日本の文化型の量的研究

I. We made a factoranalytical research of these indicators by Thurstone's centroid method and extracted five common factors.II. We calculated the Z-scores (I) of the above 12 variables which were closely related with five common factors and the Z-scores (II) of the another 10 variables by prefectures which were live birth rate, premature infant birth rate, live birth rate by hospitalization, infant mortality rate, neo-natal mortality rate, α-index, artifical foetal death rate, etc., and computed the cofficient of correlation between the Z-scores (I) and the Z-scores (II), and investigated the influence of environmental factors on mate rnaland child health.

The purpose of this investigation is to construct a new inventory for the measurement of neuroticism and extraversion-introversion. The First Trial InventoryAs the first trial, the author composed an inventory of 78 items, which was divided into 4 groups as follows; E-I Group (20 items), N Group (20 items), Eg-1Group (19 items), and Eg-2 Group (19 items). The inventory was administered to 156 junior high school pupils. The tetrachoric correlations between the items within each group were calculated, and factor analysis was executed by Thurstone's centroid method. The factors after orthogonal rotations of axes were interpreted as follows. Thinking introversion, and Social extraversion from the E-I group. Delicacy, Lack of Con fidence, and Anxiety from the N group. Self-sufficiency, and, Emotionality from the Eg-1 group. Ego-strenth, Ego-centricity, and Ego-defence from the Eg-2 group. The Second Trial InventoryAs the second step, another new inventory was composed discarding 8 items from the foregoing inventory, and adding 32 new items. The new inventory was divided into 5 groups as follows; E-I Group (20 items), D-C Group (22 items), Anx Group (20 items), S-Eg Group (20 items), As-Ce Group (20 items). The inventory was administered to 193 senior high school pupils. The factors extracted in this case were interpreted as follows; Social extraversion, and Thinking-introversion from the E-I group, Dilicacy, Emotionality, and Lack of Confidence from the D-C group, Psychogenic anxiety, and Somatic Symptoms of Anxiety from the Anx group, Self-sufficiency, and Ego-centricity, and Ego-maturity from the As-Ce group. Most of the factors corresponded to those which were extracted from the first trial inventory. Inventory for Practical UseEighty seven items loading high onto the 10 factors (excepting Self-sufficiency) were selected, and a third inventory was composed for practical use. This inventory was administered to 1262 subjects, including junior high pupils, senior high school pupils, and college students. They were divided into two groups; one the younger group and the other the elder group. Scores of traits (factors) were correlated and factor analyzed for both groups separately. Four second order factors were extracted for both groups. In regard to the patterns of factor loadings, the four factors of the one group showed close resemblances to the four factors of the other group, The factors were interpreted as follows; 1. Hypochondria. 2. Ego-tonicity and Emotionality. 3. Surgencydisurgency. 4. Thinking introversion and extraversion.

With the mechanism of common elements we designed numerous sets of variates correlated with each other in a known manner and also correlated with the primary and specific factors in the same predetermined fashion. To the correlations from theoretical populations, and also from experimental samples, Thurstone's centroid method of factoring was applied. The resulting centroid co-ordinates were rotated to yield the test vectors. These vectors were close approximations to the theoretical and sample correlations.

Pathways into and out of Poverty: A Study of Household Wealth Dynamics in Rural Kenya

India wants to be self-sufficient in food and “food secured”. Therefore, it is imperative for national food security that we need to grow sufficient food within the country. At the same time, for domestic food security, we need to sustain economic growth to raise the income levels and purchasing power of the poor people (Kumar, 2003). Irrigation has contributed significantly to boosting India’s food production and creating grain surpluses, which is used as drought buffer. On the other hand, agriculture remains as the backbone of India’s economic growth, in spite of the major structural changes that the economy is undergoing (Government of India, 2008). Several studies in the past have indicated that agricultural growth, especially growth in foodgrain production negatively impacts on rural poverty (Ghosh, 1996). After Ravallion (1998), rural poverty is correlated with relative food prices, which is affected by fluctuations in food supply (Ravallion, 1998). Recent studies also show that in the 1990s, there was no change in rural poverty ratio, while urban poverty was reduced by 10 per cent as compared to 1980s (Datt, 1999). This coincided with the period, which recorded stagnation in growth of primary sector, at 2.47-3.66 per cent (Government of India, 2008). The growth rate in production of food grains also dropped to 1.2 per cent during the 1990s from 2.3 per cent in the 1980s (Datt, 1999). All these lead to the unquestionable role that irrigation can play in stabilising food prices, and alleviating rural poverty, provided effective institutional interventions are in place (Chaturvedi, 2000). Owing to the fact that the net area under cultivation and also area under food grains remains more or less saturated at the macro level (Government of India, 2002), irrigation is the key to enhancing agricultural production, and thereby sustaining economic growth. Nearly 70 per cent of India’s workforce depends on agriculture as the primary occupation (Government of India, 2008, p.3). Irrigated agriculture remains the largest absorber of rural labour force, and therefore impacts on the livelihoods of millions of rural households, while its impact on the farmer households is more direct.

The welfare effects of impoverished rural areas: Review and research prospects

This report presents a farm household model for use in the context of developing countries to gain knowledge on food security and rural poverty alleviation under different economic conditions and agri-food policy options. This model, called FSSIM-Dev (Farming System Simulator for Developing Countries), is an extension of the FSSIM model developed within the SEAMLESS project for impact assessment of agricultural and environmental policies on farm performances across Europe.\nFSSIM-Dev is conceived to be applied for family agriculture where farm household production, consumption and labour allocation decisions are non-separable due to market imperfections. Contrary to most well-known household models which are econometric-based, FSSIM-Dev is a non-linear optimization model which simultaneously solves a set of microeconomic models reproducing the behaviour of representative farm households. \nFSIM-Dev is designed to capture five key features of developing countries' agriculture: non-separability of production and consumption decisions; interaction among farm households for market factors; heterogeneity of farm households with respect to their both consumption baskets and resource endowments; inter-linkage between transaction costs and market participation decisions; and the seasonality of farming activities and resource use. \nModel use is illustrated in this report with an analysis of the combined effects of rice support policy and improved rice cropping management on the livelihood of representative farm households in Sierra Leone.Results show that both the rice support policy and the improved crop management would significantly increase rice productivity and boost household income but they are not sufficient to fight poverty since most of the farm household types would continue to live below the extreme poverty line of 1 USD-equivalent per day.

The propose of this study is measuring ((The Relationship between National and Religious Identity and Identity Processing Styles among High School Students in Qeshm Island)). For this study we have selected 402 people sample by using standard statistical method. For data collection we used two inventory Berzonsky (ISI_6G) and national and religious identity and double cluster sampling. For data analysis we used some of statistical method such as: Pearson correlation, Regressions by usage SPSS software package .Results of this study show: first, there is a positive relationship between National identity and identity informational style as well as relationship between National identity and normative style. Second, there is a meaningful relationship between religious identity and identity informational style as well as normative style. Third, there is a meaningful relationship between diffuse/avoidant style and national identity and religious identity, but these relations are less than other variables relationship.

PurposeThe purpose of this paper evaluates the effects of the Great Western Development (GWD) policy on agricultural intensification, land use, agricultural production and rural poverty in western China.Design/methodology/approachThe authors collect county-level data on land use, input application, grain crop production, income, poverty and geophysical characteristics for 1996–2005 and use a quasi-natural experimental design of difference-in-differences (DD) in the empirical analysis.FindingsResults suggest that the GWD policy significantly increased the grain crop production in western China. This increase resulted from higher yield, with increased fertilizer use and agricultural electricity consumption per hectare, and more land allocated to grow grain crops. The policy also increased land-use concentration, reduced crop diversity and alleviated rural poverty in western China.Originality/valueThis paper makes three contributions. First, the authors add to the growing literature on the GWD policy by evaluating its effects on farm household decisions and exploring the mechanisms and broad socioeconomic impacts in western China. Second, the authors take advantage of a quasi-natural experimental design to improve the identification strategy where input use, land allocation, production and off-farm labor participation are all endogenous in a farm household. Third, the authors explore a long list of variables within one integrated dataset to present a comprehensive picture of the impact of the GWD policy.

This study aims to analyze the effect of income diversification on poverty vulnerability in rice farming households in Kabila District, Bone Bolango Regency, Gorontalo Province. Income diversification is viewed as a crucial strategy to improve the economic resilience of farming households, which are heavily dependent on the agricultural sector. The research uses a quantitative approach, analyzing data from 74 respondents selected through sampling techniques. Data was collected using a questionnaire that covered various income sources, income inequality, and the level of vulnerability to poverty. The results show that the income structure of households is dominated by income from rice farming (45%), while small businesses (25%) and formal and informal employment (15%) also contribute significantly. The Gini coefficient analysis of income inequality indicates a moderate level of inequality with a value of 0.42, with the non-agricultural sector showing higher inequality (0.45) compared to agriculture (0.38). Income diversification has a significant negative relationship with poverty vulnerability, with a correlation coefficient of −0.60. The regression model indicates that 42% of the variation in poverty vulnerability is explained by income diversification, suggesting that households with more diverse income sources are less vulnerable to poverty. The study concludes that income diversification is an effective strategy in reducing the economic vulnerability of rice farming households. The findings underline the need for policies that support farmers' access to economic opportunities outside agriculture, such as skill training and access to business capital. These results provide a basis for local governments and stakeholders to formulate effective policies aimed at reducing poverty in rural areas.

Book reviewed in this article:Understanding Green Revolutions: Agrarian Change and Development Planning in South Asia. Edited by TIM‐BAYLTSS‐SMITH and SUDHTR WANMALICFamily, Servants and Vkitors: The Farm Household in Nineteenth and Twentieth Century Devon. By MARY BOUQUETFood Economics. By ANNAB URGERAThe Economics of Coffce. By J de GRAFFAgricultural Gco ra hy:A Social and Economic Analysis. By BRIAN W. ILBERYCountryside Conjlicrs: The Politics of Farming, Forestry and Conservation. By PHILIP LOWEFood Policy: Frameworks for Analysis and Action. Edited by CHARLES K. MANN and BARBARA HUDDLESTONAgricultural Change and Rural Poverty: Variations on a Theme by Dharm Narain. Edited by JOHN W. MELLOR and GUNVANT M. DESAI

Introduction The conflict between wild elephants and rural farmers is of great concern for wildlife management and rural development in most parts of Asia and Africa. In Sri Lanka, the conflict is at a very acute stage, as revealed by the fact that between 1992 and 2001 a total of 536 people were killed by wild elephants and that elephant mortality has reached a level of more than three elephant losses per week. Habitat destruction caused by illegal human encroachments and settlements, interruption of elephant corridors and scarcity of food and water in the national parks are some of the causes that have led to the increase of the human–elephant conflicts (HEC) in all wildlife regions in Sri Lanka. Rapid economic development in the dry zone of the country led to large-scale deforestation. In addition, the civil war further increased the conflict by forcing elephants to shift from forests and national parks in the north and east to the south and the west. As their habitats are increasingly lost, wild elephants often have no other choice than invading nearby farm fields causing damage to crops, trees, storage, houses and other properties, sometimes also resulting in the loss of human lives. In response, farm households are compelled to retaliate in various ways to protect their crops, including the killing of elephants. As stated by Heffernan (2005), the factors driving HECs are complex and case specific to environmental and social variables. The intensity of the HEC depends on elephant-related parameters such as aggressiveness to post-traumatic stress disorder or propensity to feed on field crops instead of the more difficult to find forest plants. Thus, HECs are created in the interaction between the forest environment and people’s actions such as encroachment and land claims. Elephant are the largest terrestrial animal on earth and they require approximately 130–300 kg of feed and 100–200 l of water per day (WWF 2002). As their natural habitats become fragmented, elephants move from one forest patch to the other for food and water, travelling about 20–25 km per day. In Asian countries, including Bangladesh, Bhutan, China, Cambodia, India, Indonesia, Laos, Myanmar, Malaysia, Nepal, Sri Lanka, Thailand and Vietnam, elephants are restricted to a few isolated and fragmented populations, a total number of approximately 35 000–50 000 elephants thus being affected by rapid population growth (Santiapillai and Jackson 1990, Sukumar 1989). In fact, 70% of elephant habitats in Asia were lost between 1960–2000 (Wickramasinghe and Santiapillai 2000).

Introduction and Aim: Obesity is a rising public health issue in India, with the young generation particularly at risk. Lifestyle changes, increased screen time and altered sleep patterns are risk factors for obesity in students with high workload, and studies requiring significant digital accessibility. This study aims to explore the relationship between Body Mass Index (BMI), screen time, and post-9 pm awake time among first-year MBBS students in a Kolkata-based medical college, with attention to gender-specific variations. Materials and Methods: An observational cross-sectional study was conducted among 112 consenting first-year MBBS students at KPC Medical College, Kolkata. Data on BMI, 24-hour screen time, and post- 9 pm awake time were collected via structured questionnaires. Statistical analysis included central tendency and dispersion measurement, student's unpaired T- test and Pearson correlation using SPSS v27. Results: The mean BMI of participants was 23.17 kgm², mean 24-hour screen time was 5.26 hours, and mean post-9 pm awake time 4.08 hours. No significant correlation was found between BMI and screen time (R 0.0508, p 0.59). A moderate positive correlation was observed between BMI and post-9 pm awake time (R 0.5938, p

One of the main goals of SDGs is to remove of the poverty in its all forms and from everywhere. The present study examines the factors that are responsible for rural poverty; the relationship between poverty and inequality; and between poverty and debt-to-assets ratio among farm households in Bihar, one of the economically most backward states in India. We use primary data on consumption, income, and assets for the agricultural year 2010–11. The logit model has been applied to empirically identify the factors responsible for poverty. The study finds a strong positive, correlation between poverty and debtto-assets ratio, but not between inequality and poverty or any impact of inequality on poverty. The results indicate that poverty is more prevalent among the households having little access to land, livestock and other productive assets, low level of education, larger family size, and higher dependency ratio.

The inter-linkages amongst agricultural productivity, rural poverty and nutritional security have been analysed based on a study of Bagalkot district of Karnataka state using primary data for the agricultural year 2005-06 obtained from 120 farm households. The data have been processed using ratios, frequencies, percentages, regression analysis and probit model. Agricultural productivity has negatively and significantly influenced rural poverty at the farm level. Low agricultural productivity is the root cause of rural poverty. Household size and number of dependents therein have positively influenced rural poverty. Optimization of household size or increase in the number of earning members of the household would reduce poverty. Nutritional security is greatly influenced by the level of rural poverty. To upgrade the nutritional status of households, the study has suggested that effective poverty alleviation programmes aimed at enhancing agricultural productivity through transfer of productive assets instead of consumer goods to the poor, should be launched and effectively monitored. Agricultural credit being an important aspect of productivity, appropriate steps should be taken to increase the access of rural households to financial institutions.