Methods of collection analysis and interpretation of demographic data
Analysis includes the sets of methods that allow us to measure the dimensions and dynamics of populations. These methods have primarily been developed to study human populations, but are extended to a variety of areas where researchers want to know how populations of social actors can change across time through processes of birth, death, and migration. In the context of human biological populations, demographic analysis uses administrative records to develop an independent estimate of the population. 1] demographic analysis estimates are often considered a reliable standard for judging the accuracy of the census information gathered at any time. In the labor force, demographic analysis is used to estimate sizes and flows of populations of workers; in population ecology the focus is on the birth, death, migration and immigration of individuals in a population of living organisms, alternatively, in social human sciences could involve movement of firms and institutional forms. Census, da now also includes comparative analysis between independent housing estimates, and census address lists at different key time points. Data are obtained from a census of the population and from registries: records of events like birth, deaths, migrations, marriages, divorces, diseases, and employment. To do this, there needs to be an understanding of how they are calculated and the questions they answer which are included in these four concepts: population change, standardization of population numbers, the demographic bookkeeping equation, and population tion change[edit]. In this case, methods must be used to produce a standardized mortality rate (smr) or standardized incidence rate (sir). Bookkeeping is used in the identification of four main components of population growth during any given time demographic bookkeeping equation is as follows:{\displaystyle p_{2}=p_{1}+(b-d)+(m_{i}-m_{o})\,}. This data is often compared using a population tion composition is also a very important part of historical research. Information ranging back hundreds of years is not always worthwhile, because the numbers of people for which data are available may not provide the information that is important (such as population size). Lack of information on the original data-collection procedures may prevent accurate evaluation of data aphic analysis in institutions and organizations[edit].
Methods of data collection and analysis of data
Demographic analysis of labor markets can be used to show slow population growth, population aging, and the increased importance of immigration. Census bureau projects that in the next 100 years, the united states will face some dramatic demographic changes. These demographic shifts could ignite major adjustments in the economy, more specifically, in labor markets. Choosing a new location for a branch of a bank, choosing the area in which to start a new supermarket, consulting a bank loan officer that a particular location would be a beneficial site to start a car wash, and determining what shopping area would be best to buy and be redeveloped in metropolis area are types of problems in which demographers can be called rdization is a useful demographic technique used in the analysis of a business. Dee, isbn ries: demographicshidden categories: cs1 maint: multiple names: authors listarticles with limited geographic scope from march 2015articles lacking in-text citations from october 2009all articles lacking in-text citationsall articles with unsourced statementsarticles with unsourced statements from march 2015articles with unsourced statements from february logged intalkcontributionscreate accountlog pagecontentsfeatured contentcurrent eventsrandom articledonate to wikipediawikipedia out wikipediacommunity portalrecent changescontact links hererelated changesupload filespecial pagespermanent linkpage informationwikidata itemcite this a bookdownload as pdfprintable page was last edited on 15 november 2017, at 22: is available under the creative commons attribution-sharealike license;. Planning of health services can be demographic variables, for example:How many health units do we need? The data collected need to be table ,charts ,diagrams, graphs ry 1 category 2 category 3 category ry 1 category 2 category 3 category 4. Now customize the name of a clipboard to store your can see my survey data preparation, interpretation and ing survey data is an important and exciting step in the survey is the time that you may reveal important facts about your customers,Uncover trends that you might not otherwise have known existed, or table facts to support your plans. By doing in-depth data comparisons,You can begin to identify relationships between various data that will help tand more about your respondents, and guide you towards better article gives you a brief overview of how to analyze survey results. Not discusses specific usage of esurveyspro for conducting analysis as intended to provide a foundation upon which you can confidently conduct survey analysis no matter what tool you common you dive into analyzing your survey results, take a look back at the e. Do you have sufficient data to properly reach gh data analysis is the wrong time to try and rewrite your ment, it is important to remember the scope of your project and stick . To avoid this temptation, remember this 1: if you did not ask you do not r common mistake that many first time surveyors make is to attempt data to compensate for poor question design. In order to avoid this pitfall, remember this simple rule:Rule 2: do not alter data to compensate for bad survey design.
For example, if an hr manager conducts a benefits survey s all employees to participate, most people would assume that the ent all employees since everyone had an opportunity to ed that enough employees participate, the data might be , but is it really representative of all employees? If the survey collected data about employee demographics that could ed to what is known about the company, then the results do reflect y as a whole. To avoid this temptation, remember 3: do not project your data to people that did not earlier you recognize flaws in your survey design and data collection, time you will save during analysis. If you have an adequate number of responses, you'll have to get ing any survey, web or traditional, consists of a number of ses that are intended to summarize, arrange, and transform data ation. If your survey objective was simply to collect data for se or data warehouse, you do not have to do any analysis of the data. Other hand, if your objective was to understand the characteristics l customers, then you must transform you raw results in to will enable you to paint a clear picture of your ng you need to analyze the data collected from your survey, the with a quick review of the results, followed by editing, analysis, ing. To ensure you have accurate data before investing significant analysis, it is important that you do not begin analyzing results until completed the review and editing all your results. A quick review of the data can help you tand that tell you if the people that respond are the right people. A low could mean your survey invitation was not compelling enough to ipation, or your timing was off and a follow-up reminder is , the quick review of the survey can show you what areas to focus on ed analysis. As stated earlier, most surveyors already know what to get, so your quick review can show you the g and cleaning data is an important step in the survey process. Must be taken when editing survey data so that you do not alter or responses in such a way as to bias your results. Although you can g and cleaning your data as soon as results are received, caution used since any edits can be lost if the database is rebuilt. To be safe,Wait until all data is received before you begin the editing and start, find and delete incomplete and duplicate responses.
For example, if a your survey was intended to determine intentions across various demographic groups and the respondent did any of the demographic questions, you should delete the response. On hand, if the respondent answered all the demographic questions d their name or email address, then you should keep the ate responses are a unique issue for electronic surveys. For example, aphics question that asks for the respondent's role within zation may have a response like "faculty, teacher, or student" and dent selects "other" and types "professor," you would want to clean se by switching the other choice to the one for "faculty, teacher, the data preparation is complete, it is time to start analyzing the turning it into actionable is is the most important aspect of your survey research project. At , you have collected a set of data that must now be turned into ation. Mistakes during analysis can lead to costly decisions road, so extreme caution and careful review must be followed throughout s. What you do is will ultimately determine if your survey project is a successful ing on what type of information you are trying to know about ce, you will have to decide what analysis makes sense. On the other hand, more may wish to do a more complex statistical analysis using high ical tools such as spss, excel, or any number of number ations. For our purposes in this article, we will focus on basic cal analysis simply means displaying the data in a variety of s that make it easy to see patterns and identify differences among s set. Each response option is shown as an independent bar on the chart,And the length of the bar represents the frequency the response was ve to all charts, or circle graphs, have colorful "slices" representing segments data. It is easy to interpret which answer most responses in a pie chart by selecting the largest potion of the comparing two sets of data using a pie chart, it is important to make colors used for each response option remain consistent in each chart. Are other graphing options such as line charts, area charts and , which are useful when displaying the same data over a period of r these formats are not as easy to interpret for casual users, so be used ncy tables are another form of basic analysis. Cross tabs allow you to compare data from two questions ine if there is a relationship between them. Like frequency tables, appear as a table of data showing answers to one question as a series and answers to another question as a series of t marketing cal product tabs are used most frequently to look at answers to a question s demographic groups.
For analysis purposes, cross tabs are a great way to ing is the most under-utilized tool used in analysis. By viewing only the data from the people who vely, look at how they answered other questions. For instance, ually running a customer satisfaction survey, you can assess changes er attitudes over time by filtering on the date the survey was can also use a filter on date received to assess the impact of ive programs or new product offerings by comparing survey and after the s do not permanently remove the responses of those people that do the specified criteria; they simply eliminate them from the current the data, making it much easier to perform analysis. In order to know if it is "the" reason, need to ask a direct analyzing your survey data, it is time to create a report of gs. Your report should have a description of your sampling method,Including who was invited to participate, over what time frame results ted, and any issues that might exist relative to your respondent , you should include your analysis and conclusions in adequate detail the needs of your audience. After your analysis section, make recommendations that relate back to your survey endations can be as simple as conduct further studies to a major shift y direction. In either case, your recommendation must be within the your survey objective and supported by the data collected. Finally, you e a copy of your survey questions and a summary of all the data an appendix to your analysis is not as easy as downloading results and printing a chart , yet it is not so complex that it requires a phd. In this article learned that good analysis begins with good questions, ipation, and careful interpretation of the data, in order to able results. Techniques such as charting, filtering, cross tabulation,And regression analysis all help you spot trends and patterns within your helping you meet your survey objective. You now have a solid which you can confidently conduct your own survey analysis using a a look at our survey samples related to customer satisfaction. Create a survey and study effective ways to invite survey which are the most effective ways to invite people to answer your to conduct an online survey and engage customers in a ng cart abandonment ting and analyzing survey data over er satisfaction er surveys: know your customer - or how profiling can be and web survey methods onnaire er satisfaction surveys and questionnaire er satisfaction surveys: nuts and preparation, interpretation and surveys that contribute to to create an effective survey project zing on-line response tanding question types and data ing the value of your customer satisfaction up surveys to measure ad ing survey by step guide to create online list of ative to survey monkey. 2 recording a choice of method is influenced by the data gy, the type of variable, the accuracy required, the collection point skill of the enumerator.
The main data collection methods are:· registration:Registers and licences are particularly valuable for complete enumeration, limited to variables that change slowly, such as numbers of fishing their characteristics. Linkage among variables, choice of the many methods for collecting fishery depend on the variables to be measured, the source and the ble. C variables, like catch or effort, may often be best obtained records, such as the same variable, the methods can be different the type of fishery. For example, for a large-scale fishery, catch data best collected from logbooks, whilst in a small-scale fishery /or questionnaires would often be the best method. Buyers, processors and other intermediaries are likely to keep sales records, which should be used as the basis of data forms. S often do not keep any records, and data acquisition in this case restricted to one-to-one interviews, but the interview structure could collection should be conducted at intervals nt for the management purpose. For example, data for stock monitoring be collected constantly, while household data can be at much longer als. In general, frequently collected data will probably have to rely s or industry personnel providing the data. Less frequent data can ators since the costs of collection are much are cases when fishery data collection programmes operated on a regular basis because of operational limits. Under these circumstances, a number of alternative approaches can to assess the fisheries, including:· limited or sample-based pilot surveys;· household surveys or fish consumption· trade patterns;· logbook of these can be used for cross-checking landings data as providing production and socio-cultural variables can be collected by more than one method and ent points from fishers to consumers. For example, catch ted through logbooks can be cross-checked against reported landings sales slips, data collected by interview at landing sites and even trade almost all cases, many different variables can be aneously. For example, length frequency, species composition, and first sale price can all be obtained when vessels land their ting of data for different purposes reduces costs and thus due be made of this aspect when planning the data are strong links between types of data, where they obtained and the methods, which are available for their collection. N provides a guide for selecting data collection methods in relation data type and source, and gives some indication of what types of data can ted simultaneously.
These tables intend to give some guidance for ting methods and sources, and design a data collection system. The would give ideas about what types of data can be collected the same source with the same 6. If more precise measurements are required, the catch will have weighed on mes decisions on the units of measure are complicated type of data to be collected. Although registers y implemented for purposes other than to collect data, they can be in the design and implementation of a statistical system, provided data they contain are reliable, timely and complete6. Registration data typesin most countries, vessels, especially g vessels, and chartered or contract fishing vessels are registered fisheries authorities. Data on vessel type, size, gear type, country , fish holding capacity, number of fishers and engine horsepower should ered for various purposes. Data, such as the number of vessels, gear type and vessel registered fishing companies, should be recorded during such sing companies should provide basic data on the type of processing, raw material, capacity of processing, and even the source g vessels and fishing gears may often be required a valid fishing licence. If licences must be renewed each year, data licensing is particularly useful, as records are updated on an ry data also contain criteria for the classification g units into strata. These classifications are usually based tions and a priori knowledge regarding differences on catch rates,Species composition and species general, vessel registers are complex systems -established administrative procedures supported by effective ications, data storage and processing components. This method can be adopted for population or sampled onnaires may be used to collect regular or e data, and data for specialised studies. While the information in n applies to questionnaires for all these uses, examples will concern e data, whether regular or infrequent. Some of the data often h questionnaires include demographic characteristics, fishing practices,Opinions of stakeholders on fisheries issues or management, general fishers and household food budgets. If, for instance, several agencies require the same data, be made to co-ordinate its collection to avoid information that can be obtained through ts of almost any data variable.
5) for these sorts of data is more onnaires, like interviews, can contain either ons with blanks to be filled in, multiple choice questions, or they n open-ended questions where the respondent is encouraged to reply and choose their own focus to some facilitate filling out forms and data entry in a , the form should ideally be machine-readable, or at least laid out fields clearly identifiable and responses pre-coded. In an open-ended format, keywords and uring procedures should be imposed later to facilitate database entry is, if necessary. The best attempt to clarify and focus by er cannot anticipate all possible respondent interpretations. Scale test prior to actual use for data collection will assure better avoid wasting time and gh structured interviews can be used to obtain information, as with questionnaires, information is based on n. Data on variables such as catch or effort are potentially subject errors, due to poor estimates or intentional errors of ation. During that period, as a stratified random sample of people from whom data can be elicited on. While ch is more expensive, more complicated questions can be asked and data validated as it is collected, improving data quality. Interviews can aken with variety of data sources (fishers to consumers), and ative media, such as by telephone or in ured interviews form the basis for much of the tion in small-scale an interview approach for sample catch, effort , the enumerators work according to a schedule of landing site record data. Their job is vessels, obtaining data on landings, effort and prices from all boat/ that are expected to operate during the sampling day. Some additional data fishing operations may be required for certain types of fishing units, beach seines or boats making multiple fishing trips in one day. For these,The interview may cover planned activities as well as activities an interview approach for boat/gear activities, ators work according to a schedule of homeport visits to record data /gear activities. Observations), but also conduct interviews and surveys using might also be involved in data processing and analysis. Often, the amount of data and frequency tion can be established analytically with preliminary ably, observers should only collect data, not carry activities, such as enforcement, licensing or tax collection.
Problems in terms cts between data collection and law enforcement, for example, can d by clear demarcation, separating activities by location or time. Their positions on fishing vessels tasks that they perform depend significantly on a good working the captain and crew, which can be lost if they are perceived ement major data obtained through at-sea observers and effort data, which are often used for cross checking fishing logs. Same time, the at-sea observers can collect extra biological (fish size,Maturity, and sex), by-catch and environmental data, as well as ation on the gears, fishing operations etc. Frequently, discards data be collected by at-sea main data obtained from observers at landing sites,Processing plants and markets include landing (amount, quality, value ), biological (size, maturity), and effort (how many hauls, hours fishing). For the large-scale fishery where a logbook system is used, data landing sites could be used to crosscheck data recorded in logbooks. These data if collected can be validate landing ting data to estimate raising factors for processed fish weight to the whole weight equivalent may be necessary. Ng fish before and after processing, conversion factors may be ial seasonal, life history stage and other variations in body/gut suggest date, species, sex and size should be recorded ic and demographic data at each level (e. However, the data directly collected ators can also be the major source as well as supporting data for ted through other product data in processing plants can be h questionnaire (6. Methods need to be established to record whether non-fish material ed in any weighing process (e. Alternatively, each box or pallet is weighed and a note r box and pallet weight should be subtracted at a later data when. Inspectors need to be skilled sampling with enumerators/observers, inspector data should d with caution because of the high chance of sampling bias. 3 scientific researchecological research methods can be undertaken independent cial fishing operations to measure variables related to fish the environment. It is important type of research is carried out periodically in order to obtain tial rly, socio-cultural research methods can be used specific information useful to management.
Although these methods may be considered routine, they provide important data and should ered for infrequent data collection where informants are individuals with dge on a particular topic. This method is ideal ing in-depth descriptive data on beliefs and practices, ical ipant-observation is a technique whereby cher spends an extended period of time (from weeks to years, depending objective and the context) living with a target community, both behaviour and participating in their practices. However, alcs will ent in one simple respect: entry of data on the catch remains sibility of the data on fishing operations can be automatically bridge instrumentation. Once gathered, such data may be automatically transmitted to h satellite or ground technology that combines vessel position and a ment for management authorities through remote means is generally known as. Confidentiality is the key to the ance of vms, as information on current fishing grounds, and ty of position information, is a major r, vessel positions, activities and catch h these systems, directly to databases and thence to reports that ate data or remove vessel identifiers are becoming possible. 5 reportingin most complete enumeration approaches, fisheries staff directly undertake data collection, but use external data sources. Ly, these sources are data forms completed by the fishing lves, middle persons, market operators, processors and even ies and custom offices. Such methods are almost exclusively used -industrial and industrial fisheries and g companies are often a good source of ing basic data on catches and fishing effort. Landings declarations usually grouped data presented as summaries of fishing trips and catch advantage of using reports is that data are compiled other than fisheries staff and sometimes can be made available -processed computerised format directly from the company's records, ng administration costs. Confidentiality of information (such as s and catch rates) should be part of the agreement for data submission,And statistical outputs of the survey should not contain information related dual fishing vessels or companies. However, there are also risks -reporting or of deliberate distortion of data, especially fishing ground,Catch and revenue related information. 1 harvestthe collection of data from all vessels within a is sometimes needed usually from large-scale fisheries. Normally will be required to record their catch and effort data for every trip on.
For various reasons, the data collected by could be inaccurate and thus validation from time to time by important. 2 post harvestdata from post harvest operations are often used for ation on landings, biology, markets, costs and earnings. Also maintain information on their output and sales, including price, although such data may be much more difficult or impossible to legally required. Data forms will need to be customised to the type sing and the factory management system. Ideally, further data should be obtained on and level of fishing effort, although often this is similar fashion to logsheets and landings sheets, s should be prepared in appropriately identified forms in multiple required. Copies are likely to be required for the market administration (ary), the seller, the buyer and the fishery l sales records, such as volume of sales and prices t type, provide useful information for bio-economic analyses and a data on catch and landings when all other avenues for data collection lable. Three information sources on general sales are usually available:Market, processing factory and export data. The further away the data sources are from the , the more errors will be introduced, and the more details (e. These data are used in socio-economic indicators and, in ional cases, support landings ation on exports and imports is published in ies. Of course, export and import data is d use in estimating the total production of fish unless there are to establish the proportion of catch that is used in domestic r, in some particular cases, the trade data are the main source ting landings (e. If trade data are used for estimating landings, the quantities will usually need converting to lack of detail in export data can be a problem e of the form in which they are collected. Canned fish, frozen fish, fresh fish, and fishmeal may be the only relevant categories for export er with accurate raising factors, these data can be used for total tion. However, unless they are broken down by species and linked ly to sources of data closer to the harvest sector, they provide for fishery management purposes.
