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Factor structure and validity of the affect intensity measure in a Swedish sample
Personality and Individual Dierences 29 (2000) 337±350
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Factor structure and validity of the aect intensity measure in a Swedish sample Margareta Simonsson-Sarnecki*, Lars-Gunnar Lundh, Bertil ToÈrestad Department of Psychology, Stockholm University, SE-106 91, Stockholm, Sweden Received 4 February 1999; received in revised form 16 June 1999; accepted 31 August 1999
Abstract Despite the fact that Larsen's [Larsen, R.J. (1984). Theory and measurement of aect intensity as an individual dierence characteristic. Dissertation Abstracts International. 85, 2297B (University Micro®lms No. 84-22112.)] Aect Intensity Measure (AIM) is a widely used measure of aect intensity, there is an ongoing debate concerning certain of its presumed theoretical and statistical qualities and its basic, underlying assumptions. The debate has most often centered around the inventory's dimensionality; i.e., is the AIM tapping one or more dimensions of intensity? The purpose of the present study was to investigate the dimensional structure of the Swedish translation of the AIM, to ®nd the best structural model for the Swedish AIM data, and to study its validity. Data from 409 subjects (153 males, 256 females) were subjected to maximum-likelihood con®rmatory factor analysis to assess how well dierent structural models ®t the AIM data. The results showed that all of the multidimensional AIM models were superior to Larsen's original 40-item uni-dimensional model, on all the ®t indices. The best-®tting model was a newly derived three-factor model, based on 27 items resulting in the factors Positive Aectivity, Negative Intensity, and Negative reactivity. Validation of this model in a community sample of 208 adults clearly showed dierent correlational patterns between negative intensity and negative reactivity, on the one hand, and positive aectivity, on the other, which demonstrates the value of treating aect intensity as a multidimensional construct. 7 2000 Elsevier Science Ltd. All rights reserved. Keywords: Aect intensity; Measurement; Con®rmatory factor analysis
* Corresponding author. Fax: +46-8-166236. E-mail address: [email protected] (M. Simonsson-Sarnecki). 0191-8869/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 1 9 1 - 8 8 6 9 ( 9 9 ) 0 0 1 9 7 - X
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1. Introduction In 1984 Larsen introduced the Aect Intensity Measure (AIM), an inventory consisting of 40 items intended to tap individuals' habitual aect intensity. Aect intensity is described (Diener, Larsen, Levine & Emmons, 1985b; Larsen & Diener, 1987) as a stable individual trait, indicating the typical intensity with which dierent emotions, pleasant as well as unpleasant, are experienced. A basic and necessary assumption is that individuals are prone to experience both positive and negative emotions with about the same intensity. Despite the fact that the AIM has been and still is a widely used measure of aect intensity, there is an ongoing debate concerning certain of its presumed theoretical and statistical qualities. Its basic, underlying assumptions have also been questioned by some researchers (e.g., Cooper & McConville, 1993; McConville & Cooper, 1995). The debate has most often centered around the inventory's dimensionality; i.e., is the AIM tapping one or more dimensions of intensity? Already when the AIM and its theoretical underpinnings were ®rst published, Larsen (1984) and Diener, Sandvik and Larsen (1985a) stated that the AIM was multidimensional, consisting of at least ®ve factors (Positive Aect Intensity, Negative Aect Intensity, Preference for Arousal, General Emotional Intensity, and Visceral Reactivity to Emotional Events). The fact that a high internal consistency was obtained (coecient alpha >0.90 in four separate samples; Larsen & Diener, 1987) led them later to recommend a rekeying of the reversed AIM items and an averaging of scores across the 40 items to create a unidimensional, total AIM score. This seemingly contradictory ®nding of a high alpha coecient along with a factor-analytically derived multidimensionality of the AIM is recurrent in several studies. Actually, the mean intercorrelation among items in a 40item instrument only needs to be 0.20 for a Cronbach's alpha higher than 0.90 to be obtained. It is not uncommon in the literature, though, to ®nd too great a reliance on high alphas as indicators of an impressive, and more than satisfactory homogeneity in terms of unidimensionality (e.g., a simple one factor solution in factor analysis). Williams (1989), to take just one illustrative example, pointed out that ``though the coecient alpha reliability for the AIM as a whole was 0.882, the average inter-item correlation was only 0.161''. This is a statistic that can easily be calculated by means of Spearman-Brown's well-known ``prophecy formula''. Not surprisingly, Williams (1989), in his factor analysis of the AIM, arrived at nine factors with eigenvalues greater than unity. It is important to keep in mind the distinction between a test's homogeneity and internal consistency: a test's homogeneity is best approximated by calculating the mean inter-item correlation and not by the numerical value of alpha which is highly sensitive to the number of items. Most research where the AIM has been involved has used it as a factorially simple instrument. Research along these lines indicates that individual dierences in total AIM scores are related in a conceptually and psychologically meaningful way to a wide variety of other constructs, like psychosomatic symptoms (Larsen & Diener, 1987), risk for cyclothymia and bipolar aective disorder (Diener et al., 1985b), borderline personality and passive-aggressive personality (Flett & Hewitt, 1995), the degree to which mood in¯uences memory (Mueller, Grove & Thomson, 1991), and measures of emotion- and avoidance-oriented coping (Flett, Blankstein & Obertynski, 1996). However, another line of research has investigated the AIM by means of factor analytic procedures, and has then produced growing evidence of the AIM's
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factorial complexity (e.g., Bryant, Yarnold & Grimm, 1996; Lehmann, 1997; Weinfurt, Bryant & Yarnold, 1994; Williams, 1989). Unfortunately, dierent researchers have arrived at various dimensions, both as to numbers and contents. An exploratory factor analysis of AIM, carried out by Williams (1989), resulted in an orthogonal four-factor solution, consisting of two aectively positive factors, correlating positively with extraversion, and two aectively negative factors, correlating positively with neuroticism and negatively with extraversion. More recently, a con®rmatory factor analysis conducted by Weinfurt et al. (1994) revealed that Larsen and Diener's (1987) original one-factor model produced a goodness-of-®t index (GFI) as low as 0.60, whereas Williams' (1989) four-factor model, and Weinfurt et al.'s (1994) own model obtained a goodness-of-®t (GFI) of about 0.80. Weinfurt et al.'s (1994) solution was similar to Williams' (1989), as there emerged two positive dimensions (Positive Aectivity and Serenity) and two negative ones (Negative Intensity and Negative Reactivity). The ®ndings of Weinfurt et al. (1994) suggested two conceptual distinctions concerning aect intensity: (1) a distinction between positive and negative aect, which is in line with many models of emotion (e.g., Reizenstein, 1994; Russell, 1980; Tellegen, 1985); and (2) a distinction between intensity and reactivity. The latter distinction concerns the dierence between the tendency to respond emotionally to emotion-evoking stimuli (reactivity) and the characteristic strength of experienced emotions (intensity). Bryant et al. (1996) took as their starting-point these two distinctions and reduced the 40 AIM items to a subset of 27 items that they judged a priori to be indicative of either the intensity or reactivity of either positive or negative emotions. By means of con®rmatory factor analysis they compared the goodness-of-®t of two dierent structural models of the AIM [Larsen's (1984) original 40-item one-factor model and Weinfurt et al.'s (1994) 40-item four-factor model] with three new models for Aect Intensity and Reactivity (AIR), based on their 27-item version of the AIM: a one-factor model, a threefactor model (Positive Aectivity, Negative Intensity, and Negative Reactivity), and a fourfactor model (Positive Intensity, Positive Reactivity, Negative Intensity, and Negative Reactivity). Again, Larsen's (1984) one-factor model produced the lowest goodness-of-®t, whereas Bryant et al.'s (1996) three- and four-factor models produced the best goodness-of-®t. However, since Positive Intensity and Positive Reactivity showed impressive correlation coecients (r = 0.90±0.92) in the two samples studied, they selected the three-factor model as the most parsimonious and useful one. The purpose of the present study was (1) to investigate the dimensional structure of a Swedish translation of the AIM in order to ®nd the best structural model for the Swedish AIM data, and (2) to study its validity. From the outset our point of departure was that there hardly, if ever, exists any measure of a personality trait that is truly unidimensional, the moot pivotal question instead being: How many dimensions? And what is in them? 2. Method 2.1. Subjects and procedure A Swedish version of the 40-item AIM (Larsen, 1984) was administered to two independent groups of subjects. (1) A community sample of 208 adult subjects, aged 25±52 years, from
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Stockholm city (98 males, 110 females, mean age 36.57 years, SD=7.47), who were selected randomly from the SPAR register (the Swedish Government's Person and Address Register). The subjects were administered the AIM in connection with an interview at the Department of Psychology, Stockholm University, as part of a larger research project on Health and Quality of Life (the HQL project). This interview and testing occasion was followed by an 8-week period of daily recordings of somatic symptoms, emotional experiences, events, and activities in a diary questionnaire. Of the 208 participants who ®lled out the AIM, 195 individuals completed the entire project, including the diary recordings. For their participation in the entire HQL project, they received 800 SEK (around 100 USD). (2) A sample of 201 undergraduate psychology students (55 males, 146 females) at Stockholm University, who received partial course credit for their participation. The age range of the student group was 18±56 years, with a mean age of 25.87 years (SD=6.10). The combined sample consisted of 409 subjects (153 males, 256 females), with a mean age of 31.22 years (SD=8.56). In the con®rmatory factor analysis part of the study, the large, combined sample was analyzed. In the validation part of the study only the community sample was used. 2.2. Instruments 2.2.1. Aect intensity measure (AIM) A Swedish translation of the AIM was developed by Lars-Gunnar Lundh and Margareta Simonsson-Sarnecki. The translation underwent back-translation procedures, involving an independent bilingual translator with a degree in psychology. Cronbach's alpha for the Swedish translation of the 40-item AIM scale was 0.89. 2.2.2. Karolinska scales of personality (KSP) This instrument, developed by Schalling and co-workers (e.g., Schalling, 1985; Schalling, Cronholm & AÊsberg, 1975; Schalling, Cronholm, AÊsberg & Espmark, 1973), consists of 15 dierent subscales. Nine of these scales consist of ten items: Somatic Anxiety, Psychic Anxiety, Muscular tension, Psychasthenia, Detachment, Impulsiveness, Monotony Avoidance, Social Desirability, and Inhibition of Aggression. Five additional scales contain ®ve items: Irritability, Verbal Aggression, Indirect Aggression, Guilt, and Suspicion, and one scale, Socialization, contains 20 items. All items have a four-choice format, and the instruction of the inventory explicitly requests the person to report on habitual behavior and habitual feelings, i.e., traits. The KSP scales have generally demonstrated good internal consistency, and stability over a 9year period, with the possible exception of the Guilt scale which has shown less good internal consistency (Gustavsson, Weinryb, GoÈransson, Pedersen & AÊsberg, 1997). 2.2.3. Toronto Alexithymia scale (TAS-20) The TAS-20 has demonstrated good reliability and validity (Bagby, Parker & Taylor, 1994), and is considered to be the best existing measure of alexithymia (Taylor, Bagby & Parker, 1997). The present study used a Swedish translation of the TAS-20, developed by Sven Carlsson in collaboration with James Parker and Graeme Taylor. The alpha coecient for this Swedish version of the TAS-20 was 0.79 in the present study, which is comparable with similar results for English-speaking samples. Although the ®rst interviews in the HQL project were
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carried out in November 1995, the TAS-20 was not included in the project until August 1996. Therefore, there were TAS-20 data only for 137 participants. 2.2.4. Pennebaker's inventory of languid limbidness (PILL) This is a symptom checklist of 54 common physical symptoms and bodily sensations, developed by Pennebaker (1982). Respondents indicate the frequency with which they experienced each symptom during the last year, and a total score is calculated. The Swedish translation of the PILL had a Cronbach's alpha of 0.91 in the present study. 2.2.5. Diary questionnaire (DQ) The diary questionnaire contained 75 items to be rated each day during 8 weeks. The items referred to various kinds of somatic complaints, negative and positive experiences, events, and behaviours; the participants were asked to rate on a 0±3 scale how much they had experienced these during each day. The DQ was developed especially for the HQL project, and the items were largely derived from various kinds of symptom check lists, like the Hopkins Symptom Check List (Derogatis, Lipman, Rickels, Uhlenhuth & Covi, 1975), daily hassles questionnaires, etc. Several versions of the DQ were tested in pilot studies before the ®nal version of the DQ was arrived at. The participants were instructed, both orally during the ®rst interview and in writing on the DQ questionnaire, to answer these questions at the end of each day. If they forgot about this, they were told to skip this day's recordings, and not to try to reconstruct the answers from their memory. Of the 75 items on the DQ, 29 items were categorized as referring to various aspects of Negative Aect, whereas 18 items were categorized under Positive Aect, and 19 items were categorized as referring to various kinds of somatic complaints. Each participant's mean scores on these items during the 8 weeks of daily recordings, and the sum of items within each subscale during this period, were computed. Cronbach's alpha showed good internal consistency for all three subscales: alpha=0.97 for Negative Aect, alpha=0.96 for Positive Aect, and alpha=0.91 for Somatic Complaints. In the HQL project, the somatic complaints subscale correlated r = 0.60 with the PILL symptom checklist; the negative aect subscale correlated r = 0.53 with KSP somatic anxiety, r = 0.40 with KSP psychic anxiety, and r = 0.40 with KSP irritability. 2.2.6. Somatic ampli®cation scale (SAS) This ®ve-item self-report scale was developed by Barsky, Goodson, Lane and Cleary (1988) in order to measure the tendency to experience somatic sensations as intense, noxious, and disturbing. The Swedish translation of the SAS has a Cronbach's alpha of 0.64 in the present study. It showed convergent validity by correlating with the PILL (r = 0.34), and the DQ somatic complaints (r = 0.33). 2.2.7. Thirteen-item interview version of the illness attitudes scale (IAS-13) The Illness Attitudes Scale (IAS) was devised by Kellner (1987) to assess fears, beliefs and attitudes associated with hypochondriasis and abnormal illness behaviour. In its original version it includes 29 items. For the interview version of the IAS which was developed for the present study, however, the items were reduced to 13. In the HQL project, this instrument had
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a Cronbach's alpha of 0.62, and showed convergent validity by correlating with the PILL (r = 0.20), DQ somatic complaints (r = 0.20), and SAS somatosensory ampli®cation (r = 0.34). 2.2.8. Quality of life inventory (QOLI) This instrument, which was developed by Frisch, Cornell, Villanueva and Retzla (1992), is based on an additive model of life satisfaction that assumed that an individual's overall life satisfaction consists largely of the sum of satisfactions in particular areas of life deemed important. The QOLI consists of 16 items, each of which represents an area of life deemed to be potentially relevant to overall life satisfaction. The respondents are required to rate each such area in terms of its importance to their overall happiness and satisfaction (from 0=not at all important to 2=extremely important) and in terms of their satisfaction with the area (ÿ3=very dissatis®ed to 3=very satis®ed). The products of the satisfaction and importance ratings for each area are then computed, and added to make up a total QOLI score. 2.3. Con®rmatory factor analysis 2.3.1. Model ®tting Maximum-likelihood con®rmatory factor analysis (CFA) was performed (LISREL VIII; JoÈreskog & SoÈrbom, 1993), in order to assess how well dierent structural models ®t the AIM data. Hypothesizing an association between AIM factors, dierent oblique models were tested. As a guideline for interpretation of the ®t indices, the recommendation of Cole (1987) was followed and four criteria were examined: (a) the goodness-of-®t index (GFI; JoÈreskog & SoÈrbom, 1993), which varies from 0 (no ®t) to 1 (perfect ®t); (b) the adjusted goodness-of-®t index (AGFI; JoÈreskog & SoÈrbom, 1993); (c) the root-mean-square error of approximation (RMSEA; Steiger, 1990); and (d) the ratio of maximum-likelihood chi-square to the degrees of freedom (X 2/df; Bollen, 1989). Multiple criteria were used in order to compensate for the various indices' dierent strengths and weaknesses in assessing the goodness-of-®t between the hypothetical model and the actual data (Cole, 1987; Marsh, Balla & McDonald, 1988; Steiger, 1990) and because dierent ®t indices emphasize dierent aspects of model ®t (Tanaka, 1993). The following criteria were used to evaluate the satisfactory level of model ®t: GFI > 0.85; AGFI > 0.80; and RMSEA < 0.08 (Anderson & Gerbing, 1984; Browne & Cudeck, 1993; Cole, 1987). Browne and Cudeck (1993) suggested that an RMSEA value of 0.05 indicates a close ®t and that values up to 0.08 represent reasonable errors of approximation, while they would not advise employing a model with an RMSEA greater than 0.1. Bollen (1989) indicates that X 2/df below 2.0 represent good ®ts, whereas other researchers suggest a more liberal limit of 3.0 (Carmines & McIver, 1981) or 5.0 (Wheaton, Muthen, Alwin & Summers, 1977). 3. Results 3.1. Con®rmatory factor analysis Seven dierent models were tested by means of con®rmatory factor analysis. Among the examined models were Larsen's (1984) one-factor model and Weinfurt et al.'s (1994) four-
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factor model, which were imposed on the full set of 40 AIM items. CFA was also used to test the three models for Aect Intensity and Reactivity (AIR) based on Bryant et al.'s (1996) reduced set of 27 AIM items: (1) the one-factor AIR model, (2) the oblique four-factor AIR model (Positive Intensity, Negative Intensity, Positive Reactivity, Negative Reactivity), and (3) the three-factor AIR model that combines Positive Reactivity and Positive Intensity factors into one Positive Aectivity factor. In addition, two new models were subjected to CFA. One was a modi®ed three-factor AIR model (AIR-r ) based on a reanalysis of the contents of the AIM items. The rationale for this was some inconsistencies found in Bryant et al.'s (1996) a priori sorting of the AIM items into four categories. First, although these authors explicitly set out to include only positively and negatively valenced items, they chose to also include items 6 (``My emotions tend to be more intense then those of most people'') and 15 (``My friends might say I'm emotional'') into their set of 27 items. They motivated this by arguing that ``intuition'', in combination with the results of Weinfurt et al. (1994), made them believe that ``most respondents have negative emotions in mind when they consider these items'' (Bryant et al., 1996, p. 226). Since intuition, however, does not lead the present authors to the same conclusion (which may, possibly, be due to cultural or linguistic dierences), we chose to exclude items 6 and 15 from our modi®ed AIR-r analysis. Second, Bryant et al. (1996) excluded two items from their AIR model, although these items to the present authors carry clear connotations of positive aect intensity: items 2 (``I enjoy being with other people'') and 32 (``When I am excited over something I want to share my feelings with everyone''). These two items were therefore included in our AIR-r model, thus keeping the number of items to 27. Finally, a hierarchical model with a General Aect Intensity factor was tested. A hierarchical model opens for possibilities simultaneously to include a broad ``general'' aective factor (e.g., general factor Ð G ) and several more speci®c aective dimensions. Such models for human ability have earlier been proposed by Vernon (1950) and Cattell and Horn (e.g., Cattell, 1963, 1971; Horn, 1968, 1986). In the present study such a hierarchical model was tested for the AIR-r. No hierarchical model for AIR-r was obtained, however, the program not being able to arrive at a model after 500 iterations. Table 1 displays the measures of relative ®t for each of the six structural models. When compared with Bryant et al.'s (1996) three-factor AIR model, our modi®ed AIR-r three-factor model achieved the best model-®t on all four indices (GFI=0.82; AGFI=0.78; RMSEA=0.08; and Chi-square/df ratio=4.40). On two (GFI and AGFI) of these four indices criteria were Table 1 Goodness-of-®t statistics for factor models of the AIM (N = 409) Model
Chi-square
df
X 2/df
GFI
AGFI
RMSEA
Larsen's (1984) one-factor (40-items) Weinfurt et al.'s (1994) four-factor (40-items) Bryant et al.'s (1996) AIR one-factor (27-items) Bryant et al.'s (1996) AIR four-factor (27-items) Bryant et al.'s (1996) AIR three-factor (27-items) AIR-r three-factor (27-items)
4368.00 3097.20 1895.50 1593.20 1629.80 1412.50
740 734 324 318 321 321
5.90 4.22 5.85 5.01 5.08 4.40
0.64 0.75 0.77 0.80 0.80 0.82
0.61 0.73 0.73 0.77 0.77 0.78
0.096 0.078 0.095 0.087 0.087 0.080
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clearly met, on a third index (RMSEA) the criterion was almost met, and on the fourth (X 2/ df ) it was met by Wheaton et al.'s (1977) more liberal standards. An examination of the parameter item estimates for the AIR-r revealed that all estimates were signi®cant at the P < 0.01 level (see Table 2). Cronbach alpha coecients and mean inter-item correlation coecients were calculated for the AIR-r and its factors. The total AIR-r had an alpha of 0.85, and showed a mean inter-item correlation of 0.17. Table 2 shows the corresponding Cronbach's alpha values and the mean inter-item correlations (MIC) for the three factors. The parameter estimates for the relationships among the three factors of the AIR-r are presented in Table 3. All estimates were statistically signi®cant. Table 2 Con®rmatory factor analysis, parameter estimates for the AIR-r items, and Cronbach alphas and mean inter-item correlations for the three factorsa (18) (35) (7) (14) (9) (2) (8) (23) (5) (22) (38) (10) (1) (20) (27) (3) (32)
Factor 1: positive aectivity (alpha=0.90, MIC=0.34) When I'm feeling well it's easy for me to go from being in a good mood to being really joyful When I'm happy I bubble over with energy My happy moods are so strong that I feel like I'm in heaven When something good happens, I am usually much more jubilant than others If I complete a task I thought was impossible, I am ecstatic When I feel happy it is a strong type of exuberance I get overly enthusiastic When I receive an award I become overjoyed When I solve a small personal problem, I feel euphoric When I'm happy I feel very energetic When someone compliments me, I get so happy I can ``burst'' My heart races at the anticipation of some exciting event When I accomplish something dicult I feel delighted or elated When I'm happy I feel like I'm bursting with joy When things are going good I feel ``on the top of the world'' I enjoy being with other people When I'm excited over something I want to share my feelings with everyone
0.77 0.75 0.74 0.68 0.66 0.65 0.61 0.61 0.55 0.52 0.48 0.46 0.42 0.38 0.36 0.31 0.30
(36) (30) (39) (34)
Factor 2: negative intensity (alpha=0.60, MIC=0.27) When I feel guilty this emotion is quite strong When I feel anxiety it is normally very strong When I am nervous I get shaky all over My friends would probably say I'm a tense or ``high-strung'' person
0.83 0.58 0.29 0.26
(21) (11) (25) (17) (4) (13)
Factor 3: negative reactivity (alpha=0.63, MIC=0.22) Seeing a picture of some violent car accident in a newspaper makes me feel sick to my stomach Sad movies deeply touch me When I do something wrong I have strong feelings of shame and guilt The sight of someone who is hurt badly aects me strongly I feel pretty bad when I tell a lie When I talk in front of a group for the ®rst time my voice gets shaky and my heart races
0.59 0.52 0.55 0.54 0.31 0.27
a
All parameter estimates signi®cant (P < 0.01).
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Table 3 Parameter estimates among the three AIR-r factorsa Factor
F1
F2
F1 F2 F3
± 0.39 0.60
± ± 0.61
a
F1=Positive aectivity; F2=Negative intensity; F3=Negative reactivity. All parameter estimates are signi®cant at the 5%-level, at the least.
3.2. Validation of the AIR-r model In order to study the validity of our new AIR-r three-factor model, and to compare it with Larsen's (1984) original 40-item AIM, we studied correlations with the Karolinska Scales of Personality, the 20-item version of the Toronto Alexithymia Scale, the Somatosensory Ampli®cation Scale, an interview version of the Illness Attitudes Scale, Pennebaker's PILL inventory, three subscales of the Diary Questionnaire, and the Quality of Life Inventory. As Table 4 Correlations of the three AIR-r factors and the total AIM with other measures of personality, somatic symptoms, and quality of life (N = 195, except for the TAS-20, where N = 137) (P < 0.01; P < 0.001)
KSP somatic anxiety KSP psychic anxiety KSP muscular tension KSP social desirability KSP impulsiveness KSP monotony avoidance KSP detachment KSP psychasthenia KSP socialization KSP indirect aggression KSP verbal aggression KSP irritability KSP suspicion KSP guilt KSP inhibition of aggression Toronoto Alexithymia scale (TAS-20) Somatosensory ampli®cation (SAS) Illness attitudes scale (IAS-13) PILL somatic symptoms DQ somatic complaints DQ negative aect DQ positive aect Quality of life inventory (QOLI)
Positive aectivity
Negative intensity
Negative reactivity
AIM-40
0.19 0.21 0.14 0.03 0.26 0.30 ÿ0.33 0.05 ÿ0.21 0.20 0.07 0.13 0.09 0.16 0.10 ÿ0.08 0.25 0.11 0.10 0.19 0.19 0.22 0.08
0.52 0.50 0.40 ÿ0.08 0.03 0.02 ÿ0.03 0.37 ÿ0.48 0.25 0.03 0.24 0.29 0.33 0.27 0.26 0.37 0.29 0.48 0.41 0.40 ÿ0.12 ÿ0.13
0.42 0.51 0.37 0.08 0.01 ÿ0.01 ÿ0.28 0.32 ÿ0.31 0.22 ÿ0.08 0.21 0.19 0.33 0.33 0.06 0.27 0.18 0.32 0.33 0.33 ÿ0.04 0.07
0.39 0.41 0.30 ÿ0.03 0.27 0.29 ÿ0.33 ÿ0.22 ÿ0.38 0.31 0.13 0.24 0.17 0.27 0.15 ÿ0.01 0.34 0.28 0.29 0.34 0.34 0.10 ÿ0.03
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can be seen in Table 4, the three AIR-r factors showed clearly discriminant validity, in a way which supplies information that cannot be derived from the original one-dimensional AIM model. For example, although the AIM total score correlates with impulsiveness and monotony avoidance, this correlation can be traced to only one of the three AIR-r factors, i.e., positive aectivity. Conversely, the AIM correlations with psychasthenia, muscular tension, irritability, and somatic complaints were almost entirely due to correlations with the two negative AIR-r factors. Finally, there were also some variables (e.g., inhibition of aggression, suspicion, and alexithymia) which, although they showed no or little correlation with the AIM, tended to correlate with one or two of the AIR-r factors.
4. Discussion The purpose of the present study was to investigate the dimensional structure of the Swedish translation of the AIM, in order to ®nd the best structural model for the Swedish AIM data, and to validate this model. Con®rmatory factor analysis showed that all of the multidimensional AIM models were structurally superior to Larsen's (1984) original 40-item unidimensional model, on all the ®t indices. The best model-®t among the multidimensional models was obtained by our modi®ed three-factor AIR-r model: Positive Aectivity, Negative Intensity, Negative Reactivity. The results clearly showed dierent correlational patterns between negative intensity and negative reactivity, on the one hand, and positive aectivity, on the other, which indicates the value of treating aect intensity as a multidimensional construct. Psychological coherence and contents must always take precedence over statistical sophistication. Too much research in which the AIM has been involved, and especially when it comes to the question of its putative unidimensionality, suggests that many researchers try to press a priori notions of number of dimensions on their ®nal factor solutions. One point of departure for our venture was that any measure of any trait is bound to be multidimensional. Our results for the GFI and chi-square ratio correspond well with the results obtained by Bryant et al. (1996). All but one (Weinfurt et al., 1994) of the multidimensional AIM models achieved greater model-®t then Bryant et al.'s (1996) one-factor AIR on all indices. We see our results as consistent with those of other colleagues that earlier provided evidence for the multidimensionality of the AIM construct (Bryant et al., 1996; Weinfurt et al., 1994). Just as in Bryant et al.'s (1996) study, our data showed an almost identical model-®t between Bryant et al.'s (1996) AIR three-factor model and their four-factor AIR model, the models varying slightly as to the chi-square ratio. Among the ®ve multidimensional models that were tested by CFA, the best model-®t (though not entirely satisfactory) was obtained by our modi®ed threefactor AIR-r model, based on a reanalysis of the contents of the AIM items in Bryant et al.'s (1996) three-factor model. We ®nd that our analyses suggest that the AIM consists of more than one underlying factor and that the one-factor model is indistinct and possibly also conceptually misleading, since it is composed of a non-obvious conglomeration of various facets of aective experience. It is true that earlier research has indicated that the one-factor model of the AIM has external validity, as demonstrated by a number of studies of the relationships between AIM and other variables (e.g., Diener et al., 1985b; Flett et al., 1996; Flett & Hewitt, 1995; Larsen & Diener, 1987;
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Mueller et al., 1991). It is possible, however, that these associations would have appeared even more clearly if aect intensity had been treated as a multidimensional construct in these studies. The present results indicate that when the AIM is treated as a global one-dimensional construct, at least two kinds of imprecisions appear: (1) the risk of ascribing eects to global aect intensity which, in fact, are due mainly either to positive aectivity (e.g., extraversionrelated trait like impulsiveness and monotony avoidance) or negative intensity or reactivity (e.g., neuroticism-related traits like psychasthenia, muscular tension, irritability, somatic complaints); and (2) the risk of neglecting possible relations between aspects of aect intensity and other variables (e.g., inhibition of aggression, suspicion, and alexithymia) because they are not seen with global aect intensity. In addition, as can be seen in Table 4, the magnitude of several correlations between aect intensity and other variables is clearly attenuated when aect intensity is treated as a global one-dimensional construct. Validation of our three-factor model showed that negative intensity and negative reactivity, as distinct from positive aectivity, demonstrated a rather similar pattern of correlations with other variables. The main exception from this pattern was the KSP scale Detachment, which is designed to measure schizoid traits, such as need for a social distance, and coldness in social relations Ð although detachment was found to correlate negatively with both positive aectivity and negative reactivity, it showed no correlation with negative intensity. The negative correlation between detachment and positive aectivity is consistent with the de®nition of schizoid personality disorder, which has anhedonia as a component (DSM-IV; American Psychiatric Association, 1994). The dierentiated results with regard to negative intensity and negative reactivity are more dicult to explain. One possible explanation, however, is that detachment involves a lack of aective relatedness to the environment, and thereby also a relative lack of reactivity to daily situations, as speci®ed by the items in the negative reactivity subfactor (see Table 2), which produces a negative correlation between detachment and negative reactivity. On the other hand, the subfactor negative intensity has little reference to relatedness to speci®c situations and almost entirely refers to the person's experience of him- or herself, which may account for the lack of correlation between detachment and negative intensity. As suggested to us by an anonymous reviewer, it is possible that the subfactors which are here referred to as negative intensity and negative reactivity may, in fact, be distinguished in terms of whether they come solely from the self (i.e., negative intensity) or involve reactions to negative events that are mostly outside the self (i.e., negative reactivity). This may, then, contribute to the explanation of the general pattern, that was found in the present study, of negative intensity showing correlations of higher magnitude than negative reactivity with somatic distress Ð since somatic distress obviously belongs to the ``inside'' experience of the self. This applied both to symptom check lists, like Pennebacker's PILL inventory (which correlated r = 0.48 with negative intensity and r = 0.32 with negative reactivity), and the hypochondriasis-related measure Illness Attitude Scale (which correlated r = 0.29 with negative intensity and r = 0.18 with negative reactivity). Somewhat surprisingly, somatosensory ampli®cation correlated with all three AIM factors. As distinct from the other somatically related variables, this scale does not refer directly to somatic symptoms, or somatic distress, but rather to the intensity or sensitivity to somatic sensations (loud noises, heat/cold, pain, hunger contractions, etc.). In this sense, the
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somatosensory ampli®cation scales resembles the AIM, and might perhaps even be seen as a somatic sensation intensity measure, analogous to the aect intensity measure. It is notable that no association was found between any aspect of aect intensity, neither positive nor negative, and life quality as measured by the QOLI, in spite of the considerable correlation between negative intensity and somatic distress. This, however, is consistent with earlier ®ndings that although aect intensity is associated with somatic distress, it is unrelated or orthogonal to measures of psychological well-being (Diener, 1984; Larsen, Diener & Emmons, 1985). Larsen and Diener (1987, p. 16) suggest a possible explanation of this apparent paradox by assuming that, although the regular experience of strong emotions exacts ``a price to be paid in terms of wear and tear on the body'', subjects high in aect intensity apparently ``are willing to pay this price, perhaps because the regular experience of positive emotions serves some positive function for them or somehow ®ts with other aspects of their personality and temperament''. In the present study, however, although all three AIM factors showed a lack of correlation with life quality, these three subfactors correlated very dierently with somatic distress. Again, these ®ndings speak against treating aect intensity as a onedimensional construct. To summarize, the present results clearly indicate that the multidimensional model of aect intensity demonstrates superior conceptual and predictive precision relative to the unidimensional AIM total score. As expected, the most striking dierentiation was seen between positive and negative aspects of aect intensity, which, consistently with the ®ndings of Williams (1989), correlated with extraversion- and neuroticism-related trait measures, respectively. But dierences were also seen between negative intensity and negative reactivity, especially with regard to measures of somatic distress. There certainly exist a lot of varied models of the AIM scale. At ®rst glance, it may seem that we further complicate the picture by introducing still a new multi-dimensional model. It should be remembered, however, that our three-dimensional model is only a slight modi®cation of Bryant et al.'s (1996) AIR model. Still, our data suggest that this revised AIR model has both theoretical and psychometric advantages relative to the original AIR. Since our validation results are solely based on an urban community sample, the generalizability of these results needs further corroboration in other samples Ð both clinical and non-clinical. It would also be of interest to return to earlier published data, and analyze these in terms of a multidimensional model of the AIM; our prediction would be that this would clarify the meaning of associations which have been found between the AIM total score and other variables. In the long run, we will most probably need better instruments for measuring aect intensity, and the experience of aect in general (cf. Schimmack & Diener, 1997). Only one of the 40 AIM items, for example, mentions such a basic emotion as anger, and the equally basic emotion of sadness is only mentioned indirectly in one item (item 11, ``Sad movies deeply touch me''). We look forward to future endeavours in that direction.
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Factor structure and validity of the aect intensity measure in a Swedish sample Margareta Simonsson-Sarnecki*, Lars-Gunnar Lundh, Bertil ToÈrestad Department of Psychology, Stockholm University, SE-106 91, Stockholm, Sweden Received 4 February 1999; received in revised form 16 June 1999; accepted 31 August 1999
Abstract Despite the fact that Larsen's [Larsen, R.J. (1984). Theory and measurement of aect intensity as an individual dierence characteristic. Dissertation Abstracts International. 85, 2297B (University Micro®lms No. 84-22112.)] Aect Intensity Measure (AIM) is a widely used measure of aect intensity, there is an ongoing debate concerning certain of its presumed theoretical and statistical qualities and its basic, underlying assumptions. The debate has most often centered around the inventory's dimensionality; i.e., is the AIM tapping one or more dimensions of intensity? The purpose of the present study was to investigate the dimensional structure of the Swedish translation of the AIM, to ®nd the best structural model for the Swedish AIM data, and to study its validity. Data from 409 subjects (153 males, 256 females) were subjected to maximum-likelihood con®rmatory factor analysis to assess how well dierent structural models ®t the AIM data. The results showed that all of the multidimensional AIM models were superior to Larsen's original 40-item uni-dimensional model, on all the ®t indices. The best-®tting model was a newly derived three-factor model, based on 27 items resulting in the factors Positive Aectivity, Negative Intensity, and Negative reactivity. Validation of this model in a community sample of 208 adults clearly showed dierent correlational patterns between negative intensity and negative reactivity, on the one hand, and positive aectivity, on the other, which demonstrates the value of treating aect intensity as a multidimensional construct. 7 2000 Elsevier Science Ltd. All rights reserved. Keywords: Aect intensity; Measurement; Con®rmatory factor analysis
* Corresponding author. Fax: +46-8-166236. E-mail address: [email protected] (M. Simonsson-Sarnecki). 0191-8869/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 1 9 1 - 8 8 6 9 ( 9 9 ) 0 0 1 9 7 - X
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1. Introduction In 1984 Larsen introduced the Aect Intensity Measure (AIM), an inventory consisting of 40 items intended to tap individuals' habitual aect intensity. Aect intensity is described (Diener, Larsen, Levine & Emmons, 1985b; Larsen & Diener, 1987) as a stable individual trait, indicating the typical intensity with which dierent emotions, pleasant as well as unpleasant, are experienced. A basic and necessary assumption is that individuals are prone to experience both positive and negative emotions with about the same intensity. Despite the fact that the AIM has been and still is a widely used measure of aect intensity, there is an ongoing debate concerning certain of its presumed theoretical and statistical qualities. Its basic, underlying assumptions have also been questioned by some researchers (e.g., Cooper & McConville, 1993; McConville & Cooper, 1995). The debate has most often centered around the inventory's dimensionality; i.e., is the AIM tapping one or more dimensions of intensity? Already when the AIM and its theoretical underpinnings were ®rst published, Larsen (1984) and Diener, Sandvik and Larsen (1985a) stated that the AIM was multidimensional, consisting of at least ®ve factors (Positive Aect Intensity, Negative Aect Intensity, Preference for Arousal, General Emotional Intensity, and Visceral Reactivity to Emotional Events). The fact that a high internal consistency was obtained (coecient alpha >0.90 in four separate samples; Larsen & Diener, 1987) led them later to recommend a rekeying of the reversed AIM items and an averaging of scores across the 40 items to create a unidimensional, total AIM score. This seemingly contradictory ®nding of a high alpha coecient along with a factor-analytically derived multidimensionality of the AIM is recurrent in several studies. Actually, the mean intercorrelation among items in a 40item instrument only needs to be 0.20 for a Cronbach's alpha higher than 0.90 to be obtained. It is not uncommon in the literature, though, to ®nd too great a reliance on high alphas as indicators of an impressive, and more than satisfactory homogeneity in terms of unidimensionality (e.g., a simple one factor solution in factor analysis). Williams (1989), to take just one illustrative example, pointed out that ``though the coecient alpha reliability for the AIM as a whole was 0.882, the average inter-item correlation was only 0.161''. This is a statistic that can easily be calculated by means of Spearman-Brown's well-known ``prophecy formula''. Not surprisingly, Williams (1989), in his factor analysis of the AIM, arrived at nine factors with eigenvalues greater than unity. It is important to keep in mind the distinction between a test's homogeneity and internal consistency: a test's homogeneity is best approximated by calculating the mean inter-item correlation and not by the numerical value of alpha which is highly sensitive to the number of items. Most research where the AIM has been involved has used it as a factorially simple instrument. Research along these lines indicates that individual dierences in total AIM scores are related in a conceptually and psychologically meaningful way to a wide variety of other constructs, like psychosomatic symptoms (Larsen & Diener, 1987), risk for cyclothymia and bipolar aective disorder (Diener et al., 1985b), borderline personality and passive-aggressive personality (Flett & Hewitt, 1995), the degree to which mood in¯uences memory (Mueller, Grove & Thomson, 1991), and measures of emotion- and avoidance-oriented coping (Flett, Blankstein & Obertynski, 1996). However, another line of research has investigated the AIM by means of factor analytic procedures, and has then produced growing evidence of the AIM's
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factorial complexity (e.g., Bryant, Yarnold & Grimm, 1996; Lehmann, 1997; Weinfurt, Bryant & Yarnold, 1994; Williams, 1989). Unfortunately, dierent researchers have arrived at various dimensions, both as to numbers and contents. An exploratory factor analysis of AIM, carried out by Williams (1989), resulted in an orthogonal four-factor solution, consisting of two aectively positive factors, correlating positively with extraversion, and two aectively negative factors, correlating positively with neuroticism and negatively with extraversion. More recently, a con®rmatory factor analysis conducted by Weinfurt et al. (1994) revealed that Larsen and Diener's (1987) original one-factor model produced a goodness-of-®t index (GFI) as low as 0.60, whereas Williams' (1989) four-factor model, and Weinfurt et al.'s (1994) own model obtained a goodness-of-®t (GFI) of about 0.80. Weinfurt et al.'s (1994) solution was similar to Williams' (1989), as there emerged two positive dimensions (Positive Aectivity and Serenity) and two negative ones (Negative Intensity and Negative Reactivity). The ®ndings of Weinfurt et al. (1994) suggested two conceptual distinctions concerning aect intensity: (1) a distinction between positive and negative aect, which is in line with many models of emotion (e.g., Reizenstein, 1994; Russell, 1980; Tellegen, 1985); and (2) a distinction between intensity and reactivity. The latter distinction concerns the dierence between the tendency to respond emotionally to emotion-evoking stimuli (reactivity) and the characteristic strength of experienced emotions (intensity). Bryant et al. (1996) took as their starting-point these two distinctions and reduced the 40 AIM items to a subset of 27 items that they judged a priori to be indicative of either the intensity or reactivity of either positive or negative emotions. By means of con®rmatory factor analysis they compared the goodness-of-®t of two dierent structural models of the AIM [Larsen's (1984) original 40-item one-factor model and Weinfurt et al.'s (1994) 40-item four-factor model] with three new models for Aect Intensity and Reactivity (AIR), based on their 27-item version of the AIM: a one-factor model, a threefactor model (Positive Aectivity, Negative Intensity, and Negative Reactivity), and a fourfactor model (Positive Intensity, Positive Reactivity, Negative Intensity, and Negative Reactivity). Again, Larsen's (1984) one-factor model produced the lowest goodness-of-®t, whereas Bryant et al.'s (1996) three- and four-factor models produced the best goodness-of-®t. However, since Positive Intensity and Positive Reactivity showed impressive correlation coecients (r = 0.90±0.92) in the two samples studied, they selected the three-factor model as the most parsimonious and useful one. The purpose of the present study was (1) to investigate the dimensional structure of a Swedish translation of the AIM in order to ®nd the best structural model for the Swedish AIM data, and (2) to study its validity. From the outset our point of departure was that there hardly, if ever, exists any measure of a personality trait that is truly unidimensional, the moot pivotal question instead being: How many dimensions? And what is in them? 2. Method 2.1. Subjects and procedure A Swedish version of the 40-item AIM (Larsen, 1984) was administered to two independent groups of subjects. (1) A community sample of 208 adult subjects, aged 25±52 years, from
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Stockholm city (98 males, 110 females, mean age 36.57 years, SD=7.47), who were selected randomly from the SPAR register (the Swedish Government's Person and Address Register). The subjects were administered the AIM in connection with an interview at the Department of Psychology, Stockholm University, as part of a larger research project on Health and Quality of Life (the HQL project). This interview and testing occasion was followed by an 8-week period of daily recordings of somatic symptoms, emotional experiences, events, and activities in a diary questionnaire. Of the 208 participants who ®lled out the AIM, 195 individuals completed the entire project, including the diary recordings. For their participation in the entire HQL project, they received 800 SEK (around 100 USD). (2) A sample of 201 undergraduate psychology students (55 males, 146 females) at Stockholm University, who received partial course credit for their participation. The age range of the student group was 18±56 years, with a mean age of 25.87 years (SD=6.10). The combined sample consisted of 409 subjects (153 males, 256 females), with a mean age of 31.22 years (SD=8.56). In the con®rmatory factor analysis part of the study, the large, combined sample was analyzed. In the validation part of the study only the community sample was used. 2.2. Instruments 2.2.1. Aect intensity measure (AIM) A Swedish translation of the AIM was developed by Lars-Gunnar Lundh and Margareta Simonsson-Sarnecki. The translation underwent back-translation procedures, involving an independent bilingual translator with a degree in psychology. Cronbach's alpha for the Swedish translation of the 40-item AIM scale was 0.89. 2.2.2. Karolinska scales of personality (KSP) This instrument, developed by Schalling and co-workers (e.g., Schalling, 1985; Schalling, Cronholm & AÊsberg, 1975; Schalling, Cronholm, AÊsberg & Espmark, 1973), consists of 15 dierent subscales. Nine of these scales consist of ten items: Somatic Anxiety, Psychic Anxiety, Muscular tension, Psychasthenia, Detachment, Impulsiveness, Monotony Avoidance, Social Desirability, and Inhibition of Aggression. Five additional scales contain ®ve items: Irritability, Verbal Aggression, Indirect Aggression, Guilt, and Suspicion, and one scale, Socialization, contains 20 items. All items have a four-choice format, and the instruction of the inventory explicitly requests the person to report on habitual behavior and habitual feelings, i.e., traits. The KSP scales have generally demonstrated good internal consistency, and stability over a 9year period, with the possible exception of the Guilt scale which has shown less good internal consistency (Gustavsson, Weinryb, GoÈransson, Pedersen & AÊsberg, 1997). 2.2.3. Toronto Alexithymia scale (TAS-20) The TAS-20 has demonstrated good reliability and validity (Bagby, Parker & Taylor, 1994), and is considered to be the best existing measure of alexithymia (Taylor, Bagby & Parker, 1997). The present study used a Swedish translation of the TAS-20, developed by Sven Carlsson in collaboration with James Parker and Graeme Taylor. The alpha coecient for this Swedish version of the TAS-20 was 0.79 in the present study, which is comparable with similar results for English-speaking samples. Although the ®rst interviews in the HQL project were
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carried out in November 1995, the TAS-20 was not included in the project until August 1996. Therefore, there were TAS-20 data only for 137 participants. 2.2.4. Pennebaker's inventory of languid limbidness (PILL) This is a symptom checklist of 54 common physical symptoms and bodily sensations, developed by Pennebaker (1982). Respondents indicate the frequency with which they experienced each symptom during the last year, and a total score is calculated. The Swedish translation of the PILL had a Cronbach's alpha of 0.91 in the present study. 2.2.5. Diary questionnaire (DQ) The diary questionnaire contained 75 items to be rated each day during 8 weeks. The items referred to various kinds of somatic complaints, negative and positive experiences, events, and behaviours; the participants were asked to rate on a 0±3 scale how much they had experienced these during each day. The DQ was developed especially for the HQL project, and the items were largely derived from various kinds of symptom check lists, like the Hopkins Symptom Check List (Derogatis, Lipman, Rickels, Uhlenhuth & Covi, 1975), daily hassles questionnaires, etc. Several versions of the DQ were tested in pilot studies before the ®nal version of the DQ was arrived at. The participants were instructed, both orally during the ®rst interview and in writing on the DQ questionnaire, to answer these questions at the end of each day. If they forgot about this, they were told to skip this day's recordings, and not to try to reconstruct the answers from their memory. Of the 75 items on the DQ, 29 items were categorized as referring to various aspects of Negative Aect, whereas 18 items were categorized under Positive Aect, and 19 items were categorized as referring to various kinds of somatic complaints. Each participant's mean scores on these items during the 8 weeks of daily recordings, and the sum of items within each subscale during this period, were computed. Cronbach's alpha showed good internal consistency for all three subscales: alpha=0.97 for Negative Aect, alpha=0.96 for Positive Aect, and alpha=0.91 for Somatic Complaints. In the HQL project, the somatic complaints subscale correlated r = 0.60 with the PILL symptom checklist; the negative aect subscale correlated r = 0.53 with KSP somatic anxiety, r = 0.40 with KSP psychic anxiety, and r = 0.40 with KSP irritability. 2.2.6. Somatic ampli®cation scale (SAS) This ®ve-item self-report scale was developed by Barsky, Goodson, Lane and Cleary (1988) in order to measure the tendency to experience somatic sensations as intense, noxious, and disturbing. The Swedish translation of the SAS has a Cronbach's alpha of 0.64 in the present study. It showed convergent validity by correlating with the PILL (r = 0.34), and the DQ somatic complaints (r = 0.33). 2.2.7. Thirteen-item interview version of the illness attitudes scale (IAS-13) The Illness Attitudes Scale (IAS) was devised by Kellner (1987) to assess fears, beliefs and attitudes associated with hypochondriasis and abnormal illness behaviour. In its original version it includes 29 items. For the interview version of the IAS which was developed for the present study, however, the items were reduced to 13. In the HQL project, this instrument had
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a Cronbach's alpha of 0.62, and showed convergent validity by correlating with the PILL (r = 0.20), DQ somatic complaints (r = 0.20), and SAS somatosensory ampli®cation (r = 0.34). 2.2.8. Quality of life inventory (QOLI) This instrument, which was developed by Frisch, Cornell, Villanueva and Retzla (1992), is based on an additive model of life satisfaction that assumed that an individual's overall life satisfaction consists largely of the sum of satisfactions in particular areas of life deemed important. The QOLI consists of 16 items, each of which represents an area of life deemed to be potentially relevant to overall life satisfaction. The respondents are required to rate each such area in terms of its importance to their overall happiness and satisfaction (from 0=not at all important to 2=extremely important) and in terms of their satisfaction with the area (ÿ3=very dissatis®ed to 3=very satis®ed). The products of the satisfaction and importance ratings for each area are then computed, and added to make up a total QOLI score. 2.3. Con®rmatory factor analysis 2.3.1. Model ®tting Maximum-likelihood con®rmatory factor analysis (CFA) was performed (LISREL VIII; JoÈreskog & SoÈrbom, 1993), in order to assess how well dierent structural models ®t the AIM data. Hypothesizing an association between AIM factors, dierent oblique models were tested. As a guideline for interpretation of the ®t indices, the recommendation of Cole (1987) was followed and four criteria were examined: (a) the goodness-of-®t index (GFI; JoÈreskog & SoÈrbom, 1993), which varies from 0 (no ®t) to 1 (perfect ®t); (b) the adjusted goodness-of-®t index (AGFI; JoÈreskog & SoÈrbom, 1993); (c) the root-mean-square error of approximation (RMSEA; Steiger, 1990); and (d) the ratio of maximum-likelihood chi-square to the degrees of freedom (X 2/df; Bollen, 1989). Multiple criteria were used in order to compensate for the various indices' dierent strengths and weaknesses in assessing the goodness-of-®t between the hypothetical model and the actual data (Cole, 1987; Marsh, Balla & McDonald, 1988; Steiger, 1990) and because dierent ®t indices emphasize dierent aspects of model ®t (Tanaka, 1993). The following criteria were used to evaluate the satisfactory level of model ®t: GFI > 0.85; AGFI > 0.80; and RMSEA < 0.08 (Anderson & Gerbing, 1984; Browne & Cudeck, 1993; Cole, 1987). Browne and Cudeck (1993) suggested that an RMSEA value of 0.05 indicates a close ®t and that values up to 0.08 represent reasonable errors of approximation, while they would not advise employing a model with an RMSEA greater than 0.1. Bollen (1989) indicates that X 2/df below 2.0 represent good ®ts, whereas other researchers suggest a more liberal limit of 3.0 (Carmines & McIver, 1981) or 5.0 (Wheaton, Muthen, Alwin & Summers, 1977). 3. Results 3.1. Con®rmatory factor analysis Seven dierent models were tested by means of con®rmatory factor analysis. Among the examined models were Larsen's (1984) one-factor model and Weinfurt et al.'s (1994) four-
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factor model, which were imposed on the full set of 40 AIM items. CFA was also used to test the three models for Aect Intensity and Reactivity (AIR) based on Bryant et al.'s (1996) reduced set of 27 AIM items: (1) the one-factor AIR model, (2) the oblique four-factor AIR model (Positive Intensity, Negative Intensity, Positive Reactivity, Negative Reactivity), and (3) the three-factor AIR model that combines Positive Reactivity and Positive Intensity factors into one Positive Aectivity factor. In addition, two new models were subjected to CFA. One was a modi®ed three-factor AIR model (AIR-r ) based on a reanalysis of the contents of the AIM items. The rationale for this was some inconsistencies found in Bryant et al.'s (1996) a priori sorting of the AIM items into four categories. First, although these authors explicitly set out to include only positively and negatively valenced items, they chose to also include items 6 (``My emotions tend to be more intense then those of most people'') and 15 (``My friends might say I'm emotional'') into their set of 27 items. They motivated this by arguing that ``intuition'', in combination with the results of Weinfurt et al. (1994), made them believe that ``most respondents have negative emotions in mind when they consider these items'' (Bryant et al., 1996, p. 226). Since intuition, however, does not lead the present authors to the same conclusion (which may, possibly, be due to cultural or linguistic dierences), we chose to exclude items 6 and 15 from our modi®ed AIR-r analysis. Second, Bryant et al. (1996) excluded two items from their AIR model, although these items to the present authors carry clear connotations of positive aect intensity: items 2 (``I enjoy being with other people'') and 32 (``When I am excited over something I want to share my feelings with everyone''). These two items were therefore included in our AIR-r model, thus keeping the number of items to 27. Finally, a hierarchical model with a General Aect Intensity factor was tested. A hierarchical model opens for possibilities simultaneously to include a broad ``general'' aective factor (e.g., general factor Ð G ) and several more speci®c aective dimensions. Such models for human ability have earlier been proposed by Vernon (1950) and Cattell and Horn (e.g., Cattell, 1963, 1971; Horn, 1968, 1986). In the present study such a hierarchical model was tested for the AIR-r. No hierarchical model for AIR-r was obtained, however, the program not being able to arrive at a model after 500 iterations. Table 1 displays the measures of relative ®t for each of the six structural models. When compared with Bryant et al.'s (1996) three-factor AIR model, our modi®ed AIR-r three-factor model achieved the best model-®t on all four indices (GFI=0.82; AGFI=0.78; RMSEA=0.08; and Chi-square/df ratio=4.40). On two (GFI and AGFI) of these four indices criteria were Table 1 Goodness-of-®t statistics for factor models of the AIM (N = 409) Model
Chi-square
df
X 2/df
GFI
AGFI
RMSEA
Larsen's (1984) one-factor (40-items) Weinfurt et al.'s (1994) four-factor (40-items) Bryant et al.'s (1996) AIR one-factor (27-items) Bryant et al.'s (1996) AIR four-factor (27-items) Bryant et al.'s (1996) AIR three-factor (27-items) AIR-r three-factor (27-items)
4368.00 3097.20 1895.50 1593.20 1629.80 1412.50
740 734 324 318 321 321
5.90 4.22 5.85 5.01 5.08 4.40
0.64 0.75 0.77 0.80 0.80 0.82
0.61 0.73 0.73 0.77 0.77 0.78
0.096 0.078 0.095 0.087 0.087 0.080
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clearly met, on a third index (RMSEA) the criterion was almost met, and on the fourth (X 2/ df ) it was met by Wheaton et al.'s (1977) more liberal standards. An examination of the parameter item estimates for the AIR-r revealed that all estimates were signi®cant at the P < 0.01 level (see Table 2). Cronbach alpha coecients and mean inter-item correlation coecients were calculated for the AIR-r and its factors. The total AIR-r had an alpha of 0.85, and showed a mean inter-item correlation of 0.17. Table 2 shows the corresponding Cronbach's alpha values and the mean inter-item correlations (MIC) for the three factors. The parameter estimates for the relationships among the three factors of the AIR-r are presented in Table 3. All estimates were statistically signi®cant. Table 2 Con®rmatory factor analysis, parameter estimates for the AIR-r items, and Cronbach alphas and mean inter-item correlations for the three factorsa (18) (35) (7) (14) (9) (2) (8) (23) (5) (22) (38) (10) (1) (20) (27) (3) (32)
Factor 1: positive aectivity (alpha=0.90, MIC=0.34) When I'm feeling well it's easy for me to go from being in a good mood to being really joyful When I'm happy I bubble over with energy My happy moods are so strong that I feel like I'm in heaven When something good happens, I am usually much more jubilant than others If I complete a task I thought was impossible, I am ecstatic When I feel happy it is a strong type of exuberance I get overly enthusiastic When I receive an award I become overjoyed When I solve a small personal problem, I feel euphoric When I'm happy I feel very energetic When someone compliments me, I get so happy I can ``burst'' My heart races at the anticipation of some exciting event When I accomplish something dicult I feel delighted or elated When I'm happy I feel like I'm bursting with joy When things are going good I feel ``on the top of the world'' I enjoy being with other people When I'm excited over something I want to share my feelings with everyone
0.77 0.75 0.74 0.68 0.66 0.65 0.61 0.61 0.55 0.52 0.48 0.46 0.42 0.38 0.36 0.31 0.30
(36) (30) (39) (34)
Factor 2: negative intensity (alpha=0.60, MIC=0.27) When I feel guilty this emotion is quite strong When I feel anxiety it is normally very strong When I am nervous I get shaky all over My friends would probably say I'm a tense or ``high-strung'' person
0.83 0.58 0.29 0.26
(21) (11) (25) (17) (4) (13)
Factor 3: negative reactivity (alpha=0.63, MIC=0.22) Seeing a picture of some violent car accident in a newspaper makes me feel sick to my stomach Sad movies deeply touch me When I do something wrong I have strong feelings of shame and guilt The sight of someone who is hurt badly aects me strongly I feel pretty bad when I tell a lie When I talk in front of a group for the ®rst time my voice gets shaky and my heart races
0.59 0.52 0.55 0.54 0.31 0.27
a
All parameter estimates signi®cant (P < 0.01).
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Table 3 Parameter estimates among the three AIR-r factorsa Factor
F1
F2
F1 F2 F3
± 0.39 0.60
± ± 0.61
a
F1=Positive aectivity; F2=Negative intensity; F3=Negative reactivity. All parameter estimates are signi®cant at the 5%-level, at the least.
3.2. Validation of the AIR-r model In order to study the validity of our new AIR-r three-factor model, and to compare it with Larsen's (1984) original 40-item AIM, we studied correlations with the Karolinska Scales of Personality, the 20-item version of the Toronto Alexithymia Scale, the Somatosensory Ampli®cation Scale, an interview version of the Illness Attitudes Scale, Pennebaker's PILL inventory, three subscales of the Diary Questionnaire, and the Quality of Life Inventory. As Table 4 Correlations of the three AIR-r factors and the total AIM with other measures of personality, somatic symptoms, and quality of life (N = 195, except for the TAS-20, where N = 137) (P < 0.01; P < 0.001)
KSP somatic anxiety KSP psychic anxiety KSP muscular tension KSP social desirability KSP impulsiveness KSP monotony avoidance KSP detachment KSP psychasthenia KSP socialization KSP indirect aggression KSP verbal aggression KSP irritability KSP suspicion KSP guilt KSP inhibition of aggression Toronoto Alexithymia scale (TAS-20) Somatosensory ampli®cation (SAS) Illness attitudes scale (IAS-13) PILL somatic symptoms DQ somatic complaints DQ negative aect DQ positive aect Quality of life inventory (QOLI)
Positive aectivity
Negative intensity
Negative reactivity
AIM-40
0.19 0.21 0.14 0.03 0.26 0.30 ÿ0.33 0.05 ÿ0.21 0.20 0.07 0.13 0.09 0.16 0.10 ÿ0.08 0.25 0.11 0.10 0.19 0.19 0.22 0.08
0.52 0.50 0.40 ÿ0.08 0.03 0.02 ÿ0.03 0.37 ÿ0.48 0.25 0.03 0.24 0.29 0.33 0.27 0.26 0.37 0.29 0.48 0.41 0.40 ÿ0.12 ÿ0.13
0.42 0.51 0.37 0.08 0.01 ÿ0.01 ÿ0.28 0.32 ÿ0.31 0.22 ÿ0.08 0.21 0.19 0.33 0.33 0.06 0.27 0.18 0.32 0.33 0.33 ÿ0.04 0.07
0.39 0.41 0.30 ÿ0.03 0.27 0.29 ÿ0.33 ÿ0.22 ÿ0.38 0.31 0.13 0.24 0.17 0.27 0.15 ÿ0.01 0.34 0.28 0.29 0.34 0.34 0.10 ÿ0.03
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can be seen in Table 4, the three AIR-r factors showed clearly discriminant validity, in a way which supplies information that cannot be derived from the original one-dimensional AIM model. For example, although the AIM total score correlates with impulsiveness and monotony avoidance, this correlation can be traced to only one of the three AIR-r factors, i.e., positive aectivity. Conversely, the AIM correlations with psychasthenia, muscular tension, irritability, and somatic complaints were almost entirely due to correlations with the two negative AIR-r factors. Finally, there were also some variables (e.g., inhibition of aggression, suspicion, and alexithymia) which, although they showed no or little correlation with the AIM, tended to correlate with one or two of the AIR-r factors.
4. Discussion The purpose of the present study was to investigate the dimensional structure of the Swedish translation of the AIM, in order to ®nd the best structural model for the Swedish AIM data, and to validate this model. Con®rmatory factor analysis showed that all of the multidimensional AIM models were structurally superior to Larsen's (1984) original 40-item unidimensional model, on all the ®t indices. The best model-®t among the multidimensional models was obtained by our modi®ed three-factor AIR-r model: Positive Aectivity, Negative Intensity, Negative Reactivity. The results clearly showed dierent correlational patterns between negative intensity and negative reactivity, on the one hand, and positive aectivity, on the other, which indicates the value of treating aect intensity as a multidimensional construct. Psychological coherence and contents must always take precedence over statistical sophistication. Too much research in which the AIM has been involved, and especially when it comes to the question of its putative unidimensionality, suggests that many researchers try to press a priori notions of number of dimensions on their ®nal factor solutions. One point of departure for our venture was that any measure of any trait is bound to be multidimensional. Our results for the GFI and chi-square ratio correspond well with the results obtained by Bryant et al. (1996). All but one (Weinfurt et al., 1994) of the multidimensional AIM models achieved greater model-®t then Bryant et al.'s (1996) one-factor AIR on all indices. We see our results as consistent with those of other colleagues that earlier provided evidence for the multidimensionality of the AIM construct (Bryant et al., 1996; Weinfurt et al., 1994). Just as in Bryant et al.'s (1996) study, our data showed an almost identical model-®t between Bryant et al.'s (1996) AIR three-factor model and their four-factor AIR model, the models varying slightly as to the chi-square ratio. Among the ®ve multidimensional models that were tested by CFA, the best model-®t (though not entirely satisfactory) was obtained by our modi®ed threefactor AIR-r model, based on a reanalysis of the contents of the AIM items in Bryant et al.'s (1996) three-factor model. We ®nd that our analyses suggest that the AIM consists of more than one underlying factor and that the one-factor model is indistinct and possibly also conceptually misleading, since it is composed of a non-obvious conglomeration of various facets of aective experience. It is true that earlier research has indicated that the one-factor model of the AIM has external validity, as demonstrated by a number of studies of the relationships between AIM and other variables (e.g., Diener et al., 1985b; Flett et al., 1996; Flett & Hewitt, 1995; Larsen & Diener, 1987;
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Mueller et al., 1991). It is possible, however, that these associations would have appeared even more clearly if aect intensity had been treated as a multidimensional construct in these studies. The present results indicate that when the AIM is treated as a global one-dimensional construct, at least two kinds of imprecisions appear: (1) the risk of ascribing eects to global aect intensity which, in fact, are due mainly either to positive aectivity (e.g., extraversionrelated trait like impulsiveness and monotony avoidance) or negative intensity or reactivity (e.g., neuroticism-related traits like psychasthenia, muscular tension, irritability, somatic complaints); and (2) the risk of neglecting possible relations between aspects of aect intensity and other variables (e.g., inhibition of aggression, suspicion, and alexithymia) because they are not seen with global aect intensity. In addition, as can be seen in Table 4, the magnitude of several correlations between aect intensity and other variables is clearly attenuated when aect intensity is treated as a global one-dimensional construct. Validation of our three-factor model showed that negative intensity and negative reactivity, as distinct from positive aectivity, demonstrated a rather similar pattern of correlations with other variables. The main exception from this pattern was the KSP scale Detachment, which is designed to measure schizoid traits, such as need for a social distance, and coldness in social relations Ð although detachment was found to correlate negatively with both positive aectivity and negative reactivity, it showed no correlation with negative intensity. The negative correlation between detachment and positive aectivity is consistent with the de®nition of schizoid personality disorder, which has anhedonia as a component (DSM-IV; American Psychiatric Association, 1994). The dierentiated results with regard to negative intensity and negative reactivity are more dicult to explain. One possible explanation, however, is that detachment involves a lack of aective relatedness to the environment, and thereby also a relative lack of reactivity to daily situations, as speci®ed by the items in the negative reactivity subfactor (see Table 2), which produces a negative correlation between detachment and negative reactivity. On the other hand, the subfactor negative intensity has little reference to relatedness to speci®c situations and almost entirely refers to the person's experience of him- or herself, which may account for the lack of correlation between detachment and negative intensity. As suggested to us by an anonymous reviewer, it is possible that the subfactors which are here referred to as negative intensity and negative reactivity may, in fact, be distinguished in terms of whether they come solely from the self (i.e., negative intensity) or involve reactions to negative events that are mostly outside the self (i.e., negative reactivity). This may, then, contribute to the explanation of the general pattern, that was found in the present study, of negative intensity showing correlations of higher magnitude than negative reactivity with somatic distress Ð since somatic distress obviously belongs to the ``inside'' experience of the self. This applied both to symptom check lists, like Pennebacker's PILL inventory (which correlated r = 0.48 with negative intensity and r = 0.32 with negative reactivity), and the hypochondriasis-related measure Illness Attitude Scale (which correlated r = 0.29 with negative intensity and r = 0.18 with negative reactivity). Somewhat surprisingly, somatosensory ampli®cation correlated with all three AIM factors. As distinct from the other somatically related variables, this scale does not refer directly to somatic symptoms, or somatic distress, but rather to the intensity or sensitivity to somatic sensations (loud noises, heat/cold, pain, hunger contractions, etc.). In this sense, the
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somatosensory ampli®cation scales resembles the AIM, and might perhaps even be seen as a somatic sensation intensity measure, analogous to the aect intensity measure. It is notable that no association was found between any aspect of aect intensity, neither positive nor negative, and life quality as measured by the QOLI, in spite of the considerable correlation between negative intensity and somatic distress. This, however, is consistent with earlier ®ndings that although aect intensity is associated with somatic distress, it is unrelated or orthogonal to measures of psychological well-being (Diener, 1984; Larsen, Diener & Emmons, 1985). Larsen and Diener (1987, p. 16) suggest a possible explanation of this apparent paradox by assuming that, although the regular experience of strong emotions exacts ``a price to be paid in terms of wear and tear on the body'', subjects high in aect intensity apparently ``are willing to pay this price, perhaps because the regular experience of positive emotions serves some positive function for them or somehow ®ts with other aspects of their personality and temperament''. In the present study, however, although all three AIM factors showed a lack of correlation with life quality, these three subfactors correlated very dierently with somatic distress. Again, these ®ndings speak against treating aect intensity as a onedimensional construct. To summarize, the present results clearly indicate that the multidimensional model of aect intensity demonstrates superior conceptual and predictive precision relative to the unidimensional AIM total score. As expected, the most striking dierentiation was seen between positive and negative aspects of aect intensity, which, consistently with the ®ndings of Williams (1989), correlated with extraversion- and neuroticism-related trait measures, respectively. But dierences were also seen between negative intensity and negative reactivity, especially with regard to measures of somatic distress. There certainly exist a lot of varied models of the AIM scale. At ®rst glance, it may seem that we further complicate the picture by introducing still a new multi-dimensional model. It should be remembered, however, that our three-dimensional model is only a slight modi®cation of Bryant et al.'s (1996) AIR model. Still, our data suggest that this revised AIR model has both theoretical and psychometric advantages relative to the original AIR. Since our validation results are solely based on an urban community sample, the generalizability of these results needs further corroboration in other samples Ð both clinical and non-clinical. It would also be of interest to return to earlier published data, and analyze these in terms of a multidimensional model of the AIM; our prediction would be that this would clarify the meaning of associations which have been found between the AIM total score and other variables. In the long run, we will most probably need better instruments for measuring aect intensity, and the experience of aect in general (cf. Schimmack & Diener, 1997). Only one of the 40 AIM items, for example, mentions such a basic emotion as anger, and the equally basic emotion of sadness is only mentioned indirectly in one item (item 11, ``Sad movies deeply touch me''). We look forward to future endeavours in that direction.
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