...Population Characteristics in Oxford The topic of population characteristics never fail to attract individuals’ attention. This report will further investigate following themes: age and gender, ethnicity, religion, international migration and health. Part 1 Age and Gender According to 2011 census, 35% of population was aged between 15 and 29 years old in Oxford. Moreover, the city has the youngest median age 29.9 years in England and Wales. As a result of a large number of students in the city, Oxford’s population is relatively young. |Age |Males |Females |All people | |0-4 |4,600 |4,700 |9,200 | |5-9 |3,800 |3,600 |7,400 | |10-14 |3,700 |3,400 |7,100 | |15-19 |6,300 |6,600 |12,900 | |20-24 |11,400 |11,200 |22,600 | |25-29 |8,400 |8,700 |17,100 | |30-34 |6,800...
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..., population composition, physical environment and general wellness will be reported. Diverse population characteristics will be expounded in the context of their demographics change, health risks and needs, health disparities and social determinants affecting them. About 70% of the residential houses are old with peeling paint and dump-rising is evident from the mould around the base. Some have beed vacated and are at verge of collapsing. The yards are neat and clean. Commercial and public buildings are also old, but show signs of frequent paiting are repair, They have provision for accessibility by the disabled. Parks and public spaces are the best in the commnity with sports facilities function and straming with people and the parks neat...
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...Editor’s Note: This Chapter is the continuation of an adaptation of a state plan for disaster preparation and response. In total, the original chapter comprises Chapters 1, 14, 16-18. Chapter Overview Many of you are reading this manual as part of your efforts to prepare as a spiritual care professional who will be ready to respond during times of disaster. Your main role will be to provide emotional and spiritual support to those affected by disaster, but it is also critical for you to understand the context in which you will be providing this support—the bigger picture, so to speak. Disaster relief operations are complex systems having more to them than just a response mechanism. They require a significant amount of pre-planning. Disaster experts have long known that waiting until a disaster strikes to test a community’s response procedures can be disastrous. How communities respond to disaster can even induce more stress on individuals and place them at risk for developing a variety of adverse reactions and psychological consequences. This section takes the first step towards introducing you to the key elements of disaster management and response at multiple levels. The focus of this chapter is on the framework of disaster planning, preparedness, and response. In the event of a disaster in your community, you may be asked and/or may want to help. It will help you to have an understanding of the structure of the overall response so that you know how your efforts will enhance...
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...ology Chapter 13 Lecture Outline Introduction Clown, Fool, or Simply Well Adapted? A. Review: Evolution is the central theme of biology. Evolutionary adaptation is a universal characteristic of living things (see Module 1.6). NOTE: More than any other idea in biology, evolutionary theory serves to tie the discipline together. T. Dobzhansky: “Nothing in biology makes sense except in the light of evolution.” B. If you look at any organism critically, you are first struck by the differences from other organisms. 1. Further observation often reveals that an organism’s features show some relationship to where the organism lives and what it does in its environment. 2. The blue-footed booby has enormous webbed feet, an oil producing gland that keeps the booby afloat, a nostril that can close under water that prevents water from entering the lungs, a gland that secrets salt from consumed sea water, and a torpedo-like body—all adaptations that make life on the sea feasible. I. Darwin’s Theory of Evolution Module 13.1 A sea voyage helped Darwin frame his theory of evolution. A. Awareness of each organism’s adaptations and how they fit the particular conditions of its environment helps us appreciate the natural world (Figure 13.1A). B. Early Greek philosophers held various views. Anaximander (about 2,500 years ago) suggested that life arose in water and that simpler forms...
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...to test the hypothesis that small bird’s physical characteristics can greatly impact their species population growth over time. Using the Finch as a test subject for multiple trial runs on a simulated computer system we can alter the characteristics of the finch and run diagnostics for two completely different locations and set of experiments. This simultaneous testing and comparison will either prove or disprove the hypothesis that small bird’s physical characteristics can greatly impact their population growth. The base line for this comparison will be two islands labeled island A and B with population A1 of finches on island A and a population of B1 finches on island B. There will be two time allotments used to factor in the population growth for each sets of populations on both islands: beginning with a 100 year scenario and then followed by a 200 year scenario. Using extended timelines will reflect accuracy in the initial 100 year reading and or show that physical characteristics of finch birds only impacts population growth for a specific time period. Each island A and B will be of the same size and offer the same vegetation amount and size. However, the physical characteristics of each population will be altered to population A1 with a beak length of 14 mm and population B1 with a beak length of 11 mm. Given the consistency and amount of controls, compared to the one and only variable being a major physical characteristic of the...
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...summarize, for instance, the data’s typical value and the data’s variability. Descriptive statistics is summary/describing data. Inferential statistics is drawing conclusions/assumptions. The phenomenal growth in statistics is mainly in the field called inferential statistics. Generally, inferential statistics refers to drawing conclusions about a large set of data— called a population —based on a smaller set of sample data. A population is defined as all members of a specified group (not necessarily people), whereas a sample is a subset of that particular population. In most statistical applications we must rely on sample data in order to make inferences about various characteristics of the population. For example, a 2010 survey of 1,208 registered voters by a USA TODAY/Gallup Poll found that President Obama’s job performance was viewed favorably by only 41% of those polled, his lowest rating in a USA TODAY/Gallup Poll since he took office in January 2009 (USA TODAY, August 3, 2010). Researchers use this sample result, called a sample statistic, in an attempt to estimate the corresponding unknown population parameter. In this case, the parameter of interest is the percentage of all registered voters that view the president’s job performance...
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...larger groups 6-4 Census 6-5 Basics of Sampling Theory Population Element Defined target population Sampling unit Sampling frame 6-6 Sources of Error Sampling error is any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size Nonsampling error is bias that occurs in a research study regardless of whether a sample or census is used, such as bias caused by measurement error, response errors, or coding errors 6-7 Sampling Methods Probability sampling Nonprobability sampling 6-8 Classification of Sampling Techniques Sampling Techniques Nonprobability Sampling Techniques Probability Sampling Techniques Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Simple Random Sampling © 2007 Prentice Hall Systematic Sampling Stratified Sampling Cluster Sampling Other Sampling Techniques 6-9 11-9 Types of Sampling Methods Probability Simple random sampling Systematic random sampling Stratified random sampling Cluster sampling Nonprobability Convenience sampling Judgment sampling Quota sampling Snowball sampling 6-10 Forms of Probability Sampling_1 Simple random sampling is a method of probability sampling in which every unit has an equal nonzero chance of being selected Systematic random sampling is a method of probability sampling in which the defined target population is ordered and the sample is selected according to position using a...
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...worthiness or rather if they are likely to default on the loan. Therefore, appropriate models are usually applied, which are pegged upon the bank’s previous performance, and on discovering the fundamental characteristics that could be useful in establishing the prospects of new customers. Apparently,...
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...Definition of Terms Target population * the population we want to study Sampled population * the population from where we actually select the sample Sample * these are the elements from the sampled population who are actually selected to participate or to be the subject in the study Sample size * Total number of elements in the sample SAMPLING * the process of selecting a small number of elements from a larger defined target group of elements such that the information gathered from the small group will allow judgments to be made about the larger groups SAMPLING TECHNIQUES * PROBABILITY SAMPLING a method of selecting a sample wherein each element in the population has a known, nonzero chance of being included in the sample. Simple Random Sampling a probability sampling method wherein all members of the population have equal chances of being included in the study. When to use: * If the members of the population are not so spread geographically. * If the members are homogeneous with respect to the characteristic under study. Systematic Sampling probability sampling method wherein the selection of the first member (random start - r) is at random (using simple random sampling) and the selection of the other members in sample is systematic by successively taking every kth member from the random start, where k is the sampling interval. When to use: * If the arrangement of the members is according to magnitude...
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...5.5 LITERATURE REVIEW 5.5.1 DEMOGRAPHY DEFINITION Thompson (2007) : “The study of human populations – their size, composition and distribution across place – and the process through which populations change – Births, Deaths and Migration.” Weeks (1994) : “The science of population – concerned with virtually everything that influences, or can be influenced by population size, distribution, processes, structure, or characteristics.” 5.5.1.1 WHY STUDY DEMOGRAPHY To understand why the populations of some countries are growing and why some are not What happens to societies as their pattern of birth, death or migration change Understanding all these consequences of population change (either growth or decline) 5.5.1.2 SOURCES OF DEMOGRAPHIC DATA The kind of information we often seek for are: i. Population size and distribution ii. Population processes (fertility, mortality, and migration). iii. Population structure and characteristics Three major sources of information for these three population processes are: a) Census Information about persons – age, sex, marital status, source of livelihood, place of birth, number of children ever born, etc. Information can be found in official government reports. b) Registration of Vital Statistics Information about events – vital events and their rates are called vital rates – birth and deaths. - Vital registration – marriage, divorce, adoptions, fetal deaths as well as migration ...
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...Sampling techniques: Advantages and disadvantages |Technique |Descriptions |Advantages |Disadvantages | |Simple random |Random sample from whole population|Highly representative if all subjects |Not possible without complete list of | | | |participate; the ideal |population members; potentially uneconomical| | | | |to achieve; can be disruptive to isolate | | | | |members from a group; time-scale may be too | | | | |long, data/sample could change | |Stratified random |Random sample from identifiable |Can ensure that specific groups are |More complex, requires greater effort than | | |groups (strata), subgroups, etc. |represented, even proportionally, in the|simple random; strata must be carefully | | | |sample(s) (e.g., by gender), by |defined | | | |selecting individuals from strata list | ...
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...| Descriptions | Advantages | Disadvantages | Simple random | Random sample from whole population | Highly representative if all subjects participate; the ideal | Not possible without complete list of population members; potentially uneconomical to achieve; can be disruptive to isolate members from a group; time-scale may be too long, data/sample could change | Stratified random | Random sample from identifiable groups (strata), subgroups, etc. | Can ensure that specific groups are represented, even proportionally, in the sample(s) (e.g., by gender), by selecting individuals from strata list | More complex, requires greater effort than simple random; strata must be carefully defined | Cluster | Random samples of successive clusters of subjects (e.g., by institution) until small groups are chosen as units | Possible to select randomly when no single list of population members exists, but local lists do; data collected on groups may avoid introduction of confounding by isolating members | Clusters in a level must be equivalent and some natural ones are not for essential characteristics (e.g., geographic: numbers equal, but unemployment rates differ) | Stage | Combination of cluster (randomly selecting clusters) and random or stratified random sampling of individuals | Can make up probability sample by random at stages and within groups; possible to select random sample when population lists are very localized | Complex, combines limitations of cluster and stratified random...
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...Disadvantages Simple random Random sample from whole population • Highly representative if all subjects participate; the ideal • There is no scope of personal bias • Each item has an equal chance of being selected • It provides more accurate and reliable data • It becomes possible to have an idea about the errors of estimation • Not possible without complete list of population members; potentially uneconomical to achieve; • Can be disruptive to isolate members from a group; • Time-scale may be too long, data/sample could change • It is not suitable if the field of enquiry is small Stratified random Random sample from identifiable groups (strata), subgroups, etc. • Can ensure that specific groups are represented, even proportionally, in the sample(s) (e.g., by gender), by selecting individuals from strata list • It eliminates the difference between strata and thereby reduces the sampling error • It brings about a gain in the precision of the sample estimate when the strata variability is the least • Independent estimates for the different strata can be prepared • . More complex, requires greater effort than simple random. • Strata must be carefully defined • The result maybe misleading if the basis of stratification in not properly decided Cluster/block Random samples of successive clusters of subjects (e.g., by institution) until small groups are chosen as units • Possible to select randomly when no single list of population members exists, but local lists can do. ...
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... migration, and population size What is genetic diversity? Genetic diversity refers to the total number of genetic characteristics in the genetic makeup of a species. “High genetic diversity indicates many different kinds of alleles for each characteristic, and low genetic diversity indicates that nearly all the individuals in the population have the same alleles”. There are certain issues related to genetic diversity which include mutations, sexual reproduction, migration, and population size. Mutations are stable changes in the DNA sequence of a gene. Mutations can be caused by many different things. They can be caused by radiation. They can also be induced by the organism itself. Mutations happen naturally when genes have dysfunctions. Modifications in DNA caused by mutations can cause errors in protein sequence, creating partially or completely non-functional proteins. “Many mutations are harmful, but very rarely one will occur that is valuable to the organism”. Sexual reproduction is the formation of a new individual following the union of two gametes. These gametes have specific needs. A gamete has to meet and unite together. They also must have food to nourish the developing embryo. “The new member may have a unique combination of characteristics so superior to those of the other members of the population that the new member will be much more successful in producing offspring”. “Migration of individuals from one genetically distinct population to another is also...
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...questions related to how much, how often, how many, when, and who” (Cooper & Schindler, 2014), one main quantitative data collection instrument is survey which is considered a major among other ones because it throws exact measures of opinions and consumer behavior. The nature of sampling is “that by selecting some of the elements in a population, we may draw conclusions about the entire population” (Cooper & Schindler, 2014). Some examples of sampling methods available to researchers include: “Purposive sampling where Researchers choose participants arbitrarily for their unique characteristics or their experiences, attitudes, or perceptions; as conceptual or theoretical categories of participants develop during the interviewing process, researchers seek new participants to challenge emerging patterns, Snowball sampling where Participants refer researchers to others who have characteristics, experiences, or attitudes similar to or different from their own and finally Convenience sampling where Researchers select any readily available individuals as participants. Descriptive statistical methods are used to “display characteristics of the location, spread, and shape of a data array, it attempts to describe or define a subject ,often by creating a profile of a group of problems, people, or events, through the collection of data and the tabulation of the frequencies on research variables or their interaction;” (Cooper & Schindler, 2014), the article that I choose “explored...
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