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An Introduction to Research, Analysis, and Writing: Practical Skills for Social Science Students

• Edited by: Bruce Oliver Newsome
• Publisher: SAGE Publications, Inc
• Publication year: 2016
• Online pub date: January 19, 2023
• Discipline: Sociology , Criminology and Criminal Justice , Business and Management , Communication and Media Studies , Education , Psychology , Health , Social Work , Political Science and International Relations
• Methods: Case study research , Survey research , Theory
• DOI: https:// doi. org/10.4135/9781071909829
• Keywords: crime , knowledge , persons , scope , sentencing , terrorism , war Show all Show less
• Print ISBN: 9781483352558
• Online ISBN: 9781071909829
• Buy the book icon link

Subject index

This accessible guide walks readers through the process of completing a social science research project. Written specifically to meet the needs of undergraduate research classes, it introduces students to a complete skill set, including: planning, design, analysis, argumentation, criticizing theories, building theories, modeling theories, choosing methods, gathering data, presenting evidence, and writing the final product. Students can use this text as a practical resource to navigate through each stage of the process, including choices between more advanced research techniques.

Front Matter

• Acknowledgments
• About the Author
• Chapter 1: The Way Ahead
• Chapter 2: The Research Process
• Chapter 3: Research Ethics and Laws
• Chapter 4: Scoping, Justifying, Designing, and Planning
• Chapter 5: Reading and Reviewing
• Chapter 6: Analysis
• Chapter 7: Arguing and Explaining
• Chapter 8: Theorizing and Modeling
• Chapter 9: Methods
• Chapter 10: Evidence and Data
• Chapter 11: Writing

Back Matter

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Introduction to Research Methods A Hands-on Approach

• Bora Pajo - Franklin University
• Description
• Digital Option / Courseware SAGE Vantage is an intuitive learning platform that integrates quality SAGE textbook content with assignable multimedia activities and auto-graded assessments to drive student engagement and ensure accountability. Unparalleled in its ease of use and built for dynamic teaching and learning, Vantage offers customizable LMS integration and best-in-class support. It’s a learning platform you, and your students, will actually love.  Learn more.
• Assignable Video with Assessment Assignable video (available with SAGE Vantage ) is tied to learning objectives and curated exclusively for this text to bring concepts to life. Watch a sample video now.
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See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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Supplements

• Editable chapter-specific PowerPoint® slides
• Lecture notes
• Instructor Manual containing chapter activities
• All tables and figures from the textbook

“ Introduction to Research Methods is an excellent resource for students and instructors alike. With readable text in an approachable tone, it effectively covers the breadth of topics related to research methods. Its lucidity is stellar.”

“ Introduction to Research Methods: A Hands-on Approach is highly relevant to the work my undergraduate students do and is delivered at a level they can follow. It does a great job of presenting complex information in an easy-to-understand format. At the graduate level, I could see this text serving as a primary read but then supplementing with more complex articles/chapters. Accessibility of the content is a core strength of this text. It makes in-depth content easy to follow for the novice researcher, while at the same time is engaging enough to not be bland for those with research experience. Overall, this text reads as a road map for how to conduct ethical research and provides the necessary step-by-step instructions on how to be successful during the research process.”

“ Introduction to Research Methods: A Hands-on Approach is an excellent text that presents research methods in an easy-to-understand way with examples that foster critical thinking."

“The hands-on, practical approach to Pajo’s Introduction to Research Methods is a huge key strength.”

Very useful for research methods

• The new edition is available in SAGE Vantage , an intuitive learning platform that integrates quality SAGE textbook content with assignable multimedia activities and auto-graded assessments to drive student engagement and ensure accountability. Unparalleled in its ease of use and built for dynamic teaching and learning, Vantage offers customizable LMS integration and best-in-class support. Learn more.
• A new chapter on Big Data offers a window into the possibilities of what social scientists are accomplishing in the world of machine learning and the endless possibilities of data visualization. It familiarizes students with current and future opportunities in social and behavioral science.
• A completely revised chapter on qualitative designs and data collection illustrates how qualitative designs fit within scientific research methods by emphasizing similarities and identifying differences.
• Updated research examples and visuals reflect current studies in a variety of interdisciplinary fields.
• APA Style 7th Edition is used throughout the book so students see and practice the latest citation styles.
• A conversational and jargon-free writing style brings the relevance of research to life.
• Research in Action features illustrate real, annotated research examples, giving students the skills to read and interpret research they may encounter in their everyday lives.
• Research Workshop features offer step-by-step help on practical topics that extend beyond the chapter to provide hands-on tips for students conducting their own research.
• Ethical Considerations within each chapter address different aspects of research to emphasize ethics as an integral part of the research conversation.
• Chapter pedagogy includes summaries, key terms, photos, and “Taking It a Step Further” questions to extend understanding and application of text concepts.

Sample Materials & Chapters

Chapter 1: The Purpose of Research

Chapter 2: Formulating a Research Question

For instructors

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Research Methods and Data Analysis for Business Decisions

A Primer Using SPSS

• © 2021
• James E. Sallis 0 ,
• Geir Gripsrud 1 ,
• Ulf Henning Olsson 2 ,
• Ragnhild Silkoset 3

Department of Business Studies, Uppsala University, Uppsala, Sweden

You can also search for this author in PubMed   Google Scholar

Department of Marketing, BI Norwegian Business School, Oslo, Norway

Department of economics, bi norwegian business school, oslo, norway.

• Presents research methods and data analysis tools in non-technical language, using numerous step-by-step examples
• Uses QDA Miner Lite for qualitative and IBM SPSS Statistics for quantitative data analysis
• Benefits business and social science students and managers alike

Part of the book series: Classroom Companion: Business (CCB)

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Table of contents (13 chapters)

Front matter, designing the study, research methods and philosophy of science.

• James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset

The Research Process and Problem Formulation

Research design, data collection, secondary data and observation, qualitative methods, questionnaire surveys, quantitative data analysis, simple analysis techniques, hypothesis testing, regression analysis, cluster analysis and segmentation, factor analysis, reporting findings, back matter.

• research methodology
• data analysis
• decision making in business
• quantitative data analysis
• qualitative data analysis
• data analysis software
• research skills
• QDA Miner Lite
• research process
• research design
• statistical methods

About this book

This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations.

Authors and Affiliations

James E. Sallis

Geir Gripsrud, Ragnhild Silkoset

Ulf Henning Olsson

About the authors

James Sallis is a Professor at the Department of Business Studies at Uppsala University, Sweden. He teaches marketing, research methods, and statistics in the undergraduate, graduate, and executive education programs. He is statistical advisor for the department's faculty and students and is a frequent guest lecturer at business schools worldwide.

Geir Gripsrud is Professor Emeritus at the Department of Marketing at BI Norwegian Business School in Oslo, Norway, where he has acted as Dean of both Bachelor and Master Programs. An experienced teacher of marketing, marketing research, distribution channels, and international marketing, he is a co-author of widely used Norwegian textbooks on Research Methods as well as on Distribution Channels and Supply Chains.

Ulf H. Olsson is a Professor at the Department of Economics at BI Norwegian Business School in Oslo, Norway. He has held the position as Provost with responsibility for research and academic resources. Working mainly on structural equation modeling, statistical modeling and psychometrics, he has published research articles in leading statistics and psychometric journals and has also authored textbooks on statistics and mathematics.

Ragnhild Silkoset is a Professor of Marketing at BI Norwegian Business School in Oslo, Norway. She has held the position as the Head of the Department of Marketing, as well as Dean for the Executive Programs. Her areas of interest include marketing, marketing research, pricing strategy, network analysis and blockchain technology.

Bibliographic Information

Book Title : Research Methods and Data Analysis for Business Decisions

Book Subtitle : A Primer Using SPSS

Authors : James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset

Series Title : Classroom Companion: Business

DOI : https://doi.org/10.1007/978-3-030-84421-9

Publisher : Springer Cham

eBook Packages : Mathematics and Statistics , Mathematics and Statistics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021

Hardcover ISBN : 978-3-030-84420-2 Published: 31 October 2021

Softcover ISBN : 978-3-030-84423-3 Published: 01 November 2022

eBook ISBN : 978-3-030-84421-9 Published: 30 October 2021

Series ISSN : 2662-2866

Series E-ISSN : 2662-2874

Edition Number : 1

Number of Pages : XI, 258

Number of Illustrations : 26 b/w illustrations, 112 illustrations in colour

Topics : Statistics for Business, Management, Economics, Finance, Insurance , Management Education , Statistics, general , Statistics for Social Sciences, Humanities, Law , Statistics and Computing/Statistics Programs

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A blog for bibliophiles covering everything related to books from reviews and summaries to quotes and open articles.

8 Good Books on Quantitative Research

By Med Kharbach, PhD | Published: June 20, 2023 | Updated: June 6, 2024

As a scholar who navigated the complex terrain of academic research during my doctoral studies, I am intimately aware of the value that high-quality, accessible resources can bring to the table. Therefore, my aim with this post is to provide fellow researchers, scholars, and anyone interested in doing social research, with a carefully curated books on quantitative research.

Quantitative research, with its emphasis on objectivity and measurement, is a vast and rich field. The books featured in this list offer deep insights into its theoretical underpinnings, its practical applications, and the interpretative challenges it presents. Each of these texts has been selected based on its comprehensiveness, clarity, and practicality, and together they form a robust foundation for anyone wishing to delve into the world of quantitative research.

Whether you are a seasoned researcher looking to refine your approach or a novice looking to grasp the fundamentals, these books cater to all levels of understanding. From crafting sophisticated surveys to the intricacies of statistical analysis, these texts cover a range of topics that are integral to the quantitative research process.

As you explore this list, please bear in mind that it is by no means exhaustive. The field of quantitative research is ever-evolving, and new contributions are always on the horizon. Nevertheless, the knowledge contained within these pages remains as relevant and insightful as ever.

I hope that you will find these resources as valuable as I did. For those of you interested in qualitative research, check out my other post entitled best books on qualitative research .

1. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , by John W. Creswell and J. David Creswell

This widely recognized book offers a comprehensive study of qualitative, quantitative, and mixed research methodologies. Its distinguishing feature is its juxtaposition of all three research designs, making it a valuable resource for both new and experienced researchers. The authors provide a meticulous guide that starts with a fundamental understanding of philosophical suppositions, essential aspects of the research process, literature review, theory application in research, and the significance of writing and ethics in academic inquiry.

The updated edition includes a detailed discussion on research proposal design and research study steps. It sheds light on the positioning of epistemological and ontological views in relation to the selected research question and methodology. It also delves into the transformative worldview and features expanded insights into case studies, participatory action research, visual methods, and action research in mixed methods.

2. Quantitative Research in Education: A Primer , by Wayne K. Hoy and Curt M. Adams

This book is a practical guide to help you with your quantitative research. The authors first introduce readers to the nature of research and science, before presenting the meaning of concepts and research problems. They aim to dispel the notion that quantitative research is overly difficult, too theoretical, and lacks practicality.

The book is rich with concrete examples and illustrations, and emphasizes conceptual understanding and the practical use of quantitative methods while teaching strategies for developing original research hypotheses.

3. Quantitative and Statistical Research Methods , by William E. Martin and Krista D. Bridgmon

This user-friendly textbook helps students to understand and apply procedural steps in carrying out quantitative studies. It explains statistics while guiding readers through the steps of the hypothesis-testing process, from hypothesis to results.

The book offers a hands-on resource for each chapter, covering a single research problem and providing directions for implementing the research method from start to finish. It also features a Research Analysis and Interpretation Guide to help students analyze research articles.

4. Quantitative Research Methods for Communication , by Jason S. Wrench, Candice Thomas-Maddox, and Virginia Peck Richmond

This book provides a relevant and accessible guide to quantitative research in the field of communication. The authors encourage students to take an active role in the learning process, equipping them with the tools needed to locate, conduct, collect, and present their research.

The authors use real communication studies and data sets to make communication research relatable for students. The text features chapter objectives, case studies, data sheets, and key terms throughout to facilitate learning.

Related: Books on Mixed Methods Research

5. Doing Survey Research: A Guide to Quantitative Methods , by Peter M. Nardi

This book is a practical guide for those who want to learn how to conduct quantitative studies, either for an undergraduate project, a graduate-level thesis, or a survey that an employer may require. The book prepares beginners to conduct their own survey research, write up the results, and understand and critically interpret other people’s research.

Also, the book merges survey design with data analysis and interpretation. This new edition includes coverage of Big Data, Meta-Analysis, and A/B testing methodology.

6. Quantitative Methods for the Social Sciences , by Daniel Stockemer

This textbook provides an essential introduction to survey research and quantitative methods. The book guides students through the various steps of the survey research process and helps them apply those steps toward a real example.

More specifically, the book introduces students to the importance of survey research, preparing a survey, conducting a survey, and analyzing a survey.

Students are shown how to create their own questionnaire, achieve empirical findings, and use the data to test their hypotheses in a bivariate and multivariate realm. Additionally, the book explains the theory, rationale, and mathematical foundations of these tests, and provides instructions on how to conduct the tests in SPSS and Stata.

7. Quantitative Research Methods: From Theory To Publication , by Dr. Nicholas Harkiolakis

This book covers the vast majority of quantitative methods found in social sciences research. The author designed the book based on his years of experience teaching quantitative methods courses for graduate programs in Europe and the USA. The book is intended for readers who don’t have a background in quantitative research.

All concepts presented in this book are defined and introduced, and alternative and overlapping expressions and keywords used in quantitative research are presented to enhance the reader’s understanding.

The book includes examples that are easily replicated in spreadsheets like Excel, and provides commands for executing the various methods for SPSS in footnotes. The book’s website provides additional material for executing the methods discussed, as well as all book images in higher resolution and links to other sources online.

8. Quantitative Research for the Qualitative Researcher , by Laura M. O′Dwyer and James A. Bernauer

This book underlines the complementary nature of quantitative and qualitative research and elucidates the fundamental structure and purposes of design, measurement, and statistics within the framework of a research report, including a dissertation.

The text encourages the reader to perceive quantitative methodology as a process for systematically discovering new knowledge that can help describe, explain, and predict the world around us.

Final thoughts

Wrapping up this compilation of insightful reads on quantitative research, it’s clear that each of these books brings something unique to the table, making them indispensable resources for researchers at various stages of their careers. From the foundational understanding of quantitative methods to advanced statistical analysis, these books collectively cover a spectrum of topics that are vital for conducting robust and meaningful research. The practical advice, real-life examples, and step-by-step guides provided across these texts are designed to demystify the world of quantitative research, making it more accessible and less daunting, regardless of your level of expertise.

Related Posts

Meet Med Kharbach, PhD

Dr. Med Kharbach is an influential voice in the global educational landscape, with an extensive background in educational studies and a decade-long experience as a K-12 teacher. Holding a Ph.D. from Mount Saint Vincent University in Halifax, Canada, he brings a unique perspective to the educational world by integrating his profound academic knowledge with his hands-on teaching experience. Dr. Kharbach's academic pursuits encompass curriculum studies, discourse analysis, language learning/teaching, language and identity, emerging literacies, educational technology, and research methodologies. His work has been presented at numerous national and international conferences and published in various esteemed academic journals.

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8 Types of Data Analysis

The different types of data analysis include descriptive, diagnostic, exploratory, inferential, predictive, causal, mechanistic and prescriptive. Here’s what you need to know about each one.

Data analysis is an aspect of  data science and data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and modeling data to draw useful insights from it.

What Are the Different Types of Data Analysis?

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including business, science and social science, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in  decision-making , providing a better, faster and more efficacious system that minimizes risks and reduces  human biases .

That said, there are different kinds of data analysis catered with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book  R for Data Science :

• Hypothesis Generation — This involves looking deeply at the data and combining your domain knowledge to generate hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation — This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

Types of Data Analysis

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data. Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries about samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the  Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

2. Diagnostic Analysis 

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies.  

Diagnostic Analysis Example

A footwear store wants to review its website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.   

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection.

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses.

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or standard deviation to your estimation.
• The  accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the  central limit theorem .

Inferential Analysis Example

The idea of drawing an inference about the population at large with a smaller sample size is intuitive. Many statistics you see on the media and the internet are inferential; a prediction of an event based on a small sample. For example, a psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven-to-nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions.

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 US election is a popular topic and many  prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

Predictive analysis takes data from the past and present to make predictions about the future.

More on Data: Explaining the Empirical for Normal Distribution

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome.

Causal analysis is about finding out the causal relationship between variables, and examining how a change in one variable affects another.

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Many graduate-level research and complex topics are suitable examples, but to put it in simple terms, let’s say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

Mechanistic analysis is in some ways a predictive analysis, but modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science .

8. Prescriptive Analysis 

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. AI systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed. 

When to Use the Different Types of Data Analysis 

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

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Statistically Efficient Methods for Computation-Aware Uncertainty Quantification and Rare-Event Optimization

He, Shengyi

The thesis covers two fundamental topics that are important across the disciplines of operations research, statistics and even more broadly, namely stochastic optimization and uncertainty quantification, with the common theme to address both statistical accuracy and computational constraints. Here, statistical accuracy encompasses the precision of estimated solutions in stochastic optimization, as well as the tightness or reliability of confidence intervals. Computational concerns arise from rare events or expensive models, necessitating efficient sampling methods or computation procedures. In the first half of this thesis, we study stochastic optimization that involves rare events, which arises in various contexts including risk-averse decision-making and training of machine learning models. Because of the presence of rare events, crude Monte Carlo methods can be prohibitively inefficient, as it takes a sample size reciprocal to the rare-event probability to obtain valid statistical information about the rare-event. To address this issue, we investigate the use of importance sampling (IS) to reduce the required sample size. IS is commonly used to handle rare events, and the idea is to sample from an alternative distribution that hits the rare event more frequently and adjusts the estimator with a likelihood ratio to retain unbiasedness. While IS has been long studied, most of its literature focuses on estimation problems and methodologies to obtain good IS in these contexts. Contrary to these studies, the first half of this thesis provides a systematic study on the efficient use of IS in stochastic optimization. In Chapter 2, we propose an adaptive procedure that converts an efficient IS for gradient estimation to an efficient IS procedure for stochastic optimization. Then, in Chapter 3, we provide an efficient IS for gradient estimation, which serves as the input for the procedure in Chapter 2. In the second half of this thesis, we study uncertainty quantification in the sense of constructing a confidence interval (CI) for target model quantities or prediction. We are interested in the setting of expensive black-box models, which means that we are confined to using a low number of model runs, and we also lack the ability to obtain auxiliary model information such as gradients. In this case, a classical method is batching, which divides data into a few batches and then constructs a CI based on the batched estimates. Another method is the recently proposed cheap bootstrap that is constructed on a few resamples in a similar manner as batching. These methods could save computation since they do not need an accurate variability estimator which requires sufficient model evaluations to obtain. Instead, they cancel out the variability when constructing pivotal statistics, and thus obtain asymptotically valid t-distribution-based CIs with only few batches or resamples. The second half of this thesis studies several theoretical aspects of these computation-aware CI construction methods. In Chapter 4, we study the statistical optimality on CI tightness among various computation-aware CIs. Then, in Chapter 5, we study the higher-order coverage errors of batching methods. Finally, Chapter 6 is a related investigation on the higher-order coverage and correction of distributionally robust optimization (DRO) as another CI construction tool, which assumes an amount of analytical information on the model but bears similarity to Chapter 5 in terms of analysis techniques.

• Operations research
• Stochastic processes--Mathematical models
• Mathematical optimization
• Bootstrap (Statistics)
• Sampling (Statistics)

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Introduction to Research Methods and Data Analysis in Psychology 3rd Edition

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This third edition of Introduction to Research Methods and Data Analysis in Psychology provides you with a unique, balanced blend of quantitative and qualitative research methods. Highly practical in nature, the book guides you, step-by-step, through the research process and is underpinned by SPSS screenshots, diagrams and examples throughout.

• ISBN-10 0273756877
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Written in a clear and accessible style, this third edition of Introduction to Research Methods and Data Analysis in Psychology provides a unique, balanced blend of quantitative and qualitative research methods. Highly practical in nature, the book guides students step-by-step through the research process and is underpinned by SPSS screenshots, diagrams and examples throughout.

Key features:

• Fully updated with reference to version 19 of SPSS
• A new chapter on exploratory factor analysis (EFA)
• Revised and expanded coverage of ethics, report writing, regression and statistical power
• Study boxes highlight the use of different research methods and data analysis in both classic and cutting edge research in psychology.
• Activity boxes provide exercises to help students consolidate knowledge and understanding.
• Information boxes explore key issues in more detail and illustrate the concepts introduced in the text.
• Command boxes offer clear and detailed instructions of how to carry out statistical analysis using the latest version of SPSS.

Essential reading for undergraduate psychology students, and highly recommended for students on other social and health science courses.

Dr Darren Langdridge is Head of Department of Psychology at The Open University.

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• Publisher ‏ : ‎ Pearson Education; 3rd edition (October 9, 2013)
• Language ‏ : ‎ English
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Darren langdridge.

Darren Langdridge, PhD is Professor of Psychology at the Open University (UK), and a United Kingdom Council for Psychotherapy accredited existential psychotherapist working in private practice. For many years Darren has researched and written on sexualities, health, phenomenological methodology, critical theory and psychotherapy, publishing numerous books, papers and book chapters.

He is the author/co-editor of a number of books including Existential Counselling and Psychotherapy (published by Sage), Phenomenological Psychology: Theory, Research and Method (published by Pearson), Safe, Sane and Consensual: Contemporary Perspectives on Sadomasochism (with M.J. Barker, published by Palgrave Macmillan) and Understanding Non-monogamies (with M.J. Barker, published by Routledge).

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