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model (Cliff, 1992, offers various reasons for this neglect) Therefore, it is unlikely that most scales represent additivity in either an empirical, conjoint, or scaled sense On the other hand, it seems reasonable that the property of order is represented in many cases Order is much more easily justi ed than additivity because one need only argue that two individuals with different scores on a measure can be ordered in terms of the empirical amount of the construct in question, provided the scores are reasonably consistent This ordering seems sensible in many situations, even when we cannot determine meaningful distance between the ordered individuals Therefore, it seems realistic to assume that much of the data in the social sciences are ordinal The advantage of ordinal methods is that they deal explicitly with the order of the data.

Adams, J., & Kirkby, R. J. (2001). Exercise dependence and overtraining: The physiological and psychological consequences of excessive exercise. Sports Medicine, Training and Rehabilitation, 10, 199 222. Adams, J., & Kirkby, R. J. (2002). Excessive exercise as an addiction: A review. Addiction Research and Theory, 10, 415 437. Adams, J., Miller, T. W., & Kraus, R. F. (2003). Exercise dependence: Diagnostic and therapeutic issues for patients in psychotherapy. Journal of Contemporary Psychotherapy, 33, 93 107. Aidman, E. V., & Woollard, S. (2003). The influence of selfreported exercise addiction on acute emotional and physiological responses to brief exercise deprivation. Psychology of Sport and Exercise, 4, 225 236. Allen, R. M., Haupt, T. D., & Jones, W. (1964). An analysis of peak experiences reported by college students. Journal of Clinical Psychology, 20, 207 212. American College of Sports Medicine. (2006). ACSM s guidelines for exercise testing and prescription (7th ed.). Philadelphia: Lippincott, Williams, & Wilkins. Andresen, E. M., Catlin, T. K., Wyrwich, K. W., & JacksonThompson, J. (2003). Retest reliability of surveillance questions on health related quality of life. Journal of Epidemiology and Community Health, 57, 339 343. Andrews, F. M., & Withey, S. B. (1976). Social indicators of well-being: Americans perceptions of life quality. New York: Plenum Press.

Figure 2-11

32 Engineered Ligament Culture Medium (Ligament Medium) 33 Freezing Medium 34 Trypsin-EDTA 35 3-[4,5-dimethylthiazol-2-y]-2,5-dipheynyl tetrazolium bromide (MTT) Solution 36 Buffers for Scanning Electron Microscopy (SEM) Sodium Cacodylate Buffer (02 M) Karnovsky Fixative Stock Solution 37 Collagen ELISA Buffers Acetic acid, 005 M, Containing 05 M NaCl, pH 29 Tris-Base, 1 M Tris-HCl, 01 M, Containing 02 M NaCl and 005 M CaCl2 , pH 78 38 Pepsin 39 Elastase Tissue Harvest and Cell Isolation 41 Human Bone Marrow Stromal Cells (hBMSCs) Protocol 81 Primary Culture of Human Bone Marrow Stromal Cells 42 Human ACL Fibroblasts (ACLFs) Protocol 82 Primary Explant Culture of Human Ligament Silk Matrices and RGD Surface Modi cation 51 Silk Matrices 52 Surface Modi cation Protocol 83.

As I described in the sidebar earlier in this chapter (see About the Code Examples ), the Immediate window of the VBE is very useful for testing statements and trying out various VBA expressions. I generally keep the Immediate window visible at all times, and I use it frequently to test various expressions and to help in debugging code.

Surface Modi cation of Silk Fibers with RGD for Bone Marrow Stromal Cell Adhesion Cell Seeding and Cultivation 61 For Study of Silk Matrix Design (See Section 911) Protocol 84 Seeding Silk Matrix Cords with BMSCs 62 Modi cation for Study of Silk Surface Modi cation (See Section 912) 63 Modi cation for Study of Mechanical Stimulation as the Ligament Differentiation Inducer (See Section 92) Bioreactor Analytical Assays 81 Mechanical Analysis 82 Cell Viability 83 Morphology 84 Transcript Levels RNA Isolation and Real-Time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) 85 Protein Formation Extracellular Matrix Protocol 85 Isolation of Collagen from Cell-Seeded Silk Matrices 86 Statistical Analysis ..

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Most of the methods to be presented later are based on ordinal comparisons between scores Pairs of scores on a variable are compared in terms of the order relations less than, greater than, and equal to This is the most appropriate treatment when order is the only justi ed scaling property of the data Many Research Questions Are Ordinal Many of the types of research questions commonly posed tacitly acknowledge that many data are ordinal When forming research questions, few researchers take seriously the speci c interpretations provided for by highly parametric methods For example, rather than expressing questions about the correlation between two variables in unit-based functional terms, most researchers simply want to know if high scores on one variable tend to go with high (or low) scores on the other variable (Long & Cliff, 1997) The same can be said in the case of group comparisons.