Psychology
Displaying Statistical Data
Displaying statistical data in psychology involves presenting information in visual or graphical formats to help understand and interpret patterns, relationships, and trends within the data. Common methods include bar graphs, scatterplots, histograms, and pie charts, which can provide a more intuitive and accessible way to communicate complex statistical information to researchers, practitioners, and the general public.
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5 Key excerpts on "Displaying Statistical Data"
- David Anderson, Dennis Sweeney, Thomas Williams, Jeffrey Camm(Authors)
- 2019(Publication Date)
- Cengage Learning EMEA(Publisher)
The goal of data visualization is to communicate as effectively and clearly as possible, the key information about the data. In this section, we provide guidelines for creating an effective graphical display, discuss how to select an appropriate type of display given the purpose of the study, illustrate the use of data dash-boards, and show how the Cincinnati Zoo and Botanical Garden uses data visualization techniques to improve decision making. Creating Effective Graphical Displays The data presented in Table 2.16 show the forecasted or planned value of sales ($1000s) and the actual value of sales ($1000s) by sales region in the United States for Gustin Chemical for the past year. Note that there are two quantitative variables (planned sales and actual sales) and one categorical variable (sales region). Suppose we would like to develop a graphical display that would enable management of Gustin Chemical to visualize how each sales region did relative to planned sales and simultaneously enable management to visualize sales performance across regions. TABLE 2.16 Planned and Actual Sales by Sales Region ($1000 S ) Sales Region Planned Sales ($1000s) Actual Sales ($1000s) Northeast Northwest Southeast Southwest 540 420 575 360 447 447 556 341 Copyright 2020 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 72 Chapter 2 Descriptive Statistics: Tabular and Graphical Displays Figure 2.12 shows a side-by-side bar chart of the planned versus actual sales data. Note how this bar chart makes it very easy to compare the planned versus actual sales in a region, as well as across regions.
eBook - PDFQuick Answers to Quantitative Problems
A Pocket Primer
- G. William Page, Carl V. Patton(Authors)
- 2014(Publication Date)
- Academic Press(Publisher)
In performing quick analyses, descriptive statistics are essential. Descriptive statistics provide us tools that help us understand and sum-marize data. Descriptive statistics also provide us a vocabulary that we can use to communicate information concerning quantitative data. Further Readings Andrews, F. M , L. Klem, T. Davidson, P. O'Malley, and W. Rodgers, A Guide for Selecting Statistical Techniques for Analyzing Social Science Data, Second Edition. Ann Arbor: Survey Research Center, Institute for Social Research, The University of Michigan, 1981. 12 How to Describe and Display Data Blalock, Hubert M., Jr., Social Statistics, Revised Second Edition. New York: McGraw-Hill Book Company, 1979. Horwitz, Lucy, and Lou Ferleger, Statistics for Social Change. Boston: South End Press, 1980. Krueckeberg, Donald Α., and Arthur L. Silvers, Urban Planning Anal-ysis: Methods and Models. New York: John Wiley and Sons, 1974. Matlack, William F., Statistics for Public Policy and Management. Boston: Duxbury Press, 1980. Smith, G., Statistical Reasoning. Boston: Allyn and Bacon, 1985. Tufte, Edward R., Envisioning Information. Cheshire, Connecticut: Graphics Press, 1990. Chapter 2 TABULAR ANALYSIS I Definition Although a great amount of data can be summarized as means, medi-ans, modes, percentages, and proportions, there is often a need to sum-marize data in tabular form in order to allow the reader to examine relationships among components of the data. Tabular analysis involves displaying data in a logical, consistent format that permits easy and accurate interpretation. Method Since analysts typically examine and display ordinal or interval data that are usually positive in value, tables should be visualized as being contained in the χ positive, y positive quadrant of the coordinate sys-tem. The values of variables should be organized from low to high along the χ (horizontal) and y (vertical) axes.
No longer available |Learn moreSingle Case Research Methodology
Applications in Special Education and Behavioral Sciences
- Jennifer R. Ledford, David L. Gast, Jennifer R. Ledford, David L. Gast(Authors)
- 2018(Publication Date)
- Taylor & Francis(Publisher)
Graphic Displays of Data Types of Graphic Displays Line Graphs Bar Graphs Cumulative Graphs Semi-logarithmic Charts Guidelines for Selecting and Constructing Graphic Displays Figure Selection Graph Construction Data Presentation Using Computer Software to Construct Graphs Tables Summary Important Terms graphic display, abscissa, ordinate, origin, tic marks, axis labels, condition, phase, condition labels, figure caption, line graph, bar graph, cumulative graph, semi-logarithmic chart, scale break, blocking 7 Visual Representation of Data Amy D. Spriggs, Justin D. Lane, and David L. Gast Graphs should represent complex information without distortion, and should serve a clear pur- pose (Tufte, 2001). They should “induce the reader to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else” (Tufte, 2001, p. 1). Maximizing the impact of your data while minimizing consumer focus on “something else” can be done by following guidelines for graphing data that come from pro- fessional organizations (e.g., American Psychological Association [APA]), historical precedent, and empirical knowledge (i.e., research). In single case design (SCD) research, graphic displays are not only a way to share your outcomes with consumers of your research (as is also common in between-groups studies), but also to enable you to make formative decisions throughout the process of the study. Thus, well-designed graphics are essential in good SCD research. 158 • Amy D. Spriggs et al. Graphic displays (e.g., line graphs, bar graphs, cumulative graphs) and tables serve two basic purposes. First, they assist in organizing data during the data collection process, which facilitates formative evaluation. Second, they provide a detailed summary and description of behavior over time, which allows readers to analyze the relation between independent and dependent variables.
eBook - PDFVisual Statistics
Seeing Data with Dynamic Interactive Graphics
- Forrest W. Young, Pedro M. Valero-Mora, Michael Friendly(Authors)
- 2011(Publication Date)
- Wiley-Interscience(Publisher)
The data have a story to tell, but it may not be easy to hear it. We must attend to nuances. There is much art and craft in seeing what the data seem to say. Altho we have long understood that graphics can strengthen our ability to generate hypotheses, only recently have we begun to understand that graphics can also strengthen our abil-ity to evaluate hypotheses. We are coming to learn that graphics whose geometry faithfully renders the hypothesis-testing algebra of mathematical statistics can aug-ment our hypothesis-testing abilities, leading us toward a methodology for visually intuitive significance testing that is mathematically appropriate. Thus, increasingly, visual statistics is becoming influenced by mathematical statis-tics, a branch of statistics that uses the formal machinery of theorems and proofs to provide mathematically based answers to scientifically posed questions. In fact, a requirement of the dynamic interactive graphics of visual statistics is that their geom-etry be a translation of the relevant algebraic results of mathematical statistics: The subjective visual insights of the data analyst who is seeing data are firmly, though unknowingly, based on the objective rigors of mathematical statistics. Visual statistics also blends influences from computer science, cognitive science, and human-computer interaction studies. These influences inform the way that visual statistics proposes to ease and improve data analysis, leading to an emphasis on the effect of the data analysis environment on the analyst's search for understanding. See what your data seem to say —that's the bottom line: You learn about your data by interacting with their images. As you interact with images of your data, and as they respond in kind, the interaction helps you get to know your data better.
eBook - PDFSingle Subject Research
Strategies for Evaluating Change
- Thomas R Kratochwill(Author)
- 2013(Publication Date)
- Academic Press(Publisher)
2. Analysis and Presentation of Graphic Data 111 statistics so highly favored by Fisher (1942) and his successors. Instead, they developed flexible and pragmatic single-subject procedures that permitted demonstration of reliable control of observable behaviors. The data from these procedures were plotted as rates of responding across time. The demon-stration of reliable control was achieved by showing, graphically, that visible, reliable changes in the plotted response rate were correlated with the repeated introduction and removal of the independent variable. Thus, in the functional analysis of behavior, the graph attained particular importance as a comprehensive means of recording and storing data, and more impor-tant, analyzing it for evidence indicative of a functional relationship. It is this analytic function of the graph that has become of particular significance. In behavior analysis ongoing research decisions, judgments of the adequacy and meaningfulness of data, and the conclusions drawn from research are all based on the analysis of graphed data. The analysis is essen-tially a visual process; determination of change is dependent on the change being of sufficient magnitude to be apparent to the eye. Compared with the potential algebraic sophistication of statistical tests of significance (not always realized in practice), the above procedure usually is relatively insen-sitive, yet that very lack of refinement may have important and valuable consequences for the analysis of behavior (Baer, 1977). The outcome of studies employing traditional statistical data analyses usually is judged in accordance with conventions established for determining the /7-levels beyond which it is assumed that the null hypothesis safely can be rejected. In the behavioral sciences the 5% (.05) level is widely accepted, which means that the chance probability of a Type I error, i.e., of having erroneously rejected the null hypothesis, is no more than 1 in 20.
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