Implementation intentions, perfectionism, and goal progress: perhaps the road to hell is paved with good intentions

Two studies explored whether perfectionism moderates the impact of implementation intentions on goal progress. Study 1 used an implementation intention manipulation to examine the effects of these plans in interaction with perfectionism on the progress of New Year's resolutions. Study 2 added a repeated implementation intention condition and monitored affect and monthly goal progress. The results of both studies revealed a significant backfire effect of the implementation intentions on goal progress for participants high on a particular dimension of perfectionism (socially prescribed perfectionism). These perfectionists reported doing significantly worse at reaching their personal goals when they were asked to formulate implementation intentions than when they completed a control exercise. There also was evidence that implementation planning aroused negative affect for socially prescribed perfectionists. These results are the first to suggest that implementation planning may be contra-indicated for individuals with self-critical tendencies.     

From monologue to dialogue: a transition in psychoanalytic practice

During the latter part of the twentieth century, psychoanalysts of various stripes espoused the move from free association and neutrality to various forms of intersubjectivity and dialogue. This shift is studied from the vantage point of conversational rules in terms of the shift from monologue to dialogue, using the concepts of semantics, syntax, and pragmatics. Viewing the data of analysis in this manner offers a means of evaluating the contributions of both monologue and dialogue to our understanding of the conduct of an analysis and the kind of information that can be expected to emerge. Exclusive devotion to either stance, it is argued, renders less understanding than would emerge from a balanced use of both.     

Attaining personal goals: self-concordance plus implementation intentions equals success

The present article includes separate meta-analyses showing that self-concordance and implementation intentions are significantly positively associated with goal progress. Study 1 confirmed the positive relations of both self-concordance and implementation intentions to weekend goal progress. Study 2 confirmed the positive relation of self-concordance with monthly progress on New Year's resolutions but failed to find a direct benefit for implementation intentions. Both studies, however, obtained a significant interaction effect indicating that goal self-concordance and implementation intentions combined synergistically to facilitate goal progress. The article also reports a meta-analysis and results from the 2 studies that demonstrated that goal progress was associated with improved affect over time.     

Age, sex, and body mass index in performance of selected locomotor and fitness tasks by children in grades K-2

The purpose of this study was to examine the contributions of 3 predictor variables (age, sex, and body mass index) to performance of 7 fundamental movement skills (locomotor; run, gallop, hop, leap, jump, skip, and slide) and 4 fitness tasks (grip strength, step test, sit and reach, and timed sit-ups) by 65 children in Grades K-2 (M=6 yr.). A multiple regression analysis indicated that running, leaping, and skipping improved with age. No sex differences were observed on any of the seven skills. In terms of the four fitness tasks, age was positively associated with timed sit-ups and grip strength and inversely associated with step-test performance. Sex was associated with step-test and sit and reach performance (girls performed better on both). Finally, body mass index was related to increased grip strength and fewer timed sit-ups.     

A comparison of maximum covariance and K-means cluster analysis in classifying cases into known taxon groups

Maximum covariance (MAXCOV) is a method for determining whether a group of 3 or more indicators marks 1 continuous or 2 discrete latent distributions of individuals. Although the circumstances under which MAXCOV is effective in detecting latent taxa have been specified, its efficiency in classifying cases into groups has not been assessed, and few studies have compared its performance with that of cluster analysis. In the present Monte Carlo study, the classification efficiencies of MAXCOV and the k-means algorithm were compared across ranges of sample size, effect size, indicator number, taxon base rate, and within-groups covariance. When the impact of these parameters was minimized, k-means classified more data points correctly than MAXCOV. However, when the effects of all parameters were increased concurrently, MAXCOV outperformed k-means.     

Markovian processes with identifiable states: General considerations and application to all-or-none learning

It often happens that a theory specifies some variables or states which cannot be identified completely in an experiment. When this happens, there are important questions as to whether the experiment is relevant to certain assumptions of the theory. Some of these questions are taken up in the present article, where a method is developed for describing the implications of a theory for an experiment. The method consists of constructing a second theory with all of its states identifiable in the outcome-space of the experiment. The method can be applied (i.e., an equivalent identifiable theory exists) whenever a theory specifies a probability function on the sample-space of possible outcomes of the experiment. An interesting relationship between lumpability of states and recurrent events plays an important role in the development of the identifiable theory. An identifiable theory of an experiment can be used to investigate relationships among different theories of the experiment. As an example, an identifiable theory of all-or-none learning is developed, and it is shown that a large class of all-or-none theories are equivalent for experiments in which a task is learned to a strict criterion.This research was supported in part by the National Science Foundation under grant GB-319.