Story Sequence Analysis of Family TATs

One hundred twenty-six three-member families were asked to produce conjointly three TAT stories based on nine cards, which were scored by the Arnold system of Story Sequence Analysis. There were 50 families with normal children, 44 with emotionally maladjusted, 16 with schizophrenic, and 16 with delinquent children. Analysis of the results revealed that the Arnold score successfully differentiated normal from abnormal families, but that the three abnormal groups did not differ from each other. The last set of TAT cards given was the most effective.     

Exploratory Factor Analysis With Small Sample Sizes

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for N below 50. Simulations were carried out to estimate the minimum required N for different levels of loadings (λ), number of factors (f), and number of variables (p) and to examine the extent to which a small N solution can sustain the presence of small distortions such as interfactor correlations, model error, secondary loadings, unequal loadings, and unequal p/f. Factor recovery was assessed in terms of pattern congruence coefficients, factor score correlations, Heywood cases, and the gap size between eigenvalues. A subsampling study was also conducted on a psychological dataset of individuals who filled in a Big Five Inventory via the Internet. Results showed that when data are well conditioned (i.e., high λ, low f, high p), EFA can yield reliable results for N well below 50, even in the presence of small distortions. Such conditions may be uncommon but should certainly not be ruled out in behavioral research data.     

The Epistemic Integrity of Scientific Research

We live in a world in which scientific expertise and its epistemic authority become more important. On the other hand, the financial interests in research, which could potentially corrupt science, are increasing. Due to these two tendencies, a concern for the integrity of scientific research becomes increasingly vital. This concern is, however, hollow if we do not have a clear account of research integrity. Therefore, it is important that we explicate this concept. Following Rudolf Carnap’s characterization of the task of explication, this means that we should develop a concept that is (1) similar to our common sense notion of research integrity, (2) exact, (3) fruitful, and (4) as simple as possible. Since existing concepts do not meet these four requirements, we develop a new concept in this article. We describe a concept of epistemic integrity that is based on the property of deceptiveness, and argue that this concept does meet Carnap’s four requirements of explication. To illustrate and support our claims we use several examples from scientific practice, mainly from biomedical research.     

The psychophysics of sugar concentration discrimination and contrast evaluation in bumblebees

The capacity to discriminate between choice options is crucial for a decision-maker to avoid unprofitable options. The physical properties of rewards are presumed to be represented on context-dependent, nonlinear cognitive scales that may systematically influence reward expectation and thus choice behavior. In this study, we investigated the discrimination performance of free-flying bumblebee workers (Bombus impatiens) in a choice between sucrose solutions with different concentrations. We conducted two-alternative free choice experiments on two B. impatiens colonies containing some electronically tagged bumblebees foraging at an array of computer-automated artificial flowers that recorded individual choices. We mimicked natural foraging conditions by allowing uncertainty in the probability of reward delivery while maintaining certainty in reward concentration. We used a Bayesian approach to fit psychometric functions, relating the strength of preference for the higher concentration option to the relative intensity of the presented stimuli. Psychometric analysis was performed on visitation data from individually marked bumblebees and pooled data from unmarked individuals. Bumblebees preferred the more concentrated sugar solutions at high stimulus intensities and showed no preference at low stimulus intensities. The obtained psychometric function is consistent with reward evaluation based on perceived concentration contrast between choices. We found no evidence that bumblebees reduce reward expectations upon experiencing non-rewarded visits. We compare psychometric function parameters between the bumblebee B. impatiens and the flower bat Glossophaga commissarisi and discuss the relevance of psychophysics for pollinator-exerted selection pressures on plants.     

Operationalisierte Psychodynamische Diagnostik des Kindes- und Jugendalters

There are only a few studies focusing on the mental structure and its change in children and adolescents with psychiatric disorders. This study aimed at investigating change in the mental structure of children and adolescents over time. At intake 60 inpatients were interviewed with the operationalized psychodynamic diagnostic interview for children and adolescents (OPD-CA). After an average of 27 months 25 patients agreed on participating in the follow-up study. The OPD-CA was conducted again and the Kiddie-Sads present and lifetime version (Kiddie-Sads-PL) as well as parent-based ratings child behaviour checklist (CBCL) and strength and difficulties questionnaire (SDQ) were applied. A consensus rating of the OPD-CA was attained by three researchers well experienced with the use of the OPD-CA. The level of structural integration improved significantly, in particular in the dimensions “control” and “communication skills”. There were no significant changes in the dimension “self-perception/object-perception”. The improvement was also observable in parent-report questionnaires. This study makes a contribution to the idea that changes in the mental structure of children and adolescents can be shown and measured with the OPD-CA.     

Asthma severity, child security, and child internalizing: using story stem techniques to assess the meaning children give to family and disease-specific events

Children with persistent asthma are at increased risk for mental health problems. Although mechanisms of effect are not yet known, it may be that children are less trusting of the family as a source of support and security when they have more severe asthma. This study tested whether asthma severity is related to children's perceptions of insecurity in the family, and whether insecurity is in turn associated with child adjustment. Children (N = 168; mean age = 8 years) completed story stems pertaining to routine family events (e.g., mealtimes) and ambiguous but potentially threatening asthma events such as tightness in the chest. Responses were evaluated for the extent to which appraisals portrayed the family as responding in cohesive, security-provoking ways. Asthma severity was assessed by both objective lung function testing and primary caregiver report. Caregivers reported child symptomatology. Beyond medication adherence, caregiver education, and child age and gender, greater asthma severity predicted more internalizing and externalizing symptoms. Greater asthma severity, assessed using spirometry (but not parent report), was related to less secure child narratives of the family, which in turn related to more child internalizing symptoms. Results suggest that asthma can take a considerable toll on children's feelings of security and mental health. Furthermore, given the difficulty in assessing young children's perceptions, this study helps demonstrate the potential of story stem techniques in assessing children's appraisals of illness threat and management in the family.     

Hangover sensitivity after controlled alcohol administration as predictor of post-college drinking

Predicting continued problematic levels of drinking after the early 20's could help with early identification of persons at risk. This study investigated whether hangover insensitivity could predict postcollege drinking and problems beyond the variance due to drinking patterns. In a preliminary study, 134 college seniors from a laboratory study of hangover (Time 1) were contacted and assessed 1-4 years (M = 2.3) later (Time 2). Hangover severity was studied after controlled alcohol administration to a specific dose while controlling sleep and environmental influences. Hangover severity at Time 1 was used to predict Time 2 drinking volume and problems while controlling for relevant demographics and Time 1 drinking volume. Hangover insensitivity at Time 1 tended to predict a clinical level of alcohol problems with a large statistical effect size. Hangover sensitivity also correlated positively with sensitivity to alcohol intoxication. Hangover severity did not predict future drinking volume. Hangover insensitivity correlates with insensitivity to intoxication and might predict more serious alcohol problems in the future, suggesting that a future larger study is warranted. Hangover insensitivity could result from physiological factors underlying low sensitivity to alcohol or risk for alcoholism.     

Conducting behavioral research on Amazon’s Mechanical Turk

Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this article is to demonstrate how to use this Web site for conducting behavioral research and to lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments, and faster iteration between developing theory and executing experiments. While other methods of conducting behavioral research may be comparable to or even better than Mechanical Turk on one or more of the axes outlined above, we will show that when taken as a whole Mechanical Turk can be a useful tool for many researchers. We will discuss how the behavior of workers compares with that of experts and laboratory subjects. Then we will illustrate the mechanics of putting a task on Mechanical Turk, including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform, including techniques for conducting synchronous experiments, methods for ensuring high-quality work, how to keep data private, and how to maintain code security.