Strategic Management Coursework Example | Topics and Well Written Essays - 3000 words

Strategic Management - Coursework Example The rising consumer needs in the developing as well as the developed markets is generating a uniform business opportunity. Companies of local as well as international origin are actively focusing on entering newer markets as well as expanding their presence in existing markets so as to capitalize the newly emerging business opportunities. The business firms of the 21st century are actively focusing on radical as well as disruptive innovation so as to effectively fulfill the needs of the masses. It is important to highlight that the because of the presence of multiple firms offering homogenous products and services, the competition in the market is extremely high. The availability of similar kinds of product and service offerings are resulting in the increase in power for the buyers. It has to be said that to deal with the intense market competition as well as to retain their competitive edge, the organizations needs to design as well as execute successful strategies. This particular assignment focuses on the aspects of strategy development, cutting edge technology as well as the sustainable competitive advantage which are necessary for present day organizations. Traditionally, organizations around the world follow a well designed hierarchy, the top of which is often tasked with the responsibility of strategy planning as well as implementation. For the implementation as well as execution of strategies, companies in various corners of the world often follow the usual one way top down implementation approach. Over the course of execution of business, there have often been doubts about whether it is possible to design effective strategies without following the traditional top down route. In order to find a satisfying answer to this particular focus, it is important to highlight that there is a high level of persistence that is associated with the hierarchical concept of an organization. Organizations which

Approaches to the Analysis of Survey Data Essay Example for Free

Approaches to the Analysis of Survey Data Essay 1. Preparing for the Analysis 1.1 Introduction This guide is concerned with some fundamental ideas of analysis of data from surveys. The discussion is at a statistically simple level; other more sophisticated statistical approaches are outlined in our guide Modern Methods of Analysis. Our aim here is to clarify the ideas that successful data analysts usually need to consider to complete a survey analysis task purposefully. An ill-thought-out analysis process can produce incompatible outputs and many results that never get discussed or used. It can overlook key findings and fail to pull out the subsets of the sample where clear findings are evident. Our brief discussion is intended to assist the research team in working systematically; it is no substitute for clear-sighted and thorough work by researchers. We do not aim to show a totally naà ¯ve analyst exactly how to tackle a particular set of survey data. However, we believe that where readers can undertake basic survey analysis, our recommendations will help and encourage them to do so better. Chapter 1 outlines a series of themes, after an introductory example. Different data types are distinguished in section 1.2. Section 1.3 looks at data structures; simple if there is one type of sampling unit involved, and hierarchical with e.g. communities, households and individuals. In section 1.4 we separate out three stages of survey data handling – exploration, analysis and archiving – which help to define expectations and procedures for different parts of the overall process. We contrast the research objectives of description or estimation (section 1.5), and of comparison  (section 1.6) and what these imply for analysis. Section 1.7 considers when results should be weighted to represent the population – depending on the extent to which a numerical value is or is not central to the interpretation of survey results. In section 1.8 we outline the coding of non-numerical responses. The use of ranked data is discussed in brief in section 1.9. In Chapter 2 we look at the ways in which researchers usually analyse survey data. We focus primarily on tabular methods, for reasons explained in section 2.1. Simple one-way tables are often useful as explained in section 2.2. Cross-tabulations (section 2.3) can take many forms and we need to think which are appropriate. Section 2.4 discusses issues about ‘accuracy’ in relation to two- and multi-way tables. In section 2.5 we briefly discuss what to do when several responses can be selected in response to one question.  © SSC 2001 – Approaches to the Analysis of Survey Data 5 Cross-tabulations can look at many respondents, but only at a small number of questions, and we discuss profiling in section 2.6, cluster analysis in section 2.7, and indicators in sections 2.8 and 2.9. 1.2 Data Types Introductory Example: On a nominal scale the categories recorded, usually counted, are described verbally. The ‘scale’ has no numerical characteristics. If a single oneway table resulting from simple summarisation of nominal (also called categorical) scale data contains frequencies:Christian Hindu Muslim Sikh Other 29 243 117 86 25 there is little that can be done to present exactly the same information in other forms. We could report highest frequency first as opposed to alphabetic order, or reduce the information in some way e.g. if one distinction is of key importance compared to the others:Hindu Non-Hindu 243 257 On the other hand, where there are ordered categories, the sequence makes sense only in one, or in exactly the opposite, order:Excellent Good Moderate Poor Very Bad 29 243 117 86 25 We could reduce the information by combining categories as above, but also we can summarise, somewhat numerically, in various ways. For example, accepting a degree of arbitrariness, we might give scores to the categories:Excellent Good Moderate Poor Very Bad 5 4 3 2 1 and then produce an ‘average score’ – a numerical indicator – for the sample of:29 Ãâ€" 5 + 243 Ãâ€" 4 + 117 Ãâ€" 3 + 86 Ãâ€" 2 + 25 Ãâ€" 1 29 + 243 + 117 + 86 + 25 = 3.33 This is an analogue of the arithmetical calculation we would do if the categories really were numbers e.g. family sizes. 6  © SSC 2001 – Approaches to the Analysis of Survey Data The same average score of 3.33 could arise from differently patterned data e.g. from rather more extreme results:Excellent Good Moderate Poor Very Bad 79 193 117 36 75 Hence, as with any other indicator, this ‘average’ only represents one feature of the data and several summaries will sometimes be needed. A major distinction in statistical methods is between quantitative data and the other categories exemplified above. With quantitative data, the difference between the values from two respondents has a clearly defined and incontrovertible meaning e.g. â€Å"It is 5C ° hotter now than it was at dawn† or â€Å"You have two more children than your sister†. Commonplace statistical methods provide many well-known approaches to such data, and are taught in most courses, so we give them only passing attention here. In this guide we focus primarily on the other types of data, coded in number form but with less clear-cut numerical meaning, as follows. Binary – e.g. yes/no data – can be coded in 1/0 form; while purely categorical or nominal data – e.g. caste or ethnicity – may be coded 1, 2, 3†¦ using numbers that are just arbitrary labels and cannot be added or subtracted. It is also common to have ordered categorical data, where items may be rated Excellent, Good, Poor, Useless, or responses to attitude statements may be Strongly agree, Agree, Neither agree nor disagree, Disagree, Strongly disagree. With ordered categorical data the number labels should form a rational sequence, because they have some numerical meaning e.g. scores of 4, 3, 2, 1 for Excellent through to Useless. Such data supports limited quantitative analysis, and is often referred to by statisticians as ‘qualitative’ – this usage does not imply that the elicitation procedure must satisfy a purist’s restrictive perception of what constitutes qualitative research methodology. 1.3 Data Structure SIMPLE SURVEY DATA STRUCTURE: the data from a single-round survey, analysed with limited reference to other information, can often be thought of as a ‘flat’ rectangular file of numbers, whether the numbers are counts/measurements, or codes, or a mixture. In a structured survey with numbered questions, the flat file has a column for each question, and a row for each respondent, a convention common to almost all standard statistical packages. If the data form a perfect rectangular grid with a number in every cell, analysis is made relatively easy, but there are many reasons why this will not always be the case and flat file data will be incomplete or irregular. Most importantly:-  © SSC 2001 – Approaches to the Analysis of Survey Data 7 †¢ Surveys often involve ‘skip’ questions where sections are missed out if irrelevant e.g. details of spouse’s employment do not exist for the unmarried. These arise legitimately, but imply different subsets of people respond to different questions. ‘Contingent questions’, where not everyone ‘qualifies’ to answer, often lead to inconsistent-seeming results for this reason. If the overall sample size is just adequate, the subset who ‘qualify’ for a particular set of contingent questions may be too small to analyse in the detail required. †¢ If some respondents fail to respond to some questions (item non-response) there will be holes in the rectangle. Non-informative non-response occurs if the data is missing for a reason unrelated to the true answers e.g. the interviewer turned over two pages instead of one! Informative non-response means that the absence of an answer itself tells you something, e.g. you are almost sure that the missing income value will be one of the highest in the community. A little potentially informative non-response may be ignorable, if there is plenty of data. If data are sparse or if informative  non-response is frequent, the analysis should take account of what can be inferred from knowing that there are informative missing values. HIERARCHICAL DATA STRUCTURE: another complexity of survey data structure arises if the data are hierarchical. A common type of hierarchy is where a series of questions is repeated say for each child in the household, and combined with a household questionnaire, and maybe data collected at community level. For analysis, we can create a rectangular flat file, at the ‘child level’, by repeating relevant household information in separate rows for each child. Similarly, we can summarise information for the children in a household, to create a ‘household level’ analysis file. The number of children in the household is usually a desirable part of the summary; this â€Å"post-stratification† variable can be used to produce sub-group analyses at household level separating out households with different numbers of child members. The way the sampling was done can have an effect on interpretation or analysis of a hierarchical study. For example if children were chosen at random, households with more children would have a greater chance of inclusion and a simple average of the household sizes would be biased upwards: it should be corrected for selection probabilities. Hierarchical structure becomes important, and harder to handle, if there are many levels where data are collected e.g. government guidance and allocations of resource, District Development Committee interpretations of the guidance, Village Task Force selections of safety net beneficiaries, then households and individuals whose vulnerabilities and opportunities are affected by targeting decisions taken at higher levels in the hierarchy. In such cases, a relational database reflecting the hierarchical 8  © SSC 2001 – Approaches to the Analysis of Survey Data structure is a much more desirable way than a spreadsheet to define and retain the inter-relationships between levels, and to create many analysis files at different levels. Such issues are described in the guide The Role of a Database Package for Research Projects. Any one of the analysis files   may be used as we discuss below, but any such study will be looking at one facet of the structure, and several analyses will have to be brought together for an overall interpretation. A more sophisticated approach using multi-level modelling, described in our guide on Modern Methods of Analysis, provides a way to look at several levels together. 1.4 Stages of Analysis It is often worth distinguishing the three stages of exploratory analysis, deriving the main findings, and archiving. EXPLORATORY DATA ANALYSIS (EDA) means looking at the data files, maybe even before all the data has been collected and entered, to get an idea of what is there. It can lead to additional data collection if this is seen to be needed, or savings by stopping collecting data when a conclusion is already clear, or existing results prove worthless. It is not assumed that results from EDA are ready for release as study findings. †¢ EDA usually overlaps with data cleaning; it is the stage where anomalies become evident e.g. individually plausible values may lead to a way-out point when combined with other variables on a scatterplot. In an ideal situation, EDA would end with confidence that one has a clean dataset, so that a single version of the main datafiles can be finalised and ‘locked’ and all published analyses derived from a single consistent form of ‘the data’. In practice later stages of analysis often produce additional queries about data values. †¢ Such exploratory analysis will also show up limitations in contingent questions e.g. we might find we don’t have enough currently married women to analyse their income sources separately by district. EDA should include the final reconciliation of analysis ambitions with data limitations. †¢ This phase can allow the form of analysis to be tried out and agreed, developing analysis plans and program code in parallel with the final data collection, data entry and checking. Purposeful EDA allows the subsequent stage of deriving the main findings to be relatively quick, uncontroversial, and well organised. DERIVING THE MAIN FINDINGS: the second stage will  ideally begin with a clear-cut clean version of the data, so that analysis files are consistent with one another, and any inconsistencies, e.g. in numbers included, can be clearly explained. This is the stage we amplify upon, later in this guide. It should generate the summary  © SSC 2001 – Approaches to the Analysis of Survey Data 9 findings, relationships, models, interpretations and narratives, and recommendations that research users will need to begin utilising the results. first Of course one needs to allow time for ‘extra’ but usually inevitable tasks such as:†¢ follow-up work to produce further more detailed findings, e.g. elucidating unexpected results from the pre-planned work. †¢ a change made to the data, each time a previously unsuspected recording or data entry error comes to light. Then it is important to correct the database and all analysis files already created that involve the value to be corrected. This will mean repeating analyses that have already been done using, but not revealing, the erroneous value. If that analysis was done â€Å"by mouse clicking† and with no record of the steps, this can be very tedious. This stage of work is best undertaken using software that can keep a log: it records the analyses in the form of program instructions that can readily and accurately be re-run. ARCHIVING means that data collectors keep, perhaps on CD, all the non-ephemeral material relating to their efforts to acquire information. Obvious components of such a record include:(i) data collection instruments, (ii) raw data, (iii) metadata recording the what, where, when, and other identifiers of all variables, (iv) variable names and their interpretations, and labels corresponding to values of categorical variables, (v) query programs used to extract analysis files from the database, (vi) log files  defining the analyses, and (vii) reports. Often georeferencing information, digital photographs of sites and scans of documentary material are also useful. Participatory village maps, for example, can be kept for reference as digital photographs. Surveys are often complicated endeavours where analysis covers only a fraction of what could be done. Reasons for developing a good management system, of which the archive is part, include:†¢ keeping the research process organised as it progresses; †¢ satisfying the sponsor’s (e.g. DFID’s) contractual requirement that data should be available if required by the funder or by legitimate successor researchers; †¢ permitting a detailed re-analysis to authenticate the findings if they are questioned; †¢ allowing a different breakdown of results e.g. when administrative boundaries are redefined; †¢ linking several studies together, for instance in longer-term analyses carrying baseline data through to impact assessment. 10  © SSC 2001 – Approaches to the Analysis of Survey Data 1.5 Population Description as the Major Objective In the next section we look at the objective of comparing results from sub-groups, but a more basic aim is to estimate a characteristic like the absolute number in a category of proposed beneficiaries, or a relative number such as the prevalence of HIV seropositives. The estimate may be needed to describe a whole population or sections of it. In the basic analyses discussed below, we need to bear in mind both the planned and the achieved sampling structure. Example: Suppose ‘before’ and ‘after’ surveys were each planned to have a 50:50 split of urban and rural respondents. Even if we achieved 50:50 splits, these would need some manipulation if we wanted to generalise the results to represent an actual population split of 70:30 urban:rural. Say we wanted to assess the change from ‘before’ to ‘after’ and the achieved samples were in fact split 55:45 and 45:55. We would have to correct the  results carefully to get a meaningful estimate of change. Samples are often stratified i.e. structured to capture and represent particular segments of the target population. This may be much more sophisticated than the urban/rural split in the previous paragraph. Within-stratum summaries serve to describe and characterise each of these parts individually. If required by the objectives, overall summaries, which put together the strata, need to describe and characterise the whole population. It may be fine to treat the sample as a whole and produce simple, unweighted summaries if (i) we have set out to sample the strata proportionately, (ii) we have achieved this, and (iii) there are no problems due to hierarchical structure. Nonproportionality arises from various quite distinct sources, in particular:†¢ Case A: often sampling is disproportionate across strata by design, e.g. the urban situation is more novel, complex, interesting or accessible, and gets greater coverage than the fraction of the population classed as rural. †¢ Case B : sometimes particular strata are bedevilled with high levels of nonresponse, so that the data are not proportionate to stratum sizes, even when the original plan was that they should be. If we ignore non-proportionality, a simple-minded summary over all cases is not a proper representation of the population in these instances.  The ‘mechanistic’ response to ‘correct’ both the above cases is (1) to produce withinstratum results (tables or whatever), (2) to scale the numbers in them to represent the true population fraction that each stratum comprises, and then (3) to combine the results.  © SSC 2001 – Approaches to the Analysis of Survey Data 11 There is often a problem with doing this in case B, where non-response is an important part of the disproportionality: the reasons why data are missing from particular strata often correspond to real differences in the behaviour of respondents, especially those omitted or under-sampled, e.g. â€Å"We had very good response rates everywhere except in the north. There a high proportion of the population are nomadic, and we largely failed to find them.† Just  scaling up data from settled northerners does not take account of the different lifestyle and livelihood of the missing nomads. If you have largely missed a complete category, it is honest to report partial results making it clear which categories are not covered and why. One common ‘sampling’ problem arises when a substantial part of the target population is unwilling or unable to cooperate, so that the results in effect only represent a limited subset – those who volunteer or agree to take part. Of course the results are biased towards e.g. those who command sufficient resources to afford the time, or e.g. those who habitually take it upon themselves to represent others. We would be suspicious of any study which appeared to have relied on volunteers, but did not look carefully at the limits this imposed on the generalisability of the conclusions. If you have a low response rate from one stratum, but are still prepared to argue that the data are somewhat representative, the situation is at the very least uncomfortable. Where you have disproportionately few responses, the multipliers used in scaling up to ‘represent’ the stratum will be very high, so your limited data will be heavily weighted in the final overall summary. If there is any possible argument that these results are untypical, it is worthwhile to think carefully before giving them extra prominence in this way. 1.6 Comparison as the Major Objective One sound reason for disproportionate sampling is that the main objective is a comparison of subgroups in the population. Even if one of two groups to be compared is very small, say 10% of the total number in the population, we now want roughly equally many observations from each subgroup, to describe both groups roughly equally accurately. There is no point in comparing a very accurate set of results from one group with a very vague, ill-defined description of the other; the comparison is at least as vague as the worse description. The same broad principle applies whether the comparison is a wholly quantitative one looking at the difference in means of a numerical measure between groups, or a much looser verbal comparison e.g. an assessment of differences in pattern across a range of cross-tabulations. 12  © SSC 2001 – Approaches to the Analysis of Survey Data If for a subsidiary objective we produce an overall summary giving ‘the general picture’ of which both groups are part, 50:50 sampling may need to be re-weighted 90:10 to produce a quantitative overall picture of the sampled population. The great difference between true experimental approaches and surveys is that experiments usually involve a relatively specific comparison as the major objective, while surveys much more often do not. Many surveys have multiple objectives, frequently ill defined, often contradictory, and usually not formally prioritised. Along with the likelihood of some non-response, this tends to mean there is no sampling scheme which is best for all parts of the analysis, so various different weighting schemes may be needed in the analysis of a single survey. 1.7 When Weighting Matters Several times in the above we have discussed issues about how survey results may need to be scaled or weighted to allow for, or ‘correct for’, inequalities in how the sample represents the population. Sometimes this is of great importance, sometimes not. A fair evaluation of survey work ought to consider whether an appropriate tradeoff has been achieved between the need for accuracy and the benefits of simplicity. If the objective is formal estimation, e.g. of total population size from a census of a sample of communities, we are concerned to produce a strictly numerical answer, which we would like to be as accurate as circumstances allow. We should then correct as best we can for a distorted representation of the population in the sample. If groups being formally compared run across several population strata, we should try to ensure the comparison is fair by similar corrections, so that the groups are compared on the basis of consistent samples. In these cases we have to face up to problems such as unusually large weights attached to poorly-responding strata, and we may need to investigate the extent to which the final answer is dubious because of sensitivity to results from such subsamples. Survey findings are often used in ‘less numerical’ ways, where it may not be so important to achieve accurate weighting e.g. â€Å"whatever varieties they grow for sale, a large majority of farm households in Sri Lanka prefer traditional red rice varieties for home consumption because they prefer their flavour†. If this is a clear-cut finding which accords with other information, if it is to be used for a simple decision process, or if it is an interim finding which will prompt further investigation, there is a lot to be said for keeping the analysis simple. Of course it saves time and money. It makes the process of interpretation of the findings more accessible to those not very involved in the study. Also, weighting schemes depend on good information to create the weighting factors and this may be hard to pin down.  © SSC 2001 – Approaches to the Analysis of Survey Data 13 Where we have worryingly large weights, attaching to small amounts of doubtful information, it is natural to want to put limits on, or ‘cap’, the high weights, even at the expense of introducing some bias, i.e. to prevent any part of the data having too much impact on the result. The ultimate form of capping is to express doubts about all the data, and to give equal weight to every observation. The rationale, not usually clearly stated, even if analysts are aware they have done this, is to minimise the maximum weight given to any data item. This lends some support to the common practice of analysing survey data as if they were a simple random sample from an unstructured population. For ‘less numerical’ usages, this may not be particularly problematic as far as simple description is concerned. Of course it is wrong – and may be very misleading – to follow this up by calculating standard deviations and making claims of accuracy about the results which their derivation will not sustain! 1.8 Coding We recognise that purely qualitative researchers may prefer to use qualitative analysis methods and software, but where open-form and other verbal responses occur alongside numerical data it is often sensible to use a quantitative tool. From the statistical viewpoint, basic coding implies that we have material, which can be put into nominal-level categories. Usually this is recorded in verbal or pictorial form, maybe on audio- or videotape, or written down by interviewers or self-reported. We would advocate computerising the raw data, so it is archived. The following refers to extracting codes, usually describing the routine comments, rather than unique individual ones which can be used for subsequent qualitative analysis. By scanning the set of responses, themes are developed which reflect the items noted in the material. These should reflect the objectives of the activity. It is not necessary to code rare, irrelevant or uninteresting material. In the code development phase, a large enough range of the responses is scanned to be reasonably sure that commonly occurring themes have been noted. If previous literature, or theory, suggests other themes, these are noted too. Ideally, each theme is broken down into unambiguous, mutually exclusive and exhaustive, categories so that any response segment can be assigned to just one, and assigned the corresponding code value. A ‘codebook’ is then prepared where the categories are listed and codes assigned to them. Codes do not have to be consecutive numbers. It is common to think of codes as presence/absence markers, but there is no intrinsic reason why they should not be graded as ordered categorical variables if appropriate, e.g. on a scale such as fervent, positive, uninterested/no opinion, negative. 14  © SSC 2001 – Approaches to the Analysis of Survey Data The entire body of material is then reviewed and codes are recorded. This may be in relevant places on questionnaires or transcripts. Especially when looking at ‘new’ material not used in code development, extra items may arise and need to be added to the codebook. This may mean another pass through material already reviewed, to add new codes e.g. because a  particular response is turning up more than expected. From the point of view of analysis, no particular significance attaches to particular numbers used as codes, but it is worth bearing in mind that statistical packages are usually excellent at sorting, selecting or flagging, for example, ‘numbers between 10 and 19’ and other arithmetically defined sets. If these all referred to a theme such as ‘forest exploitation activities of male farmers’ they could easily be bundled together. It is of course impossible to separate out items given the same code, so deciding the right level of coding detail is essential at an early stage in the process. When codes are analysed, they can be treated like other nominal or ordered categorical data. The frequencies of different types of response can be counted or cross-tabulated. Since they often derive from text passages and the like, they are often particularly well-adapted for use in sorting listings of verbal comments – into relevant bundles for detailed non-quantitative analysis. 1.9 Ranking Scoring A common means of eliciting data is to ask individuals or groups to rank a set of options. The researchers’ decision to use ranks in the first place means that results are less informative than scoring, especially if respondents are forced to choose between some nearly-equal alternatives and some very different ones. A British 8-year-old offered baked beans on toast, or fish and chips, or chicken burger, or sushi with hot radish might rank these 1, 2, 3, 4 but score them 9, 8.5, 8, and 0.5 on a zero to ten scale! Ranking is an easy task where the set of ranks is not required to contain more than about four or five choices. It is common to ask respondents to rank, say, their best four from a list of ten, with 1 = best, etc. Accepting a degree of arbitrariness, we would usually replace ranks 1, 2, 3, 4, and a string of blanks by pseudo-scores 4, 3, 2, 1, and a string of zeros, which gives a complete array of numbers we can summarise – rather than a sparse array where we don’t know how to handle the blanks. A project output paper†  available on the SSC website explores this in more detail. †  Converting Ranks to Scores for an ad hoc Assessment of Methods of Communication Available to Farmers by Savitri Abeyasekera, Julie  Lawson-Macdowell Ian Wilson. This is an output from DFID-funded work under the Farming Systems Integrated Pest Management Project, Malawi and DFID NRSP project R7033, Methodological Framework for Combining Qualitative and Quantitative Survey Methods.  © SSC 2001 – Approaches to the Analysis of Survey Data 15 Where the instructions were to rank as many as you wish from a fixed, long list, we would tend to replace the variable length lists of ranks with scores. One might develop these as if respondents each had a fixed amount, e.g. 100 beans, to allocate as they saw fit. If four were chosen these might be scored 40, 30, 20, 10, or with five chosen 30, 25, 20, 15, 10, with zeros again for unranked items. These scores are arbitrary e.g. 40, 30, 20, 10 could instead be any number of choices e.g. 34, 28, 22, 16 or 40, 25, 20, 15; this reflects the rather uninformative nature of rankings, and the difficulty of post hoc construction of information that was not elicited effectively in the first place. Having reflected and having replaced ranks by scores we would usually treat these like any other numerical data, with one change of emphasis. Where results might be sensitive to the actual values attributed to ranks, we would stress sensitivity analysis more than with other types of numerical data, e.g. re-running analyses with (4, 3, 2, 1, 0, 0, †¦) pseudo-scores replaced by (6, 4, 2, 1, 0, 0 , †¦). If the interpretations of results are insensitive to such changes, the choice of scores is not critical. 16  © SSC 2001 – Approaches to the Analysis of Survey Data 2. Doing the Analysis 2.1 Approaches Data listings are readily produced by database and many statistical packages. They are generally on a case-by-case basis, so are particularly suitable in  EDA as a means of tracking down odd values, or patterns, to be explored. For example, if material is in verbal form, such a listing can give exactly what every respondent was recorded as saying. Sorting these records – according to who collected them, say – may show up great differences in field workers’ aptitude, awareness or approach. Data listings can be an adjunct to tabulation: in Excel, for example, the Drill Down feature allows one to look at the data from individuals who appear together in a single cell. There is a place for the use of graphical methods, especially for presentational purposes, where simple messages need to be given in easily understood, and attentiongrabbing form. Packages offer many ways of making results bright and colourful, without necessarily conveying more information or a more accurate understanding. A few basic points are covered in the guide on Informative Presentation of Tables, Graphs and Statistics. Where the data are at all voluminous, it is a good idea selectively to tabulate most ‘qualitative’ but numerically coded data i.e. the binary, nominal or ordered categorical types mentioned above. Tables can be very effective in presentations if stripped down to focus on key findings, crisply presented. In longer reports, a carefully crafted, well documented, set of cross-tabulations is usually an essential component of summary and comparative analysis, because of the limitations of approaches which avoid tabulation:†¢ Large numbers of charts and pictures can become expensive, but also repetitive, confusing and difficult to use as a source of detailed information. †¢ With substantial data, a purely narrative full description will be so long-winded and repetitive that readers will have great difficulty getting a clear picture of what the results have to say. With a briefer verbal description, it is difficult not to be overly selective. Then the reader has to question why a great deal went into collecting data that merits little description, and should question the impartiality of the reporting. †¢ At the other extreme, some analysts will skip or skimp the tabulation stage and move rapidly to complex statistical modelling. Their findings are just as much to be distrusted! The models may be based on preconceptions rather than evidence, they may fit badly and conceal important variations in the underlying patterns.  © SSC 2001 – Approaches to the Analysis of Survey Data 17 †¢ In terms of producing final outputs, data listings seldom get more than a place in an appendix. They are usually too extensive to be assimilated by the busy reader, and are unsuitable for presentation purposes. 2.2 One-Way Tables The most straightforward form of analysis, and one that often supplies much of the basic information need, is to tabulate results, question by question, as ‘one-way tables’. Sometimes this can be done using an original questionnaire and writing on it the frequency or number of people who ‘ticked each box’. Of course this does not identify which respondents produced particular combinations of responses, but this is often a first step where a quick and/or simple summary is required. 2.3 Cross-Tabulation: Two-Way Higher-Way Tables At the most basic level, cross-tabulations break down the sample into two-way tables showing the response categories of one question as row headings, those of another question as column headings. If for example each question has five possible answers the table breaks the total sample down into 25 subgroups. If the answers are subdivided e.g. by sex of respondent, there will be one three-way table, 5x5x2, probably shown on the page as separate two-way tables for males and for females. The total sample size is now split over 50 categories and the degree to which the data can sensibly be disaggregated will be constrained by the total number of respondents represented. There are usually many possible two-way tables, and even more three-way tables. The main analysis needs to involve careful thought as to which ones are necessary, and how much detail is needed. Even after deciding that we want some cross-tabulation with categories of ‘question J’ as rows and ‘question K’ as columns, there are several other  decisions to be made: †¢ The number in the cells of the table may be just the frequency i.e. the number of respondents who gave that combination of answers. This may be rephrased as a proportion or a percentage of the total. Alternatively, percentages can be scaled so they total 100% across each row or down each column, so as to make particular comparisons clearer. †¢ The contents of a cell can equally well be a statistic derived from one or more other questions e.g. the proportion of the respondents falling in that cell who were economically-active women. Often such a table has an associated frequency table to show how many responses went in to each cell. If the cell frequencies represent 18  © SSC 2001 – Approaches to the Analysis of Survey Data small subsamples the results can vary wildly, just by chance, and should not be over-interpreted. †¢ Where interest focuses mainly on one ‘area’ of a two-way table it may be possible to combine rows and columns that we don’t need to separate out, e.g. ruling party supporters vs. supporters of all other parties. This simplifies interpretation and presentation, as well as reducing the impact of chance variations where there are very small cell counts. †¢ Frequently we don’t just want the cross-tabulation for ‘all respondents’. We may want to have the same table separately for each region of the country – described as segmentation – or for a particular group on whom we wish to focus such as ‘AIDS orphans’ – described as selection. †¢ Because of varying levels of success in covering a population, the response set may end up being very uneven in its coverage of the target population. Then simply combining over the respondents can mis-represent the intended population. It may be necessary to show the patterns in tables, sub-group by sub-group to convey the whole picture. An alternative, discussed in Part 1, is to weight up the results from the sub-groups to give a fair representation of the whole. 2.4 Tabulation the Assessment of Accuracy Tabulation is usually purely descriptive, with limited effort made to assess the ‘accuracy’ of the numbers tabulated. We caution that confidence intervals are sometimes very wide when survey samples have been disaggregated into various subgroups: if crucial decisions hang on a few numbers it may well be worth putting extra effort into assessing – and discussing – how reliable these are. If the uses intended for various tables are not very numerical or not very crucial, it is likely to cause unjustifiable delay and frustration to attempt to put formal measures of precision on the results. Usually, the most important considerations in assessing the ‘quality’ or ‘value’ or ‘accuracy’ of results are not those relating to ‘statistical sampling variation’, but those which appraise the following factors and their effects:†¢ evenness of coverage of the target (intended) population †¢ suitability of the sampling scheme reviewed in the light of field experience and findings †¢ sophistication and uniformity of response elicitation and accuracy of field recording †¢ efficacy of measures to prevent, compensate for, and understand non-response †¢ quality of data entry, cleaning and metadata recording †¢ selection of appropriate subgroups in analysis  © SSC 2001 – Approaches to the Analysis of Survey Data 19 If any of the above factors raises important concerns, it is necessary to think hard about the interpretation of ‘statistical’ measures of precision such as standard errors. A factor that has uneven effects will introduce biases, whose size and detectability ought to be dispassionately appraised and reported with the conclusions. Inferential statistical procedures can be used to guide generalisations from the sample to the population, where a  survey is not badly affected by any of the above. Inference addresses issues such as whether apparent patterns in the results have come about by chance or can reasonably be taken to reflect real features of the population. Basic ideas are reviewed in Understanding Significance: the Basic Ideas of Inferential Statistics. More advanced approaches are described in Modern Methods of Analysis. Inference is particularly valuable, for instance, in determining the appropriate form of presentation of survey results. Consider an adoption study, which examined socioeconomic factors affecting adoption of a new technology. Households are classified as male or female headed, and the level of education and access to credit of the head is recorded. At its most complicated the total number of households in the sample would be classified by adoption, gender of household head, level of education and access to credit resulting in a 4-way table. Now suppose, from chi-square tests we find no evidence of any relationship between adoption and education or access to credit. In this case the results of the simple twoway table of adoption by gender of household head would probably be appropriate. If on the other hand, access to credit were the main criterion affecting the chance of adoption and if this association varied according to the gender of the household head, the simple two-way table of adoption by gender would no longer be appropriate and a three-way table would be necessary. Inferential procedures thus help in deciding whether presentation of results should be in terms of one-way, two-way or higher dimensional tables. Chi-square tests are limited to examining association in two-way tables, so have to be used in a piecemeal fashion for more complicated situations like that above. A more general way to examine tabulated data is to use log-linear models described in Modern Methods of Analysis. 2.5 Multiple Response Data Surveys often contain questions where respondents can choose a number of relevant responses, e.g. 20  © SSC 2001 – Approaches to the Analysis of Survey Data If you are not using an improved fallow on any of your land, please tick from the list below, any reasons that apply to you:(i) Don’t have any land of my own (ii) Do not have any suitable crop for an improved fallow (iii) Can not afford to buy the seed or plants (iv) Do not have the time/labour There are three ways of computerising these data. The simplest is to provide as many columns as there are alternatives. This is called a multiple dichotomy†, because there is a yes/no (or 1/0) response in each case indicating that the respondent ticked/did not tick each item in the list. The second way is to find the maximum number of ticks from anyone and then have this number of columns, entering the codes for ticked responses, one per column. This is known as â€Å"multiple response† data. This is a useful method if the question asks respondents to put the alternatives in order of importance, because the first column can give the most important reason, and so on. A third method is to have a separate table for the data, with just 2 columns. The first identifies the person and the second gives their responses. There are as many rows of data as there are reasons. There is no entry for a  person who gives no reasons. Thus, in this third method the length of the columns is equal to the number of responses rather than the number of respondents. If there are follow-up questions about each reason, the third method above is the obvious way to organise the data, and readers may identify the general concept as being that of data at another level, i.e. the reason level. More information on organising this type of data is provided in the guide The Role of a Database Package for Research Projects. Essentially such data are analysed by building up counts of the numbers of mentions of each response. Apart from SPSS, few standard statistics packages have any special facilities for processing multiple response and multiple dichotomy data. Almost any package can be used with a little ingenuity, but working from first principles is a timeconsuming business. On our web site we describe how Excel may be used. 2.6 Profiles Usually the questions as put to respondents in a survey need to represent ‘atomic’ facets of an issue, expressed in concrete terms and simplified as much as possible, so that there is no ambiguity and so they will be consistently interpreted by respondents.  © SSC 2001 – Approaches to the Analysis of Survey Data 21 Basic cross-tabulations are based on reporting responses to such individual questions and are therefore narrowly issue-specific. A rather different approach is needed if the researchers’ ambitions include taking an overall view of individual, or small groups’, responses as to their livelihood, say. Cross-tabulations of individual questions are not a sensible approach to ‘people-centred’ or ‘holistic’ summary of results. Usually, even when tackling issues a great deal less complicated than livelihoods, the more important research outputs are ‘complex molecules’ which bring together  responses from numerous questions to produce higher-level conclusions described in more abstract terms. For example several questions may each enquire whether the respondent follows a particular recommendation, whereas the output may be concerned with overall ‘compliance’ – the abstract concept behind the questioning. A profile is a description synthesising responses to a range of questions, perhaps in terms of a set of abstract nouns like compliance. It may describe an individual, cluster of respondents or an entire population. One approach to discussing a larger concept is to produce numerous cross-tabulations reflecting actual questions and to synthesise their information content verbally. This tends to lose sight of the ‘profiling’ element: if particular groups of respondents tend to reply to a range of questions in a similar way, this overall grouping will often come out only weakly. If you try to follow the group of individuals who appear together in one corner cell of the first cross-tab, you can’t easily track whether they stay together in a cross-tab of other variables. Another type of approach may be more constructive: to derive synthetic variables – indicators – which bring together inputs from a range of questions, say into a measure of ‘compliance’, and to analyse those, by cross-tabulation or other methods. See section 2.8 below. If we have an analysis dataset with a row for each respondent and a column for each question, the derivation of a synthetic variable just corresponds to adding an extra column to the dataset. This is then used in analysis just like any other column. A profile for an individual will often comprise a set of values of a suite of indicators. 2.7 Looking for Respondent Groups Profiling is often concerned with acknowledging that respondents are not just a homogeneous mass, and distinguishing between different groups of respondents. Cluster analysis is a data-driven statistical technique that can draw out – and thence characterise – groups of respondents whose response profiles are similar to one another. The response profiles may serve to differentiate one group from another if they are somewhat distinct. This might be needed if the aim were, say, to define 22  © SSC 2001 – Approaches to the Analysis of Survey Data target groups for distinct safety net interventions. The analysis could help clarify the distinguishing features of the groups, their sizes, their distinctness or otherwise, and so on. Unfortunately there is no guarantee that groupings derived from data alone will make good sense in terms of profiling respondents. Cluster analysis does not characterise the groupings; you have to study each cluster to see what they have in common. Nor does it prove that they constitute suitable target groups for meaningful development interventions Cluster analysis is thus an exploratory technique, which may help to screen a large mass of data, and prompt more thoughtful analysis by raising questions such as:†¢ Is there any sign that the respondents do fall into clear-cut sub-groups? †¢ How many groups do there seem to be, and how important are their separations? †¢ If there are distinct groups, what sorts of responses do â€Å"typical† group members give? 2.8 Indicators Indicators are summary measures. Magazines provide many examples, e.g. an assessment of personal computers may give a score in numerical form like 7 out of 10 or a pictorial form of quality rating, e.g. Very good Good Moderate à  Poor Very Poor à ® This review of computers may give scores – indicators – for each of several characteristics, where the maximum score for each characteristic reflects its importance e.g. for one model:- build quality (7/10), screen quality (8/20), processor speed (18/30), hard disk capacity (17/20) and software provided (10/20). The maximum score over all characteristics in the summary indicator is in this case (10 + 20 + 30 + 20 + 20) = 100, so the total score for each computer is a percentage e.g. above (7 + 8 + 18 + 17 + 10) = 60%. The popularity of such summaries demonstrates that readers find them accessible, convenient and to a degree useful. This is either because there is little time to absorb detailed information, or because the indicators provide a baseline from which to weigh up the finer points. Many disciplines of course are awash with suggested indicators from simple averages to housing quality measures, social capital assessment tools, or quality-adjusted years of life. Of course new indicators should be developed only if others do nor exist or are unsatisfactory. Well-understood, well-validated indicators, relevant to the situation in hand are quicker and more cost-effective to use. Defining an economical set of meaningful indicators before data collection ought ideally to imply that at  © SSC 2001 – Approaches to the Analysis of Survey Data 23 analysis, their calculation follows a pre-defined path, and the values are readily interpreted and used. Is it legitimate to create new indicators after data collection and during analysis? This is to be expected in genuine ‘research’ where fieldwork approaches allow new ideas to come forward e.g. if new lines of questioning have been used, or if survey findings take the researchers into areas not  well covered by existing indicators. A study relatively early on in a research cycle, e.g. a baseline survey, can fall into this category. Usually this means the available time and data are not quite what one would desire in order to ensure well-understood, well-validated indicators emerge in final form from the analysis. Since the problem does arise, how does the analyst best face up to it? It is important not to create unnecessary confusion. An indicator should synthesise information and serve to represent a reasonable measure of some issue or concept. The concept should have an agreed name so that users can discuss it meaningfully e.g. ‘compliance’ or ‘vulnerability to flooding’. A specific meaning is attached to the name, so it is important to realise that the jargon thus created needs careful explanation to ‘outsiders’. Consultation or brainstorming leading to a consensus is often desirable when new indicators are created. Indicators created ‘on the fly’ by analysts as the work is rushed to a conclusion are prone to suffer from their hasty introduction, then to lead to misinterpretation, often over-interpretation, by enthusiast would-be users. It is all too easy for a little information about a small part of the issue to be taken as ‘the’ answer to ‘the problem’! As far as possible, creating indicators during analysis should follow the same lines as when the process is done a priori i.e. (i) deciding on the facets which need to be included to give a good feel for the concept, (ii) tying these to the questions or observations needed to measure these facets, (iii) ensuring balanced coverage, so that the right input comes from each facet, (iv) working out how to combine the information gathered into a synthesis which everyone agrees is sensible. These are all parts of ensuring face (or content) validity as in the next section. Usually this should be done in a simple enough way that the user community are all comfortable with the definitions of what is measured. There is some advantage in creating indicators when datasets are already available. You can look at how well the indicators serve to describe the relevant issues and groups, and select the most effective ones. Some analysts rely too much on data reduction techniques such as factor analysis or cluster analysis as a substitute for thinking hard about the issues. We argue that an intellectual process of indicator development should build on, or dispense with, more data-driven approaches. 24  © SSC 2001 – Approaches to the Analysis of Survey Data Principal component analysis is data-driven, but readily provides weighted averages. These should be seen as no more than a foundation for useful forms of indicator. 2.9 Validity The basic question behind the concept of validity is whether an indicator measures what we say or believe it does. This may be quite a basic question if the subject matter of the indicator is visible and readily understood, but the practicalities can be more complex in mundane, but sensitive, areas such as measurement of household income. Where we consider issues such as the value attached to indigenous knowledge the question can become very complex. Numerous variations on the validity theme are discussed extensively in social science research methodology literature. Validity takes us into issues of what different people understand words to mean, during the development of the indicator and its use. It is good practice to try a variety of approaches with a wide range of relevant people, and carefully compare the interpretations, behaviours and attitudes revealed, to make sure there are no major discrepancies of understanding. The processes of comparison and reflection, then the redevelopment of definitions, approaches and research instruments, may all be encompassed in what is sometimes called triangulation – using the results of different approaches to synthesise robust, clear, and easily interpreted results. Survey instrument or indicator validity is a discussion topic, not a statistical measure, but two themes with which statistical survey analysts regularly need to engage are the following. Content (or face) validity looks at the extent to which the questions in a survey, and the weights the results are given in a set of indicators, serve to cover in a balanced way the important facets of the notion the indicator is supposed to represent. Criterion validity can look at how the observed values of the indicator tie up with something readily  measurable that they should relate to. Its aim is to validate a new indicator by reference to something better established, e.g. to validate a prediction retrospectively against the actual outcome. If we measure an indicator of ‘intention to participate’ or ‘likelihood of participating’ beforehand, then for the same individuals later ascertain whether they did participate, we can check the accuracy of the stated intentions, and hence the degree of reliance that can in future be placed on the indicator. As a statistical exercise, criterion validation has to be done through sensible analyses of good-quality data. If the reason for developing the indicator is that there is no satisfactory way of establishing a criterion measure, criterion validity is not a sensible approach.  © SSC 2001 – Approaches to the Analysis of Survey Data 25 2.10 Summary In this guide we have outlined general features of survey analysis that have wide application to data collected from many sources and with a range of different objectives. Many readers of this guide should be able to use its suggestions unaided. We have pointed out ideas and methods which do not in any way depend on the analyst knowing modern or complicated statistical methods, or having access to specialised or expensive computing resources. The emphasis has been on the importance of preparing the appropriate tables to summarise the information. This is not to belittle the importance of graphical display, but that is at the presentation stage, and the tables provide the information for the graphs. Often key tables will be in the text, with larger, less important tables in Appendices. Often a pilot study will have indicated the most important tables to be produced initially. What then takes time is to decide on exactly the right tables. There are three main issues. The first is to decide on what is to be tabulated, and we have considered tables involving either individual questions or indicators. The second is the complexity of table that is  required – one-way, two-way or higher. The final issue is the numbers that will be presented. Often they will be percentages, but deciding on the most informative base, i.e. what is 100% is also important. 2.11 Next Steps We have mentioned the role of more sophisticated methods. Cluster analysis may be useful to indicate groups of respondents and principal components to identify datadriven indicators. Examples of both methods are in our Modern Methods of Analysis guide where we emphasise, as here, that their role is usually exploratory. When used, they should normally be at the start of the analysis, and are primarily to assist the researcher, rather than as presentations for the reader. Inferential methods are also described in the Modern Methods guide. For surveys, they cannot be as simple as in most courses on statistics, because the data are usually at multiple levels and with unequal numbers at each subdivision of the data. The most important methods are log-linear and logistic models and the newer multilevel modelling. These methods can support the analysts’ decisions on the complexity of tables to produce. Both the more complex methods and those in this guide are equally applicable to cross-sectional surveys, such as baseline studies, and longitudinal surveys. The latter are often needed for impact assessment. Details of the design and analysis of baseline surveys and those specifically for impact assessment must await another guide! 26  © SSC 2001 – Approaches to the Analysis of Survey Data  © SSC 2001 – Approaches to the Analysis of Survey Data 27 The Statistical Services Centre is attached to the Department of Applied Statistics at The University of Reading, UK, and undertakes training and consultancy work on a non-profit-making basis for clients outside the University. These statistical guides were originally written as part of a contract with DFID to give guidance to research and support staff working on DFID Natural Resources projects. The available titles are listed below. †¢ Statistical Guidelines for Natural Resources Projects †¢ On-Farm Trials – Some Biometric Guidelines †¢ Data Management Guidelines for Experimental Projects †¢ Guidelines for Planning Effective Surveys †¢ Project Data Archiving – Lessons from a Case Study †¢ Informative Presentation of Tables, Graphs and Statistics †¢ Concepts Underlying the Design of Experiments †¢ One Animal per Farm? †¢ Disciplined Use of Spreadsheets for Data Entry †¢ The Role of a Database Package for Research Projects †¢ Excel for Statistics: Tips and Warnings †¢ The Statistical Background to ANOVA †¢ Moving on from MSTAT (to Genstat) †¢ Some Basic Ideas of Sampling †¢ Modern Methods of Analysis †¢ Confidence Significance: Key Concepts of Inferential Statistics †¢ Modern Approaches to the Analysis of Experimental Data †¢ Approaches to the Analysis of Survey Data †¢ Mixed Models and Multilevel Data Structures in Agriculture The guides are available in both printed and computer-readable form. For copies or for further information about the SSC, please use the contact details given below. Statistical Services Centre, The University of Reading P.O. Box 240, Reading, RG6 6FN United Kingdom tel: SSC Administration +44 118 931 8025 fax: +44 118 975 3169 e-mail: [emailprotected] web: http://www.reading.ac.uk/ssc/

The Disproval Of Spontaneous Generation :: essays research papers

From the beginning of time it was believed that living things could come from nonliving things. This process was known as spontaneous generation. However, in the middle of the 17th century and then through the next 100 years, this idea was disproved by three important experiments. We now know that a nonliving object or group of objects can not turn into a living organism. Spontaneous generation is impossible in the atmosphere that we have today.   Ã‚  Ã‚  Ã‚  Ã‚  In the early 1600’s, people believed that living organisms could evolve from nonliving organisms. They proved this by saying that if a piece of meat was left out uncovered, that maggots would appear in a few days. These worms did not come from anything that they could see, so they assumed they came from the nonliving meat. In 1668, a man named Redi designed and completed an experiment that showed how this was not true. He took two pieces of raw meat, and left them out. He covered one so that nothing could get in, and left the other one open. The open one grew maggots, and the covered one did not, proving that the dead meat did not produce the worms as they had previously thought.   Ã‚  Ã‚  Ã‚  Ã‚  In the 1700’s a man named Spallanzani proved Redi’s idea to a further extent. He noticed microbial growth on boiled pond water after being exposed to the air. To prove that this growth came from something living in the air, and not from the nonliving water, he designed an experiment. He boiled pond water to kill all the microbial growths. He then poured that water into two separate test tubes. He sealed one so that no air could get in, and left one open to the air. The one that was left open slowly became more and more cloudy with microbial growths. The sealed tube stayed as clear as it had been when it was boiled. This experiment proved that the growths could not come from nonliving organisms, but had to have been transported there through the air. When Spallanzani presented his results to the public, he was criticized. Other scientists said that he made the air unfit for living growth, and that they needed the air to change from nonliving to living .   Ã‚  Ã‚  Ã‚  Ã‚  Pasteur did the third experiment, in 1862. He took Spallanzani’s experiment, and the critic’s statements, and combined the two. He boiled pond water to kill all the living organisms.

Parents of Pre-term Infants Essay -- social workers, parental stress,

Article Review One The first article, Parents of Pre-term Infants Two Months after Discharge from the Hospital: Are They Still at (Parental) Risk? (Olshtain-Mann, O. & Auslander, G. K , 2008), describes a study in Israel that was designed to gain further understanding of † the emotional state and functioning of parents of pre-term infants, after an initial period of adjustment following the infants’ discharge from a Neonatal Intensive Care Unit (NICU)†. Specifically, this study compared the stress levels of parents and self-perceptions of competence as parents among mothers and fathers, two months after discharge of their babies from the hospital. The study compared parents of pre-term babies with parents of full-term babies. A target group of 80 pairs of parents of pre-term babies and a non-matched comparison group of 80 pairs of parents of full-term babies were interviewed for the study. Respondents were selected as follows: All couples (both mothers and fathers) in both groups were Hebrew speaking. The target group had pre-term infants who were hospitalized in the NICU of any of three hospitals in Jerusalem during 2001-2002. These premature babies weighed less than 3.85 pounds and were born in the 36th week or earlier. All were treated in the NICU for one week or more. According to the article, exclusions included parents of children who were not expected to survive or those who had congenital abnormalities. Parents of the full-term babies had infants who were born in the same time period, and in the same hospitals, as the target group. All babies in both groups were singletons. The interviews were conducted by social workers and followed a specific protocol. A further questionnaire was completed by both mothers and fa... ...e employed to develop programs that will offer support to the new parents of higher risk children. Works Cited Olshtain-Mann, O. & Auslander, G. K. (2008). Parents of pre-term infants two months after discharge from the hospital: Are they still at (parental) risk? Health & Social Work, 33(4), 299-308. Retrieved from http://search.proquest.com/docview/210554738?accountid=8289 Pierrehumbert, B., Nicole, A., Muller-Nix, C., Forcada-Guex, M., Ansermet, F. Parental post- traumatic reactions after premature birth: implications for sleeping and eating problems in the infant. Arch Dis Child Fetal Neonatal Ed 2003;88:5 F400-F404 doi:10.1136/fn.88.5.F400 Dacey, J., Travers, J. & Fiore, L. (2009) Human Development Across the Lifespan. (7th ed). New York, NY: McGraw-Hill Higher Education. Cogburn, N., Cogburn, N. personal communications, April 5, 2014.

Write About The Ways In Which Chaucer Presents Chauntecleer’s Dream?

In the Nun's priest's tale, the denizens of the widow's barnyard, in particular Chauntecleer and Pertelote are used to poke fun at very human sorts of behavior. The rooster's dream is significant as it and the discussion that follows takes up much of the tale itself. The focus is not on the action (Chauntecleer's capture by the fox) but on who is correct. Is Chauntecleer's position on dreams correct or is Pertelote's? The extensive discussion of the dream steers the story away from the â€Å"moral† of Chauntecleer's vanity. Chaucer uses numerous diverse techniques in-order to present chauntecleer's dream to the audience; I feel that he presents his dreams very successfully. For instance in the opening line, the use of a discourse marker is extremely effective, ‘and so bifel', it immediately catches the readers attention. ‘Bifel', meaning ‘it happened', and so the audience ask themselves, ‘what happened?' Furthermore, in line 5 and 6, the use of alliteration helps empathize that chauntecleer is somewhat distressed, slightly troubled. For example, ‘gan gronen' and followed, ‘ dreem is drecched'. Several times in the passage, Chaucer refers to religion; he uses the word, ‘God', as part of his sentence or in order to explain something. This highlights that they are significant points in which he is trying to get across. ‘For by that God above', almost means that God is watching at all times. Further down Chaucer creates a sense of imagery, implying that he was almost captured, held in captivity. He does this by involving the words, ‘prisoun' and ‘beest' sequentially to generate tension. When describing what the fox looked like on lines 20-25, you also notice that imagery of colour is put into effect, to stress how influential the animal is. Chaucer chooses very fiery colours to do this. ‘bitwixe yellow and reed'. Once more, Chaucer includes the technique alliteration, when describing the animal, this in a sense signifies his power. ‘Tipped was his tayl' and ‘Snowte Smal'. On the same line, line 24, Chaucer describes the animal in great detail, very insignificant aspects are included. Again a sense of imagery is created for the audience, ‘Glowynge eyen tweye', this is talking about the eyes of the animal. The use of discourse markers on line 27 and 28 brings the passage to a climax as Pertelote implies that Chauntecleer is a coward. ‘Avoy', which is followed by, how could you? You heartless coward! Beneath, is followed by, ‘Allas', in order to take the tension away from the point just brought up. The way Chaucer prevails his dream allows Pertelote to think differently of him, note that Pertelote's indignation at the thought that Chauntecleer might be a coward (and thus unworthy of her love); Chauntecleer's gallant compliments to his â€Å"lady† and statements concerning the effect of her beauty upon him; his references to the physical side of their passion. All the way through the tale Chaucer perceives the chickens as humans, and he continues to do this in his description of the dream. ‘To han housbondes hardy, wise and free'. This is basically indicating that they are husband and wife almost. But in fact they are just rooster and hen, which are made out to be more than that. In a sense mock-heroic by where Chaucer is exaggerating extensively. When talking about the fox, Chaucer uses the technique, rhetoric, which is the clever use of language which I have already touched upon, for example when describing his eyes the use of language is so complicated yet it is describing something very simple. Overall I feel that Chaucer have been very effective in presenting Chauntecleer's dream to the audience, this is only been helped in the techniques that he has included. Personally he interacts very well with the audience because of the way he makes out the two to be elderly humans instead of a rooster and a hen.

Muslim Women Rights

Muslim Women Rights The human right awareness in the world has taken a quite interesting shape in the attempt to achieve sisterhood and recognize women rights as human rights. This has been a subject of debate and extensive criticism as it is perceived as having potential to â€Å"homogenizing the issues that might be different to different women. There is also the risk of universalizing feminist ideas that are practiced in the western countries and use them to solve the problems facing all women around the globe. Advertising We will write a custom essay sample on Muslim Women Rights specifically for you for only $16.05 $11/page Learn More Societal development and political maturity of a society is currently appraised by the extent the women rights are being enforced. In my opinion, is support that women problems should not at all be homogenized or feminist ideas universalized because the problems of women are not similar around the world and different women take different cou rse in addressing their problems. The issue of women rights has brought about hot debates in the world especially regarding veil put by Muslim women and other issues like polygamy. As presented in this essay, the issue of veil which is a discursive issue in the world is discussed as described by Leila Ahmed. Leila Ahmed tries to seek the discourse about women and gender in the Islamic has emerged and exploring what is the root cause of such debates in current worlds. She seeks from the debate whether the Islamic culture and societies are really oppressive to women. Te center of her concern was the discourse of veil where women in the Islamic societies were required to appear in veils and other clothing that are connected to the Islamic traditions. The mail question that lingered in her mind is whether the issue of veil and the traditional clothing was meant to boost pure Islam favoring both sexes or otherwise. Ahmed first focuses on the gender pattern in the Middle East prior to th e emergence of the Islam in order to gain ground to describe the Islamic doctrine on women that were practiced in the past. She describes how the Arab societies propagated the debates about women and gender within the Islamic societies that have become so prevalent in the world today. She explores the issue of women and gender both in Islamic contexts, social and also the historical background. She conducts an extensive study of debates and ideologies about women within the Islamic societies and demonstrates how the debate is so prevalent in the current world. She advocated tat unveiling women could be a great step to transforming the social status of the Islamic societies. His survey acted as a strong onset of the feminism within the Arab culture. According to Ahmed (23), Muslim men and women have expended a lot of effort in the attempt to discard the veil from their culture but others believe that the veil is important for feminist struggle. She demonstrates that the veil was a w ay used by the colonizers in order to promote their cultures in other territories forcing them to undermine their native culture. It is among other things a strategy of colonial domination in an area.Advertising Looking for essay on history? Let's see if we can help you! Get your first paper with 15% OFF Learn More Some Muslim women in different part of the world demand it as their right to be allowed to choose whether to veil or not. The concept explored by Leila Ahmed is very vital and closely connected to the Inderpal Grewal’s â€Å"the regime of human rights†? According to Grewal (1), similar to Ahmed argument, oppression of women by global feminism comes in form of universalization and generalization of women issues and approaches. This is oppressive because the women issues are not the same in all places in the world. There is very high correlation between the issues presented by Ahmed and that by Grewal because they both discuss issues r elated to women rights. They both argue that human rights are based on western notion that is generalized. The international organization present the western women as superior and they spill over their feminism notions to their third world counterparts without considering the cultural differences. To achieve â€Å"a radical complexity in the practice of feminist politics† as termed by Grewal, women should be allowed to pursue their demand for women rights their own way depending on their culture. Homogenization and universalizing women problems deprives some women the right to deal with their issues the best way they can by forcing them to adopt the western culture. Women oppression should not be homogenized or universalized because women in different countries differ in culture, politics or have social system that only suits their own problems. According to Ahmed, these are strategies to achieve western domination and erode the culture of women in minority communities. Ahme d, Leila. â€Å"The Discourse of the Veil†. Women and Gender in Islam: Historical Roots of a Modern Debate. New Haven: Yale University Press, 1992. Grewal, Inderpal. Women’s Rights as Human Right: Feminist Practices, Global Feminism and Human Rights Regimes in Trans-nationality. (PDF) Citizenship Studies 3, no. 3 (1999): 337–54

Capital Markets and Market Efficiency The WritePass Journal

Capital Markets and Market Efficiency Part 1 Capital Markets and Market Efficiency Part 11) Weak form efficiency2) Semi-strong efficiencyStrong-form efficiencyPart 2 Arguments For the Efficient Market Hypothesis Arguments against the Efficient Market Hypothesis Evaluation and Implications for Investors BibliographyRelated Part 1 The Efficient market hypothesis states that all financial markets are efficient in their use of information to determine prices. This means that investors cannot expect to achieve excess profits that are more than the average market profits with similar risk factors, given all available information at the current time of investment, aside from through some form of luck. In part 1 of this report we will discuss the three different forms of market efficiency that Eugene Fama identified in her 1970 report. These can be explained as follows: 1) Weak form efficiency Fama (1970) observes that a market is efficient in weak form if past returns cannot be used to predict current stock price changes. It also assumes that prices on assets that are traded publicly already have and use all available information on the stock at any moment in time. It therefore stands to reason that the weak form of the market efficiency hypothesis means that past returns on stock are uncorrelated with future returns on the same stock. Future prices cannot be predicted by studying carefully the past prices of the stock. Excess returns cannot be earned over an extended period of time by using investment strategies that are based only upon the historical prices of shares or differing forms of historical analysis. This means that this style of technical analysis will not be able to produce high levels of returns on a consistent basis for investors. Overall one cannot expect future price changes to be predicted by using the past stock prices. Simply put weak form efficiency a ssumes that historical analysis on past stock data is of no use in predicting future price changes on stocks. 2) Semi-strong efficiency The semi-strong market efficiency form progresses from the aforementioned weak form market efficiency by stating that markets can adjust easily and very quickly to new information that is provided about various stocks. Fama (1970: 383) cites semi- strong efficiency as whether prices efficiently adjust to other information that is publicly available. e.g. announcements of stock splits, etc†¦   Here it is assumed that asset prices fully reflect all of the publicly available information on the stocks meaning that only those investors who manage to possess additional unique information about the stocks could have an advantage over the market to make large gains. This form also asserts that any price outliers are found quickly and on this basis the stock market manages to adjust. In a semi-strong form efficiency share prices are able to react quickly to new information made available publicly in a quick manner so that no large returns can be gained from using the recent information . This leads us to imply that neither fundamental analysis or technical analysis will be able to produce consistent excess returns. Strong-form efficiency Strong-form efficiency assumes that prices reflect completely any type of new information about the market be that public or private information. Fama (1970: 383) says that strong form tests are concerned with whether given investors or groups have monopolistic access to any information relevant for formation, however Fama claims that the efficient hypothesis model still stands up well. The strong form claims the market price also includes different forms of insider information and not solely public information, and this is how it differs from the semi-strong form. The implications of this is that no one at all can therefore have any kind of advantage over the market in prediction of the stock prices as no possible additional data exists which would provide additional value to any investor. However, if any legal barriers exist which prevents the spread of useful information, such as insider trading laws for example, then this form of market efficiency is not possible. Part 2 The Efficient Markets Hypothesis was introduced by Eugene Fama in 1970. The main idea of the Efficient Market Hypothesis is predominantly that market prices must take into account all available information at any given point. Therefore meaning that no one can outperform the market by using readily available public information aside from through luck. A market is said to be efficient if the price fully reflects information about that market, for example if the price of the stock would be unaffected if all information surrounding it was revealed to all stakeholders in that market. Part two of this report will be critically discussing the evidence for and against the Efficient Market Hypothesis and whether it is possible to exploit market inefficiencies. The implications for investors and companies of the Efficient Markey Hypothesis will also be considered. Arguments For the Efficient Market Hypothesis To begin with following the birth of the efficient market hypothesis the theory was widely accepted, and it was widely assumed that the markets were very efficient in taking this information into account (Malkiel, 2003). It was accepted that when information came to the fore this would spread rapidly and would then be incorporated almost instantaneously into the share prices without hesitation. This meant that technical analysis, study of prior stock prices, nor any analysis of relevent information of a financial sense would lead an investment to achieve more successful returns than holding random stocks which have a comparable risk factor.   Dimson and Mussavian (1998) observe that the evidence accumulated during the 1960s and 1970s was consistent with the Efficient Market Hypothesis view. There was a substantial backing for the weak and semi strong Efficient Market Hypothesis forms. Even though more recent times have seen an attack against the Efficient Market Hypothesis, Roll (1994) still observes that it remains incredibly difficult to make a high level of profit on a consistent basis even with the wildest variants of stock market efficiency. These violations of market efficiency are often sporadic events that do not last for a period of time. This can be seen by looking at the fact that on the whole profitable investment successes are referred to on a consistent basis as outliers (Dimson and Mussavian, 1998). Malkiel (2005: 2) says that: the strongest evidence suggesting that markets are generally quite efficient is that professional investors do not beat the market. Indeed, the evidence accumulated over the past 30-plus years makes me more convinced than ever that our stock markets are remarkably efficient at adjusting correctly to new information. This is showing that the markets must be efficient due to the fact that professional investors do not on the whole beat the market, and therefore all available information must be taken into account by the market prices and thus there is no gain to be had by any investors by using past prices, or publicly or privately readily available information. Arguments against the Efficient Market Hypothesis Malkiel (2003: 60) observes that by the beginning of the twenty first century the intellectual dominance of the efficient market hypothesis had become far less universal and academics were starting to question the premise and were not accepting it as they had done previously. Shiller (2003 ; 83) states that,   Ã¢â‚¬Å"[contained in the EMH is] the idea that speculative asset prices such as stock prices always incorporate the best information about fundamental values and that prices change only because of good, sensible information.† However he then moves on to discuss how not all information is sensible and not all actors are rational, this would conflict with the efficient market hypothesis which relies on information having a large impact on the prices of stock. As well as this several recent reports have shown a range of empirical evidence which suggests that stock returns can actually possess components of a predictable nature, therefore also rejecting parts of the efficient market hypothesis which profess that looking at past trends do not allow for excess gains when investing on the stocks against the market. Keim and Stambaugh (1986) state that using forecasts based on a number of factors can find statistically significant predictability in a range of different stock prices. Lo and MacKinlay (1988) reject the random walk hypothesis, which is so often considered with the efficient market hypothesis theory, and show that it is not at all consistent with the stochastic nature of weekly returns. Empirical evidence of return behaviour which has been anomalous in the form of variables such as price to earnings ratio (Fama and French, 1992) has defied any kind of usual rational explanation and has resulted in a great number of researchers cons idering their views and opinions of market efficiency. Evaluation and Implications for Investors In conclusion, it is clear to see that market prices are not always predictable and that the markets have made large errors at certain points in time, for example at the recent dotcom internet bubble. Here it was obviously possible to exploit the market inefficiency to make money for investors. In the short run it may be possible to exploit these sporadic inefficiencies, but in the long term true value will always come to the fore. As long as these markets do exist, due to it being reliant on the judgement of investors, there will occasionally be errors made and some participants In the market are likely to behave in a less than rational manner, as is inherent in human nature. As well as this all information will not necessarily be sensible and investors are not likely to necessarily use it rationally. Thus irregular pricing or predictable patterns on stocks can appear and be exploited from time to time. In terms of the implications for investors in terms of the efficient market hypothesis, it is plain to see that all markets cannot be one hundred percent efficient all of the time or there would not be an incentive for people who are professionals in the field to discover different facets of information that is often quickly reflected by market prices (Grossman and Stiglitz, 1980). However, things such as the 1999 dot com bubble are exceptions rather than the rule to providing investors with extraordinary returns on their investments to exploit market inefficiencies. Therefore one could assume that the markets are efficient more often than not, and Fama (1970) is on the whole correct. This could lead to the conclusion in agreeing with Ellis (1998) and the overall idea that active equity management is indeed a losers game. Malkiel (2005) further advises on Ellis claim and professes that indexing is likely to produce higher rates of return than active portfolio management. This is beco ming more and more likely to impact investors as markets become more and more efficient, as Toth and Kertesz (2006) show in their examination of an increase in efficiency of the New York stock exchange. Therefore investors are required to question if it is indeed possible or feasible to exploit market inefficiencies using strategies the efficient market hypothesis calls into question. Bibliography Dimson, E. and Mussavian, M. (1998). A Brief History of Market Efficiency. European Financial Management. 4(1): 91-103. Ellis, C. (1998). Winning the Loser’s Game, McGraw-Hill: New York. Fama. E.G, (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance. 25(2): 383-417 Fama, E. and French, K. (1988) Dividend yields and expected stock returns. Journal of Financial Economics.(22): 3-25. Fama, E. and French, K. (1992). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics. (33): 3-56. Grossman, S. and J, Stiglitz. (1980). On the Impossibility of Informationally Efficient Markets. American Economic Review. 70(3). 393-408. Keim and Stambaugh (1986). Predicting returns In the Stock and Bond Markets. Journal of Financial Economics. 357-290. Lo and MacKinlay. (1988) Stock Market prices do not follow random walks : Evidence from a simple specification test. Review of Financial Studies. (1): 41-66. Malkiel, B. (2003). The Efficient Market Hypothesis and Its Critics Authors.   The Journal of Economic Perspectives, 17(1): 59-82 Malkiel, B. (2005). Reflections on the Efficient Market Hypothesis: 30 Years Later. The Financial Review (40):1-9 Shiller, R. (2003). ‘From Efficient Markets Theory to Behavioral Finance’. Journal of Economic Perspectives. 17(1) : 83-104. Toth, B. and Kertesz, J. (2006). Increasing market efficiency: Evolution of cross-correlations of stock returns. Physica 360(2): 505–515.

What to do if your career plans fall through

What to do if your career plans fall through Maybe you applied for your dream job and never heard back. Maybe you made it months into the process, and after 3 rounds of interviews finally received a rejection. Or maybe you got what you thought was the perfect job and then realized it’s not the career for you. It’s tough to put everything you have into building a career and then have it not pan out. When your work life is out of balance, you’re in a vulnerable place, and that uncertainty can color all aspects of your life.Here are a few actions you can take to dig yourself out of a career slump and get yourself going in the right direction.Take a time out.When you get the rejection- or the realization hits you that you’re in the wrong job- give yourself a minute to wallow. Really, it’s okay to be frustrated and sad. Sulk, cry, and vent to your partner and friends. Take a moment to process before getting back on your A-game again.Accept the reality of the situation.The fact is, you’re not where you want to be. No matter how many good vibes you send into the world, you have to face reality. Figure out exactly where you are now, and accept that place as your new square one. This will give you a solid base from which to start and get done what you need to get done.Turn your anger into action.Instead of grumbling about how unfair life is, put all of that mental energy to better use figuring out how you’re going to move on. Sure, you can be annoyed and mad, but if you obsess over negative emotions, your bitterness will seep into your job hunt and your life.Work on yourself.We all have areas that need improvement. What are yours? Take some time to identify your trouble spots- or even just places where you could broaden your awesomeness. Nothing boosts your self worth- or your resume- like targeting weaknesses and eventually turning them into strengths.This is the professional version of turning lemons into lemonade. Sit down with a piece of paper or a blank screen and list the reason why you might have fallen short. Take a moment to figure out what you can learn from where you’ve ended up. What can you do in the future to improve and not make the same mistakes? How can you be better?Solicit feedback.Hiring managers are often happy to help you along your journey. Ask what you could have improved in order to be a better candidate for the position you didn’t get. You can also ask mentors, bosses, and colleagues how they would rate your past job performance. See how people view you from the outside, and then work on the skills that you lack.Get to work.Okay, you had your pity party and have assessed your strengths and weaknesses. Now’s the time to muster all your positivity and motivation and really get out there to get things done. Do your homework: figure out what you need to do to end up in your dream job and start all over again. You’ll get there.

The global economy--answer all three questions Essay

The global economy--answer all three questions - Essay Example With growing or declining market rates, which are dependent on demand for funds or economic conditions, interest rate is changing respectively. There are many differences between official cash rate and the market rate of interest. Nevertheless, relation between these two rates determines a special influence exerted on the economy of the country. Cash rate is also known as interest rate paid on overnight funds. Business cycle is under a strong influence of interest rate. Changes are sent to other interest rates in the economy and economic activity is under the influence of interest rates changes. Changes in the cash rate exert influence on interest rates in the economy. Nevertheless, some rates are not subjected to external influence from other rates. There are different options for interest rate changes and movements. It can be claimed that market rates are quick and complete in the markets. From another perspective, there are numerous cases of regression and the coefficients of cash rate change are less than one, as a rule. Maturity of security can be defined in the following way: â€Å"the long-run pass-through coefficient and the coefficient on the cash rate in the "levels" regressions are positive for all three rates, but the size of the coefficients declines the longer is the maturity of the security† (Wylie, 2009). It is possible to define the measure of governmental bonds over a certain period of time and the rates of maturities should be correlated during different periods. It is evident that the interest rate, when decrease, exerts a serious negative influence on consumption and investment expenditures, as well as the level of aggregate demand, the inflation and the unemployment rates. This influence can be interpreted in the following manner: when there is a price increase, borrowing is decreasing. When the price is decreasing, the effect is quite different. Borrowing increases in this case at once. Consequently, it is evident that changes in

The role and importance of creativity and innovation in generating Essay

The role and importance of creativity and innovation in generating competitive advantage - Essay Example Porter has emphasized that the motivation behind the formulation and adoption of any strategy is the achievement of competitive advantage. To achieve competitive advantage a business organization is required to build a steadfast choice about the form of competitive advantage it wants to accomplish and the range of resources within which it would strive to achieve that level. Competitive advantage sought by firms can be classified into two basic types; low cost and differentiation (IFM, n.d.). On the basis of these two competitive advantages Porter has come up with three generic strategies (Porter, 2008, p. 12) namely, cost leadership, differentiation, and focus. The last strategy has two sub categories, â€Å"cost focus and differentiation focus† (IFM, n.d.). These strategies help the firm deliver a better than average performance. Cost leadership Any firm that follows the strategy of cost leadership, targets at becoming the only producer in the entire industry whose cost of p roduction would be lower than all its competitors. The producer seeks to exploit economies of scale and follow competitive pricing (Richardson and Dennis, 2003). Cost leadership strategy is a key to success for several successful companies; one among them is Walmart (Baroto, Abdullah and Wan, 2012). Differentiation Under this strategy the firm concentrates on becoming unique in the products it offers. It does this by identifying certain product dimensions that consumers value the most. The firm develops its production and marketing strategies in such a way that it can satisfy the customers’ demand for those attributes and hence receives premium price for that uniqueness. For example, Apple Computers makes â€Å"differentiation by technology† (Baroto, Abdullah and Wan, 2012, p. 120) to preserve its competitive advantage. Focus The firm selects either a group of segments or a single segment from the industry in which it belongs and optimizes its strategies to serve these segments so well, as to gain competitive advantage over all its competitors. A firm can pursue this by either creating cost advantage in a targeted segment (cost focus) or by developing a differentiation in a targeted segment (differentiation). Tesco follows the focus strategy to blend elements of both differentiation and low cost (Baroto, Abdullah and Wan, 2012). Total Quality Management Total quality management (TQM) is â€Å"an art of management† (Singh, Qureshi and Butt, 2007) that became popular with business organizations in 1980s. Clark (1996) has explained that this management strategy focuses on maintaining quality of in all processes running in an organization; manufacturing, human resource, financial procurements, R&D and administration. Implementation of total quality management provides a framework that guides the organization to select competitive advantages in the face of uncertainty. These competitive advantages become the foundation on which operational deci sions are made regarding the marketplace (Tseng and Lin, 2008). Quality management is an approach that many firms consider the basis for making differentiation from competing firms (Singh, Qureshi and Butt, 2007). The role played by TQM in a firm is that of creating a demanding work environment and also lay down ways to fulfil the demands through team spirit, mutual trust, honesty, open communication and fun. In this framework, changes are appreciated, fear is defeated and resistance towards change is

How too conduct an accident investigation in the workplace Essay

How too conduct an accident investigation in the workplace - Essay Example Once at the scene take photographs and examine the site thoroughly for any possible causes of the accident or underlying causes that may have attributed to the event. For example if the site itself is an open plan work-room, describe the layout of the room with the position of all furniture, noting whether it was fixed or movable and in what condition it is in. Take a note of all, if any, cables and wiring and their location in regards to the accident, also note the type of lighting in the room, and if there are any bulbs or lights not working in case the room dimmed enough to cause an accident. Note what type of flooring the room has, if it carpeted or tiled and if there are any frayed or loose edges, or if the floor is made of polished wood or linoleum, if so is it polished enough to cause a person to slip. Is there any possibility of spilt liquids or leakages from over head pipes or under floor pipes. Once the site has been investigated and thoroughly examined the next stage is to interview the injured person or persons and any witnesses present or involved in the accident, including those who had a role after the event. Keep a copy of the report to hand, but as the injured party to describe the event prior to and leading up to their having the accident.