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 Development Of Social Work Social Work Essay

The Development Of Social Work Social Work Essay The problems that came about from industrialisation proved there was a severe lack of help for those who truly needed it. No profession already existed to help these people in society, and from that social care gradually came about, progressing into social work as it is today. Industrialisation meant that everyone left the country to move into the city, as it was a lot easier to find work, however with more people in the cities this meant more social problems could easily arise. Older people and younger children were given no help or education, as they were seen as no benefit to society, as they werent fit to work. From the mid 1700s Britain began to change dramatically, those who had formerly lived in the country and worked on the land, moved to the cities and sought employment in the factories. Work conditions were harsh and many were working 12 hours days on very low wages, and without laws people were exploited. Home conditions were not much better, and the large urban populations led to poor sanitary and social conditions which went on the lead to very poor public health, and high numbers of those being effected by diseases such as cholera and typhoid. The governing social policy of the time was laissez faire, leaving the caring self less citizens of society to help those worse of than them, with the policy having its roots in religious benevolence. However laissez faire had appeared to have failed and the effects included the poor living conditions of the time. In the fight against poverty and poor sanitation the Poor Law Amendment Act (1834) and The Public Health Act (1848) were created, and this was said to be ..the first example of the state taking direct responsibility for the poor (Sheldon McDonald, 2009, p13). The Poor Law Amendment Act was put in place to make sure that those who truly deserved relief were receiving it. The poor were separated into two categories; the undeserving and the deserving. The deserving poor received practical and financial support from charities, and consisted of those who were not physically fit to work such as the elderly, sick, and disabled. The undeserving poor were those who were fit to work but chose not to for whatever reason; these people were turned down for support from charity and voluntary services. They were forced to turn to the workhouse or the state, conditions in the workhouse were deliberately harsh, to try and deter those who we able to work to seek work instead. Workhouses soon became home to those who were not well enough to work, but were eliminated in 1930. The Public Health Act came as a result of Chadwicks Sanitary Report, and the aim was to improve the sanitary conditions in towns and cities. The General Board of Health had responsibility over water supplies and drainage; the first main focuses were on public places, especially hospitals. Diseases slowly killed fewer and fewer people, due to the drainage systems and clean water put into place in London following the act. At first sight the Poor Law and the workhouses it introduced may seem a far cry from social wok (State Social Work, BJSW, p.665, John Harris 2008) Financial and practical support provided was and still is set below that of minimum wage which therefore dissuaded people from seeking help from state intervention and instead encourages them to find employment. Today the same values still hold. The Charity Organisation Charity (COS) was founded in 1869, to help manage the relief that was given out to the poor. The system was designed to stop charitable relief being given out to those who didnt require it as much as others, as they were seen to be taking advantage. Only the deserving poor could receive aid from charities. The COS introduced a case work practice, which meant that those who applied for support from charities could be thoroughly assessed as to whether they were legible for help or not. A caseworker would work closely with an applicant to build up a bigger picture of their personal background to determine what action plan would be put in place. Just as a social worker would today, the caseworker visited the client and built a relationship. A client would then be referred to a local charity or voluntary organisation which would best help provide for their needs. The COS was the first organisation to introduce the idea of casework, which was later developed and ex panded due to the work of Mary Richmond. Richmonds beliefs really conveyed the importance of casework, and her ideas focused on the social theory instead of a psychological theory. Her ideas are still recognised today and are said to be the root of social work education. Post World War I, psychiatry in social work started to play a big part. Many men returned from war with what we now recognise as post traumatic stress disorder, however a hundred years ago little was known about it. Care centres employed psychiatrists to treat these men, and from this psychiatry became much more recognised and started to play a bigger part in treatment for mental health patients. Social workers and psychiatrists focused more on psychology for their answers and a more head over heart (Sheldon McDonald, 2009, p.21) approach was used more widely in practices. During World War 2, over 3 million children were removed from their homes in the cities and sent to live in the country for their own safety; however children experienced adverse effects due to their separation from their parents. It was from this psychologist John Bowlby produced his theory of attachment and effects of maternal separation. 1944 saw the beginning of the Education Act, ensuring that children were entitled to education, free of charge. In the following 5 years after the end of World War 2, towns and cities were rebuilt and following Sir William Beveridges suggestions, a welfare state was set up, as the former welfare system was not accepted anymore by those in society. In 1948 the Nation Health Service was founded, providing care to everyone who needed it. Even though voluntary organisations were still in place, the state had eventually become the biggest provider of care. During the 1950s the COS no longer had such a major impact upon the development of social work, as it had had previously. Times were changing for social work as a profession, and psychological theories became one of the main influences of social work practice. The COS identified individuals social problems and sought the best way to deal with them, however they stuck too strongly to their social theory that in the end it began to have less and less relevance to the real problems the poor were experiencing. An in depth understanding of the individual was the basis to good social work, according to Younghusband. She wrote that the social worker needs to understand their client as a person, their individuals needs and relationships but must not forget the reality of the situation they are in. Before the 1950s, social work training had always been specialised in a specific area of practice, so the social worker would be specifically trained to do the job they were doing. However the 1950 s saw the first general social work training scheme being set up. The Seebohm Committee was set up in 1965 to assess the social services, in reaction to the growing pressure for a reform. The Seebohm report was finally published in 1968 stating their recommendations and beliefs of improving the social work profession. To start with the committee set up area teams, where social workers worked within one community. It stated the profession needed to provide a more coordinated and comprehensive approach to the problems of individuals, families and communities. (Seebohm Report, cited in Social Work: An Introduction to Contemporary Practice, p.58) The report is said to be a major landmark in social policy (State Social Work, p.670, BJSW, 2008). Social work was given more status as a profession and the Local Authority Social Services Bill was passed in Parliament in May of 1970. The conservative government came into power in June 1970, from then on social work saw a massive growth. To begin with there was a huge increase in qualified social workers, however this stirred conflict between those who had qualified before 1970, and those who qualified after, due to the difference in their training, and there failed to be an established understanding of the social work role in society. The 1970s saw the formation of the Radical Social Work Movement, which start when a magazine named Case Con was published, which passed judgment upon social work practices, in hope to come up with a new theory of Social Work. This theory was built on the need for Social workers and clients working together to bring about social change. Rather than offering suggestions on what needed to be changed, radical social work instead just summarised the faults within social work. Consequently the movement did not change policy and practices within social work, yet the writings of the movement still played a big part in the growth of Social Work throughout the 80s. The Barclay Committee report published in 1982 recognised three approaches to relationships between the state and those in need. The first was known as the safety net approach, with the main principle that help from the state should only be given to those who really need it, as a last resort, once again with a focus on who are the deserving and undeserving poor. With the welfare state approach it was a responsibility of the states to offer services to everyone. The third approached focused upon the communitys responsibility to look after each other, and this was the most popular approach to the committee. Social workers main focus should be on a certain community and having an in depth knowledge of that community. With helping those in that community they encourage citizens to care more for one another. Overall the Barclay report did not make much change to community social work. In social works reform as a profession, marketisation and managerialism became very important, introducing new techniques such as audits and inspections. With following wider market trends and models, social work became more cost effective and sustainable, focusing on the outcome rather than the quality in which the work gets done. In recent times procedures and bureaucratic practices have become the main framework around which social work practice is structured, at the expense of the professional relationship, (Gupta and Blewitt, 2007, cited in Social Work: An Introduction to Contemporary Practice, p.5). With growing advances in technology it meant that social workers could pursue more office base practices, which had its advantages in storing, accessing and organising files and data, however it can be argued that it has had a negative effect on the client/social worker relationship. In 2003 the Laming Report was published, after the well known case of Victoria Climbie and how she eventually died. The safeguarding of children became a priority, and it was questioned how effectively social work was doing its job. More emphasis was put onto the importance of inter professional working and communication, as it was due to the lack of communication between services that Climbie was not saved. The Children Act of 1989 was modified and The Children Act of 2004 was put into place to help protect and safeguard children. Social work has its beginnings in voluntary based services which were mainly based on religious principles and over the last 150 years the profession has grown to what we know it as today. Although laws and policies have changed and developed over the years, social work is still based upon the same values as it was all those years ago. Social workers still do the same service, in helping clients improve their lifestyle, however due to all the laws and legislation that have been put into over the years, the way in which social workers have to do their job has changed. Today the job is less hands on as it used to be, more paper work based, and more time is spent in the office, due to technological advances and managerial direction. The relationship with the client has always been at the heart of good social work practice, and social workers strive to do their best in improving their clients situation, no matter what their problems may be. There is still a focus on who is deserving and undeserving, and there are limitations on who can receive what services. For example the benefits the unemployed can receive are lower than minimum wage, encouraging the unemployed to seek jobs rather than rely on the state. In social work assessments always have to be carried out on the client to determine whether they are eligible for the services on offer. In the last 10 years we have seen the development of the GSCC (General Social Care Council) which was set up in 2001, in reaction to criticisms, mainly stemming from the death of Victoria Climbie. As of April 2005 it became policy that all trainee and qualified social workers had to be registered with the GSCC. When registered with the GSCC, social workers must oblige by the codes of practice. The codes of practice are in place to show practitioners what is expected of them, and to ensure that clients are receiving the best care possible. The codes of practice of the GSCC are based on social work values developed over the years, giving us the underpinning beliefs of contemporary social work.

Voice Disorders in Child Communication

Voice Disorders in Child Communication Voice disorders are the most fairly common communication disorder in children.   Voice disorders can be developed throughout the lifespan of an individual.   Currently around 7% to 9% of children develop a voice disorder.   Voice disorders can be characterised by hoarseness, occasional loss of voice, vocal fatigue and unusually low or high pitch. Voice disorders are generally classified as: Vocal abuse Neurogenic disorders Psychogenic disorders Alaryngeal communication They are often associated with: Lots of screaming and yelling; Recurrent infections of the upper airway; Reflux Different subtypes: Vocal Cord Paralysis Vocal Cord Nodules and Polyps Paradoxical Vocal Fold Movement Spasmodic Dysphonia References: Justice, L. (2006). Communication Sciences and Disorders: An Introduction (1st ed., pp. Chp 11.14-23). New Jersey: Pearson/Merrill Prentice Hall. Voice Fact Sheets. (2016). Speechpathologyaustralia.org.au. Retrieved 14 March 2017, from http://www.speechpathologyaustralia.org.au/spaweb/Document_Management/Public/Fact_Sheets.aspx#anchor_voice A Speech Pathologists role in working with a child who has a voice disorder is to treat them.   Speech pathologists treat children with a voice disorder through common treatments such as vocal techniques, therapies, and work in conjunction with teachers and Ear, Nose and Throat doctors to help a child produce the best possible speech quality and normal vocal sound production. Assessment of voice disorders: †¢ Voice quality can be screened, (evaluation of vocal characteristics) †¢ A comprehensive assessment is conducted for children suspected of having a voice disorder, using both standardized and nonstandardized measures. †¢ Disorders that relate to the structure and function are physical characteristics that must be diagnosed by a physician (ear nose and throat doctor specialist (ENT)) Reference: BuildnCare Therapy,. (2017). Retrieved from http://buildncare.com/speech-therapy/ Voice Fact Sheets. (2016). Speechpathologyaustralia.org.au. Retrieved 14 March 2017, from http://www.speechpathologyaustralia.org.au/spaweb/Document_Management/Public/Fact_Sheets.aspx#anchor_voice The International Classification of Functioning Disability and Health (ICF) is the best framework to use to understand and asses the impact that a voice disorder has on a childs quality of life.   As it covers all aspects of an individuals life that a voice disorder may impact. By using an internationally recognised model that consists of; à ¯Ã‚ Ã‚ ± Body Functions and Structures à ¯Ã‚ Ã‚ ± Activity and Participation à ¯Ã‚ Ã‚ ± Environmental Factors. à ¯Ã‚ Ã‚ ± Personal Factors References: 1st day as an english teacher,. (2013). Retrieved from http://1st-day-as-an-english-teacher.blogspot.com.au/2013/02/how-to-teach-kids-to-not-talk-during.html Rebecca is a 9 year old girl who has an outgoing, loud, bubbly personality. Rebecca loves to sing and act and is the lead in both her school choir and drama group.   At a weekly rehearsal with her choir group Rebecca began to experience frequent coughing and clearing of her throat. Her choir teacher suggest that Rebecca should go visit the school speech pathologist.   After her session with the school speech pathologist Rebecca was diagnosed with Vocal Cord Nodules which is benign growths on both her vocal cords. Rebecca is now receiving treatment to correct the behaviour that was causing the problem.   As a result Rebecca can no longer for the time being be the lead in her school choir and drama club and as a result cannot perform in the yearly singing under the moonlight concert. Rebecca now also find it difficult to verbally communicate as she experiences discomfort which has lead to her spending no time with her friends who are all part of the drama or choir group. This has lead to Rebecca feeling lonely and left out as she is unable to effectively participate in her groups.    Body Functions and Structures †¢ Diagnosed with vocal nodules due to vocal abuse †¢ Rough vocal quality Activities and Participation †¢ Unable to perform for longer than 2 minutes without vocal discomfort †¢ Not able to fully participate in choir and drama club †¢ Unable to perform lead role in concert †¢ Reduced ability to talk due to discomfort Environmental and Personal Factors †¢Age:9 †¢School girl singer and actor †¢Talkative and outgoing person References: Australian girls choir,. (2012). Retrieved from http://www.ausgirlschoir.com.au/About-agc/Latest-News/2012/AGC-National-Tour

Catch A Yawn :: Biology Essays Research Papers

Catch A Yawn A trick in every girl's handbook: If you want to know if someone is checking you out, yawn and check to see who, if anyone, yawns back. While we may be using the contagious phenomenon of yawning to our advantage, the age-old question still lingers on - why, in fact, is yawning contagious? Plausible explanations range from historic origins to muscular requirements. However, one answer that encompasses all other questions about the cause and traits of yawning has yet to be found. First, let's tackle the question of why we yawn. An evolutional/psychological theory has claimed that yawning was once used as a non-verbal form of communication to synchronize group behavior among animals (9). For example, the leader of a pack of wolves would yawn to set a certain mood or signal a change of activity. Humans also being group-oriented animals may have assimilated to this form of agreement. In the same way that one pumped up team member can influence the level of aggression and team-spirit of an entire team, one yawning client can also affect the mood of sales-pitch meeting. Another good example of synchronization among humans is if a group is sitting around a campfire and the leader yawns, it most likely will act as a signal to the others that it may be time to call it a night. Yawning is commonly perceived to be a sign of boredom or tiredness. Dr. Robert Provine, known as the yawn-expert from the University of Maryland, performed a study on 17-19 year old students to test this perception. In comparison to a group of students who watched music videos for 30 minutes, a group who watched an uninteresting color test bar pattern for 30 minutes yawned more (10). Dr. Provine also suggested that yawning is like stretching (5). Much like stretching, blood pressure and heart rate can be increased just by yawning. Perhaps animals yawn instinctively when bored or tired to get their blood pumping so that they may be physically stimulated to move or seek a new activity. But then why is it that we yawn after waking up? If we yawn after waking as a physical prompt to become active that's one thing. But yawning as a sign of tiredness can be ruled out if we yawn after waking from a restful sleep. Maybe a study could be done in which a comparison could be made between the ho urs of sleep and the occurrence of yawning when waking.

How the Media Distorts Male Self-Perception Essay -- Exploratory Essay

How the Media Distorts Male Self-Perception Women are insecure. They constantly diet and scrutinize their bodies. They fall victims to the anorexically thin models appearing in the media. Why do men have it so easy? For years these questions are what women asked themselves. In a world where appearance is everything, women have been the main source of all the hype concerning the image and body. Advertisements have been criticized for years about putting the pressures of the â€Å"perfect† body into the heads of millions of women. Up until a few years ago, it was believed that only women had the eyes of society on them. Now the scales are balancing. More men are beginning to feel pressured, by the same society, to have a muscular body and to portray a perfect male image. However, some men are taking this pressure to the extremes. The media is having a negative effect on the way men view their bodies making them feel as if they need to look like the models shown, which can often result in muscle dysm orphia. Through exposure to the media, primarily advertisements and movies, the physical appearance of the male body has drastically changed, from barely seeing the torso to men in their briefs. In past decades, the male body was portrayed in a â€Å"rugged† sort of way (Luciano 4). It didn’t matter how many muscles the man had or the degree of atonement, but the way that the man carried himself. He was his own seller in a busy market. If he wanted to appear tough and manly, the way he presented and carried himself would make all the difference. For example, in the first two Rocky films, Rocky, played by Sylvester Stallone, is â€Å"beefy† yet buff (Law). He is the idol of women portrayed as ... ...s in Magazines.† Journal and Mass Communication Quarterly. Autumn 2002: 697-711. ABI/INFORM Global. Proquest Horn Lib., Babson Park, MA. 11 Feb.. 2003 . Luciano, Lynne. Looking Good: Male Body Image in Modern America. New York: Hill and Wang, 2001. Olivardia, Roberto. â€Å"Muscle Dysmorphia in Male Weightlifter: A Case Control Study.† American Journal of Psychiatry. Aug 2000: 1291-6. ABI/INFORM Global. Proquest Horn Lib., Babson Park, MA. 11 Feb 2003 . Pope, H.G.. â€Å"Muscle Dysmorphia: a New Syndrome in Weightlifter.† British Journal Of Sports Medicine. Oct. 2002: 375-8 Expanded Academic ASAP. Infotrac. Horn Lib., Babson Park, MA 11 Feb 2003

Total Quality Management (TQM) :: GCSE Business Marketing Coursework

Total Quality Management Total Quality Management is a structured system for satisfying internal and external customers and suppliers by integrating the business environment, continuous improvement, and breakthroughs with development, improvement, and maintenance cycles while changing organizational culture. A remarkable thing is happening as we see the awakening of the individual and the collaboration of empowered people in the team effort of total quality management. It is a renewing, a reinforcing and a building of a bridge of trust among the individuals responsible for accomplishing a common goal (The Total Quality Review; May 1994). One of the goals is to build an organizational environment conducive to job redesign and cross training in order to facilitate job flexibility. TQM initiatives in areas of common concern provide an opportunity to form and better control the relationship with a company's external vital customers and suppliers. TQM is essential for companies seeking to provide quality goods and services to their customers. The end result is that they will enjoy prosperity and long-term growth. "In order to compete in a global economy, our products, systems and services must be of a higher quality than our competition. Increasing Total Quality is our number on priority here at Hewlett-Packard. -John Young, President of Hewlett-Packard" (www.dmu.ac.uk.htm). Often companies find themselves faced with a dilemma that is rooted to a lack of TQM. This was the case with Apple Computers. They were unable to conceive a working, productive relationship between their managers and engineers. Apple found themselves unable to alleviate this problem, which ultimately hindered it from responding to environmental changes. The positive effects of TQM has transcended down through the ranks. Non-profit organizations have begun to embrace TQM throughout their organization, relying on the positive results found throughout the business world. Even the Health care industry has initiated TQM to promote excellence in nursing, urging nurses to apply quality improvement.

Letter To My Children

As I lay awake in bed with Carol by my side, sharing my insomnia, it occurred to me, totally out of the blue, that Neo had opened up for me a new world of understanding, which is to say that my conversation with him had led me to the realization that there was a way out of my conflicts here and now.   I was being selfish aforetime: it occurred to me.   Although I could not go to the Vatican all the way from America in order to express my new understanding of Christianity – rather than to pose questions that the authorities on religion there were not expected to appreciate – I could express myself before my children, as though nakedly.But what would I teach them?   I certainly did not wish to confuse them by sharing my conflicts.   Neither did I desire for them to catch on my negative emotions surrounding the wonderful truths I was learning my entire life.   One such negative emotion was my slight fear of various authorities on religion because I could not get over my miserable misunderstandings with them.   I understood that the authorities on religion are meant to be peacemakers, and yet I could not see eye to eye with them on many issues of peace, which, in my awareness, is often a result of increased knowledge and new understanding of faith.These misunderstandings, I knew, were tormenting me alone, while they slept soundly night after night.   I recalled then the words of Jesus, virtually unaware at the time that Carol had started to sing in bed a song from the film, Evita:Blessed are the poor in spirit: for theirs is the kingdom of heaven.Blessed are they that mourn: for they shall be comforted.Blessed are the meek: for they shall inherit the earth.  Blessed are they that hunger and thirst after righteousness: for they shall be filled (Gospel  of Matthew, Chapter 5, Verses 3-6).Did I have to share myself with the various authorities on religion in order to find peace?   I did not believe so.   All the same, my torment was asking me to somehow stand in front of the whole wide world and declare myself to be a true seeker of truth.   Then, and only then, I thought, would I find peace.   However, it was impossible for me to stand on the stage before the entire Christian world and declare myself to be true.   As soon as I realized that my mind was straying away from my children, I said â€Å"Yes.†Ã‚   Carol asked me right away, â€Å"What?†   Ã¢â‚¬Å"Oh, nothing,† I said.   â€Å"I am enjoying your song!†Ã‚   Carol continued singing from that point on.I got back to my reflections through insomnia.   I thought that I knew that I could die very soon, and I did not have the kind of oneness Jesus experienced with God Almighty to know when.   I could express my understanding to my children – yes I could do that, I thought again – and my children in turn would develop their own understanding of religion based on my teachings and their own experiences in lif e.As I lay in bed reflecting on the new questions that had perhaps occurred to me through supernatural inspiration, it did not take me long to figure out what exactly I would be teaching my children and how.   I would write a letter to them, directing Carol to deliver it to them only  when they have all reached maturity at the same time.   I would not want one child wanting to know more than the others at any given time.   I would like them to grow in spirituality altogether.   I did not want them to have to go through the experience of single handedly dealing with the problems of realizing the truth as I did.   I did not want them to feel as alone in the world as I felt.But perhaps I would eventually leave the letter in Carol’s hand to decide when to give it to each of my kids, I thought.   I was â€Å"hungering and thirsting after righteousness.†Ã‚   I knew that expressing my understanding of the religion to my children would grant me a sense of complet ion from the Almighty – somehow.   I did not know how He worked on such completions.By the time I had reached this point in my train of thoughts, I realized that Carol had gone to sleep.   Her singing was over and done with; she was snoring, in fact.   I suddenly realized a sense of greater freedom in my thoughts.   Increased confidence was aroused to boot.   Although I knew that Jesus had said, â€Å"Blessed are they that have been persecuted for righteousness’ sake, for theirs is the kingdom of heaven† (Matthew, Chapter 5, Verse 10), I neither had the strength nor the courage to stand before the world and discuss my concept of religion.   I should have had the courage, I thought, but sadly, I did not.   I then realized that perhaps this kind of courage is unnecessary, given that Jesus had also said: â€Å"Agree with thine adversary quickly, lest haply the adversary deliver thee to the judge, and the judge deliver thee to the officer, and thou be cast into prison† (Matthew, Chapter 5, Verse 25).Was Jesus right there with me to teach me something of the essence?   How was it that verses from the Gospel of Matthew were appearing in my self without notice, and perhaps out of context?  I got out of bed then, with the awareness that I had the courage to teach my understanding of Christianity to my children, even if I could not teach the whole world.   Yet I did not want to express my entire self to my children.   I only believed that they had a right to know the basics that I was working with.   I believed in their right to question religious practices of the world.   I also trusted that they would eventually find the truth using the tools I would provide them with.I turned on the lamp on my desk.   Carol moved a little in bed, responding to the click of the lamp.   Fortunately, she did not get up to ask what I was up to.   I did not want to bother her at all, and so I sat down to write, as though in a wh isper:â€Å"Dear children,  Ã‚  By this time you must be old enough to feel the need to know God.   You will be exposed to many practices in the Church†¦.†Ã‚  I gave up writing at this point, tore the page in half, and took a fresh sheet of paper to begin again.â€Å"Dear Children,  For I say unto you, that except your righteousness shall exceed the righteousness of the scribes and the Pharisees, ye shall in no wise enter into the kingdom of heaven.â€Å"’Ye have heard that it was said to them of old time, Thou shalt not kill; and whoever shall kill shall be in danger of the judgment:â€Å"’But I say unto you, that every one who is angry with his brother shall be in danger of the judgment; and whosoever shall say to his brother, Raca, shall be in danger of the council; and whoever shall say, Thou fool, shall be in danger of the hell of fire.â€Å"’If therefore thou art offering thy gift at the altar, and there rememberest that thy brother h ath aught against thee,â€Å"’Leave there thy gift before the altar, and go thy way, first be reconciled to thy brother, and then come and offer thy gift’† (Matthew, Chapter 5, Verses 20-24).I comprehended what I was doing.   Comparing the Pharisees and the scribes to the majority of religious authorities in the world, I was directing my children to go to the scriptures whenever they require guidance from the divine authority.   I did not have another way of teaching them the truth.   I wanted to say that they must never go to any religious authorities except the scriptures in the process of seeking truth; and that they must be good to each other, no matter what.I had written the words of Jesus in my memory with great enthusiasm.   And I did not doubt that my children could – given the right direction – show equal respect to the word of righteousness.   Yet I doubted whether I should tell them everything about the Church as it existed in the world today.I gave a moment of attention to Carol at this point.   She was snoring, fast asleep, and looking lovely.   I thought with tenderness that her children were mine, and I had a right to teach them whatever I felt I must.   I did not have to mention the Church and the circumstances surrounding my role in it that very day.   It was not important, given that the circumstances surrounding the Church were expected by me to change, perhaps drastically, in the years of my offspring’s maturity.I went back to my letter.   Without explaining myself in it, besides the purpose of my letter, I felt that I had to write something else I remembered from the Gospel of Matthew:  Ã¢â‚¬Å"’At that season Jesus answered and said, I thank thee, O Father, Lord of heaven and earth, that thou didst hide these things from the wise and understanding, and didst reveal them unto babes:â€Å"’Yea, Father, for so it was well-pleasing in thy sight’ (Chapter 11, Verses 25-26).Confident in my writing, I continued:  Ã¢â‚¬Å"My dear children, after reading the above scriptures you must be thinking that perhaps I felt in my years in Church that these are some of the most perfect verses in the Gospels.   As a matter of fact, I did not think so.   And the only reason I am writing you today is that I want you to learn how to trust yourselves in seeking the truth.   The scriptures are the best guidance I can offer you now.   Hold on to them, learn from them, and do not mind questioning the practices of others  when you do not believe them to be correct.   This is, in my opinion, the best attitude to take into maturity.   Let us leave the rest in God’s hand.  Ã¢â‚¬Å"Your loving father on earth.†