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PubMedDate not supplied

Sadistic statistics.

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Data Interpretation, Statistical↗
PubMed1991

Improper statistics.

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Data Interpretation, Statistical↗
PubMedDate not supplied

Using statistics.

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Data Interpretation, Statistical↗
PubMed1985

Aboriginal socio-economic characteristics: issues affecting the interpretation of statistics.

Statistical data on aborigines in Australia are examined, and it is recognized that internationally accepted census definitions and criteria may not be appropriate for analyzing the situation of aboriginal groups. In particular, the author "uses 1981 census-based comparisons of some Aboriginal and non-Aboriginal socio-economic characteristics and discusses how these assessments are affected by Aboriginal attributes. Attention is confined to three commonly used socio-economic indicators: labour force status, occupational category and income (on an individual and per capita basis)--all examined on an Australia-wide basis and specifically for the Northern Territory."

Australia↗
PubMed1989

The box plot: a simple visual method to interpret data.

Exploratory data analysis involves the use of statistical techniques to identify patterns that may be hidden in a group of numbers. One of these techniques is the "box plot," which is used to visually summarize and compare groups of data. The box plot uses the median, the approximate quartiles, and the lowest and highest data points to convey the level, spread, and symmetry of a distribution of data values. It can also be easily refined to identify outlier data values and can be easily constructed by hand. We apply box plots to tabular data from two recently published articles to show how readers can use box plots to improve the interpretation of data in complex tables. The box plot, like other visual methods, is more than a substitute for a table: It is a tool that can improve our reasoning about quantitative information. We recommend that the box plot be used more frequently.

Alcohol Drinking↗
PubMed1994

The statistical analysis of single-subject data: a comparative examination.

BACKGROUND AND PURPOSE: The purposes of this study were to examine whether the use of three different statistical methods for analyzing single-subject data led to similar results and to identify components of graphed data that influence agreement (or disagreement) among the statistical procedures. METHODS: Forty-two graphs containing single-subject data were examined. Twenty-one were AB charts of hypothetical data. The other 21 graphs appeared in Journal of Applied Behavioral Analysis, Physical Therapy, Journal of the Association for Persons With Severe Handicaps, and Journal of Behavior Therapy and Experimental Psychiatry. Three different statistical tests--the C statistic, the two-standard deviation band method, and the split-middle method of trend estimation--were used to analyze the 42 graphs. RESULTS: A relatively low degree of agreement (38%) was found among the three statistical tests. The highest rate of agreement for any two statistical procedures (71%) was found for the two-standard deviation band method and the C statistic. A logistic regression analysis revealed that overlap in single-subject graphed data was the best predictor of disagreement among the three statistical tests (beta = .49, P < .03). CONCLUSION AND DISCUSSION: The results indicate that interpretation of data from single-subject research designs is directly influenced by the method of data analysis selected. Variation exists across both visual and statistical methods of data reduction. The advantages and disadvantages of statistical and visual analysis are described.

Data Interpretation, Statistical↗
PubMed1991

Interpretation of interaction in factorial analysis of variance design.

The validity of statistical conclusions in medical research depends on proper analysis and interpretation of collected data. One potential area of invalidity is the inappropriate post hoc analysis of statistically significant interactions in the analysis of variance of factorial designs. This paper examines the statistical explanations included in 83 studies published in three leading medical journals where the findings indicated significant interaction effects. Only 24 per cent of the reported statistically significant interactions had an accompanying correct interpretation. The most common form of misinterpretation involved a comparison of individual cell means within a row or column of one factor used in the design. This interpretation did not conform to the factorial ANOVA model with interaction. This misinterpretation occurs when the correct omnibus test of a hypothesis is followed by an incorrect post hoc analysis and/or an inaccurate assessment of the original statistical result.

Analysis of Variance↗
PubMed1980

Interpretation of research data: selected statistical procedures.

Selected statistical procedures used in the analysis of research data are presented. The relationship of significance testing to research hypotheses is explained in terms of tests of differences and correlation. Also, the differences, assumptions, and advantages and disadvantages of parametric and nonparametric statistics are discussed. With regard to each statistic presented, emphasis is placed on the hypotheses that would be tested, the kinds of data for which the statistic is appropriate, the method of calculation, and how to test for "significance." The selected statistical procedures include the Student's t-test and chi square. An explanation of the concept of correlation is provided, and several correlation coefficients are discussed, including the Pearson r, Spearman rho, Kendall's tau, the point biserial, biserial, phi coefficient, and contingency coefficient. Pharmacists must know basic statistical procedures in order to be able to effectively interpret the results of published research or to appropriately analyze data that have been collected in their own research endeavors.

Methods↗
PubMed1996

Technology assessment in critical care: understanding statistical analyses used to assess agreement between methods of clinical measurement.

Many new measurement methods that employ various technologies to measure physiological parameters have been introduced into the field of critical care. Clinical assessment of these new methods occurs through the conduct of method-comparison studies in which the level of agreement between a new measurement method and a clinical standard method is determined. Clinicians and researchers are often faced with the complicated task of analyzing and interpreting the results of method-comparison studies. Use of correlation and linear regression techniques has been prevalent in method-comparison studies but has proven inappropriate and inadequate in determining how well methods compare. The purposes of this article are to briefly review the terms of accuracy, agreement, and the precision in context with method-comparison studies, and discuss inappropriate and appropriate statistical analyses and their interpretation. Appropriate data analysis of method-comparison studies will aid in determining not only whether new monitoring methods can be interchanged or used in place of existing methods, but whether new methods warrant further research of their effect on patient outcomes.

Critical Care↗
PubMed2000

Vocational students' learning preferences: the interpretability of ipsative data.

A number of researchers have argued that ipsative data are not suitable for statistical procedures designed for normative data. Others have argued that the interpretability of such analyses of ipsative data are little affected where the number of variables and the sample size are sufficiently large. The research reported here represents a factor analysis of the scores on the Canfield Learning Styles Inventory for 1,252 students in vocational education. The results of the factor analysis of these ipsative data were examined in a context of existing theory and research on vocational students and lend support to the argument that the factor analysis of ipsative data can provide sensibly interpretable results.

Adolescent↗