The documents that follow, which are from the Bancroft Library transcripts of the California archives, are examples of some of the materials that shed light on the frontier of Alta and Baja California between 1774 and 1781.

In hierarchical data structures, observational units at one level are nested within units at other levels. For longitudinal data on individuals, within-person observations are nested within individuals. Within-person (micro) observations are generally more like each other than observations sampled randomly across individuals (contexts). In generalized regression models, several approaches can accommodate this lack of independence: (i) random effects; (ii) fixed effects; (iii) marginal models in the non-Gaussian case, and (iv) regression coefficient covariance matrix adjustment. Each approach has specific advantages and drawbacks. Fixed effects are often used in situations in which a key assumption of the random effects approach is thought to be implausible. In those instances, the use of marginal models or regression coefficient covariance matrix adjustment would also merit reconsideration. Where more than one approach can justifiably be applied—and there are many such cases—estimation results can be method-dependent, leading to different conclusions about the effects of specific covariates. In addition, particularly for the random effects approach, alternative methods of estimation can yield somewhat different results, as can different algorithmic implementations of the same method across software packages. The impact of small within-context sample sizes in unbalanced designs merits further exploration. Where possible, researchers should consider whether their conclusions are method- or algorithm-dependent.

The high sex ratio at birth in China has attracted considerable attentions from demographers. Previous studies assert that female infanticide, underreporting of female births, and prenatal sex-selective abortion were the immediate causes of the initial increase of the sex ratio at birth. Recent studies suggest that prenatal sex selection became the leading immediate cause of more recent increase in the sex ratio at birth. A snowball sampling survey conducted in rural Henan in 2001 collected information on women’s abortion histories. Using these data, we analyze the practice and mechanisms regarding to prenatal sex selection, as well as its impact on SRB. Results show that prenatal sex selection is widely known and commonly practiced in the studied area. The SRB is the highest among births that have only sister(s), and close to normal for other births. Using a population-average model, we found that female fetuses that have only sister(s) are most likely to be aborted. Finally, our imputation suggests that sex-selective abortion has been the predominant, if not the sole, immediate cause of the high SRB.

No official public use micro-sample of the 1990 Chinese census has yet been released. There are, however, two extant micro-samples of the census that have circulated within China and abroad. The first, in order of creation, is a one percent sample of administrative villages. The second is a one percent sample of households. We refer to the former, the subject of this article, as the "one percent clustered sample," and refer to the latter as the "one percent household sample." The State Statistical Bureau of China has provided the one percent household sample to several researchers, but to our knowledge has never released the corresponding mortality data. Provision of the one percent clustered sample has been on an informal basis, generally without mortality data. We obtained the clustered sample and the corresponding mortality data separately, from sources that prefer to remain anonymous.

Background and Objectives. Population parameter estimates for behavioral and contextual risk factors of STDs in individuals are currently unavailable. Surveillance data systems and clinical studies inherently can not provide this information. Nationally representative sample surveys that use self-reports of STD history are potentially better suited for this purpose. It remains to be established that self-reports are valid.

Goal of this Study. Assess the validity of self-reports of STD history among school-going adolescents.

Study Design. We compare gender and race/ethnicity differentials in self-report STD information in the mid-1990s National Longitudinal Survey of Adolescent Health (Add Health) with corresponding differentials obtained from CDC surveillance statistics.

Results. Add Health and CDC surveillance data yield similar results. Further Add Health analysis demonstrates that family structure, socioeconomic status, and neighborhood context contribute to STD presence at the individual level.

Conclusion. Self-reports of STD infection in a national sample of adolescents can be used to study behavioral and contextual risk factors.

Cohort analysis treats an outcome variable as a function of cohort membership, age, and period. The linear dependency of the three temporal dimensions always creates an identification problem. Resolution of this problem requires external knowledge that is often difficult to acquire. Most satisfactory is the introduction of variables held to measure the dimensions that underlie at least one of age, period and cohort. Such measured, substantive variables can provide direct tests of cohort-based explanations. A promising path for future technical development is a hierarchical Bayes approach, which treats appropriately defined cohort, age, and period contrasts as randomly distributed and allows for their dependence on substantive, measured variables. Models that include age, period, and cohort can also include interactions between these dimensions, but not all such interactions are identified. This extends the realism of cohort models, since many phenomena seem to require specifications that allow for interactions between two or more of age, period, and cohort. Panel studies and cross-sectional studies with retrospective information not only support cohort analyses, they engender them. These longitudinal data structures do not, however, provide the basis for a solution to the identification problem.

Understanding how mathematics is taught in the classroom is an important first step in connecting mathematics instruction to student learning. This study sheds light on mathematics teaching in kindergarten and first grade—the grades at which initial understandings, as well as obstacles to later progress, begin to emerge. Using the Early Childhood Longitudinal Study Kindergarten Class of 1998-99 (ECLS-K) survey, we consider (i) how much time teachers spend on mathematics on days when they teach this subject, (ii) the content of mathematics instruction, and (iii) the pedagogical techniques used. We find that time spent on mathematics instruction, content coverage, and pedagogical techniques varies between teachers as a function of school location and type, classroom composition, and a range of teacher attributes that includes demographics, preparation, level of effort, and professional development activities.

Fox et al. (1998) carried out a logistic regression analysis with discrete covariates in which one of the covariates was missing for a substantial percentage of respondents. The missing data problem was addressed using the "approximate Bayesian bootstrap." We return to this missing data problem to provide a form of case study. Using the Fox et al. (1998) data for expository purposes we carry out a comparative analysis of eight of the most commonly used techniques for dealing with missing data. We then report on two sets of simulations based on the original data. These suggest, for patterns of missingness we consider realistic, that case deletion and weighted case deletion are inferior techniques, and that common simple alternatives are better. In addition, the simulations do not affirm the theoretical superiority of Bayesian Multiple Imputation. The apparent explanation is that the imputation model, which is the fully saturated interaction model recommended in the literature, was too detailed for the data. This result is cautionary. Even when the analyst of a single body of data is using a missingness technique with desirable theoretical properties, and the missingness mechanism and imputation model are supposedly correctly specified, the technique can still produce biased estimates. This is in addition to the generic problem posed by missing data, which is that usually analysts do not know the missingness mechanism or which among many alternative imputation models is correct.

The Paiute of Owens Valley had by early historic time progressed to a substantial extent along the path toward large-scale food production. They are perhaps the best instance in North America of a group that developed its own system of vegeculture—a system carried over to include irrigation of a variety of seedbearing plants as well. The Owens Valley Paiute thus offer a better example of agricultural origins than any presently known archaeological cultures that already had domesticated crop plants. And this remarkable achievement of indigenous agriculture occurred in a group which, as Julian Steward (1970) concluded after nearly fifty years of study, had evolved only "proto-bands." This was a retraction of his earlier statement that they were grouped in true composite landowning bands (Steward 1938:50). Comment on that classification we leave for a future time.

Steward (1930:153) himself deserves credit for recognizing that the Owens Valley Paiute use of irrigation could contribute knowledge within the broader framework of the "origins of agriculture." Curiously, during Steward's own time, geographer Carl O. Sauer was carrying out research on the problems of agricultural origins and dispersals. Sauer believed that vegetative propagation had preceded seed cultivation and set out to develop a theoretical basis for locating the cradle of agriculture (Harlan 1975:46). Between Sauer (1952) and Edgar Anderson (1954) a model evolved suggesting that agricultural peoples were sedentary fisherfolk living in wooded lands and bringing aggressive plants back from their riverbanks that found natural places to sprout in the kitchen middens of their homes.

Evidence since has shown that some of the presuppositions of Sauer and Anderson were simplistic or incorrect (Harlan 1975:45). Nevertheless, it seems odd that Sauer, living in California, failed to note that Steward had called attention to practices that so nearly coincided with his own model for agricultural origins. Nearly fifty years have elapsed since Steward wrote his seminal paper on irrigation in Owens Valley, but as yet no anthropologists have mustered interest in closely studying the problem. It may well be too late to acquire much of the information which still remains unknown about Owens Valley agriculture— such as the dating of its origin and the conditions under which it began. Yet research in this neglected area by archaeologists, linguists, plant scientists, and other scholars could probably tell us as much about agricultural origins as current research on the subject being carried out elsewhere in the world.