Behavioral Statistics

For each problem, Download the data from the Problem Description (see below).  Calculate all appropriate descriptive statistics and test the hypothesis with an appropriate alpha (α) level.  Also draw a bar or line graph.  Don't forget an APA-style write up of your results. APA-style examples are located here.

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In a study by Eysenck (1974; see Problem Description 2) in which he compared the recall of older subjects under one of 5 levels of processing. Another aspect of Eysenck’s study compared Younger and Older subjects on their ability to recall material in the face of instructions telling that they should memorize the material for later recall. (Presumably this task required a high level of processing, which older subjects may not do well.) The data on 10 subjects in each group follow, where the dependent variable is the number of items recalled.

        (a) Conduct the analysis of variance comparing the means of these two groups.
        (b) Conduct an independent-samples t-test on the data and compare the results to those you obtained in part (a).

Craik and Lockhart (1972) proposed as a model of memory that the degree to which verbal material is remembered by the subject is a function of the degree to which it was processed when it was initially presented. You have probably noticed that I frequently insert questions to you in the text, asking about alternative interpretations of the data, about what it would mean if the test statistic came out differently, and so on. The main purpose of these questions is to encourage you to “process” the information you have just read rather than to just let it flow past. I’m talking here about the same thing that Craik and Lockhart were. But to put the example in their terms, imagine that you are asked to memorize a list of words. Repeating a word to yourself (a low level of processing) would not be expected to lead to as good recall as thinking about each word and trying to form associations between that word and some other word. Eysenck (1974) was interested in testing this model and, more important, in looking to see whether it could help to explain reported differences between young and old subjects in their ability to recall verbal material. An examination of Eysenck’s data on age differences will be postponed the Factorial ANOVA; here, you will concentrate on differences due to the level of processing.

Eysenck randomly assigned 50 subjects between the ages of 55 and 65 years to one of five groups: four incidental-learning groups and one intentional-learning group. (Incidental learning is learning in the absence of the expectation that the material will need to be recalled later.) The Counting group was asked to read through a list of words and simply count the number of letters in each word. This involved the lowest level of processing because subjects did not need to deal with each word as anything more than a collection of letters. The Rhyming group was asked to read each word and to think of a word that rhymed with it. This task involved considering the sound of each word but not its meaning. The Adjective group had to process the words to the extent of giving an adjective that could reasonably be used to modify each word on the list. The Imagery group was instructed to try to form vivid images of each word, and this condition was assumed to require the deepest level of processing. None of these four groups was told that they would later be asked to recall the items. Finally, the Intentional group was told to read through the list and to memorize the words for later recall. After the subjects had gone through the list of 27 items three times, they were given a sheet of paper and asked to write down all the words they could remember. If learning involves nothing more than being exposed to the material (the way most of us read a newspaper or, heaven forbid, a class assignment), then the five groups should have shown equal recall—after all, they all saw all the words, If the level of processing of the material is important, then there should have been noticeable differences among the group means.

The Null Hypothesis
Eysenck was interested in testing the null hypothesis that the level of recall was equal under the five conditions. In other words, if µ₁ represents the population mean for all subjects who could potentially be tested under the Counting conditions, µ₂ represents the population mean corresponding to the Rhyming condition, and so on, up to µ₅ (for the Intentional condition), then the null hypothesis is

            H₀: µ₁ = µ₂ = µ₃ = µ₄ = µ₅

The alternative hypothesis H₁ will be the hypothesis that at least one mean is different from the others.

An additional example of a one-way analysis of variance will illustrate the treatment of unequal sample sizes. In a study of the development of low birthweight (LBW) infants (Nurcombe, Howell, Rauh, Teti, Ruoff, & Brennan, 1984), three groups of newborn infants differed in terms of birth- weight and whether their mothers had participated in a training program about the special needs of low-birthweight infants. The mothers were then interviewed when the infants were 6 months old. There were three groups in the experiment—an LBW—Experimental group, an LBW—Control group, and a Full-Term—Control group. The two control groups received no special training, and so serve as reference points against which to compare the performance of the trained (experimental) group. The LBW—Experimental group was part of the intervention program, and we hoped to show that those mothers would adapt to their new role as well as mothers of full-term infants, On the other hand, we expected that mothers of low-birthweight infants who did not receive the intervention program would have some trouble adapting. (Being a parent of a low-birthweight baby is not an easy task, especially for the first few months. For rather dramatic results from tracking these children for nine years, see Achenbach, Howell, Aoki, & Rauh, 1993.)  The dependent variable is the score on a maternal adaptation scale.  The actual data from this study are presented in the file.

What does marijuana do, and how does it do it? Aside from its better-known effects, marijuana increases, or in some cases decreases, locomotor (walking around) behavior. The nucleus accumbens is a forebrain structure that has been shown to be involved in locomotor activity in rats. (It is also thought to control feelings of pleasure.) Administration of low doses of tetrahydrocannabinol (THC, the major active ingredient in marijuana) is known to increase locomotor activity, whereas high doses are known to lead to a decrease in activity. In an attempt to examine whether THC is acting within the nucleus accumbens to produce its effects on activity, Conti and Musty (1984) bilaterally injected either a placebo or 0.1, 0.5, 1, or 2 micrograms (µg) of THC directly into the nucleus accumbens of rats. The investigators recorded the change in the activity level of the animals after injection. It was expected that activity would increase more with smaller injections than with larger ones. The data in the file represent the amount of change (decrease) in each animal.  First, we will set up the null hypothesis, which states that all of the samples were drawn from populations with the same mean. In other words, H₀: µ₁ = µ₂ = µ₃ = µ₄ = µ₅.  For consistency we will test this null hypothesis with a significance level of α = .05.
 

Another way to look at the Eysenck study mentioned in Problem Descriptions 1 and 2 is to compare four groups of subjects. One group consisted of Younger subjects who were presented the words to be recalled in a condition that elicited a Low level of processing. A second group consisted of Younger subjects who were given a task requiring the Highest level of processing (as in Problem Description 2). The two other groups were Older subjects who were given tasks requiring either Low or High levels of processing. The data follow:

What effect does smoking have on performance? Spilich, June, and Renner (1992) asked nonsmokers (NS), smokers who had delayed smoking for three hours (DS), and smokers who were actively smoking (AS) to perform a pattern recognition task in which they had to locate a target on a screen. The dependent variable was latency (in seconds). The data are presented below. Plot the resulting means and run the analysis of variance. On the basis of these data is there support for the hypothesis that smoking has an effect on performance?

           
 

In the study referred to in Problem Description 6, Spilich et al. (1992) also investigated performance on a cognitive task that required the subject to read a passage and then to recall it later. This task has much greater information processing demands than the pattern recognition task. The independent variable was the three smoking groups referred to in Problem Description 6.
 

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© 2008 David M. Compton, Ph.D.