Quantitative Data Analysis: An Introduction
Edition: 1
Copyright: 2021
Pages: 172
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Quantitative Data Analysis: An Introduction provides an introduction to quantitative research methodology. The first chapters discuss fundamental topics such as developing research questions, stating hypotheses, identifying variables, and commonly used quantitative research designs. The book explains concepts frequently used in quantitative research, such as distributions and standardized scores, and presents a series of widely used data analysis procedures. Additionally, the book describes data management techniques and provides step-by-step directions on analyzing and manipulating data using the IBM SPSS and Microsoft Excel software. The information covered in this book is critical for students conducting research projects or practitioners working with quantitative data.
CHAPTER 1 Using Data to Make Decisions and Address Problems
Using Data to Address Problems
Examples of Problems and Concerns That Can Be Addressed Using Data
Strategies for Obtaining Information and Addressing Problems
Research Questions
Investigable Questions
Feasible Questions
Precise Questions
Types of Investigation
Exercise: Drafting Research Questions
Worksheet: Drafting Research Question
CHAPTER 2 Designing a Quantitative Study
Quantitative versus Qualitative Studies
Mixed-Method Studies
Variables
Identifying the Variables That Need to Be Measured
Types of Quantitative Studies
1. Descriptive Studies
2. Correlational Studies
3. Causal-Comparative Studies
Worksheet: Planning the Investigation
CHAPTER 3 Data Sources
Identifying the Information Needed
Identifying Variables and Entities That Need to Be Measured
The Concept of Data
Collecting Data versus Using Existing Data
Sampling and Instrumentation
Population versus Sample
Sampling Procedures
Data Collection Worksheet
CHAPTER 4 Data Management
Organizing Data
Defining Variables
Coding Variables
Recoding Variables
Creating New Variables
Preparing Data for Analysis
Removing Duplicate Observations
Sorting Data
Selecting a Subsample
Examining Missing Values
Data Management Using IBM SPSS
Organizing Data and Defining Variables
Recoding Variables
Creating New Variables
Preparing Data for Analysis
Identifying Duplicate Observations
Sorting Data
Selecting a Subsample
Examining the Distribution of Missing Values
Imputation of Missing Values
Manage Data Using Excel
Sorting Cases
CHAPTER 5 Data Analysis: Exploratory Procedures
Summarizing Data
Categorical Variables
Quantitative Variables
1. Descriptive Statistics
2. Graphs
Descriptive Analysis Using SPSS
Quantitative Variables
Categorical Variables
Descriptive Analyses Using Excel
Quantitative Variables
Categorical Variables
CHAPTER 6 Data Analysis: Examining Distributions
Distributions
Density Curves
Types of Distributions
The Normal Distribution
The t Distribution
Skewness
CHAPTER 7 Standardized Scores
Norm-Referenced Scores
Standardized Scores
z Scores
Standard Errors
The t Statistic
Calculating z-Scores Using IBM SPSS
Calculating z Scores Using EXCEL
CHAPTER 8 The t Confidence Interval
Assumptions for Making Inferences about the Mean of One Group
Parameters and Statistics
Estimating a t Confidence Interval for the Population Mean
The Margin of Error
The Upper and Lower Bounds of the Confidence Interval
Confidence Intervals, Margin of Error, and Sample Size
Calculate Confidence Intervals Using IBM SPSS
Calculate Confidence Intervals Using Excel
CHAPTER 9 Hypothesis Testing
Tests of Significance
1. Stating Hypotheses
2. Calculating the Test Statistic
3. Determining the Probability of the Test Statistic
4. Determining the Significance of the Test Statistics
Type I and Type II Errors
Exercise: One-Sided versus Two-Sided Alternative Hypotheses
Worksheet: Stating Hypotheses
CHAPTER 10 The t-Test of Significance for One Sample Mean
The t Test of Significance
The t Test for One Sample Mean
Step 1. State the Null and Alternative Hypotheses
Step 2. Calculate the t-Test Statistics
Step 3. Determine the Statistical Significance of the Test Statistic
Step 4: Decide Whether to Accept or Reject the Null Hypothesis
Conducting a t-Test for One Population Mean Using IBM SPSS
Conducting a t-Test for One Population Mean Using Excel
CHAPTER 11 The t-Tests of Significance for Comparing Two Independent Sample Means
Comparing Two Sample Means
The t-Test for Independent Samples
Step 1. State the Hypotheses
Step 2. Calculate the Test Statistic
Step 3. Determine the Significance of the Test Statistic
Step 4. Decide Whether to Accept or Reject the Null and Alternative Hypotheses
Conducting Independent-Samples t-Tests Using IBM SPSS
Conducting Independent-Samples t-Tests Using Excel
CHAPTER 12 The t-Tests of Significance for Comparing Two Paired Sample Means
Comparing Two Paired Sample Means
The t-Test for Paired Samples
Step 1. State the Hypotheses
Step 2. Calculate the Test Statistic
Step 3. Determine the Significance of the Test Statistic
Step 4. Decide Whether to Accept or Reject the Null and Alternative Hypotheses
Conducting Paired-Samples t-Tests Using IBM SPSS
Conducting a Paired-Samples t-Test Using Excel
Appendix
References
Dr. Diana Mindrila is an Associate Professor of Educational Research at the University of West Georgia. She teaches quantitative research methodology, research design, and educational assessment and directs doctoral student research. Dr. Mindrila's expertise is in latent variable modeling and multivariate classification procedures. She published theoretical studies and empirical research using latent class analysis, factor analysis, structural equation modeling, cluster analysis, etc.