# Quantitative Data Analysis: An Introduction

**Author(s):**
Diana L.
Mindrila

**Edition:
**
1

**Copyright:
**
2021

**Pages:
**
172

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## $54.02

* 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**

**Diana L. Mindrila**

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.

* 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**

**Diana L. Mindrila**

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.