## Data Theory and Dimensional Analysis, Issues 77-78By examining some of the basic scaling questions, such as the importance of measurement levels, the kinds of variables needed for Likert or Guttman scales and when to use multidimensional scaling versus factor analysis, Jacoby introduces readers to the most appropriate scaling strategies for different research situations. He also explores data theory, the study of how real world observations can be transformed into something to be analyzed, in order to facilitate more effective use of scaling techniques. |

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### Contents

Series Editors Introduction | 1 |

Date Theory | 13 |

Date Theory and Scaling Methods | 38 |

Alternaling Least Squares Optimal Scaling | 74 |

Conclusions | 81 |

About the Author | 89 |

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### Common terms and phrases

approach assumptions attribute axes coefficients Coombs correlations correspond cutting point data matrix data set data source data theory data values dichotomous DIMENSIONAL ANALYSIS dimensionality dissimilarities dominance relation empirical objects empirical observations estimate example factor analysis factor space Figure function geometric model Guttman scale input interpretation interval level involves latent dimension levels of measurement measurement characteristics measurement levels measurement theories modes Mokken Scales monograph Multidimensional Scaling nominal level number line numerical assignments observed variables ordinal level ordinal variables Output Matrix paired comparisons parameters point locations point pairs point representing positive response preferential choice data ratio level regression replications researcher scalar products scaling analysis scaling errors scaling procedures scaling solution scaling strategies scaling technique scores set of objects set of points similarities data simply single stimulus data specific stimulus comparison data stimulus points structure student subject point substantive types of data underlying dimension unidimensional usually variable vectors