Chapter 12: Descriptive Statistics
Expressing Your Results
Learning Objectives
 Write out simple descriptive statistics in American Psychological Association (APA) style.
 Interpret and create simple APAstyle graphs—including bar graphs, line graphs, and scatterplots.
 Interpret and create simple APAstyle tables—including tables of group or condition means and correlation matrixes.
Once you have conducted your descriptive statistical analyses, you will need to present them to others. In this section, we focus on presenting descriptive statistical results in writing, in graphs, and in tables—following American Psychological Association (APA) guidelines for written research reports. These principles can be adapted easily to other presentation formats such as posters and slide show presentations.
Presenting Descriptive Statistics in Writing
When you have a small number of results to report, it is often most efficient to write them out. There are a few important APA style guidelines here. First, statistical results are always presented in the form of numerals rather than words and are usually rounded to two decimal places (e.g., “2.00” rather than “two” or “2”). They can be presented either in the narrative description of the results or parenthetically—much like reference citations. Here are some examples:
The mean age of the participants was 22.43 years with a standard deviation of 2.34.
Among the low selfesteem participants, those in a negative mood expressed stronger intentions to have unprotected sex (M = 4.05, SD = 2.32) than those in a positive mood (M = 2.15, SD = 2.27).
The treatment group had a mean of 23.40 (SD = 9.33), while the control group had a mean of 20.87 (SD = 8.45).
The testretest correlation was .96.
There was a moderate negative correlation between the alphabetical position of respondents’ last names and their response time (r = −.27).
Notice that when presented in the narrative, the terms mean and standard deviation are written out, but when presented parenthetically, the symbols M and SD are used instead. Notice also that it is especially important to use parallel construction to express similar or comparable results in similar ways. The third example is much better than the following nonparallel alternative:
The treatment group had a mean of 23.40 (SD = 9.33), while 20.87 was the mean of the control group, which had a standard deviation of 8.45.
Presenting Descriptive Statistics in Graphs
When you have a large number of results to report, you can often do it more clearly and efficiently with a graph. When you prepare graphs for an APAstyle research report, there are some general guidelines that you should keep in mind. First, the graph should always add important information rather than repeat information that already appears in the text or in a table. (If a graph presents information more clearly or efficiently, then you should keep the graph and eliminate the text or table.) Second, graphs should be as simple as possible. For example, the Publication Manual discourages the use of colour unless it is absolutely necessary (although colour can still be an effective element in posters, slide show presentations, or textbooks.) Third, graphs should be interpretable on their own. A reader should be able to understand the basic result based only on the graph and its caption and should not have to refer to the text for an explanation.
There are also several more technical guidelines for graphs that include the following:
 Layout
 The graph should be slightly wider than it is tall.
 The independent variable should be plotted on the xaxis and the dependent variable on the yaxis.
 Values should increase from left to right on the xaxis and from bottom to top on the yaxis.
 Axis Labels and Legends
 Axis labels should be clear and concise and include the units of measurement if they do not appear in the caption.
 Axis labels should be parallel to the axis.
 Legends should appear within the boundaries of the graph.
 Text should be in the same simple font throughout and differ by no more than four points.
 Captions
 Captions should briefly describe the figure, explain any abbreviations, and include the units of measurement if they do not appear in the axis labels.
 Captions in an APA manuscript should be typed on a separate page that appears at the end of the manuscript. See Chapter 11 for more information.
Bar Graphs
As we have seen throughout this book, bar graphs are generally used to present and compare the mean scores for two or more groups or conditions. The bar graph in Figure 12.11 is an APAstyle version of Figure 12.4. Notice that it conforms to all the guidelines listed. A new element in Figure 12.11 is the smaller vertical bars that extend both upward and downward from the top of each main bar. These are error bars, and they represent the variability in each group or condition. Although they sometimes extend one standard deviation in each direction, they are more likely to extend one standard error in each direction (as in Figure 12.11). The standard error is the standard deviation of the group divided by the square root of the sample size of the group. The standard error is used because, in general, a difference between group means that is greater than two standard errors is statistically significant. Thus one can “see” whether a difference is statistically significant based on a bar graph with error bars.
Line Graphs
Line graphs are used to present correlations between quantitative variables when the independent variable has, or is organized into, a relatively small number of distinct levels. Each point in a line graph represents the mean score on the dependent variable for participants at one level of the independent variable. Figure 12.12 is an APAstyle version of the results of Carlson and Conard. Notice that it includes error bars representing the standard error and conforms to all the stated guidelines.
In most cases, the information in a line graph could just as easily be presented in a bar graph. In Figure 12.12, for example, one could replace each point with a bar that reaches up to the same level and leave the error bars right where they are. This emphasizes the fundamental similarity of the two types of statistical relationship. Both are differences in the average score on one variable across levels of another. The convention followed by most researchers, however, is to use a bar graph when the variable plotted on the xaxis is categorical and a line graph when it is quantitative.
Scatterplots
Scatterplots are used to present relationships between quantitative variables when the variable on the xaxis (typically the independent variable) has a large number of levels. Each point in a scatterplot represents an individual rather than the mean for a group of individuals, and there are no lines connecting the points. The graph in Figure 12.13 is an APAstyle version of Figure 12.7, which illustrates a few additional points. First, when the variables on the xaxis and yaxis are conceptually similar and measured on the same scale—as here, where they are measures of the same variable on two different occasions—this can be emphasized by making the axes the same length. Second, when two or more individuals fall at exactly the same point on the graph, one way this can be indicated is by offsetting the points slightly along the xaxis. Other ways are by displaying the number of individuals in parentheses next to the point or by making the point larger or darker in proportion to the number of individuals. Finally, the straight line that best fits the points in the scatterplot, which is called the regression line, can also be included.
Expressing Descriptive Statistics in Tables
Like graphs, tables can be used to present large amounts of information clearly and efficiently. The same general principles apply to tables as apply to graphs. They should add important information to the presentation of your results, be as simple as possible, and be interpretable on their own. Again, we focus here on tables for an APAstyle manuscript.
The most common use of tables is to present several means and standard deviations—usually for complex research designs with multiple independent and dependent variables. Figure 12.14, for example, shows the results of a hypothetical study similar to the one by MacDonald and Martineau (2002)^{[1]} discussed in Chapter 5. (The means in Figure 12.14 are the means reported by MacDonald and Martineau, but the standard errors are not). Recall that these researchers categorized participants as having low or high selfesteem, put them into a negative or positive mood, and measured their intentions to have unprotected sex. Although not mentioned in Chapter 5, they also measured participants’ attitudes toward unprotected sex. Notice that the table includes horizontal lines spanning the entire table at the top and bottom, and just beneath the column headings. Furthermore, every column has a heading—including the leftmost column—and there are additional headings that span two or more columns that help to organize the information and present it more efficiently. Finally, notice that APAstyle tables are numbered consecutively starting at 1 (Table 1, Table 2, and so on) and given a brief but clear and descriptive title.
Another common use of tables is to present correlations—usually measured by Pearson’s r—among several variables. This kind of table is called a correlation matrix. Figure 12.15 is a correlation matrix based on a study by David McCabe and colleagues (McCabe, Roediger, McDaniel, Balota, & Hambrick, 2010)^{[2]}. They were interested in the relationships between working memory and several other variables. We can see from the table that the correlation between working memory and executive function, for example, was an extremely strong .96, that the correlation between working memory and vocabulary was a medium .27, and that all the measures except vocabulary tend to decline with age. Notice here that only half the table is filled in because the other half would have identical values. For example, the Pearson’s r value in the upper right corner (working memory and age) would be the same as the one in the lower left corner (age and working memory). The correlation of a variable with itself is always 1.00, so these values are replaced by dashes to make the table easier to read.
As with graphs, precise statistical results that appear in a table do not need to be repeated in the text. Instead, the writer can note major trends and alert the reader to details (e.g., specific correlations) that are of particular interest.
Key Takeaways
 In an APAstyle article, simple results are most efficiently presented in the text, while more complex results are most efficiently presented in graphs or tables.
 APA style includes several rules for presenting numerical results in the text. These include using words only for numbers less than 10 that do not represent precise statistical results, and rounding results to two decimal places, using words (e.g., “mean”) in the text and symbols (e.g., “M”) in parentheses.
 APA style includes several rules for presenting results in graphs and tables. Graphs and tables should add information rather than repeating information, be as simple as possible, and be interpretable on their own with a descriptive caption (for graphs) or a descriptive title (for tables).
Exercises
 Practice: In a classic study, men and women rated the importance of physical attractiveness in both a shortterm mate and a longterm mate (Buss & Schmitt, 1993)^{[3]}. The means and standard deviations are as follows. Men / Short Term: M = 5.67, SD = 2.34; Men / Long Term: M = 4.43, SD = 2.11; Women / Short Term: M = 5.67, SD = 2.48; Women / Long Term: M = 4.22, SD = 1.98. Present these results
 in writing
 in a graph
 in a table
Long Descriptions
“Convincing” long description: A fourpanel comic strip. In the first panel, a man says to a woman, “I think we should give it another shot.” The woman says, “We should break up, and I can prove it.”
In the second panel, there is a line graph with a downward trend titled “Our Relationship.”
In the third panel, the man, bent over and looking at the graph in the woman’s hands, says, “Huh.”
In the fourth panel, the man says, “Maybe you’re right.” The woman says, “I knew data would convince you.” The man replies, “No, I just think I can do better than someone who doesn’t label her axes.” [Return to “Convincing”]
Figure 12.11 long description: A sample APAstyle bar graph, with a horizontal axis labelled “Condition” and a vertical axis labelled “Clinician Rating of Severity.” The caption of the graph says, “Figure X. Mean clinician’s rating of phobia severity for participants receiving the education treatment and the exposure treatment. Error bars represent standard errors.” At the top of each data bar is an error bar, which look likes a capital I: a vertical line with short horizontal lines attached to its top and bottom. The bottom half of each error bar hangs over the top of the data bar, while each top half sticks out the top of the data bar. [Return to Figure 12.11]
Figure 12.12 long description: A sample APAstyle line graph with a horizontal axis labelled “Last Name Quartile” and a vertical axis labelled “Response Times (z Scores).” The caption of the graph says, “Figure X. Mean response time by the alphabetical position of respondents’ names in the alphabet. Response times are expressed as z scores. Error bars represent standard errors.” Each data point has an error bar sticking out of its top and bottom. [Return to Figure 12.12]
Figure 12.13 long description: Sample APAstyle scatterplot with a horizontal axis labelled “Time 1” and a vertical axis labelled “Time 2.” Each axis has values from 10 to 30. The caption of the scatterplot says, “Figure X. Relationship between scores on the Rosenberg selfesteem scale taken by 25 research methods students on two occasions one week apart. Pearson’s r = .96.” Most of the data points are clustered around the dashed regression line that extends from approximately (12, 11) to (29, 22). [Return to Figure 12.13]
Figure 12.14 long description: Sample APAstyle table presenting means and standard deviations. The table is titled “Table X” and is captioned, “Means and Standard Deviations of Intentions to Have Unprotected Sex and Attitudes Toward Unprotected Sex as a Function of Both Mood and SelfEsteem.” The data is organized into negative and positive mood and details intentions and attitudes toward unprotected sex.
Negative mood:
 Intentions
 High—Mean, 2.46
 High—Standard Deviation, 1.97
 Low—Mean, 4.05
 Low—Standard Deviation, 2.32
 Attitudes
 High—Mean, 1.65
 High—Standard Deviation, 2.23
 Low—Mean, 1.95
 Low—Standard Deviation, 2.01
Positive mood:
 Intentions
 High—Mean, 2.45
 High—Standard Deviation, 2.00
 Low—Mean, 2.15
 Low—Standard Deviation, 2.27
 Attitudes
 High—Mean, 1.82
 High—Standard Deviation, 2.32
 Low—Mean, 1.23
 Low—Standard Deviation, 1.75
Figure 12.15 long description: Sample APAstyle correlation matrix, titled “Table X: Correlations Between Five Cognitive Variables and Age.” The five cognitive variables are:
 Working memory
 Executive function
 Processing speed
 Vocabulary
 Episodic memory
The data is as such:
Measure  1  2  3  4  5 


—  

.96  —  

.78  .78  —  

.27  .45  .08  —  

.73  .75  .52  .38  — 

−.59  −.56  −.82  .22  −.41 
Media Attributions
 MacDonald, T. K., & Martineau, A. M. (2002). Selfesteem, mood, and intentions to use condoms: When does low selfesteem lead to risky health behaviours? Journal of Experimental Social Psychology, 38, 299–306. ↵
 McCabe, D. P., Roediger, H. L., McDaniel, M. A., Balota, D. A., & Hambrick, D. Z. (2010). The relationship between working memory capacity and executive functioning. Neuropsychology, 24(2), 222–243. doi:10.1037/a0017619 ↵
 Buss, D. M., & Schmitt, D. P. (1993). Sexual strategies theory: A contextual evolutionary analysis of human mating. Psychological Review, 100, 204–232. ↵
A figure in which the heights of the bars represent the group means.
Small bars at the top of each main bar in a bar graph that represent the variability in each group or condition.
The standard deviation of the group divided by the square root of the sample size of the group.
A graph used to present correlations between quantitative variables when the independent variable has, or is organized into, a relatively small number of distinct levels.
A graph which shows correlations between quantitative variables; each point represents one person’s score on both variables.
A table showing the correlation between every possible pair of variables in the study.