This textbook offers an essential introduction to survey research and quantitative methods. Building on the premise that statistical methods need to be learned in a practical fashion, the book guides students through the various steps of the survey research process and helps to apply those steps toward a real example.
In detail, the textbook introduces students to the four pillars of survey research and quantitative analysis: (1) the importance of survey research, (2) preparing a survey, (3) conducting a survey and (4) analyzing a survey. Students are shown how to create their own questionnaire based on some theoretically derived hypotheses to achieve empirical findings for a solid dataset. Lastly, they use said data to test their hypotheses in a bivariate and multivariate realm.
The book explains the theory, rationale and mathematical foundations of these tests. In addition, it provides clear instructions on how to conduct the tests in SPSS and Stata. Given the breadth of its coverage, the textbook is suitable for introductory statistics, survey research or quantitative methods classes in the social sciences.
Author(s): Daniel Stockemer
Publisher: Springer International Publishing
Year: 2018
Language: English
Pages: x+182
Chapter 1: IntroductionChapter 2: The nuts and bolts of empirical social science2.1 What is empirical research in the social sciences?2.2 Qualitative and quantitative research2.3 Theories, concepts, variables and hypotheses2.3.1 Theories2.3.2 Concepts2.3.3. Variables2.3.4 Hypotheses2.4 The quantitative research processChapter 3: A short introduction to survey research3.1 What is survey research?3.2 A short history of survey research3.3 The importance of survey research in the social sciences and beyond3.4 Overview of some of the most widely-used surveys in the social sciences3.4.1 The Comparative Study of Electoral Systems (CSES)3.4.2 World Value Survey (WVS)3.4.3 The European Social Survey (ESS)3.5 Different types of surveys3.5.1 Cross-sectional surveys3.5.2 Longitudinal surveysChapter 4: Constructing a survey4.1 Question design4.2 Ordering of questions4.3 Number of questions4.4 Getting the questions right4.5 Social desirability4.6 Open-ended and closed-ended questions4.7 Types of closed-ended survey questions4.7.1 Scales4.7.2 Dichotomous survey questions4.7.3 Multiple choice questions4.7.4 Numerical continuous questions4.7.5 Categorical survey questions4.7.6 Rank order questions4.7.7 Matrix table questions4.8 Different variables4.9 Coding of different variables in a dataset4.9.1 Coding of nominal variables4.10 Drafting a questionnaire: General information4.10.1 Drafting a questionnaire A step-by-step approach4.11 Sample questionnaire4.12 Background information about the questionnaireChapter 5: Conducting a survey5.1 Population and sample5.2 Representative, random, and biased samples5.3 Sampling errors5.4 Non-random sampling techniques5.5 Different types of surveys5.6 Which type of survey should researchers use?5.7 Pre-tests5.7.1 What is a pre-test?5.7.2 How to conduct a pre-testChapter 6: Univariate statistics6.1 SPSS and Stata6.2 Putting data into an SPSS spreadsheet6.3 Putting data into a Stata spreadsheet6.4 Frequency tables6.4.1 Constructing a frequency table in SPSS6.4.2 Constructing a frequency table in Stata6.5 The measures of central tendency - mean, median, mode, and range6.6 Displaying data graphically - pie charts, boxplots, and histograms6.6.1 Pie charts6.6.2 Doing a pie chart in SPSS6.6.3 Doing a pie chart in Stata6.7 Boxplots6.7.1 Doing a boxplot in SPSS6.7.2 Doing a boxplot in Stata6.8 Histograms6.8.1 Doing a histogram in SPSS6.8.2 Doing a histogram in Stata6.9 Deviation, variance, standard deviation, standard error, sampling error, and confidence interval6.9.1 Calculating the confidence interval in SPSS6.9.2 Calculating the confidence interval in StataChapter 7: Bivariate statistics with categorical variables7.1 Independent samples t-test7.1.1 Doing an independent samples t-test in SPSS7.1.2 Interpreting an independent samples t-test SPSS output7.1.3 Reading an SPSS independent samples t-test output column by column7.1.4 Doing an independent samples t-test in Stata7.1.5 Interpreting an independent samples t-test Stata output7.1.6 Reporting the output of an independent samples t-test7.2 F-test or one way anova7.2.1 Doing an f-test in SPSS7.2.2 Interpreting an SPSS anova output7.2.3 Doing a post-hoc or multiple comparison test in SPSS7.2.4 Doing an f-test in Stata7.2.5 Interpreting an f-test in Stata7.2.6 Doing a post-hoc or multiple comparison test with unequal variance in Stata7.2.7 Reporting the results of an f-test7.3 Cross-tabulation table and chi-square test7.3.1 Cross-tabulation table7.3.2 Chi-square test7.3.3 Doing a chi-square test in SPSS7.3.4 Interpreting a SPSS chi-square test7.3.5 Doing a chi-square test in Stata7.3.6 Reporting a chi-square test resultChapter 8: Bivariate relationships featuring two continuous variables8.1 What is a bivariate relationship between two continuous variables?8.1.1 Positive and negative relationships8.2 Scatterplots8.2.1 Positive relationships displayed in a scatterplot8.2.2 Negative relationships displayed in a scatterplot8.2.3 No relationship displayed in a scatterplot8.3 Drawing the line in a scatterplot8.4 Doing scatterplots in SPSS8.5 Doing scatterplots in Stata8.6 Correlation analysis8.6.1 Doing a correlation analysis in SPSS8.6.2 Interpreting an SPSS correlation output8.6.3 Doing a correlation analysis in Stata8.7 Bivariate regression analysis8.7.1 Gauging the steepness of a regression line8.7.2 Gauging the error term8.8 Doing a bivariate regression analysis in SPSS8.9 Interpreting an SPSS (bivariate) regression output8.9.1 The model summary table8.9.2 The regression anova table8.9.3 The regression coefficients table8.10 Doing a (bivariate) regression analysis in Stata8.10.1 Interpreting a Stata (bivariate) regression output8.10.2 Reporting and interpreting the results of a bivariate regression model
