Statistics Essay: Interpreting Social Data

Statistics Essay interlingual rendition Social entropyInterpreting Social DataThe British mob Panel Survey of 1991 measured many opinions, among otherthings, of the UK cosmos. One of the questions asked was whether thehusband should be the primary(a) breadwinner in the household, piece the wifestayed at home. Answers to the questions were provided on an no. scale,progressing in voltsome ordinances from powerfully disagree to Strongly agree.Results for each ordinance were recorded from mannish serveents and young-bearing(prenominal)respondents. Of survey respondents, 96.75, or N = 5325.162 dish uped thisquestion of a total survey population of N = 5500.829. 3.2%, or N = 175.667 ofsurvey respondents did non resolving the question. In redact terms, this means about 97% of the survey respondents answered the question, while 3% did non.The study presents ordinal ranking, or ranking in a qualitative manner, of fivesets of concordant pairs of shiftings the priapic and fe ma nly imagine for those whostrongly agree the husband be the primary earner while the wife stays at home,the ph all toldic and female count for those who agree, the male and female count forthose who are neutral, the male and female count for those who disagree, andthe male and female count for those who strongly disagree. The sexcross-tabulation presents numeric data for results for each of the ten unsettleds, place in five variable pairs with male and female rejoinders foreach variable pair. Data is presented in terms of modus operandi of responses for eachof the ten variables.The counts or military issue of responses for each variable are pendent variables in the data analysis. We know they are dependentvariables because first, they are presented on the y-axis in the chart lifelikely representing the data. parasitic variables are graphicallyrepresented on the y-axis, with self-sustaining variables presented on the x-axis.Causally it becomes more than difficult to disting uish between dependent andindependent variables at first glance. Dependent variables usually change as aresult of independent variables. For example, if champion were studying the effectof a certain medication on source sugar in diabetics, the independent variablewould be the descend of medication addicted to the patient of. In a essay group orcohort of patients, each would be given a set superman and their blood sugarresponses recorded. One patient may respond with a blood sugar reading of 110when given 20mg of medical specialty. Another day the patient, again given 20mg ofmedicine, may respond with a blood sugar reading of 240. The amount ofmedicine provided to the patient is fixed, or the independent variable. Theresponse of the patient is variable, and believed to be influenced by, ordependent on, the amount of medicine provided. The dependent variable wouldtherefore be the responding blood sugar reading in each patient.In this survey, independent variables are the five choices of answers available to the survey takers. These five realizableresponses are presented to each survey respondent, just as the medicine isprovided to the patient in the example above. The respondent then chooses hisor her reply to the five possible answers, or chooses not to answer thequestion at all. The amount of those choosing not to answer at all, 3.2%, isconsidered statistically irrelevant in the analysis of this data. Data relatedto non-response is not considered from either an independent variable ordependent variable standpoint.The amount of responses or response count for a givenindependent variable in the survey is a dependent variable. The response countwill change, at least slightly, from survey to survey. This could be a out-of-pocket tochange in survey size, response rate or number of those choosing to respond tothe statement, or possible minor fluctuation in percentage response for thefive answer possibilities. Although the statistical results of the respons esshould be similar, given a large profuse and representative sample for eachsurvey attempt, some variance is likely to occur. The independent dependentvariable kinship in the economize should earn, wife should stay at homeanalysis is trickier to get ones point around than the medical example givenabove. In the medical example, it is easy to image how a medicine could affectblood sugar, and the resulting cause-effect relationship. In this survey, thecreation of five answer groups causes the respondents to categorise theiropinion into one of the groups, a much more difficult mental construction thanmore straight cause-result examples.Fourexamples of dependent variables in these statistics are the number of men whoagreed with the statement (525), the number of women who agreed with thestatement (520), the number of men who disagreed with the statement (688), andthe number of women who disagreed with the statement (997). As describedabove, we know these are dependent variables be cause they are caused by theindependent variables, the five ordinal answer groups, in the survey.Overall,empirical data for the results is skewed towards the Disagree / Stronglydisagree end of the survey. trio of the independent variables are ofparticular note. Strongly agree is the humbleest response for both men and women,with Disagree being the highest response for both men and women althoughaccording to Gaussian predictions the Not agree/disagree variable should have thehighest distribution.Inlay terms, the graphical representation of each of the five possible answersshould have looked like a bell-shaped curve. The dickens independent variables oneach end of the chart, Strongly agree and Strongly disagree, should have had alow but approximately equal response. The middle independent variable on thechart, Not agree / disagree, should have been the largest response. Thisshould have produced dependent variables of approximately 935 each for both menand women for the Not agree / disagree variable. Instead, the response for menwas 586, or 63% of typical distribution of answers. The response for women was702, or 75% of the typically distributed answers. The mean, or average, of allresponses in this survey is 1065.2, with the mean or average of male responsesbeing 464.6 and the mean or average of female responses being 600.6. Were theresponses distributed evenly amongst all five possible answers, these would bethe anticipated response counts.Inexamining this data, a supposal mountain be put forth that the correlation betweenthe counts on two of the answer possibilities (two of the dependent variables)will be some look upon other than zero, at least in the population represented bythe survey respondents. This hypothesis can be tested using the ordinalsymmetric measures produced in the data analysis. As Pilcher describes, whendata on two ordinal variables are grouped and given in categorical order, wewant to finalize whether or not the relative positions of categories on twoscales go together (1990, 98). ternary ordinal symmetric measures, Kendallstau-b, Kendalls tau-c, and Gamma, were therefore calculated to determine ifthe order of categories on the amount of agreement to the question would helpto predict the order of categories on the count or amount of those selectingeach ordinal category. The most appropriate measures of association toevaluate this hypothesis are the two Kendalls tau measures. The Kendall tau-cmeasure allows for tie correction not considered in the Kendall tau-b measure.The results of these measures, value .083 and .102 with approximate Tbof 6.75 indicate there is neither a perfect affirmatory or perfect negativecorrelation between variables. Results do indicate a low level of predictionand approximation of sampling distribution. The correlation between two of thedependent variables is thence a value other than zero, proving the hypothesiscorrect.Three nominal symmetric measures were also calculated.These show ed weak relationship between category and count variables, with avalue of only .096 for Phi, Cramers V, and fortuity Coefficient. Thesewere not used in testing the above hypothesis.Atheory of distribution, Chebyshevs theorem states that the standard of deviationwill be increased when data is spread out, and smaller when data is compacted.While the data may or may not present according to the empirical rule(bell-shaped), Chebyshevs theorem contends that specify percentages of thedata will always be within a certain number of standard deviations from themean (Pilcher 1990).Inthis example, data is compressed into five possible answer variables. The datadoes not present according to the empirical rule, but is skewed towards thedisagreement end of the variable scale. However, Chebyshevs theorem doesapply relating to the distribution of data according to standard deviation fromthe mean for night club of the ten dependent variables. The response count of womenwho Disagree with the state ment the Husband should earn, the wife stay at home,was proportionately larger than would be indicated on normal distribution.While the response count for men is also statistically high, it is not beyondthe predictions of Chebyshevs theorem. If the survey had been conducted withfewer independent variables, say three ordinances instead of five, theresulting data would be more tightly compacted. If the survey had beenconducted with ten ordinances, the data would have been more spread out.REFERENCESPilcher, D., 1990. Data Analysis forthe Helping Professions. Sage Publications, London.