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Data Analysis for Research Designs

Data Analysis for Research Designs covers the analytical techniques for the analysis of variance (ANOVA) and multiple regression/correlation (MRC), emphasizing single-degree-of-freedom comparisons so that students focus on clear research planning. This text is designed for advanced undergraduates and graduate students of the behavioral and social sciences who have an understanding of algebra and statistics.

This text is designed for advanced undergraduates and graduate students of the behavioral and social sciences who have an understanding of algebra and statistics.

Using R for Data Analysis in Social Sciences

A Research Project-Oriented Approach

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and ...

Research Methods & Data Analysis for Multicultural Social Work and Human Services

Research Methods & Data Analysis for Multicultural Social Work and Human Services introduces research methodology to social work students and practitioners. It provides hands-on examples of how to conduct data analysis in SPSS and Stata. It equips readers with the skills needed to become critical research consumers and to engage in agency-based research and evaluation. The text teaches students how to collect appropriate data and analyze data that is suitable for each type of research design. It prepares them to conduct applied social science research in a variety of fields, such as health and mental health, ethnic studies, acculturation, family violence, LGBT studies, and more. Topics addressed include the process of research, ethical issues, the validity and reliability of research instruments, design types, and relevant statistical tools. Research Methods & Data Analysis for Multicultural Social Work and Human Services provides a solid foundation and knowledge base for students and researchers. It is an excellent resource for undergraduate and graduate level research methods and design classes and courses on research and statistics in social work. Thanh V. Tran holds a Ph.D. and a master of science degree in social work from the School of Social Work at the University of Texas, Arlington. Dr. Tran is a professor in the Boston College School of Social Work in Chestnut Hill, Massachusetts. Ce Shen earned a Ph.D. in sociology at Boston College in Chestnut Hill and is now an associate professor in the college's School of Social Work. Siyon Rhee earned a Ph.D. at the School of Social Welfare, University of California, Los Angeles. Dr. Rhee teaches in the School of Social Work at California State University, Los Angeles.

The text teaches students how to collect appropriate data and analyze data that is suitable for each type of research design.

Data Analysis for Social Science and Marketing Research Using Python

A Non Programmer's Guide

The book is written for researchers in social science and marketing field, especially for those with little or no knowledge in computer programming. Data analytics has become part and parcel in the contemporary technologically fast paced world. We have amazing tools and software that allow us to analyse data available in various formats. However, most of the popular paid software and packages for data analysis is not affordable or not even accessible for the students, researchers. This is true in the case of many NGOs and agencies how are involved in community based research in developing countries. We have popular open source platforms and tools such as R and Python for data analysis. This book makes use of Python because of its simplicity, adaptability, broader scope and greater potential in advanced data mining and text mining contexts. We found it as a need to educate and train the researchers from social science and marketing research background, so that they could make use of Python, a promising tool to meet simple to extremely complex data analyses needs free of cost. The learnings from this book will not only help them in doing their conventional data analyses but also enable them to pursue advanced knowledge in machine learning algorithms, text analytics and other new generation techniques with the support of freely accessible open source platforms. Since the objective of the book is to educate the researchers with no programming background, we have made every effort to give hands-on experience in learning some basic coding in Python, which is sufficient for the readers to follow the book. The step-by-step procedure to do various data processing and analysis described in this book will make it easy for the users. Apart from that, we have tried our level best to give explanations on specific codes and how they perform to get us the desired output. We also request you to give you valuable comments and suggestions on the book, via our blog, so that we could improve the same in the upcoming volumes. We commit ourselves to providing explanations to the readers' questions related to the codes and analysis provided in this book. The book specifically deals with data sets of row and column format, as the general format commonly used in social science research, which most of the researchers are familiar with. So we do not work with arrays and dictionaries, except in one or two occasions (only to make you familiar with that) instead prefer to make use of Excel data and pandas data frame. The book consists of thirteen chapters. The first chapter gives an introduction to Python and its relevance and scope in contemporary data analysis contexts. Ch. 2 teaches the basics and Python coding, Ch. 3-7, provide a step-by-step narration of how to enter data, process it, preliminary analysis and data cleaning with the help of Python, Ch.8-9, present data visualizations and narration techniques using Python; Ch.10.demonstrate how Python can use for statistical analysis. The remaining chapters are focusing on giving more real life situations in data analysis and the practical solutions to handle them. The exercises provided in the book are similar to real analysis situations, and that will help the reader for an easy transition to the data analyst jobs. The authors have taken utmost care identifying and providing solutions to all practical difficulties the readers may face while using Python for data analysis purpose. The authors have developed a series of codes and have incorporated them to make data processing and analysis convenient and easy for the researchers. The self-learning materials given in this book will help social science and marketing researchers to deepen their understanding of various steps in data processing and analyses and to gain advanced skills in using Python for this purpose.

This is true in the case of many NGOs and agencies how are involved in community based research in developing countries. We have popular open source platforms and tools such as R and Python for data analysis.

Qualitative Research: Data Collection and Data Analysis Techniques -2nd Edition (UUM Press)

Qualitative Research: Data Collection & Data Analysis Techniques (2nd Edition) has been systematically revised with additional content, more in-depth explanations, and latest references to enhance the knowledge and skills required for those interested in conducting qualitative research. The reader-friendly organisation and writing style of this edition provides guaranteed accessibility to a wide array of readers ranging from established scholars to novice researchers and undergraduates. Each chapter in this edition is set to provide a clear, contextualised and comprehensive coverage of the main qualitative research methods (interviews, focus groups, observations, diary studies, archival document analysis, and content analysis) aimed at equipping readers with a thorough understanding of the design, procedures and skills to effectively undertake qualitative research. At the same time, the authors have anticipated major concerns such as ethical issues that qualitative researchers often face and addressed them in the various chapters. This effort has been made possible through the collaboration involving notable qualitative research scholars from different tertiary institutions – Assoc. Prof. Dr. Puvensvary Muthiah (ELT Consultant), Dr. R. Sivabala Naidu (Taylor’s College), Assoc. Prof. Dr. Mastura Badzis (International Islamic University Malaysia), Dr. Radziah Abdul Rahim (formerly attached to National Defense University of Malaysia), Dr. Noor Fadhilah Mat Nayan (University of Reading), and Assoc. Prof. Noor Hashima Abd Aziz (Universiti Utara Malaysia).

Each chapter in this edition is set to provide a clear, contextualised and comprehensive coverage of the main qualitative research methods (interviews, focus groups, observations, diary studies, archival document analysis, and content ...

Nursing Research Using Data Analysis

Qualitative Designs and Methods in Nursing

Nursing Research Using Data Analysis: Qualitative Designs and Methods in Nursing is one book in a series of seven volumes that presents concise, how-to guides to conducting qualitative research -- for novice researchers and specialists seeking to develop or expand their competency, health institution research divisions, in-service educators and students, and graduate nursing educators and students.

Nursing Research Using Data Analysis: Qualitative Designs and Methods in Nursing is one book in a series of seven volumes that presents concise, how-to guides to conducting qualitative research -- for novice researchers and specialists ...

Analysis

Back to Basics? ; [transcript] Broadcast on 16th and 19th December 1993

Islamic Economics: Principles and Analysis

With the impressive emergence of Islamic finance as a branch of Islamic economics, the need for a solid knowledge base that encompasses theories, thoughts and applications related to the subject increased in importance. However, writing about Islamic economics is a great challenge due to the differences in opinion on many of its issues. This includes methodologies for determining the Islamic perspective on economic concepts and issues as well as applicable solutions for today’s economic and social problems. It is further argued that Islamic economics topics are not as clear as those in conventional economics as they have their own religious, spiritual and social dimensions. The points of controversy have generated lengthy discussions. Moreover, Islamic economics encompasses a vast array of topics and approaches, from the purely theoretical, which may include philosophy or religious ideas, to mathematical and quantitative analyses. We tried our best throughout this textbook to simplify, clarify and summarise these concepts to make them accessible to all readers including students, practitioners, academics and even interested non-specialists. This textbook presents, discusses and analyses various topics and issues related to Islamic economics ranging from philosophical, epistemological and methodological to microeconomic and macroeconomic perspectives. In this endeavour, the social aspect of Islamic economics—an essential part of the discipline—is not neglected. The textbook compares Islamic ideas and concepts related to economics with those in conventional economics to highlight Islamic economics as a distinct field of knowledge with an emphasis on the ethical and social aspects. The authors have tried their level best to explain the theoretical concepts as simply as possible without ignoring today’s realities and without compromising Sharīʿah principles and objectives. One of the main objectives of the book is to provide the reader with Islamic economic ideas and solutions that are realistic and applicable within the current highly globalised economic and business environment, which is largely dominated by conventional interest-based systems and institutions. Despite being written for an elementary-level audience, this textbook can also be beneficial to a wide range of specialist and non-specialist readers and seekers of knowledge. For those specialising in Islamic economics, it is an appropriate source of reference to gain an overview on different topics relating to the foundations of Islamic economics. At this point, however, it must be mentioned that each topic deliberated upon, by its nature, would require a book on its own to cover all its aspects. Therefore, further exploration is required for Islamic economics specialists. A list of references and recommended readings is provided for that purpose at the end of each chapter. On the other hand, students of mainstream economics, finance and other academic majors will find this textbook an excellent resource for comprehensive knowledge of Islamic economics and its related issues. Universities may benefit from the different topics presented in this textbook in designing or preparing their economics courses at different levels based on their own curriculums and classes. This textbook could be used at the undergraduate level or even for a master’s level economics or Islamic economics course, especially in an Islamic banking and finance programme or for an MBA having a specialisation in Islamic banking and finance where an economics or Islamic economics course is offered. Furthermore, practitioners and interested readers who are seeking essential and simple knowledge about Islamic economics will also find this textbook to be a helpful guide. It is important to mention here that Islamic economics literature shows wide differences among the scholars in almost every subtopic. Presenting all opinions within a limited number of pages is almost impossible. However, with the great contribution of more than 60 scholars from a wide span of countries and from various economic schools, this book represents an important attempt to present the topics and issues from various perspectives with the maximum objectivity possible. Through comprehensive content editing, the editors have striven to improve the flow of arguments, remove inconsistencies and put the ideas together in as coherent a manner as possible. However, the editors acknowledge that some biases and overlaps may still persist.

However, with the great contribution of more than 60 scholars from a wide span of countries and from various economic schools, this book represents an important attempt to present the topics and issues from various perspectives with the ...

Efisiensi dan Produktifitas Rumah Sakit: Teori dan Aplikasi Pengukuran dengan Pendekatan Data Envelopment Analysis

Pengelolaan rumah sakit yang efisien dan produktif dengan tetap memperhatikan kendali mutu dan biaya menjadi kunci agar mampu bertahan di era Jaminan Kesehatan Nasional saat ini. Perubahan sistem pembayaran dari sistem fee for services ke sistem prospective payment system dengan menggunakan tarif paket INA-CBGs telah memaksa rumah sakit untuk dapat beroperasional lebih efisien tanpa mengurangi mutu pelayanan. Salah satu pendekatan yang paling sering digunakan untuk mengukur efisiensi dan produktifitas rumah sakit adalah Data Envelopment Analysis (DEA). Kelebihan DEA adalah mampu mengakomodasi banyak input maupun output dalam banyak dimensi. Pengukuran efisiensi yang didapatkan pun lebih akurat. DEA telah diaplikasikan secara luas dalam evaluasi efisiensi dan produktivitas pada berbagai bidang termasuk rumah sakit. Saat ini buku yang membahas tentang penggunaan metode DEA dalam bidang perumahsakitan masih sangat kurang. Oleh karena itu, buku ini disusun berdasarkan hasil penelitian untuk membantu para praktisi dan manajer rumah sakit untuk dapat mengelola tingkat efisiensi dan produktifitas rumah sakit serta disertai pembahasan mengenai strategi yang dapat ditempuh. Buku ini juga disusun untuk membantu para mahasiswa dan praktisi perumahsakitan dalam mempelajari konsep-konsep Badan Layanan Umum serta efisiensi dan produktifitas di rumah sakit dengan menggunakan pendekatan Data Envelopment Analysis (DEA). Pada akhirnya buku ini juga diharapkan dapat memberi masukan kepada para pengambil kebijakan tentang bagaimana dampak Kebijakan Jaminan Kesehatan Nasional di Indonesia terhadap tingkat efisiensi dan produktifitas rumah sakit serta implikasi kebijakan yang dapat diambil untuk mengatasinya.

... Theory : An Open System Approach. Journal ofAdvanced Nursing, 66(12), 2828 – 2838. Mitropoulos, Panagiotis ... Sistem Perencanaan dan Pengendalian Manajemen: Sistem Pelipatganda Kinerja Perusahaan (3 ed.). Jakarta: Salemba Empat. Murni ...

Remote Sensing Digital Image Analysis

An Introduction

Contents: Sources and Characteristics of Remote Sensing Image Data.- Error Correction and Registration of Image Data.- The Interpretation of Digital Image Data.- Radiometric Enhancement Techniques.- Geometric Enhancement Using Image Domain Techniques.- Multispectral Transformations of Image Data.- Fourier Transformation of Image Data.- Supervised Classification Techniques.- Clustering and Unsupervised Classification.- Feature Reduction.- Image Classification Methodologies.- Data Fusion.- Interpretation of Hyperspectral Image Data.- Appendices

Contents: Sources and Characteristics of Remote Sensing Image Data.