SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions (2015) are officially named IBM SPSS Statistics. Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection, now divested under UNICOM Intelligence), data mining (IBM SPSS Modeler), text analytics, and collaboration and deployment (batch and automated scoring services).
The software name originally stood for Statistical Package for the Social Sciences (SPSS), reflecting the original market, although the software is now popular in other fields as well, including the health sciences and marketing.
Stata is a general-purpose statistical software package created in 1985 by StataCorp. Most of its users work in research, especially in the fields of economics, sociology, political science, biomedicine and epidemiology.
Stata’s capabilities include data management, statistical analysis, graphics, simulations, regression, and custom programming. It also has a system to disseminate user-written programs that lets it grow continuously.
The name Stata is a syllabic abbreviation of the words statistics and data. The FAQ for the official forum of Stata insists that the correct English pronunciation of Stata “must remain a mystery”; any of “Stay-ta”, “Sta-ta” or “Stah-ta” is considered acceptable.
EViews (Econometric Views)is a statistical package for Windows, used mainly for time-series oriented econometric analysis. It is developed by Quantitative Micro Software (QMS), now a part of IHS. Version 1.0 was released in March 1994, and replaced MicroTSP. The TSP software and programming language had been originally developed by Robert Hall in 1965. The current version of EViews is 10, released in June 2017.
Qualitative Research is primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations. It provides insights into the problem or helps to develop ideas or hypotheses for potential quantitative research. Qualitative Research is also used to uncover trends in thought and opinions, and dive deeper into the problem. Qualitative data collection methods vary using unstructured or semi-structured techniques. Some common methods include focus groups (group discussions), individual interviews, and participation/observations. The sample size is typically small, and respondents are selected to fulfill a given quota.
Quantitative Research is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics. It is used to quantify attitudes, opinions, behaviors, and other defined variables – and generalize results from a larger sample population. Quantitative Research uses measurable data to formulate facts and uncover patterns in research. Quantitative data collection methods are much more structured than Qualitative data collection methods. Quantitative data collection methods include various forms of surveys – online surveys, paper surveys, mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations.
This graduate level course provides an introduction to the basic concepts of probability, common distributions, statistical methods, and data analysis. It is intended for graduate students who have one undergraduate statistics course and who wish to review the fundamentals before taking additional 500 level statistics courses. This course is cohort-based, which means that there is an established start and end date, and that you will interact with other students throughout the course.
Upon completion of this course students will:
• Appreciate and understand the role of statistics in their own field of study.
• Develop an ability to apply appropriate statistical methods to summarize and analyze data for some of the more routine experimental settings.
• Make sense of data and be able to report the results in appropriate table or statistical terms for inclusion in their thesis or paper.
• Interpret results from various computer packages (Minitab, SPSS, SAS) and be able to use Minitab to perform appropriate statistical techniques.
The course aims to provide students with important skills which are of both academic and vocational value, being an essential part of the intellectual training of an economist and social scientist and also useful for a career.
By the end of the module students should have competencies in the following: An awareness of the empirical approach to economics and social science; review and extend fundamental statistical concepts; methods of data collection and analysis; regression analysis, its extensions and applications; use of spreadsheets and statistical packages such as SPSS (Or STATA).
The module will typically cover the following topics:
Review of random variables, associated distributions and moments; review of statistical estimation, estimator sampling distributions and population inference; causality and selection bias; experimental versus non-experimental data; simple linear regression (SLR) model, assumptions, interpretation and hypothesis testing; multiple linear regression (MLR) model, assumptions, interpretation and hypothesis testing; modelling non-linear relationships; dummy variables; interaction terms; the failure of MLR assumptions; tests and implications for hypothesis testing; problems of endogeneity; instrumental variables; short panel data methods; Stata.