Quantitative Analysis using Stata
Quantitative Analysis using Stata is one of the commonly discussed queries. In this simple tutorials, we will explain what Stata does offer to conduct Quantitative Analysis. Stata has been one our best choice for Quantitative Analysis for academic and commercial research solutions. Quantitative Analysis using Stata will describe the core features of Stata that one can employ to analyse survey data and write research reports. If you need any technical support to complete your research projects involving Quantitative Analysis using Stata, we would be happy to provide customized solution.
Stata offers many tools and techniques to complete statistical analysis of data to write academic reports. In this regard, some of the techniques, commonly applied are listed below:
- One way tabulation of data.
- Two way tabulation of data
- Ploting categorical data through pie charts and bar charts
- Running simple and multiple regression with normal and continous variables
- Running regression methods for categorical data involving binary and multi-categorical expected values
- Running count data models
- Generalized Linear regression models
- Structural Equation models using covariance approach
- Generalized Structural Equation models
- Multigroup analysis for SEM and GSEM using SEM Builder of Stata
These are only the few methods that we can easily employ to conduct Quantitative Analysis using Stata and write our technical business reports or academic papers including PhD thesis and Master level dissertation.
Most of the commonly asked questions about these techniques related to when to use a technique or how to interpret a technique. On this, we will continue working to produce specific tutorials over the next few weeks. We recommend you bookmark this page and our website to get udpates about our weekly tutorials on Quantitative Analysis using Stata. There will be announcements about courses and project support for PhD and Master students and hence to availe a discounted enrollment, do follow our social media groups on Facebook and Linkedin as well.
Quantitative Analysis using SPSS
SPSS is one of the most conventional and easier Statistical software preferred for Quantitative Analysis using SPSS. Academics, professionals and students all like SPSS equally for its convenience and suitability of processes. In this simple introduction to Quantitative Analysis using SPSS, we will cover the basic processes that one can do perform in SPSS for Quantitative Analysis.
The use of SPSS is more common among the academic users within the fields of Social Sciences, Public Health, Business Studies and Management. This is mainly due to the application of techniques they have to follow is readily available within few clicks in SPSS and the results produced can simply be copy-pasted into Word for processing and writing reports. Quantitative Analysis using SPSS is thus, one of the most value series we will cover on these pages to help the academic and business community to enable them write technical reports with the results from Quantitative Analysis using SPSS.
The following list produces some commonly available statistical methods that we will cover to write more tutorials on Quantitative Analysis using SPSS:
- One way tabulation
- Two way tabulation
- Correlation, Bivariate Correlation and Canonical Correlation
- Generalized Linear Methods
- Linear Regression and Multiple Regression
- Logist and Multinomial Logistic Regression
- Probit and Ordinal Regression
- Partial least square and Hiearchical Regression
- Scale Reduction, Factor Analysis, Principle Compnent Analysis
- Confirmatory Factor Analysis and SEM
- Multigroup Analysis using SEM
We also offer completely private and instructor led courses in Quantitative Analysis using SPSS to PhD and Master students in Economics, Behavioural Finance, Management Sciences and Project Management and Social Sciences. We will help the students to conduct Quantitative Analysis using SPSS and write their academic papers and thesis with high impact.
Also, we help business professionals in conducting Quantitative Analysis using SPSS for survey data related to their businesses and hence enable them get deeper insights through advanced multivariate analysis and completely customized Business Research reports.
Quantitative Analysis using SmartPLS
Quantitative Analysis using SmartPLS helps to conduct structural equation modeling following the variance based partial least squares approach. SmartPLS also allows us to estimate consistent SEM-PLS and then conduct bootstrapping of the estimated partial least squares and consitent pls estimates. In addition to these common Quantitative Analysis using SmartPLS, we can implement many other advanced techniques for small samples of data collected through questionnaires. These Quantitative Analysis approaches include:
- Partial least squares approach to structural equation models
- Consistent PLS approach to SEM
- Bootstrapping PLS
- Bootstrapping Consistent PLS
- Mediation Regression
- Moderation Regression
- Moderated Mediation or Medicated Moderation through PLS or Consistent PLS
- Multigroup Analysis using SEM-PLS
- Multigroup Analysis using Consistent SEM-PLS
- Confirmatory Titrad Analysis or CTA
- Importance-performance matrix analysis (IPMA)
- Multi-group analysis (MGA)
- Hierarchical component models (second-order models)
- Nonlinear relationships (e.g. quadratic effect)
- Confirmatory tetrad analysis (CTA)
- Finite mixture (FIMIX) segmentation
- Prediction-oriented segmentation (POS)
We recommend SmartPLS for Quantitative Analysis during our lectures because of its simplicity, convenience and robust estimation with small samples because we believe students can save money and time to complete their academic papers by using advanced Quantitative Analysis using Smartpls, produce efficient estimators and write effective papers.
Quantitative Analysis using SmartPLS is also recommended based on many other reasons. The "A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)" is the key books which mention the reasons to use PLS and hence SmartPLS for practical data analysis for small samples because it offers various advantages over the conventional SEM based on covariance.
Another important reason that we recommend to follow is the robust helping community from the producers and users of SmartPLS (link here) provides an opportunity to ask any question related to Quantitative Analysis using Smartpls. The authors and expert users provide interesting replies to all questions within hours.
Also, we provide technical support related to Quantitative Analysis using Smartpls to PhD and Master students in Social Sciences, Management Sciences and Economics to help them estimate complex path diagrams and write effective research reports.