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An Introduction to Statistics and Data Analysis Using Stata

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Table of Contents

Part 1: The research process and data collection Chapter 1: The research process and data collection Read the literature and identify gaps or ways to extend the literature Examine the theory Develop your research questions and hypotheses Develop your research method Analyze the data Write the research paper Chapter 2: Sampling techniques Sample design Selecting a sample Sampling weights Chapter 3: Questionnaire design Structured and semi-structure questionnaires Open- and closed-ended questions General guidelines for questionnaire design Designing the questions Collecting the response data Skip patterns Ethical issues Part 2: Describing Data Chapter 4: An Introduction to Stata Opening Stata and Stata Windows Working with existing data Entering your own data into Stata Using log files and saving your work Getting help Summary of commands used in chapter Chapter 5: Preparing and transforming your data Checking for outliers Creating new variables Missing values in Stata Summary of commands used in chapter Chapter 6: Descriptive statistics Types of variable and measurement Descriptive statistics for all types of variables -- frequency tables and modes Descriptive statistics for variables measured as ordinal, interval, and ratio scales -- median and percentiles Descriptive statistics for continuous variables -- mean, variance, standard deviation, and coefficient of variation Descriptive statistics for categorical variables measured on a nominal or ordinal scale -- cross tabulation Applying sampling weights Formatting output for use in a document (Word, Google Docs, etc.) Graphs to describe data Summary of code used in chapter Part 3: Testing Hypotheses Chapter 7: The Normal distribution The normal distribution and standard scores Sampling distributions and standard errors Examining the theory and identifying the research question and hypothesis Testing for statistical significance Rejecting or not rejecting the null hypothesis Interpreting the results Central limit theorem Presenting the results Summary of commands used in chapter Chapter 8: Testing a hypothesis about a single mean When to use the one-sample t test Calculating the one-sample t test Conducting a one-sample t test Interpreting the output Presenting the results Summary of commands used in chapter Chapter 9: Testing a hypothesis about two means When to use a two independent-samples t test Calculating the t statistic Conducting a t test Interpreting the output Presenting the results Summary of commands used in chapter Chapter 10: Analysis of variance When to use one-way analysis of variance Calculating the F ratio Conducting a one-way analysis of variance test Interpreting the output Is one mean different or are all of them different? Presenting the results Summary of commands used in chapter Chapter 11: Cross-tabulation and the chi-squared test When to use the chi-squared test Calculating the chi-squared test Conducting a chi-squared test Interpreting the output Presenting the results Summary of commands used in chapter Part 4: Exploring relationships Chapter 12: Linear regression analysis When to use a regression analysis Correlation Simple regression analysis Multiple regression analysis Presenting the results Summary of commands used in chapter Chapter 13: Regression Diagnostics Measurement error Specification error Multicollinearity Heteroskedasticity Endogeneity Non-normality Presenting the results Summary of commands used in chapter Chapter 14: Regression analysis with categorical dependent variables When to use logit or probit analysis Understanding the logit model Running logit and interpreting the results Logit vs probit regression models Regression analysis with other types of categorical dependent variables Presenting the results Summary of commands used in chapter Chapter 15: Writing a research paper Introduction section of a research paper Literature review Data and methods Results Discussion Conclusions

About the Author

Lisa Daniels is the Hodson Trust Professor of Economics at Washington College in Chestertown, Maryland. She specializes in development in Africa, where she worked for ten years, beginning as a Peace Corps volunteer. During this time, she studied agricultural markets, market information systems, poverty trends, and micro- and small-scale enterprises. As part of her research on micro- and small-scale enterprises, she directed national surveys of 7,000 to 56,000 households and business in Bangladesh, Botswana, Kenya, Malawi, and Zimbabwe funded by the United States Agency for International Development. In each survey, she was she was responsible for the questionnaire design, sample selection, data collection and analysis, and report preparation. Her work from these surveys and other research in Africa and Asia appears as both consulting reports and in peer-reviewed journals. In addition to research and fieldwork, she has taught a range of courses over the past 22 years, including a Research Methods course and a Data Analysis course that she has taught 16 times. She has also presented her work related to teaching at over a dozen workshops. Nicholas Minot is a Senior Research Fellow at the International Food Policy Research Institute (IFPRI) in Washington, DC. Since joining IFPRI in 1997, he has carried out research on the agricultural market reform, income diversification, spatial patterns in policy, and food price volatility in developing countries. This research often involves carrying out surveys of farmers, cooperatives, traders, and consumers to better understand changes in food marketing systems. In addition to research, he is involved in outreach and capacity building activities, including offering short courses on the use of Stata for survey data analysis. Before joining IFPRI he taught at the University of Illinois in Urbana-Champaign, served as a policy adviser in Zimbabwe and analyzed survey data in Rwanda. Overall, he has worked in more than two dozen countries in Latin America, sub-Saharan Africa, North Africa, and Asia.


"This book introduces statistical methods to students while, at the same time, walking them through the process by which to apply those methods to real-world problems using Stata. This is something that is severely lacking in methods texts at this time."

-- Steven P. Nawara

"This is so far one of the best introductions to statistics and Stata that I have seen, particularly for my students who really need a bit of hand holding. This will likely make it less intimidating for students with no exposure to statistics."

-- Holona LeAnne Ochs

"I found the style of the book very sound for today's student. The style wasn't overly formal nor was the material presented in an overly complicated fashion. The author kept to a somewhat casual, approachable writing style that should be perfect for the modern college student."

-- Wendy L. Hicks

"This is a much needed book that encompasses research methods through to the analysis stage and reporting writing."

-- Eileen M. Ahlin

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