﻿ Decision Tree Flowchart Examples

Graziano & Raulin
Research Methods (9th edition)

## Examples of Using the Decision-Tree Flowchart

Listed below are several examples of using the decision-tree flowcharts to determine an appropriate statistical procedure for a given study. We will walk you through each step of the process.

1. DESCRIPTION: Panic disorder patients, either with or without agoraphobia, are compared on (1) whether they qualify for a diagnosis of depression, (2) their age at the time of the first panic attack, and (3) the number of panic attacks that they have had in the last 30 days.

SELECTION: We start by listing the study characteristics. We have two independent groups in a differential study defined by their diagnosis (panic with agoraphobia, panic without agoraphobia). This is a differential research study with three dependent variables. The diagnosis of depression is a nominal measure, and the age at first panic attack and the number of panic attacks are both ratio measures.

We always want to include descriptive statistics. We would compute frequency counts for the diagnosis of depression and measures of central tendency (mean, median, and mode) and variability (variance and standard deviation) for the other two variables.

There is no manipulation; therefore, no manipulation check is necessary. In differential research studies, one must be very cautious about the possibility of confounding. Therefore, we will want to compute correlations between the dependent variables and possible confounding variables and will want to look for mean differences on potential confounding variables using the same inferential statistics used to test the hypotheses.

For the first dependent variable (presence of depression, nominal variable), we are comparing two groups. Therefore, the appropriate inferential statistic is a chi-square test for independence. For the next two variables, we have one independent variable, two independent groups, and score data. There are two possibilities (independent groups t-test or one-way ANOVA). Both are correct and both will lead to the same decision.

2. DESCRIPTION: High school class rankings are compared for those who are engaged in interscholastic athletics and those who are not.

SELECTION: We are comparing two groups in a differential research study. The dependent measure is a ranking (ordinal data), and we want to see if the groups are different on this ranking. We should compute frequencies or average rankings for descriptive statistics and a Mann-Whitney U-test for the inferential statistic.

3. DESCRIPTION: We are comparing the rate of salary increase over a two-year period in two industries (auto manufacturing and computer distribution and sales). We randomly select 50 people from each of those two industries. Half of the 50 from each industry is randomly selected for a two-day workshop on time and priority management. Then the 100 participants are followed in their career for two years. The hypothesis is that the workshop will improve the functioning of participants and will thus contribute to a more rapid rise in their incomes. Furthermore, it is expected that this workshop will have a bigger impact on people in a rapidly-changing field like computers than a more traditional field like heavy manufacturing.

SELECTION: We have a mixed factorial design, which is mixed in two ways. One factor is manipulated, and one factor is nonmanipulated. One factor is a between-subjects factor, and one factor is a within-subjects factor. The manipulated/nonmanipulated distinction will not affect the choice of statistics, but will affect the interpretation. The within-subjects/between-subjects distinction will affect the statistical procedure. See Chapter 12 for details about these issues.

Our dependent measure (rate of salary increase) will produce score data (a ratio measure). Therefore, we would compute measures of central tendency (mean, median, and mode) and variability (variance and standard deviation) as descriptive statistics. For inferential statistics, we are directed to a factorial ANOVA, although we are warned that there are many possible variations on factorial ANOVAs.

This is a complex ANOVA, which was not covered in the text and cannot be computed using the version of SPSS for Windows that can be bundled with this text. However, many statistical analysis packages can handle this complex design. If you had to compute such a complex ANOVA, you may want to contact your computer center to learn about the statistical analysis packages available for your use.

4. DESCRIPTION: In a cognitive study, 60 randomly selected college students are tested on a recognition task. They are all shown 10 objects on a computer screen that are easy to identify and distinguish. They are then flashed pictures of those objects or new objects (i.e., not part of the original set of 10) to either the right visual field or the left visual field. Their task is to respond as quickly as possible to indicate whether the picture was new or part of the original set. The scores for each participant include both an error score and a reaction time measure. Those scores were computed separately for each visual field.

SELECTION: We have a single group of participants who are tested under two conditions (right vs. left visual field presentation), and we have two dependent variables (error rate and reaction time), which are computed for each condition. We are hypothesizing a shorter reaction time, but no difference in error rate, for pictures that are projected to the left visual field (processed initially in the right cerebral hemisphere).

Our dependent measures both produce score data. Therefore, we should compute measures of central tendency (mean, median, and mode) and variability (variance and standard deviation) as our descriptive statistics. Since we are using a within-subjects designs, our two groups (conditions) are correlated. Therefore, we have two possibilities (correlated t-test or repeated measures ANOVA). Both are correct, and both will lead to the same statistical decision.