.  Why do I make things complicated for myself?  Also, does anyone search or follow analysis tags on tumblr besides me?  I think I know the answer to that.... does this mean that I need to find myself a life?    

>10 is very strong evidence of improvement

Unlock advanced insights with the best Structural Equation Modeling service, designed to help you analyze complex relationships between variables with precision and accuracy. Our expert team uses state-of-the-art SEM techniques to provide comprehensive analysis tailored to your research or business needs. Whether for academic, industrial, or market research, our service delivers reliable, data-driven results that empower decision-making. Trust us for unparalleled expertise, personalized support, and a commitment to delivering the best in Structural Equation Modeling

Every company's primary goal is to attract and keep customers. However, many businesses are more concerned with attracting new clients than with keeping old ones. However, increasing client retention can result in higher long-term profits. The goal of this study was to see how the service marketing mix affected customer retention of Islamic banking savings accounts in Pekanbaru. The study was quantitative and used a causal and descriptive research approach. By employing a purposive sampling strategy, 154 consumers of multiple Islamic banks were sampled. Surveys and observations were employed to gather information for this study. Structural Equation Modeling was utilised to analyse the data (SEM). Except for price, location, and physical proof, the results demonstrated that all elements of the service marketing mix have a considerable beneficial impact on client retention. Please see the link :- http://mbimph.com/index.php/AJOAIR/article/view/1656

#Dataanalysis

Website: http://www.statswork.com/

Website: http://www.statswork.com/