Book Review of Improved Earth: Prairie Space as Modern Artefact, 1869-1944 by Rod Bantjes

In this short but suggestive study, sociologist Rod Bantjes examines how contending visions of modernity shaped the social and physical landscapes of the Canadian prairies. "[B]oth statesmen and prairie farmers were infused with the modernist spirit of innovation, the will creatively (and destructively) to transform their worlds," Bantjes argues. His provocative view of farmers as agents of modernity reflects recent scholarship that seeks to explore "multiple modernities," or the notion that ideas and practices of modernism must be regarded not as monolithic but rather as contested and multivocal, and must be examined in their historical and geographical contexts.

A Review of the Genus Echinococcus Rudolphi, 1801

1. The genus Echinococcus Rudolphi, 1801, is reviewed. The recorded measurements of all species are shown in tables, and their taxonomic status is discussed. 2. Three species of Echinococcus can be distinguished by morphological criteria and are considered valid: E. granulosus, E. multilocularis and E. oligarthrus. The status of E. felidis and E. patagonicus is uncertain, but both may be conspecific with E. granulosus. Six species are considered synonyms of E. granulosus: E. cameroni, E. intermedius, E. longimanubrius, E. lycaontis, E. minimus and E. ortleppi.

The Taxonomic Value and Variability of Certain Structures in the Cestode Genus Echinococcus (Rudolphi, 1801) and a Review of Recognized Species

Although cestodes of the genus Echinococcus have been much studied in the past, there is need for an evaluation of these morphological characters used as the basis for species differentiation. The generous cooperation of other investigators in providing necessary foreign material and the results of nearly five years of field work in Alaska make possible such a study. It is the purpose of the paper to evaluate morphological characters used at the species level to differentiate these cestodes, and to review the status of species currently considered valid.

Evaluating Measurement Invariance with Censored Ordinal Data: A Monte Carlo Comparison of Alternative Model Estimators and Scales of Measurement

Evaluations of measurement invariance provide essential construct validity evidence. However, the quality of such evidence is partly dependent upon the validity of the resulting statistical conclusions. The presence of Type I or Type II errors can render measurement invariance conclusions meaningless. The purpose of this study was to determine the effects of categorization and censoring on the behavior of the chi-square/likelihood ratio test statistic and two alternative fit indices (CFI and RMSEA) under the context of evaluating measurement invariance. Monte Carlo simulation was used to examine Type I error and power rates for the (a) overall test statistic/fit indices, and (b) change in test statistic/fit indices. Data were generated according to a multiple-group single-factor CFA model across 40 conditions that varied by sample size, strength of item factor loadings, and categorization thresholds. Seven different combinations of model estimators (ML, Yuan-Bentler scaled ML, and WLSMV) and specified measurement scales (continuous, censored, and categorical) were used to analyze each of the simulation conditions. As hypothesized, non-normality increased Type I error rates for the continuous scale of measurement and did not affect error rates for the categorical scale of measurement. Maximum likelihood estimation combined with a categorical scale of measurement resulted in more correct statistical conclusions than the other analysis combinations. For the continuous and censored scales of measurement, the Yuan-Bentler scaled ML resulted in more correct conclusions than normal-theory ML. The censored measurement scale did not offer any advantages over the continuous measurement scale. Comparing across fit statistics and indices, the chi-square-based test statistics were preferred over the alternative fit indices, and ΔRMSEA was preferred over ΔCFI. Results from this study should be used to inform the modeling decisions of applied researchers. However, no single analysis combination can be recommended for all situations. Therefore, it is essential that researchers consider the context and purpose of their analyses.