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Books on the topic 'Correlation (Statistics) – Research'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Keppel, Geoffrey. Data analysis for research designs. Freeman, 1995.

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2

Sheldon, Zedeck, ed. Data analysis for research designs: Analysis-of-variance and multiple regression/correlation approaches. W.H. Freeman, 1989.

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3

Gaver, Donald Paul. Research in quantitative bioassay methodology and risk analysis and characterization. Naval Postgraduate School, 1996.

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4

Keppel, Geoffrey. Data Analysis For Research Designs. 2nd ed. Worth Pub, 2008.

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5

Ray, Sumantra (Shumone), Sue Fitzpatrick, Rajna Golubic, Susan Fisher, and Sarah Gibbings, eds. Navigating research methods: basic concepts in biostatistics and epidemiology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199608478.003.0002.

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This chapter provides an overview of the basic concepts in biostatistics and epidemiology. Section 1: Basic concepts in biostatistics The concepts in biostatistics include: 1. descriptive statistical methods (which comprise of frequency distribution, distribution shapes, and measures of central tendency and dispersion); and 2. inferential statistics which is applied to make inferences about a population from the sample data. Non-probability and probability sampling methods are outlined. This section provides simple explanation of the complex concepts of significance tests and confidence interv

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6

Miksza, Peter, and Kenneth Elpus. Correlational Design and Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0006.

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Interests in how variables may relate to each other and how systems of relationships among variables may be at play often underlie the questions music education researchers pose. This chapter describes basic design and analysis considerations in research that involves the systematic investigation of whether and how variables are related; in other words, correlational research. The chapter poses correlational research as an extension of the book’s previous discussion of descriptive research. The chapter briefly describes the role of correlational studies in advancing theory, presents several is

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7

de, Waterbeemd Han van, ed. Structure-property correlations in drug research. Academic Press, 1996.

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8

de, Waterbeemd Han van, ed. Structure-property correlations in drug research. R.G. Langes Co., 1996.

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9

Li, Quan. Using R for Data Analysis in Social Sciences. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656218.001.0001.

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This book seeks to teach undergraduate and graduate students in social sciences how to use R to manage, visualize, and analyze data in order to answer substantive questions and replicate published findings. This book distinguishes itself from other introductory R or statistics books in three ways. First, targeting an audience rarely exposed to statistical programming, it adopts a minimalist approach and covers only the most important functions and skills in R that one will need for conducting reproducible research projects. Second, it emphasizes meeting the practical needs of students using R

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10

Rubin, Donald B., Ralph L. Rosnow, and Robert Rosenthal. Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press, 1999.

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11

Tran, Thanh V., Tam Nguyen, and Keith Chan. Assessing and Testing Cross-Cultural Measurement Equivalence. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780190496470.003.0004.

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A cross-cultural comparison can be misleading for two reasons: (1) comparison is made using different attributes and (2) comparison is made using different scale units. This chapter illustrates multiple statistical approaches to evaluating the cross-cultural equivalence of the research instruments: data distribution of the items of the research instrument, the patterns of responses of each item, the corrected item–total correlation, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability analysis using the parallel test and tau-equivalence test. Equivalence is th

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12

Bobko, Philip. Correlation and Regression: Principals and Applications for Industrial/Organizational Psychology and Management (Organizational Research Methods). Sage Publications, Inc, 2001.

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13

Bobko, Philip. Correlation and Regression: Principals and Applications for Industrial/Organizational Psychology and Management (Organizational Research Methods). Sage Publications, Inc, 2001.

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14

Schiff, Brian. Out of the Head. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199332182.003.0003.

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Chapter 2, “Out of the Head,” in A New Narrative for Psychology argues that psychology’s conceptual problem becomes even more acute when one considers conclusions drawn about the relationship between variables. Interpreting the statistical relationship between two or more variables, researchers commit a serious error; they misinterpret correlations derived from group data as an indication of what must be happening inside persons. Only an approach that allows one to get close enough to persons, to observe how they tell and interpret their experience, can allow one to understand how subjectivity

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15

Halperin, Sandra, and Oliver Heath. 16. Patterns of Association. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0016.

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This chapter discusses the principles of bivariate analysis as a tool for helping researchers get to know their data and identify patterns of association between two variables. Bivariate analysis offers a way of establishing whether or not there is a relationship between two variables, a dependent variable and an independent variable. With bivariate analysis, theoretical expectations can be compared against evidence from the real world to see if the theory is supported by what is observed. The chapter examines the pattern of association between dependent and independent variables, with particu

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