What is topic population in research?

I Demographics of Adolescent Religiousness and Spirituality

Current demographic research confirms young people's interest and participation in religion and spirituality. Although statistics varied by country, one trend suggested by the World Values Survey indicates that young adults aged 1824 surveyed across 41 countries reported higher levels of belief in God compared to levels of importance of religion in their lives [see Lippman & Keith, 2006]. Religious attendance is another indicator of adolescent religiousness and spirituality. An international study of 14-year-olds across 28 countries showed that average youth attendance is 42% in America, 14% in Western Europe, 28% in Southern Europe, 20% in Asia/Pacific regions, 13% in Northern Europe, and 10% in Eastern Europe [see Lippman & Keith, 2006].

From among the industrialized nations, the United States reported the highest rates of adolescent religion and spirituality. Two landmark nationally representative studies found that 8487% of U.S. adolescents are affiliated with a specific religious group [Smith & Denton, 2005; Wallace, Forman, Caldwell, & Willis, 2003]. Although a significant portion of U.S. youth report being engaged and valuing being religious, this is clearly not the case for all adolescents. Smith and Denton [2005] highlight that the other half of U.S. teenagers express weak or no subjective attachment to religion and have fewer or no religious experiences [p. 68]. Among respondents, 54% of the NYSR said very true or somewhat true when asked if they considered themselves spiritual but not religious. In short, although still relatively religious when compared to other industrialized nations, North American youth show a high degree of religious diversity and a growing number who self-identify as spiritual but not religious.

Existing statistics illustrate the complexity of religious and spiritual engagement in adolescents around the world. Although there is no set trend, it is clear that many young people across the globe are engaged in various forms of religion and spirituality. The next section provides an overview of concepts related to adolescent religiousness, spirituality, and thriving.

Introduction

A goal of population research is to isolate causal effects of, for example, policies, programs, circumstances, and behavioral choices. For example, what is the causal effect of improved access to family planning services on the health and economic status of women? What is the impact of marriage on health and well-being? What is the impact of migration on those who move and those who are left behind? Answering these types of questions with observational studies is challenging because the counterfactual is typically not observed: what would have happened if the migrant had not moved is not known. It is difficult to separate the impact of the stimulus [the decision to migrate] from other, unobserved factors that distinguish those who experience the stimulus [those who choose to migrate] from those who do not [those who do not choose to migrate]. The promise of social experiments is that they provide a method for putting aside these unobserved differences and, thereby, identify causal effects.

In the ideal randomized controlled trial [RCT], studysubjects are randomly assigned to receive a stimulus [thetreatment group] or a placebo [the control group]. After the stimulus, the difference between the average outcome of thetreatment group and the average outcome of the control group provides a measure of the causal effect of the stimulus on the outcome. Because the study subjects are randomly assigned to receive the stimulus, concerns about what drives selection into each group [or unobserved differences between the groups] are, by design, put aside.

Implementation of the ideal RCT is far from straightforward in the best of circumstances. Implementing social experiments to address important questions in the population sciences is very challenging. To examine the impact of migration, for example, one cannot simply move a randomly selected group of people to a new location and force them to remain there while forcing those in the control group to stay put. It may be possible, however, to subsidize the costs of migrating, broadly defined, or, in the case of international migration, adopt a policy of randomly assigning visas to a subset of those who apply. Aspects of each of these designs raise important issues regarding the question that is addressed and the interpretation of the estimates. The central point is that social experiments have the potential to be very valuable in population research but, as is the case with observational studies, they have strengths and weaknesses.

Indeed, beyond identifying causal effects, it is important to understand the mechanisms that underlie behavioral choices. To this end, it seems the greatest promise of RCTs and social experiments lies in their fuller integration with theory and observational designs to advance understanding of population processes, a position advocated by Heckman [2010].

Conceptualizations

Since the discovery of the Gompertz law, studies of the Aeffects have long existed in the history of population science. Studies that jointly consider P and C variations as distinct entities from A effects, termed as the age-period-cohort [APC] analysis, appeared relatively recently [see, e.g., Hobcraft etal., 1982, for a review]. Of central focus in the APC analysis is the distinction of P and C effects as the other two components of temporal variation in demographic outcomes. The meanings of P and C effects can be most differentiated by contrasting them in specific demographic contexts. Here we focus on their conceptualizations in the context of health, chronic disease, and mortality.

Period [P] effects are variations over time periods or calendar years that simultaneously influence all individuals. They subsume a complex set of historical events and environmental factors such as world wars, famine, and pandemics of infectious diseases, rapid adoption of technological advances, or economic expansions and contractions. Period effects are often evident from a correspondence in the timing of changes in outcomes and conditions that induced these outcomes. They can also arise from changes in disease classification or diagnostic techniques. For example, increases in breast cancer death rates from 1970 to 2000 were found to result from increases in mammography usage, and hence detections of malignant cases during this period, whereas the subsequent downturn in death rates starting around year 2000 was due to the sudden reductions of hormone replacement therapy usage [Yang and Land, 2013: Figures 8.7 and 8.9]. Similar period-based changes in mortality from heart disease have been observed in the U.S. population following the introduction and widespread use of pharmacological technologies to manage cholesterol [e.g., statins], hypertension [e.g., beta blockers and other antihypertensive drugs], and myocardial infarction [e.g., stents] [Lloyd-Jones etal., 2009].

Cohort [C] effects are also observed in many processes related to population health and mortality. A birth cohort ages together, encountering the same historical and social events at the same stage of the life course. The exit of an old cohort through death is constantly being replenished by the entry of a new cohort through birth, thereby continuously shaping and reshaping a population's composition. This continual process of cohort succession and replacement has long been recognized as an essential component of demographic metabolism driving social change [Ryder, 1965]. Further, variation across cohorts can arise from differential exposure to early life conditions and life-long accumulation of exposures to physiological, socioeconomic, behavioral, and environmental risk factors from gestation to old age [Fogel and Costa, 1997; Ben-Shlomo and Kuh, 2002; Finch and Crimmins, 2004]. Importantly, cohort membership operates as a contextual characteristic for the understanding of the heterogeneous experiences of aging. Indeed, the principle of cohort differences in aging posits that each cohort ages across the life course in unique and identifiable ways [Riley, 1987].

3.09.2.5 Minnesota Population Center [MPC]

Operationalizing Cultural Traits in Statistical Taxonomies

As promising as anthropology's approach and methods can be in demographic research, what anthropology has usually offered the demographic enterprise has largely reflected the state of its own field. In the middle of the twentieth century, for example, a particular methodological approach, sometimes called the cross-cultural method, dominated American anthropology. This was a kind of statistical taxonomy in which societies were described by traits that could be operationalized cast in the form of discrete variables that could be coded and counted in order to test quantitative hypotheses and look for cross-cultural patterns. The primary data used in this endeavor, the Human Relations Area Files [HRAF] [Murdock, 1975], contained detailed codes of the organizational features of hundreds of ethnic groups. Prevalent as well in American anthropology at this time were questions about the relationship of culture to personality from Freudian perspectives. Anumber of anthropologists sought to combine this theoretical agenda with cross-cultural methodologies. Harvard anthropologists led by John and Beatrice Whiting [Whiting etal., 1958], for example, argued from HRAF data that life under harsh environmental conditions required parents to protect a child by long periods of breastfeeding and a ban on sexual relations. For boys, such practices, because they were thought to produce strong psychological attachments between mother and child, were said to necessitate dramatic manhood initiation rituals that separated boys from their mothers and fostered the development of their masculinity. Though personality development itself drew limited demographic interest, there was considerable interest in the potentials of such studies to shed light on observed durations of breastfeeding and postpartum sexual abstinence, which had more obvious connections to fertility through its biological bases. Applying this kind of analysis to data on kinship and economic subsistence, demographer Frank Lorimer [1954] argued that populations like those on small islands, with highly constrained and intensively utilized resources, developed forms of social organization and customs that effectively checked family size. In Polynesia, he maintained, such customs included coitus interruptus, abortion, infanticide, the encouragement of sea voyaging by young men, and banning further reproduction to the mother of a married son.

B Laboratory Procedures

In contrast to the relative homogeneity of an undergraduate student research pool, research populations in real-world organizations can vary considerably. In the military, the participant population may range from new recruits with a minimum of education or experience to high-level officers with advanced education and training. Care must be taken to develop laboratory procedures that are appropriate for the participants. Furthermore, because of the heterogeneity of the population, it is especially important to ensure random assignment of participants to experimental conditions. Unless precautions are taken, it may be likely that one week, participants will come from a mechanical division, the next week from a military police division, and so on.

In applied research, it is often easier to garner research participants interest and motivation because you are using a research task and an environment that is important to them. Therefore, it is important to establish clearly in the experimental instructions not only what you are doing and why you are doing it but also why this research is important for the military. In general, we have found that military research participants are genuinely interested in research that is being done in their world, and they are quite motivated to take part. On the other hand, one problem with using a research task that often only approximates the real-world task of interest is that the participants know from experience that this is not the exact task that they do in the real world, and they will let you know this in no uncertain terms.

Finally, in debriefing participants, the same theme of why the research is valuable to them and the organization should be emphasized. The research participants are part of a larger organization, and especially in the case of the military, it is an organization to which there is a high level of individual commitment. Thus, participants are particularly interested in the value of the research for the military. This emphasis on relevance can help avoid contamination of the research population, which refers to individual subjects telling others about critical features of the experimental situation, rendering these others unfit for participation in the study. Contamination of future research participants is less likely to be an issue if debriefing procedures are comprehensive and emphasize why the organization would be harmed if future participants are told of experimental details in advance.

Other Widely Used Death Rates

Although IMR measures infant deaths from birth through the first year of life, the most dangerous time for infants is just before and just after birth. There are special measures of infant death that take these risks into account. For example, late fetal mortality refers to fetal deaths that occur after at least 28 weeks of gestation. Neonatal mortality refers to deaths of infants within 28 days after birth. Postneonatal mortality, then, covers deaths from 28 days to 1 year after birth. In addition, there is an index called perinatal mortality, which includes late fetal deaths plus deaths within the first 7 days after birth. All of these values are typically divided by the number of live births. Technically, they should be divided by the number of conceptions in order to measure the risk of various pregnancy outcomes, but the data on conceptions are generally not available or are not very reliable even if they are available.

Maternal mortality refers to those deaths that are associated with complications of pregnancy and childbearing. The maternal mortality ratio measures the number of maternal deaths per 100,000 live births. At the end of the twentieth century, the world average was estimated by the World Health Organization to be 400 per 100,000. Another way of measuring maternal mortality that takes into account the number of pregnancies that a woman will have is to estimate a woman's lifetime risk of a maternal death. As a woman begins her reproductive career by engaging in intercourse, the question that is asked is: What is the probability that she will die from complications of pregnancy and childbirth? This risk represents a combination of how many times she will get pregnant and the health risks that she faces with each pregnancy, which are influenced largely by where she lives. For the average woman in the world, that probability is 0.013 or 1 chance in 75, but for women in sub-Saharan Africa, the risk is 1 in 11.

This article reviews the recent transition in thinking regarding the fertility-inhibiting consequences of breastfeeding. Early demographic research, while not generally measuring lactational amenorrhea directly, nonetheless helped focus attention on the importance of breastfeeding in reducing the risk of pregnancy worldwide. Since the 1960s, a large number of scientific studies from many parts of the world have consistently demonstrated that breastfeeding is closely associated with prolonged periods of postpartum amenorrhea, long birth intervals, low completed fertility, and optimal health and wellbeing for the mother and her infant. Clinical research has also played an important role in elucidating the underlying neuroendocrine mechanisms of postpartum lactational infertility. More recently, innovative demographic studies utilizing hazard model regression techniques have allowed researchers to quantify the fertility reducing effects of breastfeeding unambiguously by incorporating into their models knowledge of the biology of postpartum infertility along with measures of breastfeeding behavior. Breastfeeding behavior is also currently being promoted as a family planning method. Finally, due to the fact that the HIV virus causing AIDS can be transmitted by breastfeeding, ethical issues have arisen surrounding the promotion of breastfeeding for health reasons and family planning.

Definitions of Infertility

As Ulla Larsen carefully explains, there are different definitions of infertility in clinical practice, epidemiological research, and demographic research. In the clinical setting, where there is an interest in initiating treatment in a timely fashion, the definition is inability to conceive after one year of regular unprotected sexual intercourse. With epidemiological research, where it is important to reduce the number of false positives [where fertile individuals or couples are misclassified as infertile], the preferred definition of infertility is inability to conceive after two years of regular unprotected sexual intercourse [as recommended by the World Health Organization [WHO]]. For demographers, the definition of infertility is the inability of a non-contracepting sexually active women to have a live birth; information about couples infertility is inferred from information about womens birth histories. Recently, the term subfertility has been introduced. This term seems more accurate and less stigmatizing for persons who experience fertility problems.