Quantitative Research in Education : A Primer (eBook)

<p style="e;background: white; margin: 0in 0in 0pt;"e;><strong>Quantitative Research in Education: A Primer, <em>Second Edition</em></strong> is a brief and practical text designed to allay anxiety about quantitative research. Award-winning authors Wayne K. Hoy and Curt M. Adams first introduce readers to the nature of research and science, and then present the meaning of concepts and research problems as they dispel notions that quantitative research is too difficult, too theoretical, and not practical. Rich with concrete examples and illustrations, the Primer emphasizes conceptual understanding and the practical utility of quantitative methods while teaching strategies and techniques for developing original research hypotheses. </p><p style="e;background: white; margin: 0in 0in 0pt;"e;> </p><p style="e;margin: 0in 0in 0pt;"e;><strong>NEW TO THIS EDITION</strong></p><p style="e;margin: 0in 0in 0pt;"e;><strong> </strong></p><ul style="e;list-style-type: disc; margin-top: 0in;"e;><li style="e;background: white; color: black; margin: 0in 0in 0pt;"e;><strong>A new chapter devoted to the practical applications of quantitative research</strong> uses the concepts of collective trust, organizational climate, and improvement science to illustrate the utility of quantitative research for practice. </li><li style="e;background: white; color: black; margin: 0in 0in 0pt;"e;><strong>New hypotheses found in research studies</strong> are available for readers to analyze and diagram. </li><li style="e;background: white; color: black; margin: 0in 0in 0pt;"e;><strong>A new section on self-determination theory</strong> has been added to demonstrate the relationship between theory and practice. </li><li style="e;background: white; color: black; margin: 0in 0in 0pt;"e;><strong>A new section on self-regulatory climate gives readers an opportunity to explore an exciting new area they are likely to encounter in practice.</strong> </li><li style="e;background: white; color: black; margin: 0in 0in 0pt;"e;><strong>A conceptual description of Hierarchical Linear Modeling (HLM)</strong> has been added to help readers understand statistical data organized at more than one level. </li></ul><p style="e;background: white; margin: 0in 0in 0pt;"e;> </p>