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
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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