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Running Head: a REVIEW and EVALUATION of an OPEN 1 Running Head: A REVIEW AND EVALUATION OF AN OPEN 1 A Review and Evaluation of an Open-Source Mood Assessment, the Brief Mood Introspection Scale Rachael M. Cavallaro University of New Hampshire Victoria Bryan University of New Hampshire John D. Mayer University of New Hampshire A REVIEW AND EVALUATION OF AN OPEN 2 Abstract The Brief Mood Introspection Scale (BMIS) is an open-source, 16-item assessment of current mood regularly used in psychological research. The scale has two sets of scales: one pair of scales assesses mood using Pleasant-Unpleasant and Arousal-Calm dimensions; the other (less used alternative) assesses mood using Positive and Negative Affect. However, since its publication in 1988, there have been no systematic reports of its usage pattern, norms or of evidence for its validity. The present meta-analysis aims to (1) identify the nature of the studies that employed the BMIS (i.e. research areas and design), (2) establish the norms for the BMIS the scale means, standard deviations, and reliabilities, and (3) evaluate for the BMIS’s validity. One hundred and fifty studies that utilized the BMIS over a five-year timespan (2016-2011) were identified, including 27 studies that reported information pertinent to the scale’s norms. Our findings indicated that BMIS was primarily used in experimental research, and that its norms were similar to those from the original report. We make recommendations for use of Likert response scales and scoring conventions. A more qualitative review suggested that the Pleasant- Unpleasant mood scale had considerable evidence for its validity from its relation to criteria. KEY WORDS: Brief Mood Introspection Scale; Mood; Meta-Analysis; Mood Assessment A REVIEW AND EVALUATION OF AN OPEN 3 A Review and Evaluation of an Open-Source Mood Assessment, the Brief Mood Introspection Scale The Brief Mood Introspection Scale (BMIS) is an open-source mood scale, consisting of 16-mood adjectives such as “happy” and “fed up” that participants use to rate their current mood state (Mayer & Gaschke, 1988). Since its introduction in 1988, the BMIS has been used in more than 200 published articles across a variety of research areas including self-control, mood and emotion, social psychology and relationships, and cognition, according to a search on PsycINFO. The first mood adjective scales originated in the early 1950’s in response to the development of the mood-altering pharmaceuticals—which had shown early promise for the treatment of mood and anxiety (McNair & Lorr, 1964; Nowlis, 1965; Nowlis & Nowlis, 1956). For such scales, respondents were instructed to rate the degree to which adjectives such as happy, angry, or sad described their current mood. Renewed interest in mood scales grew during the 1980’s, as the understanding of emotion increased (e.g., Ekman, Levenson & Friesen, 1983), and interest grew as to the possible influence of mood on cognition, and of cognition on mood (e.g., Beck, Epstein & Harrison, 1983; Bower, 1981; Isen, Shalker, Clark and Karp, 1978, Zajonc, 1980). These interests led to a fresh look at measures of mood, including mood adjectives (Watson & Tellegen, 1985). The Brief Mood Introspection Scale (BMIS) is one of several mood-adjective scales developed in the 1980s as a result, including the widely used Positive and Negative Affect Schedule (PANAS; see Watson, Clark, & Tellegen, 1988) and the Russell Affect Grid (Russell, Weiss, & Mendelsohn, 1989). The present work re-examines the psychometric properties of the BMIS by employing a meta-analysis of 98 articles (203 studies) that utilized the scale between January 1st, 2011 and July 31st, 2016. Normative data for the assessment is provided along with some additional A REVIEW AND EVALUATION OF AN OPEN 4 evidence of the scale’s validity. From our findings, we make suggestions regarding the use and reporting of results using the BMIS. The Brief Mood Introspection Scale Structure of the Brief Mood Introspection Scale The 16-item Brief Mood Introspection Scale (BMIS) was originally developed as a subset of the 62-adjective Mood-State Introspection Scale (Mayer & Gaschke, 1988); the general layout of the scale is depicted in Figure 1. The two-dimensional simple structure of mood In the 1950s, researchers had asked participants to describe how they were feeling on the first mood adjective checklists: Lists of moods such as “happy,” “angry,” and “afraid.” Such scales often included many terms—sometimes over 100. The correlations among the test items were factor analyzed in the hopes of reducing the number of variables to a smaller number of affective dimensions. In one of the first such studies, Green and Nowlis (1957) suggested there were eight such dimensions; McNair and Lorr (1964) recommended between five and seven . Revisiting the issue in the 1980s, researchers such as Russell, and Watson and Tellegen focused on a far simpler analysis of mood using two broad dimensions (Russell et al., 1989; Watson & Tellegen, 1985). Specifically, in their unrotated factor extractions forcing two factors, they obtained two dimensions labeled I. Pleasant-Unpleasant mood and II. Arousal-Calm mood. This appealing two-dimensional solution mapped onto early theories of mood and simplified these earlier solutions in a way that was conducive to further research. There emerged an at-least temporary consensus in the 1980s that two-dimensions provided a relatively good, more parsimonious, depiction of mood than what had come before (Mayer & Gashke, 1988; Russell et A REVIEW AND EVALUATION OF AN OPEN 5 al., 1989; Watson & Tellegen, 1985), but see also some contemporary revisions (e.g., Gregg & Shephard, 2009; Wilhelm & Schoebi, 2007). There was, however, one unresolved controversy: whether the unrotated solution described above, or a varimax-rotated version of the factor solution was best. Factor rotations align items on a test into different clusters depending upon specified criteria. Varimax rotation maximizes “simple structure” of factor dimensions: That is, it finds the solution for which moods load as much as possible on one dimension and load minimally on another, “turning up the contrast” between moods. Watson, Clark, & Tellegen (1988) proposed a varimax approach which led to variations on the original two-factor solution, which they labeled Positive and Negative Affect. They argued the varimax solution was serious alternative for several reasons. Emerging research at the time suggested that, perhaps, the brain areas responsible for positive and negative emotions might be split across hemispheres (Davidson, Schwartz, Saron, Bennett, & Goleman, 1979). Moreover, Edward Diener, who was then studying subjective well-being, argued that such well-being might best be conceived as a person’s overall positive and negative feelings about their lives (Diener & Emmons, 1984). Conceived of that way, positive and negative emotion should be measured relatively independent of one another. The argument for the original, unrotated Pleasant-Unpleasant dimension, that was equally compelling, was that psychological models of affect dating from Wundt & Judd (1897) forward, depicted emotion as falling along such Pleasant-Unpleasant and Arousal-Calm continua. Moreover, contemporaneous cognitive research emphasized the correspondence between a single Pleasant-Unpleasant mood dimension and the cognitive evaluation of stimuli as taking place along a Pleasant-Unpleasant continuum (e.g. Osgood & Suci, 1955). This became useful, for example, in research on mood-congruent cognition, in which people more readily retrieved A REVIEW AND EVALUATION OF AN OPEN 6 cognitive stimuli that matched their moods (Bower, 1981; Drače, 2013; Drače, Efendić & Marić, 2015; Mayer, Gaschke, Braverman & Evans, 1992; Mayer & Hanson, 1995). Initial Normative Information and Validity Evidence of the Brief Mood Introspection Scale Mayer and & Gaschke (1988) established the initial normative information for the BMIS, specifically its means, standard deviations and reliabilities. They employed subtractive scoring to calculate participants’ scores on either the unrotated or rotated dimensions of the BMIS. That is, items that are oppositely-scored (e.g., the adjective sad on the Pleasant-Unpleasant Scale) were summed and then subtracted from the sum of the scores of the positively-phrased items (e.g., happy). Since the scale’s introduction, however, Mayer has recommended reverse scoring opposite-scored items. For example, to calculate how pleasant someone’s current mood is, a participant who rated how sad they currently felt by reporting a 1 on a 4-point Likert scale would have their score “reversed” or converted as 1=4, 2=3, 3=2 and 4=1. All the items are then summed. The procedure eliminates negative scores and allows for the easy calculation of an average endorsement level across all items. Fortunately, one can convert subtractive to reverse- scored procedures algebraically (Mayer & Cavallaro, 2019). Table 1 reproduces the original-reported means, standard deviations, and reliabilities of the BMIS norms, converted to a 4-point reverse-scored scale. We will use this procedure as our default here and in the remainder of the article. The original report indicated reliabilities for the pleasant-unpleasant of α = 0.83, arousal- calm mood scale’s reliability lower at α = 0.58, positive-tired of α = 0.77, and negative-relaxed α = 0.76. Mayer & Gaschke (1988) further suggested that a possible method to raise the reliability of the arousal-calm mood scale would be to use a 7-point Likert scale, instead of a 4-point Likert A REVIEW AND EVALUATION OF AN OPEN 7 scale—the response scale used in the original work— based on a suggestion by (Nunnally, 1975) that more response options would increase the reliability of a response scale. In the current meta- analysis, we will test if the reliability of the BMIS pleasant-unpleasant and arousal-calm mood scales are affected by the length of the response scale employed. Lastly, Mayer & Gaschke (1988) also provided evidence for the validity of the BMIS from its content, by choosing eight mood-item pairs from across the mood spectrum, and evidence for its test structure based on factor analysis.
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