Extension of Feldt's approach to testing homogeneity of coefficients of reliability

Limitations and extensions of Feldt's approach to testing the equality of Cronbach's alpha coefficients in independent and matched samples are discussed. In particular, this approach is used to test equality of intraclass correlation coefficients.This work was supported by the National Institute of Mental Health Specialized Research Center Grant # MH-30854.     

Insensitivity and oversensitivity to answer diagnosticity in hypothesis testing

Two experiments examined how people perceive the diagnosticity of different answers (“yes” and “no”) to the same question. We manipulated whether the “yes” and the “no” answers conveyed the same amount of information or not, as well as the presentation format of the probabilities of the features inquired about. In Experiment 1, participants were presented with only the percentages of occurrence of the features, which most straightforwardly apply to the diagnosticity of “yes” answers. In Experiment 2, participants received in addition the percentages of the absence of features, which serve to assess the diagnosticity of “no” answers. Consistent with previous studies, we found that participants underestimated the difference in the diagnosticity conveyed by different answers to the same question. However, participants' insensitivity was greater when the normative (Bayesian) diagnosticity of the “no” answer was higher than that of the “yes” answer. We also found oversensitivity to answer diagnosticity, whereby participants valued as differentially diagnostic two answers that were normatively equal in terms of their diagnosticity. Presenting to participants the percentages of occurrence of the features inquired about together with their complements increased their sensitivity to the diagnosticity of answers. We discuss the implications of these findings for confirmation bias in hypothesis testing.     

A jackknife test of homogeneity of variance with paired replicates data

A nonparametric test of dispersion with paired replicates data is described which involves jackknifing logarithmic transformations of the ratio of variance estimates for the pre- and post-treatment populations. Results from a Monte Carlo simulation show that the test performs well underH _o and has good power properties. Examples are given of applying the procedure on psychiatric data.A referee called our attention to valuable references which related to our work, and he suggested the inclusion of competing jackknife procedures into our studies.     

Strategies of clinical hypothesis testing

This research examines whether Psychology students, when they test clinical hypotheses, follow either confirmatory or disconfirmatory reasoning strategies. Two hundred and six psychology students, divided in four groups, participated. One group received information about the probability that the hypothesis was correct by means of verbal labels, and another group, by means of numerical expressions. An additional group received the information that getting a precise diagnosis was clinically important. In a last group, diagnostic tests allowed them to increase certainty about the hypothesis. Results show a partial use of confirmatory strategies because, although participants did not seek confirming information, they indeed avoided collecting disconfirming information. When the information increased certainty about the hypothesis, confirmatory strategies became more likely. Neither the increase in the task importance nor the numerical expression of the likelihood that the hypothesis was correct seemed to affect the testing strategy used.     

Using three-dimensional graphics in neuropsychological hypothesis testing

This paper presents the application of an overlapping sampling technique used with a fast Fourier transform on electroencephalographic data. The use of this sampling and analysis technique is demonstrated on data in which steady state, rate of change, and critical event hypotheses are tested. The benefit of applying computer-generated three-dimensional graphics to the results of using this innovative sampling and analysis technique is emphasized.     

Factors affecting hypothesis testing in kindergarten children

In a previous experiment, the authors demonstrated that kindergarten and first-grade children can be trained to test hypotheses sequentially within the context of a discrimination learning task. The present experiment is concerned with delineating various aspects of the pretraining that contribute to the improved hypothesis-testing strategies of kindergarten children (mean CA = 71.6 months). It was found that children who have learned to anticipate an invariant cue-reward relation in such tasks manifest improved hypothesis-testing behavior, as well as improved discrimination performance, whereas children who have been trained to identify and name the various stimulus components of the discriminanda do not perform better than those without such training. It was also found that children who have had practice in shifting from an irrelevant to a relevant dimension perform better than those who have not had such experience. Moreover, children who have been given explicit instruction and training in the use of win-stay and lose-shift rules, as well as in the use of valid hypotheses, manifest strategies superior to those without such training. Finally, extensive pretraining over two sessions, administered on separate days, resulted in a marked reduction in the proportion of children who were dimensionally fixated while solving discrimination problems with two genuine dimensions.     

Learning and hypothesis testing in probabilistic inference tasks

The relation between learning of probabilistic inference tasks and the availability of correct hypotheses for the tasks in individual subjects was investigated in two experiments. The results showed that having the correct hypotheses as measured before learning the tasks was neither a sufficient nor a necessary condition for learning the task. It was concluded that the hypothesis measurements are not reliable enough to allow predictions on an individual level. The results also showed that subjects have not only the four basic functions: positive linear, negative linear, U-shaped and inversely U-shaped, in their hypothesis hierarchies but also J-shaped functions. That the subjects can leam such functions is thus no discodinnation of the hypotheses sampling conception of learning as assumed in earlier studies.     

Note on hypothesis testing in probabilistic inference tasks

Subjects' ability to test hypotheses about the form of functional relations in probabilistic inference tasks was investigated in two experiments which varied the functional relation in the task, the hypotheses to be tested, and the validity of the cue. The results showed that the subjects used the same amount of information regardless of the validity of the cue, that nonlinear hypotheses were harder to test accurately than linear hypothesis, and that hypotheses were harder to test when the relation in the task was nonlinear than when it was linear.     

Omnibus hypothesis testing in dominance-based ordinal multiple regression

Often quantitative data in the social sciences have only ordinal justification. Problems of interpretation can arise when least squares multiple regression (LSMR) is used with ordinal data. Two ordinal alternatives are discussed, dominance-based ordinal multiple regression (DOMR) and proportional odds multiple regression. The Q2 statistic is introduced for testing the omnibus null hypothesis in DOMR. A simulation study is discussed that examines the actual Type I error rate and power of Q2 in comparison to the LSMR omnibus F test under normality and non-normality. Results suggest that Q2 has favorable sampling properties as long as the sample size-to-predictors ratio is not too small, and Q2 can be a good alternative to the omnibus F test when the response variable is non-normal.     

Five studies testing two new egoistic alternatives to the empathy-altruism hypothesis

The empathy-altruism hypothesis claims that prosocial motivation associated with feeling empathy for a person in need is directed toward the ultimate goal of benefiting that person, not toward some subtle form of self-benefit. We explored two new egoistic alternatives to this hypothesis. The empathy-specific reward hypothesis proposes that the prosocial motivation associated with empathy is directed toward the goal of obtaining social or self-rewards (i.e., praise, honor, and pride). The empathy-specific punishment hypothesis proposes that this motivation is directed toward the goal of avoiding social or self-punishments (i.e., censure, guilt, and shame). Study 1 provided an initial test of the empathy-specific reward hypothesis. Studies 2 through 4 used three procedures to test the empathy-specific punishment hypothesis. In Study 5, a Stroop procedure was used to assess the role of reward-relevant, punishment-relevant, and victim-relevant cognitions in mediating the empathy-helping relationship. Results of these five studies did not support either the empathy-specific reward or the empathy-specific punishment hypothesis. Instead, results of each supported the empathy-altruism hypothesis. Evidence that empathic emotion evokes altruistic motivation continues to mount.     

Robust techniques for testing heterogeneity of variance effects in factorial designs

Several ways of using the traditional analysis of variance to test heterogeneity of spread in factorial designs with equal or unequaln are compared using both theoretical and Monte Carlo results. Two types of spread variables, (1) the jackknife pseudovalues ofs ² and (2) the absolute deviations from the cell median, are shown to be robust and relatively powerful. These variables seem to be generally superior to the Z-variance and Box-Scheffé procedures.This research was sponsored by Public Health Service Training Grant MH-08258 from the National Institute of Mental Health. The author thanks Mark I. Appelbaum, Elliot M. Cramer, and Scott E. Maxwell for their helpful criticisms of this paper. An earlier version of this work was presented at the Annual Meeting of the Psychometric Society, Murray Hill, New Jersey, April, 1976.     

Confirming expectations in asymmetric and symmetric social hypothesis testing

This article examines individuals' expectations in a social hypothesis testing task. Participants selected questions from a list to investigate the presence of personality traits in a target individual. They also identified the responses that they expected to receive and the likelihood of the expected responses. The results of two studies indicated that when people asked questions inquiring about the hypothesized traits that did not entail strong a priori beliefs, they expected to find evidence confirming the hypothesis under investigation. These confirming expectations were more pronounced for symmetric questions, in which the diagnosticity and frequency of the expected evidence did not conflict. When the search for information was asymmetric, confirming expectations were diminished, likely as a consequence of either the rareness or low diagnosticity of the hypothesis-confirming outcome. We also discuss the implications of these findings for confirmation bias.     

Goal specificity effects on hypothesis testing in problem solving

Previous research has found that having a nonspecific goal (NSG) leads to better problem solving and transfer than having a specific goal (SG). To distinguish between the various explanations of this effect requires direct evidence showing how a NSG affects a participant's behaviour. Therefore we collected verbal protocols from participants learning to control a linear system consisting of 3 outputs by manipulating 3 inputs. This system was simpler than the one we had used previously, so in Exp. 1 we generalized our earlier goal specificity findings to this system. In Exp. 2 protocol analysis confirmed our prediction (based on dual-space theories of problem solving) that NSG participants focused on hypothesis testing whereas SG participants focused on the goal. However, this difference only emerged over time. We also replicated the goal specificity effect on performance and showed that giving participants a hypothesis to test improved performance.     

Residual costs in task switching: testing the failure-to-engage hypothesis

Reaction time is typically longer on trials on which the task changes. Thisswitch cost is reduced by the opportunity to prepare for the change before the stimulus onset, but there remains aresidual cost that resists reduction by further opportunity for preparation. De Jong (2000) proposed a model for evaluating the contribution to the residual cost of (1) failure to achieve endogenous task-set reconfiguration on a proportion of trials, and (2) limitations to the completeness of reconfiguration attainable by endogenous means. We report good fits of the model to the data from one previous and one new task-switching experiment, suggesting that the residual switch cost may indeed be attributable to a probabilistic failure to complete advance preparation. But strong incentives for preparation only marginally increased the estimated preparation probability, suggesting some intrinsic limitation to the ability to achieve endogenous preparation for a task switch on every trial.     

Answering two criticisms of hypothesis testing: a comment

In a recent article, Leventhal (1999) responds to two criticisms of hypothesis testing by showing that the one-tailed test and the directional two-tailed test are valid, even if all point null hypotheses are false and that hypothesis tests can provide the probability of decisions being correct which are based on the tests. Unfortunately, the falseness of all point null hypotheses affects the operating characteristics of the directional two-tailed test, seeming to weaken certain of Leventhal's arguments in favor of this procedure.     

The Fisher-Pitman permutation test when testing for differences in mean and variance

The Fisher-Pitman permutation test can detect any type of difference between two samples: hence, a significant Fisher-Pitman permutation test does not necessarily provide evidence for a difference in means. It is possible, however, to test separately for differences in means and variances. Here, we present a recently proposed two-stage procedure to decide whether there are differences in means or variances that can be applied when samples may come from nonnormal distributions with possibly unequal variances.