Learn structural equation modeling using SPSS AMOS in private and instructor led online courses at AnEc Center for Econometrics Research.
The course aims to develop a strong foundation of the theory and application of modeling relationships between observed and unobserved variables, path analysis and confirmatory factor analysis using structural equation modeling with SPSS and AMOS.
Structural Equation Modeling using SPSS AMOS is an online, private and instructor led course from AnEc Center for Econometrics Research led by Professor Anees Muhammad since 2010.
The course is designed to help the students in Economics, Finance, Business, Management, Psychology, Political Sciences, Social Sciences and Healthcare pursuing PhD and MS research. The course can be enrolled in private or in group. Private courses are recommended for students who need to use SEM during their writing of PhD thesis.
Statistics for Business and Management professionals and academic researchers is of critical importance. Although there are specialized courses in Statistics for the students of Business and Management but none of the courses offer specialization in either, Stata, Eviews or SPSS and other computer packages. The current course introduces the application of Stata, Eviews and SPSS and other computer application while emphasis is laid on the development of theoretical background of the learners as well as with strong application of the techniques in Statistics for Business. The contents of the course offered to students include (but are not limited) to:
Basic Data Management and Presentation
Hypothesis Testing and Research Problems
Causal Relationships, Regression Analysis and Applications
Qualitative Data Analysis for Binary and Multi-chotomous variables
Also the students of the course would be provided additional support via other mediums like phone, SMS, email beside the traditional and favorite WizIQ!
This course will benefit the students of MBA, BBA and Doctorate students in Business and Management courses have to study and develop the skills of Quantitative Data Analysis. This course will master these students in their Statistical courses.
Apply Statistical Techniques to real world data
Apply Data Analysis to relevant research problems
Wide range of contents which are usually required in broader contexts
The course package:
15 LIVE online classes
Duration: Two to Four Weeks (Negotiable)
All Days: 0500 PM to 1000 PM GMT (1000 PM to 0200 AM Pakistan Times)
The course outline:
Deterministic Data and Random Data
Multivariate Tables, Scatter Plots and D Plots
Measures of Association for Continuous Variables
Measures of Association for Ordinal Variables
Measures of Association for Nominal Variables
Estimating Data Parameters
Parametric Tests of Hypotheses
Non-Parametric Tests of Hypotheses
General Linear Regression Model
General Linear Regression in Matrix Terms
Inferences on Regression Parameters
ANOVA and Extra Sums of Squares
Polynomial Regression and Other Models
Building and Evaluating the Regression Model
Principal Components of Correlation Matrices
The Exponential Model
The Weibull Model
The Cox Regression Model
Recommended books for this course:
Probability and Statistics, Fourth Edition by Morris H. DeGroot & Mark J. Schervish
SPSS for Introductory Statistics Second Edition by Morgan et al
A First Course in Business Statistics, Eight Edition by JAMES T. McCLAVE, P. GEORGE BENSON & TERRYSlNClCH
Note: Students must purchase these books themselves. These are not available as part of the course. Discount coupons may be asked from the teacher who would be happy to share if any available right now.
In this simple outline on the issue of commonly raised douts on how to select an economic model, I would like to sum the issue in this 300 words article. Econometric Models Selection is one key problem for all. The model selection is based on the type of data and type of variables. Here, we mention an order following which helps an econometric model strategy development for us. We assume to use Panel data to develop econometric model selection strategy. Initially, to determine the right set of models, the nature of data structure for the panel data follows like this: A. Check data structure. 1. if N>T, then follows B series of steps. 2. If T>N, then follow the steps in C series.
If we assume the panel data has N sufficiently greater than T, then simple panel data or instrumental/GMM models for panel data can be achieved using the following order. This helps in Econometric Models selection looking into the key steps of the econometric analysis of the given panel data.
For Case 1, follow these steps.
B1. The first stepOLS
B4. Assumptions of RE or FE from B3.
B5. Endogeneity Tests
B6. Instrumental Regression
B7. GMM if seens dynamics/autocorrelation in B4.
If the data at hands is such that T is sufficiently larger than N, the panel time series structure will be evidenced and hence our econometric models selection strategy will follow the following procedures. The unit roots and cointegration is for panel time series data which are also sometimes considered as longer time panel data or longer panel data. In such cases, the time series properties in estimators are heavily observed and hence the steps to select an econometric models selection strategy will include these.
For Case 2, follow these steps
C1. Unit Root
If you have on entity (N=1) like one country, one company, one person etc and you collect data about this entity for many time periods (T=many), it is called Time Series Data. For example if we download data on GDP, Consumption, Investment, Savings, Exports and Imports for from world bank indicators (data.worldbank.org) for Pakistan only and 30 year, this will be N=Pakistan and T=(1970 to 2000) which defines the data is time series data.
If you many entities (N=many) like 10 countries, 100 companies and 1000 persons etc and you collect data about these entities for one time period (T=1), then it is called cross sectional data. Similar to earlier example of (World Bank Data), if we download data for 50 countries for only year 2010, then it becomes cross section data. For example, if I collect data for GDP, Consumption, Investment, Savings, Exports and Imports for Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Antigua and Barbuda, Arab World , Argentina , Armenia , Aruba , Australia , Austria , Azerbaijan , Bahamas, The , Bahrain , Bangladesh , Barbados , Belarus , Belgium , Belize , Benin , Bermuda , Bhutan, Bolivia , Bosnia and Herzegovina , Botswana , Brazil , British Virgin Islands, Brunei Darussalam , Bulgaria, Burkina Faso for the year 2010, then it can be cross sectional data.
If you have many entities (N=many) and collect data about these many entities for many time periods (T=many), it is called Cross Sectional Time Series data. So, if we collect data onGDP, Consumption, Investment, Savings, Exports and Imports for Afghanistan, Albania, Algeria, American Samoa, Andorra, Angola, Antigua and Barbuda, Arab World , Argentina , Armenia , Aruba , Australia , Austria , Azerbaijan , Bahamas, The , Bahrain , Bangladesh , Barbados , Belarus , Belgium , Belize , Benin , Bermuda , Bhutan, Bolivia , Bosnia and Herzegovina , Botswana , Brazil , British Virgin Islands, Brunei Darussalam , Bulgaria, Burkina Faso for the year 2000 to 2010, it will be panel data. Note, panel data might include T>N or N>T so selection of a relevant method can decided here:
Note panel data is what entities remains same over the time periods. Logitudinal data might contain some entities to appear only in one or the other time periods.