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Journal of Behavior Therapy and... Mar 1994The Self-Assessment Manikin (SAM) is a non-verbal pictorial assessment technique that directly measures the pleasure, arousal, and dominance associated with a person's... (Comparative Study)
Comparative Study
The Self-Assessment Manikin (SAM) is a non-verbal pictorial assessment technique that directly measures the pleasure, arousal, and dominance associated with a person's affective reaction to a wide variety of stimuli. In this experiment, we compare reports of affective experience obtained using SAM, which requires only three simple judgments, to the Semantic Differential scale devised by Mehrabian and Russell (An approach to environmental psychology, 1974) which requires 18 different ratings. Subjective reports were measured to a series of pictures that varied in both affective valence and intensity. Correlations across the two rating methods were high both for reports of experienced pleasure and felt arousal. Differences obtained in the dominance dimension of the two instruments suggest that SAM may better track the personal response to an affective stimulus. SAM is an inexpensive, easy method for quickly assessing reports of affective response in many contexts.
Topics: Adult; Affect; Arousal; Emotions; Factor Analysis, Statistical; Female; Humans; Internal-External Control; Male; Personality Inventory; Psychometrics; Semantic Differential
PubMed: 7962581
DOI: 10.1016/0005-7916(94)90063-9 -
Journal of Religion and Health Aug 2022This article describes the psychometric properties of a semantic differential scale developed to evaluate the attitudes towards menstruation among Roman Catholics in...
This article describes the psychometric properties of a semantic differential scale developed to evaluate the attitudes towards menstruation among Roman Catholics in India, which is referred to as Menstrual Semantic Differential Scale (MSDS). For this purpose, the south Indian state of Kerala was chosen as the location. First, exploratory factor analysis was conducted on the data collected from a sample of participants of different ages, gender, and caste groups. The analysis produced a 10 item scale with a tri-factorial structure that explained 74 percent of the variance. Subsequently, confirmatory factor analysis on a different sample established that the three-factor model was a good fit. Moreover, Cronbach's alpha coefficients of the subscales ranged from 0.73 to 0.93, confirming the acceptable reliability of the instrument. The findings suggest that the MSDS is a reliable and valid measure for assessing attitudes towards menstruation among Roman Catholics in India.
Topics: Attitude; Catholicism; Female; Humans; Menstruation; Psychometrics; Reproducibility of Results; Semantic Differential; Surveys and Questionnaires
PubMed: 34537935
DOI: 10.1007/s10943-021-01429-w -
Journal of Clinical Psychology Jan 1988A set of bipolar semantic differential type adjective scales were constructed to assess five mood states. The response format chosen serves to control for response bias,...
A set of bipolar semantic differential type adjective scales were constructed to assess five mood states. The response format chosen serves to control for response bias, reduces the number of items by half, and measures both positive and negative affect. Each of the six bipolar mood states hypothesized was defined by seven five-point items. Two studies were conducted on samples of high school boys (N = 210); both confirmed the presence of five mood factors: Cheerful-Depressed, Energetic-Tired, Good natured-Grouchy, Confident-Unsure, and Relaxed-Anxious. The mood state factors isolated were compared with the scales in the Eight State Questionnaire and Bipolar POMS.
Topics: Adolescent; Female; Humans; Male; Mood Disorders; Psychometrics; Reference Values; Semantic Differential
PubMed: 3343359
DOI: 10.1002/1097-4679(198801)44:1<33::aid-jclp2270440106>3.0.co;2-n -
Perceptual and Motor Skills Dec 1964
Topics: Humans; Psychological Tests; Semantic Differential; Semantics
PubMed: 14238247
DOI: 10.2466/pms.1964.19.3.968 -
Human Factors Apr 1977
Topics: Communication; Female; Humans; Male; Semantic Differential; Visual Perception
PubMed: 858631
DOI: 10.1177/001872087701900208 -
Nursing Feb 1980
Topics: Emotions; Humans; Myocardial Infarction; Nursing Assessment; Nursing Process; Psychological Tests; Semantic Differential
PubMed: 6898897
DOI: 10.1097/00152193-198002000-00018 -
Research on Aging 2021This study validated a Swedish translation of the Aging Semantic Differential Scale (ASD, 32-items) distributed online. Translation and back-translation were conducted....
This study validated a Swedish translation of the Aging Semantic Differential Scale (ASD, 32-items) distributed online. Translation and back-translation were conducted. A convenience sample of nursing students completed the online questionnaire (N = 292) in spring 2020. Confirmatory factor analysis tested a validated four-factor structure consisting of 26 items, and the reliability and validity of the scale were tested. The Swedish version of the ASD was found to be reliable and valid. Model fit indices, internal reliability, and scale validity were acceptable. Construct validity was verified, and mean differences were observed, in accord with previous research regarding participants' age, sex, clinical experience, and personal relationships with older individuals. The findings provide cross-cultural validation of the ASD by extending its international use. The validation of an online version expands data collection flexibility. As this modified instrument required only 26 items, it may be beneficial for use in future studies and practical settings.
Topics: Aging; Humans; Psychometrics; Reproducibility of Results; Semantic Differential; Surveys and Questionnaires; Sweden
PubMed: 34524931
DOI: 10.1177/0164027520963618 -
Journal of Consulting Psychology Apr 1961
Topics: Humans; Psychological Tests; Self Report; Semantic Differential; Semantics
PubMed: 13765033
DOI: 10.1037/h0046272 -
The American Journal of Psychology Dec 1963
Topics: Concept Formation; Humans; Semantic Differential; Semantics; Thinking
PubMed: 14082653
DOI: No ID Found -
Multivariate Behavioral Research 2020Differential item functioning (DIF) is a pernicious statistical issue that can mask true group differences on a target latent construct. A considerable amount of... (Comparative Study)
Comparative Study
Differential item functioning (DIF) is a pernicious statistical issue that can mask true group differences on a target latent construct. A considerable amount of research has focused on evaluating methods for testing DIF, such as using likelihood ratio tests in item response theory (IRT). Most of this research has focused on the asymptotic properties of DIF testing, in part because many latent variable methods require large samples to obtain stable parameter estimates. Much less research has evaluated these methods in small sample sizes despite the fact that many social and behavioral scientists frequently encounter small samples in practice. In this article, we examine the extent to which model complexity-the number of model parameters estimated simultaneously-affects the recovery of DIF in small samples. We compare three models that vary in complexity: logistic regression with sum scores, the 1-parameter logistic IRT model, and the 2-parameter logistic IRT model. We expected that logistic regression with sum scores and the 1-parameter logistic IRT model would more accurately estimate DIF because these models yielded more stable estimates despite being misspecified. Indeed, a simulation study and empirical example of adolescent substance use show that, even when data are generated from / assumed to be a 2-parameter logistic IRT, using parsimonious models in small samples leads to more powerful tests of DIF while adequately controlling for Type I error. We also provide evidence for minimum sample sizes needed to detect DIF, and we evaluate whether applying corrections for multiple testing is advisable. Finally, we provide recommendations for applied researchers who conduct DIF analyses in small samples.
Topics: Adolescent; Computer Simulation; Data Interpretation, Statistical; Female; Humans; Logistic Models; Male; Models, Theoretical; Psychometrics; Reaction Time; Sample Size; Semantic Differential; Substance-Related Disorders
PubMed: 31583903
DOI: 10.1080/00273171.2019.1671162