Measurement of the Effects of Abstinence from Tabacco - Research Paper Example

?“Fifteen-Year Follow-Up of a Behavioral History of Attention Deficit Disorder A Re-Analysis and Heather Walen-Frederick Walden University INSTRUCTOR’S NAME COURSE TITLE AND NUMBER August 22, 2011 Abstract Type your abstract here. An abstract “is a brief, comprehensive summary of the contents of the article” (p. 25). Review section 2.04 in the APA manual for important additional guidance. Does not yet count for purposes of present page fulfillment, take note of page number above. Table of Contents NOT COUNTED EITHER “Fifteen-Year Follow-Up of a Behavioral History of Attention Deficit Disorder”: A Re-Analysis Introduction Attention deficit disorder is a gray area of dysfunction, chiefly developmental though also observed in adults, that exemplifies the great urge of the mental health profession to understand and resolve every area of human frailty. Such an all-encompassing impulse commenced as a reaction to the radical behaviorism of Watson, Pavlov and Skinner. The latter had boasted that any and every behavior could be learned or “extinguished” by applying the principles of operant conditioning alone. Behaviorism failed as a universal paradigm because it could not account for resilience, motivation and altruism, among others. Humanistic psychology emerged as a return to the inward-looking thrust of the great pioneers of psychiatry (e.g. Freud). Subsequently, discoveries in neurology and biochemical psychology led to the other extreme of viewing human dysfunction as singular or cascading biochemical phenomena. Following that line of reasoning, any source of discomfort or disequilibrium should be treated forthwith with the appropriate drugs. Hence, even “childhood hyperkinesis”, the other term for ADD, was routinely treated with psychoactive drugs such as the CNS stimulants Methylphenidate/Dexmethylphenidate, the high-risk norepinephrine reuptake inhibitors, and amphetamine derivatives (Zieve & Berger, 2011), the latter classed by the Drug Enforcement Administration as Type II addictive substances. Such an aggressive approach has been called into question, partly on the grounds that long-term drug use fail to reverse depressive states in adults, for example, and the observed bias for referring male pupils as so rowdy that they “must be” afflicted by chronic ADD (Sciutto, Nolfi, & Bluhm, (2004). In 1970, Howell, Huessy and Hassuk (1985) embarked on a longitudinal study that followed a cohort of school children for the next decade-and-a-half. That rigorous research design, which produced the data set subject of this paper, aimed to strengthen construct validity. If it could be shown that children who scored high on an ADD screening study instrument also had adverse outcomes in point of academic achievement, grade point average, dropping out of school, social adjustment, and failing to graduate from high school despite controlling for intelligence, then it could reasonably be concluded that ADD was a crippling condition and not just a transient phenomenon in child development. Method Participants (ALREADY WRITTEN IN STUDENT DRAFT) Procedure (DITTO) Measures (DITTO) Results Descriptive Statistics Tables 1 and 2 (below) show, first of all, the variables that have to do purely with participant profiles for gender and IQ, from the Howell, Huessy and Hassuk (1985) data set of 216 students. Table 1 Descriptive Statistics for Gender Descriptive Statistics: GENDER N 216 Range 1 Sum 316 Mode 1 Median 1 Mean N.A. Std. Deviation N.A. N.A. = Not applicable because nominal-type variable. Table 2 Descriptive Statistics for IQ N Valid 216 Missing 0 Mean 102.35 Std. Error of Mean .854 Median 103.00 Mode 95 Std. Deviation 12.558 Range 82 Sum 22109 Figure 1: Distribution of IQ Scores We next examine the descriptive statistics for the criterion variable, ADD scores. Table 3: ADD-like Behavior Score (Mean of 3) N Valid 216 Missing 0 Mean 52.8480 Std. Error of Mean .71118 Median 53.0000 Mode 51.67 Std. Deviation 10.45221 Range 52.00 Sum 11415.17 Percentiles 10 39.2333 20 42.8000 25 46.0000 30 47.6667 40 50.3333 50 53.0000 60 55.3333 70 58.3000 75 60.3333 80 62.0000 90 67.4333 Figure 2: Distribution of Scores on Five-Point ADD Behavior Scale Table 4 Repeated Grade? Frequency Percent Valid Percent Cumulative Percent Valid No 196 90.7 90.7 90.7 Yes 20 9.3 9.3 100.0 Total 216 100.0 100.0 Table 5 9th Grade English Level Frequency Percent Valid Percent Cumulative Percent Valid College Prep 29 13.4 13.4 13.4 General 154 71.3 71.3 84.7 Remedial 33 15.3 15.3 100.0 Total 216 100.0 100.0 Table 6 9th Grade English Grade Frequency Percent Valid Percent Cumulative Percent Valid F 3 1.4 1.4 1.4 D 25 11.6 11.6 13.0 C 78 36.1 36.1 49.1 B 82 38.0 38.0 87.0 A 28 13.0 13.0 100.0 Total 216 100.0 100.0 Table 7 Social Adjustment Problems in 9th Grade? Frequency Percent Valid Percent Cumulative Percent Valid No 191 88.4 88.4 88.4 Yes 25 11.6 11.6 100.0 Total 216 100.0 100.0 Table 8 Dropped out of High School? Frequency Percent Valid Percent Cumulative Percent Valid No 196 90.7 90.7 90.7 Yes 20 9.3 9.3 100.0 Total 216 100.0 100.0 Table 9 Descriptive Statistics N Range Sum Mean Std. Deviation GPA in 9th Grade 216 3.75 526.73 2.4386 .84507 Valid N (listwise) 216 Results for t Test Results for Analysis of Variance (ANOVA) Results for Correlation Discussion The descriptive statistics runs on the Howell et al. (1985) data set reveal that: Male pupils outnumbered girls by a 54:46 ratio. This is unimportant unless it can be shown that such a gender breakdown is unusual for the area sampled (within 40 miles of Burlington, VT) or for the cohort that entered the second grade when the study commenced. For the purposes of the original study, IQ should be considered an intervening variable in that above-average mental ability can offset the academic consequences of ADD. Given a mean and median of 102 and 103 IQ points, respectively, Figure 1 above roughly approximates a normal curve. However, the distribution is bimodal. The mode is at 95, suggesting a marginally negative skew that does not materially affect study results. Given that the diagnostic instrument was a 21-item questionnaire that tapped behavioral components commonly associated with ADD – each item being a scale of 1 to 5 (where 1 always meant “low ADD-like behavior”, 3 “average” and 5 “high ADD-like behavior”) – and that was administered three times prior to being averaged, the true range of scores is 21 to 105. The outcome (see Table 3 above) is that all measures of central tendency clustered at 52 and 53. These scores are below the true midpoint of the scale, 63 if all students scored 3 on all 21 items each time. On the face of it, most students are below-average or low in ADD-like behavior. This stands to reason, given the known minimal prevalence of ADD in the population: about 3 to 5% of school aged children (Zieve & Berger, 2011). Both the construction of the scale and the results from the dataset create conflicting views about the incidence of behavior that teachers interpret as ADD. Given the standard deviation of ±10.45, Chebyshev’s rule would have us expect that at least 75.0% will lie within ? ± 2?. Taking only the right tail of such a distribution, about 12.5% of pupils are more than 2? away from the mean. This small subgroup would have comprised all those scoring >73.75 on average across all three assessments taken or >3.5 on the five-point scale. Under the Empirical Rule, about 95.44% would lie within ? ± 2?. In this case, 2.8% of children ought to be found scoring 3.6 or higher on the five-point scale. At the same time, the percentile breakdowns shown in Table 3 above suggest that 90% of the pupils score 3.2 or less on the five-point scale. Absent information on the rating scale cut-offs that Howell et al. (1985) applied, caution dictates that an average score of 67.4 across 21 items be applied as the cut-off for diagnosing ADD. References Appendix SPSS Syntax and Output Files for the Final Project Transform/Recode Variable Types GET FILE='D:\Work\Academia\June2011\Stat01Tobacco\3148983_howell_dataset_cleaned.sav'. * Define Variable Properties. *GENDER. VARIABLE LEVEL GENDER(NOMINAL). *REPEAT. VARIABLE LEVEL REPEAT(NOMINAL). *ENGL. VARIABLE LEVEL ENGL(ORDINAL). *ENGG. VARIABLE LEVEL ENGG(ORDINAL). FORMATS ENGG(F8.0). *SOCPROB. VARIABLE LEVEL SOCPROB(NOMINAL). FORMATS SOCPROB(F8.0). *DROPOUT. VARIABLE LEVEL DROPOUT(NOMINAL). FORMATS DROPOUT(F8.0). *IQ. FORMATS IQ(F8.0). *GPA. VARIABLE LEVEL GPA(ORDINAL). EXECUTE. DESCRIPTIVES VARIABLES=GENDER IQ /STATISTICS=MEAN SUM STDDEV RANGE. Descriptive Statistics Descriptive Statistics N Range Sum Mean Std. Deviation Gender 216 1 316 1.46 .500 IQ Score 216 82 22108 102.35 12.558 Valid N (listwise) 216 Statistics Gender N Valid 216 Missing 0 Mean 1.46 Median 1.00 Mode 1 Gender Frequency Percent Valid Percent Cumulative Percent Valid Male 116 53.7 53.7 53.7 Female 100 46.3 46.3 100.0 Total 216 100.0 100.0 FREQUENCIES VARIABLES=IQ /STATISTICS=STDDEV RANGE SEMEAN MEAN MEDIAN MODE SUM /HISTOGRAM NORMAL /ORDER=ANALYSIS. IQ Score Frequency Percent Valid Percent Cumulative Percent Valid 55 1 .5 .5 .5 74 1 .5 .5 .9 75 1 .5 .5 1.4 78 1 .5 .5 1.9 79 2 .9 .9 2.8 80 1 .5 .5 3.2 81 2 .9 .9 4.2 82 2 .9 .9 5.1 83 3 1.4 1.4 6.5 84 4 1.9 1.9 8.3 85 3 1.4 1.4 9.7 86 1 .5 .5 10.2 86 3 1.4 1.4 11.6 88 5 2.3 2.3 13.9 89 2 .9 .9 14.8 90 1 .5 .5 15.3 90 2 .9 .9 16.2 91 1 .5 .5 16.7 91 4 1.9 1.9 18.5 92 2 .9 .9 19.4 92 5 2.3 2.3 21.8 93 6 2.8 2.8 24.5 94 1 .5 .5 25.0 94 3 1.4 1.4 26.4 95 1 .5 .5 26.9 95 9 4.2 4.2 31.0 96 1 .5 .5 31.5 96 4 1.9 1.9 33.3 97 3 1.4 1.4 34.7 98 3 1.4 1.4 36.1 98 2 .9 .9 37.0 99 1 .5 .5 37.5 99 4 1.9 1.9 39.4 100 1 .5 .5 39.8 CONTINUES FOR 8 MORE PAGES, DOES NOT YET INCLUDE INFERENTIAL t Test Copy and paste your SPSS syntax and output files from Week 8 here. ANOVA Copy and paste your SPSS syntax and output files from Week 9 here. Correlation Copy and paste your SPSS syntax and output files from Week 10 here. Read More