Arts, Faculty of
Psychology, Department of
DSpace
UBCV
Hibbert, Anita
Doctor of Philosophy - PhD
University of British Columbia
Self-report personality measures are commonly used across all of psychology, both in research and clinical settings. Given their ubiquity in psychological research and practice, it is important to characterize potential threats to the validity of self-report personality measures, so that this information can be considered during research design. One potential threat to the validity of self-report personality measures which has yet to be investigated is the influence of current mood state on self-reported personality ratings. The current research used an experimental, within-person design in two undergraduate samples to investigate: 1) whether current mood state in fact influences self-reported Big Five personality ratings or structure; 2) to identify whether these changes are primarily driven by a positive mood state, a negative mood state, or both; and 3) to explore potential moderators of this effect, including clinical disorder symptoms and the social desirability of the individual personality items. Study 1 (N=185) demonstrated an effect of current mood state on self-reported personality ratings, where individuals in a positive mood state rated themselves as more extraverted, more agreeable, more conscientious, and less neurotic than when they were in a negative mood state (ps<.03). The results from Study 2 (N=431) suggested that extraversion and openness to experience ratings were increasing in response to the positive mood state, whereas neuroticism ratings were increasing in response to the negative mood state. Clinical symptoms did not moderate the effect of mood on personality ratings, and the structure of the personality factors did not appear to significantly change between the mood conditions. Although the social desirability of the personality items did not moderate the effect of mood on self-report personality ratings in the models we tested, there was some evidence that highly socially desirable items were more stable in their ratings across mood conditions. Overall, the results from the current research provide evidence that mood state at the time of questionnaire completion can influence self-report ratings of certain personality traits. However, the size of the effect appears to be very small, and unlikely to represent a large threat to the validity of self-report personality measures under typical research conditions.
THE INFLUENCE OF MOOD ON SELF-REPORTED PERSONALITY by Anita Hibbert M.A., The University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2018 © Anita Hibbert, 2018 ii Abstract Self-report personality measures are commonly used across all of psychology, both in research and clinical settings. Given their ubiquity in psychological research and practice, it is important to characterize potential threats to the validity of self-report personality measures, so that this information can be considered during research design. One potential threat to the validity of self-report personality measures which has yet to be investigated is the influence of current mood state on self-reported personality ratings. The current research used an experimental, within-person design in two undergraduate samples to investigate: 1) whether current mood state in fact influences self-reported Big Five personality ratings or structure; 2) to identify whether these changes are primarily driven by a positive mood state, a negative mood state, or both; and 3) to explore potential moderators of this effect, including clinical disorder symptoms and the social desirability of the individual personality items. Study 1 (N=185) demonstrated an effect of current mood state on self-reported personality ratings, where individuals in a positive mood state rated themselves as more extraverted, more agreeable, more conscientious, and less neurotic than when they were in a negative mood state (ps<.03). The results from Study 2 (N=431) suggested that extraversion and openness to experience ratings were increasing in response to the positive mood state, whereas neuroticism ratings were increasing in response to the negative mood state. Clinical symptoms did not moderate the effect of mood on personality ratings, and the structure of the personality factors did not appear to significantly change between the mood conditions. Although the social desirability of the personality items did not moderate the effect of mood on self-report personality ratings in the models we tested, there was some evidence that highly socially desirable items were more stable in their ratings across mood conditions. Overall, the results from the current research provide evidence that mood state at the iii time of questionnaire completion can influence self-report ratings of certain personality traits. However, the size of the effect appears to be very small, and unlikely to represent a large threat to the validity of self-report personality measures under typical research conditions. iv Lay Summary Self-report personality measures are frequently used in psychology research. As such, it is important to characterize potential threats to the validity of these measures. One potential threat which has yet to be investigated is the influence of current mood state on self-reported personality ratings. The current research aimed to investigate: 1) whether current mood state has an impact on self-report personality ratings; 2) to identify which mood states are driving the observed changes in ratings; and 3) to explore potential moderators of these changes (i.e. clinical symptoms, and social desirability). Overall, the results suggest that our ratings of certain personality traits are impacted by our mood state at the time we complete the measure – particularly the personality traits of extraversion, neuroticism and agreeableness. However, the effect appears to be small and so it is unlikely to pose a threat to the validity of self-report personality measures in most contexts. v Preface The author was the primary contributor to the work presented in this dissertation, and was generally responsible for all study design, data analysis, and manuscript writing. The author took part in data collection for Study 1 and Study 2 with the help of numerous research assistants whom the author helped to train and supervise. Dr. E. David Klonsky assisted with study design, provided feedback and other intellectual contributions, and edited the manuscript. Dr. Jeremy Biesanz provided the social desirability dataset used in Study 2 to conduct the social desirability analyses. Dr. William Hall and Dr. Jeremy Biesanz provided statistical consultation for the analyses in Study 2. The research described in this dissertation was approved by the UBC Behavioural Research Ethics Board, under the ethics certificate numbers: H12-00528 and H13-03421. vi Table of Contents Abstract .......................................................................................................................................... ii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x Acknowledgements ..................................................................................................................... xii Chapter 1: Introduction ................................................................................................................1 1.1 Structural Models of Personality .................................................................................... 3 1.2 The Role of Self-Report Measures ................................................................................. 5 1.3 The Influence of Mood on Self-Reported Personality: Theoretical Support .................. 6 1.3.1 Associative network model. ........................................................................................ 6 1.3.2 Affect-as-information theory ...................................................................................... 8 1.3.3 Affect Infusion Model............................................................................................... 10 1.3.4 Theoretical Support: Summary ................................................................................. 14 1.4 The Influence of Mood on Personality Ratings: Empirical Support ............................. 15 1.4.1 Empirical Support: Summary ................................................................................... 18 1.5 Additional Exploratory Aims of the Current Research ................................................. 19 1.5.1 Affective disorders .................................................................................................... 19 1.5.2 Borderline Personality Disorder ............................................................................... 21 1.5.3 Social Desirability ..................................................................................................... 22 1.5.4 The Influence of Mood on Personality Structure ...................................................... 23 1.6 Summary of the Primary Research Questions .............................................................. 24 vii 1.6.1 Does Mood State Influence Self-Reported Personality Ratings? ............................. 25 1.6.2 Clarifying the Nature of the Change in Self-Reported Personality Ratings ............. 25 1.6.3 Additional Exploratory Aims .................................................................................... 26 Chapter 2: Study 1 .......................................................................................................................27 2.1 Methods......................................................................................................................... 27 2.1.1 Study Sample and Recruitment ................................................................................. 27 2.1.2 Procedure .................................................................................................................. 28 2.1.3 Measures ................................................................................................................... 30 2.1.3.1 Demographics ................................................................................................... 30 2.1.3.2 Self-Reported Personality ................................................................................. 30 2.1.3.3 Current Affective State ..................................................................................... 30 2.1.4 Missing Data and Data Cleaning .............................................................................. 31 2.2 Results ........................................................................................................................... 32 2.2.1 Manipulation Check .................................................................................................. 32 2.2.2 The Influence of Mood on Mean Personality Ratings .............................................. 33 2.2.3 The Influence of Mood State on Personality Structure ............................................. 35 2.3 Summary of Study 1 Results......................................................................................... 37 Chapter 3: Study 2 .......................................................................................................................39 3.1 Methods......................................................................................................................... 41 3.1.1 Study Sample and Recruitment ................................................................................. 41 3.1.2 Procedure .................................................................................................................. 42 3.1.3 Measures ................................................................................................................... 43 3.1.3.1 Demographics ................................................................................................... 43 viii 3.1.3.2 Self-Reported Personality Traits ....................................................................... 43 3.1.3.3 Current Affective State ..................................................................................... 43 3.1.3.4 Clinical Screening Measures............................................................................. 43 3.1.3.4.1 Depression, Anxiety and Stress Symptoms ................................................ 44 3.1.3.4.2 Borderline Personality Disorder Symptoms ................................................ 44 3.1.4 Missing Data and Data Cleaning .............................................................................. 44 3.1.5 General Statistical Method ........................................................................................ 46 3.2 Results ........................................................................................................................... 47 3.2.1 Manipulation Check .................................................................................................. 47 3.2.2 The Impact of Mood State on Personality Ratings ................................................... 49 3.2.2.1 Agreeableness ................................................................................................... 50 3.2.2.2 Conscientiousness ............................................................................................. 50 3.2.2.3 Extraversion ...................................................................................................... 50 3.2.2.4 Neuroticism ....................................................................................................... 50 3.2.2.5 Openness to Experience .................................................................................... 51 3.3 Moderation by Clinical Variables ................................................................................. 52 3.3.1 Social Desirability Findings ...................................................................................... 56 3.3.2 The Influence of Mood State on Personality Structure ............................................. 60 3.4 Summary of Study 2 Results......................................................................................... 63 Chapter 4: Conclusion .................................................................................................................65 4.1 Overall Summary of Research Findings ....................................................................... 65 4.2 Implications of the Current Findings ............................................................................ 69 4.3 Limitations and Future Directions ................................................................................ 72 ix 4.4 Summary ....................................................................................................................... 76 References .....................................................................................................................................77 x List of Tables Table 1 - Study 1 Sample Demographics ..................................................................................... 28 Table 2 - SAM ratings of study completers vs. non-completers pre- and post-induction ............ 31 Table 3 - Comparison of study completers vs. non-completers on BFI ratings post-induction ... 32 Table 4 – Paired samples t-tests comparing SAM ratings pre- and post-mood induction ............ 33 Table 5 - Paired samples t-tests comparing SAM ratings post-mood induction........................... 33 Table 6 - Paired Samples t-tests of BFI Scales Comparing Happy versus Sad Mood Inductions 34 Table 7 - Paired Samples t-tests of BFI scales (‘non-responders’ removed) ................................ 35 Table 8 – Correlations among personality ratings across and within mood conditions in study 136 Table 9 - Study 2 Sample Demographics ..................................................................................... 41 Table 10 - SAM ratings of study completers vs. non-completers pre- and post-induction .......... 45 Table 11 - Comparison of study completers vs. non-completers on BFI ratings post-induction . 46 Table 12 - Mean SAM ratings pre- and post-mood inductions .................................................... 48 Table 13 – Paired Samples t-tests of SAM ratings pre- to post-mood induction ......................... 48 Table 14 - SAM contrasts post-mood induction only ................................................................... 49 Table 15 - Mean BFI personality factor ratings by condition ....................................................... 51 Table 16 - Pairwise comparisons of mean ratings across conditions............................................ 52 Table 17 – Descriptive Statistics of Clinical Variables ................................................................ 53 Table 18 - Moderation effects across all clinical variables ........................................................... 54 Table 19 - Correlation between mean item social desirability and trait ratings, by mood condition....................................................................................................................................................... 56 Table 20 - Results from the linear mixed model........................................................................... 58 Table 21 – Social desirability and variance of the BFI items ....................................................... 59 xi Table 22 – Correlations among personality ratings across and within mood conditions in study 2....................................................................................................................................................... 62 xii Acknowledgements I would like to extend my sincerest gratitude to my advisor, Dr. E. David Klonsky. I am loath to think of what my time in graduate school would have been like without his unwavering encouragement, his genuine enthusiasm and intellectual curiosity, and his seemingly endless patience. I am also thankful for the support from my supervisory committee members, Dr. Jessica Tracy and Dr. Jeremy Biesanz, and for the mentorship that I have received from my clinical supervisors over the years. Finally, my heartfelt thanks go out to my friends, family, and William Hall, for walking this road with me. 1 Chapter 1: Introduction The study of personality has evolved significantly since the ancient Greeks first formulated their humoral theory of temperament in the 5th century B.C. (Zuckerman, 1998). With each shift to a new dominant paradigm the measurement, the scope, and even the definition of ‘personality’ has been debated. Among the current diversity of definitions, there appears to be a level of consensus that: a) personality has its origins in an individual’s biology; b) these innate tendencies are shaped by external forces, including family context, social context, culture, and other external pressures; and c) this results in patterns of behavioural, cognitive, and emotional tendencies, which together are understood as ‘personality’ (S. Cloninger, 2009). Implicit in the current understanding of ‘personality’ is the assumption that our quantitative tools for measuring ‘personality’ have a high level of validity. Much research has been conducted to verify that our assessment measures are in fact measuring these behavioural, cognitive and emotional patterns, and that they are not being systematically influenced by extraneous factors. However, it is unlikely that our current measures are yet impervious to all potential threats to validity, particularly as self-report measures are very vulnerable to biases and other sources of inaccuracy (Paulhus & Vazire, 2009). This issue is particularly important given that the overwhelming majority of research involving personality variables relies on self-report measures. Although alternative measures of personality traits exist (Adelstein et al., 2011; Back, Schmukle, & Egloff, 2009; Mehl & Pennebaker, 2003; Schnabel, Asendorpf, & Greenwald, 2008; Schnabel, Banse, & Asendorpf, 2006), examples of research involving these measures are limited in comparison to the widespread use of self-report measures. As an illustration of the overwhelming popularity of self-report measures of personality, the professional manual for the revised NEO personality 2 inventory and NEO five-factor inventory (Costa & McCrae, 1992) – a commonly used self-report measure of personality – has been cited over 10,000 times. Given the widespread usage of self-report personality measures, it is important for researchers to continue to investigate factors that have the possibility of biasing the results obtained from personality assessment tools. One factor which has not received much attention in the literature is the potential influence of current mood state on responses to self-report personality measures. The current research aims to investigate the influence of current mood on self-reported personality traits. Several theoretical models and empirical lines of research detailed below suggest that an individual’s current mood may influence their judgments about, or perceptions of, themselves. By extension, mood may influence an individual’s responses on self-report measures, as self-report measures draw on the ability of an individual to perceive and make judgments about the ‘self’. If this effect does indeed occur, and current mood state alters how people respond on personality self-report measures, it would be important for personality researchers and others who use self-report personality measures to be aware of this source of meaningful error variance. Additionally, by examining the influence of mood on self-reported personality, the current research aims to contribute to the literature on the stability of self-perception. Previous research has found that mood can influence perceptions of the self in a number of ways, including influencing perceptions of general well-being (Schwarz & Clore, 1983), global self-descriptions (Wright & Mischel, 1982), and endorsement of personal characteristics (Sedikides, 1995). The current research is the first to examine the influence of mood on self-perceptions of the Big Five traits experimentally. Additionally, if mood can be demonstrated to impact self-3 perceptions of purportedly stable behavioural, cognitive, and emotional tendencies, it would provide evidence in support of the variability of self-perception. 1.1 Structural Models of Personality Structural, or trait-based theories have emerged as the most prevalent perspectives in personality psychology today (Campbell, 2008). Prior to this development, the study of personality was often conducted by clinicians or clinically oriented researchers, and their psychoanalytic or psychodynamic theories of personality were highly influential. The emphasis of these approaches was on understanding the individual from an ideographic perspective, and the focus was largely on personality as a process. In the psychoanalytic or psychodynamic tradition, projective tests were often used to assess an individuals’ personality (Westen, Gabbard, & Ortigo, 2008). In contrast to this ideographic approach, the structural or trait-based movement has been characterized by a more nomothetic emphasis on personality measurement across individuals and the hierarchical organization of traits using psychometric tools such as factor analysis (Campbell, 2008; Wiggins & Pincus, 1992). Of the structural or trait-based models of personality, the Five-Factor Model or FFM (Costa & McCrae, 1985b, 1992) – also referred to as the ‘Big Five’ structure (Goldberg, 1990) – has perhaps been most the widely studied and empirically supported (Digman, 1990; McCrae, 2009). According to the FFM/Big Five factor structure, personality can be described using five overarching and enduring factors or trait domains: neuroticism (or conversely, emotional stability), extraversion (or surgency), openness to experience (or intellect/imagination/culture), agreeableness (or conversely, antagonism), and conscientiousness (or will to achieve). These five factors were first identified using a lexical approach, where a comprehensive list of lay language 4 personality descriptors were transformed into scales, and then factor analyzed (Goldberg, 1990; Tupes & Christal, 1961, 1992). Additionally, this five factor structure has also emerged when using measures based on an aggregation of previously studied personality constructs (Costa & McCrae, 1985b), as well as those based on clinical measures (McCrae, Costa, & Busch, 1986). Although the FFM or Big Five factor structure is not without critics (Block, 1995), there is substantial empirical support for the model. The Big Five structure has been replicated using different types of self-report instruments (e.g. adjective lists and questionnaire scales), and has also been identified across different rating sources (e.g. observer ratings and self-report ratings; (Funder, Kolar, & Blackman, 1995; McCrae & Costa, 1987). The five factor structure has been replicated cross-culturally, and research has been generally consistent in demonstrating that the five factor structure emerges in Germanic language cultures. In non-Western, non-Germanic language cultures, the five factor model has also been replicated (McCrae & Costa, 1997), although the factor of Openness-Intellect has not been identified as consistently as the other four traits. When studies have been conducted using an emic methodology (e.g. trait terms are derived from the native language, as opposed translated from Western questionnaires) they have not always seen the emergence of the five factors. However, substantial overlap between the culturally derived facets and the Western five factors has been identified (John & Srivastava, 1999). Finally, the five factors have also demonstrated theoretically consistent relationships to real-world outcomes. For example, the five facets have been shown to predict job performance (Barrick, Mount, & Judge, 2001), academic performance (Poropat, 2009), as well as a number of outcomes over the lifespan such as career success, income, use of psychiatric services, and substance abuse (Soldz & Vaillant, 1999). 5 1.2 The Role of Self-Report Measures As is evident in the aforementioned research, structural models of personality like the FFM largely rely on self-report measures, both in the development and validation of the models themselves, as well as in the final measures used to assess personality traits. Alternatives to self-report measures have been used, such as the use of brain imaging (Adelstein et al., 2011) and implicit measures of personality (Back et al., 2009; Schmukle, Back, & Egloff, 2008; Steffens & König, 2006). However, self-report measures such as the Revised NEO Personality Inventory (Costa & McCrae, 1992) or the Big Five Inventory (Benet-Martínez & John, 1998; John, Donahue, & Kentle, 1991; John & Srivastava, 1999) remain the most popular and well-validated method of assessing personality traits. Self-report personality measures are popular because they offer a number of benefits over alternative methods (Paulhus & Vazire, 2009). Specifically, self-report questionnaires represent a cost-effective and straightforward method of data collection. They are also more easily interpretable than alternative methods, such as brain imaging, where the exact meaning of increased or decreased brain region activity can be ambiguous. Additionally, to a certain extent it is assumed that participants will have a higher quality of information about their own personalities; whereas outside observers can view the external behaviours of the target in limited roles or contexts, an individual has access to memories of their own behaviours across all situations, as well as unique knowledge of their internal experiences. In addition to the benefits of self-report measures, self-report personality questionnaires are also susceptible to a number of well-known sources of inaccuracy (Paulhus & Vazire, 2009), such as socially desirable responding or acquiescence responding. However, if researchers are knowledgeable about these effects and how they can impact responding, steps can be taking 6 during scale construction or during the study design process to minimize the influence of these sources of inaccuracy (Nederhof, 1985). It is therefore important to identify as many possible sources of inaccuracy or bias, to enable researchers to better design their measures or studies. The current research aimed to clarify whether current mood may be another source of bias that researchers should be taking into account when using self-reported personality measures. 1.3 The Influence of Mood on Self-Reported Personality: Theoretical Support Self-report measures of personality are frequently used without consideration for whether they are or are not mood-invariant. However, there are several theories related to the impact of mood on cognitions which support the possibility that responses to self-report personality measures may be systematically influenced by mood state. Three examples of such theories are the associative network model (Bower, 1981), the affect-as-information model (Clore, Gasper, & Garvin, 2001), and the affect infusion model (Forgas, 1995). 1.3.1 Associative network model. The associative network model (Bower, 1981; Bower & Forgas, 2000) has direct implications for the influence of mood on self-reported personality. The associative network model considers moods or emotions to be informational ‘nodes’. Nodes can also be objects, events, autobiographical memories, or other memory representations. These nodes are linked to form networks. For example, the node of ‘sad mood’ may be linked to objects associated with sadness (e.g. tears, the colour blue), sad events (e.g. endings, death), and also specific autobiographical memories. In a process called ‘affect priming’, the elicitation of a mood or emotion in an individual will activate the node corresponding to that mood or emotion. This 7 activation is thought to spread through the network of nodes linked to that mood or emotion, which in turn makes those associated nodes more easily accessible by the individual. Mood congruent memory is an extension of the associative network model of emotion. Mood congruent memory hypothesizes that individuals will more easily recall memories that are associated with their current affective state (Bower, 1981; Bower & Forgas, 2000). As such, it is likely that individuals who are currently in a negative mood may remember mood-congruent memories more easily than non-congruent memories. In other words, an individual who is experiencing a sad mood may recall sad memories more frequently and more easily than happy memories. In support of mood congruent memory and the affective network model, Bower (1981) demonstrated that happy and sad moods were associated with increased recall of mood-congruent details and events from childhood, as well as from the most recent few weeks. Mood congruent memory has also been demonstrated in relation to naturally occurring mood states (Loeffler, Myrtek, & Peper, 2013; Mayer, McCormick, & Strong, 1995) and has been demonstrated to occur in depressed samples (Gaddy & Ingram, 2014; Matt, Vázquez, & Campbell, 1992; Watkins, Vache, Verney, & Mathews, 1996) of individuals with chronically low mood. If individuals base their personality ratings on the memory of their past experiences and behaviours, the associative network model and mood-congruent memory suggest that mood state would likely influence the types of memories which are most easily retrieved. This biased recall therefore would have the potential to impact ratings of many self-report personality items. Using the Big Five Inventory (John et al., 1991) as an example, when in a negative mood state individuals might be more likely to recall memories supporting the endorsement of personality items such as: “is depressed, blue” (neuroticism item); “starts quarrels with others” 8 (agreeableness item, reverse scored); “is sometimes shy, inhibited” (extraversion item, reverse scored); “tends to be lazy” (conscientiousness, reverse scored). Conversely, when in a more positive mood state, it might be easier for individuals to recall examples supporting greater endorsement of items such as: “is relaxed, handles stress well” (neuroticism item, reverse scored); “is helpful and unselfish with others” (agreeableness item); “generates a lot of enthusiasm” (extraversion, reverse scored); “is a reliable worker” (conscientiousness item); “is original, comes up with new ideas” (openness to experience item). In other words, according to the associative network model and mood-congruent memory, when individuals are in a negative mood state, we would expect to see increases in their endorsement of personality traits associated with negative memories, whereas when individuals are in a positive mood state, we would expect them to endorse higher levels of personality traits associated with more positive memories. 1.3.2 Affect-as-information theory According to the affect-as-information theory (Clore et al., 2001; Schwarz & Clore, 1983), individuals implicitly use their current affective state as relevant data when they are making judgements, as long as their current affective state is not consciously attributed to something outside of the judgement at hand. In other words, individuals may misattribute their current emotional state as an indication of how they ‘feel’ about a certain target of judgment, and use this ‘feeling’ as heuristic cue in making that judgement. This model presumes the potential for individuals to mistake pre-existing affect as information on current judgements. For example, mild positive affect induced by an external event may skew the judgement of an unrelated construct in a more positive direction. In support of the affect-as-information model, individuals who rated their life satisfaction on sunny days 9 reported higher life satisfaction than those interviewed on rainy days (Schwarz & Clore, 1983), although this difference disappeared when participants were made explicitly aware of the weather. The use or misattribution of pre-existing affect to judgment targets has also been seen in judgments made by individuals who had just viewed a happy or sad film (Forgas & Moylan, 1987), and in individuals who had been primed with positive images (Tobin & Tidwell, 2013). Certain contextual conditions have been theorized to increase the likelihood that individuals will use their current affective state as a heuristic cue. For example, the use of affect as information increases when: the individual is constrained in time or attentional resources, when the task has little relevance to the individual, when the judgement or decision is too complex to work through in a systematic fashion, or when there is little information on which to base a judgment (Forgas & Koch, 2013). These conditions are present in many research designs involving self-reported personality measures (e.g. the participant wishes to complete the study visit in a timely fashion, the participant is not particularly invested in the study outcomes, and the participant is asked to summarize their complex understanding of themselves via scale responses), and may increase the likelihood of research participants using current affect as a heuristic cue in their judgements. Overall, the affect-as-information model suggests that, provided they remain unaware of the influence that their current affective state may have on their ratings, individuals will have a tendency to rate themselves more positively when in a positive mood state than when they are in a negative mood state. As many of the questions in self-report personality measures have either positive or negative connotations (e.g. “is full of energy” versus “can be cold and aloof”), the potential for current affective state to influence self-report ratings of personality measures is high. 10 1.3.3 Affect Infusion Model The affect infusion model (Forgas, 1995) was constructed to integrate the prior work on how affect comes to be used in the formation of judgements. This model incorporates and builds upon both the associative network model and the affect-as-information model by predicting when affect will influence judgments. According to the affect infusion model, the influence of affect will vary depending on the type of processing strategy which is required in any given situation. More specifically, the affect infusion model distinguishes between four types of processing strategies: direct access, motivated, heuristic, and substantive. The first two strategies are unlikely to be influenced by current affective state, whereas the latter two are strategies which are likely to incorporate current affective state into the formation of judgments. The ‘direct access’ processing strategy refers to the simple retrieval of a pre-stored or crystallized evaluation. For example, being asked whether one’s favourite colour is green would involve little deliberation, as one likely has a pre-formulated opinion about their favourite colour. The ‘motivated’ processing strategy refers to processing that occurs under conditions where there is an outcome or judgment that an individual wishes to arrive at. For example, an individual may be motivated to conclude that they were not at fault in a car accident, and so may selectively attend to information that supports this position. The direct access and the motivated processing strategies are considered to be ‘low affect infusion strategies’, where individuals using these strategies are unlikely to incorporate current affect into their judgment process. In the case of direct access, this is due to the information being pre-stored and easily accessible. In the case of motivated processing, this is due to attention and/or recall narrowing include only the 11 information required to reach the desired judgment, and so current affect (unless it is congruent with the judgment) is likely to be disregarded. As noted above, the two remaining processing strategies (‘heuristic’ and ‘substantive’, respectively) are thought to be ‘high affect infusion strategies’, where current affect is likely to influence judgments. The heuristic processing strategy refers to when judgments are based on an individual’s prevailing affective state or irrelevant environmental variables. The heuristic processing strategy is thought to be employed when: there is little motivation for an individual to come to a specific conclusion, the target of the judgment is simple/typical or global, there is little personal relevance in the judgment, the individual has limited cognitive capacity and/or the situation does not elicit a high degree of accuracy or elaborated consideration. In the previously noted study where individuals’ ratings of life satisfaction were being influenced by the weather at the time of their rating (Schwarz & Clore, 1983), individuals would have been engaging in heuristic processing: they were using their current affective state to as a heuristic for how satisfied they were in general with their life. In fact, it is likely that any example illustrating the affect-as-information model (Clore et al., 2001; Schwarz & Clore, 1983) would be considered an example of heuristic processing, as by definition, individuals would be using their current affective state as a heuristic for how they ‘feel’ about the target of their judgment. Conversely, the ‘substantive processing strategy’ is thought to occur when individuals are processing more complex targets, and when less cognitively demanding processing strategies are not adequate or appropriate to use. It is used in situations where the judgment task is demanding, the target is complex or atypical, the individual has the cognitive resources to dedicate to the judgment and is motivated to be accurate, and there are no ready-made responses or other motivational goals which would guide the response. For example, if an individual is attempting 12 to assimilate novel information into pre-existing knowledge structures, or to integrate or interpret information about a target, they would be engaging in substantive processing. This processing strategy is thought to be highly susceptible to affect infusion, because of the influence that affect can have on the information which is recalled or attended to. As such, the associative network model’s explanation of how affect influences judgments (Bower, 1981; Bower & Forgas, 2000) would fall into this ‘substantive processing’ category: mood congruent memory and affective priming will influence the information that is most easily available when individuals are forming their judgments. It is somewhat unclear which of the AIM processing strategies would be employed by individuals while they are completing self-report personality questionnaires, as arguments could be made to justify all of the processing strategies. Of the four strategies, direct access would seem to be the least likely to be used. In order for individuals to use the direct access processing strategy when completing the self-report measures, they would need to have a pre-stored response to the personality items. It is possible that individuals completing personality questionnaires may have been previously exposed to the questionnaire items, or that the individuals may have previously reflected on the quality or behaviour being assessed. Individuals may also have developed a general sense or opinion about whether their behaviours and experiences do or do not fit with the trait being assessed. However, it is unlikely that this pre-stored knowledge would extend to an exact rating on the item’s scale. In other words, individuals may have a sense that they are generally ‘talkative’, but not have a pre-formed response to how talkative they are, or how much they agree with the statement ‘[I am] talkative’ on the specific scale being used. This would suggest that a different processing strategy would need to be employed. 13 It would also be possible to argue that individuals might use motivated processing when rating the personality items, although it not clear that this would occur across all of the items or personality traits. As noted above, motivated processing occurs when individuals are motivated to arrive at a predetermined conclusion. Certain personality questionnaire items are more or less socially desirable (e.g. “tends to be lazy” could be considered low in social desirability), and individuals are likely motivated to respond in more socially desirable ways, either due to self-deception or impression management (Paulhus, 1986). However, not all personality items are high or low in social desirability: an item such as “is quiet” on the Big Five Inventory is unlikely to be universally viewed as either highly socially desirable or undesirable. There is also evidence that individuals may vary in how socially desirable they view certain personality traits to be (Smeding, Dompnier, & Darnon, 2017), and so it is not clear that individuals would be consistently motived to come to a similar conclusion. To the extent that individuals may be using motivated processing when completing self-report personality questionnaires, we would expect the influence of mood on personality ratings to be attenuated. Given that this specific question has not been investigated experimentally, the extent to which individuals may be using motivated processing remains an empirical question. Similarly, although it could be possible that participants are engaging in substantive processing when completing self-report personality questionnaires, it is fairly unlikely that they would dedicate the significant cognitive resources necessary to do so. If they were highly motivated to be accurate and were able to dedicate the cognitive resources necessary to engage in substantive processing, participants would need to use this strategy in order to synthesize information across various situations and contexts where their behaviours may differ. Should 14 participants engage in substantive processing while completing personality questionnaires, their responses would likely be highly susceptible to influence by their current affective state. Of the four processing strategies outlined in AIM, the fourth and final strategy of heuristic processing is that which appears most likely to be used by participants completing personality questionnaires. Many of the conditions associated with the heuristic processing strategy described above are likely to occur in the context of participants completing self-report personality questionnaires. Specifically, when participants are completing self-report questionnaires for research purposes: they are being asked to make global judgments about themselves, they are likely not dedicating significant cognitive resources to the task, they are often asked explicitly to make their best guess and not spend too much time on any one question, and there may be little personal relevance to them in terms of the ultimate outcome from their ratings. All of these situational factors would be thought to encourage the use of a heuristic processing strategy, where individuals are more likely to rely on their current affective state in informing their ratings. Given the high likelihood of heuristic processing, a ‘high affect infusion’ strategy, being used during the completion of self-report personality questionnaires, it seems appropriate to investigate the possible influence of mood on self-report personality measures. 1.3.4 Theoretical Support: Summary Mood congruent memory, the affect-as-information theory, and the affect infusion model all predict that current mood could impact responses on personality self-report measures. Mood congruent memory suggests that current mood may increase the recall of memories associated with that mood state, and this biased recall may impact endorsement of personality scale items. Affect-as-information theory and the affect infusion model both suggest that current mood may 15 be used as a heuristic cue when making ratings about the self, thereby increasing endorsement of personality scale items in the direction of the current mood state (positive vs. negative). The convergence of predictions by three separate theories of how affect may influence cognition and judgment supports the possibility that mood may influence self-reported personality. As such, it appears that an empirical investigation into this possibility is warranted. 1.4 The Influence of Mood on Personality Ratings: Empirical Support Despite the theoretical rationale for the potential influence of mood on self-reported personality ratings, little research has been done in this specific area. This is particularly surprising given very early concerns about the possibility that mood could influence self-reported personality (Johnson, 1934). W. B. Johnson (1934) conducted a study where participants completed the Bernreuter Personality Inventory at two time-points spaced approximately two weeks apart: once when they were in an “unusually depressed” mood, and once when they were in an “unusually gay” mood (p. 517). No significant differences were observed, but this is unsurprising given the small sample size (N=15). The author noted that the pattern of results suggested that depressed mood (as compared to elated mood) was associated with small increases in ‘neurotic tendency’ and ‘introversion’, and small decreases in self-sufficiency and dominance. Although these results are interesting, the small sample size and lack of experimental control over mood make the findings difficult to interpret. More recently, a correlational study was conducted on the relationship between mood and personality traits in a non-clinical sample, as assessed by the Tridimensional Personality Questionnaire (R. C. Cloninger, Przybeck, & Svrakic, 1991). They concluded that personality dimensions were largely independent from mood, although they noted that the dimension of 16 ‘harm avoidance’ did appear to covary with mood. Harm avoidance in this context refers to a bi-polar personality dimension characterized by anticipatory worry, fear of uncertainty, shyness with strangers, and fatigability on one pole, versus optimism, confidence, gregariousness, and vigor on the other. The authors found that higher levels of harm avoidance (anxiety, worry) were related to low or depressed mood states. Surprisingly, when operationalising ‘current mood’ the authors chose to assess mood during the weeks leading up to the completion of the personality measure, rather than the mood that participants were experiencing at the time of completing the self-report personality measure. This leaves the question of whether current mood (aka. mood state at the time of actual questionnaire completion) has an impact on self-report personality ratings unanswered. In a similar correlational study conducted with psychiatric outpatients (S. L. Brown, Svrakic, Przybeck, & Cloninger, 1992), the authors reached the same conclusion: the personality dimension of ‘harm avoidance’ appeared to be related to ‘current mood’, but the other dimensions of personality as measured by the Tridimensional Personality Questionnaire were unaffected by ‘current mood’. However, current mood in this study was again operationalized in a somewhat counterintuitive fashion: through scales assessing depressive and anxious symptoms. It appears that ‘current mood’ in this study was referring to the degree of clinical severity of affective disorders, as opposed to actual mood at the moment of personality measure completion. Interestingly, in the discussion section, the authors acknowledge that “the results presented so far may reflect an artifact: because of the current mood, the subjects evaluate their actually unchanged HA dimension in a biased way” (p. 208), although they subsequently provide reasons why this may not be the case. Although it is again unclear whether the authors’ use of ‘current mood’ here refers to severity of mood disorder symptoms or the participants’ mood state when 17 completing the questionnaires, this comment illustrates that the impact of mood on self-reported personality is of concern to researchers in the field. The three studies described above suggest that there may be an impact of current mood on self-reported personality. However, the studies suffered from methodological issues such as small sample size, operationalization of ‘current mood’ with measures that do not necessarily capture mood state at the time of the completion of the self-report measures, and a lack of experimental control over mood. Little other research has been done on the specific influence of mood on self-reported personality, and to the author’s knowledge, no studies have specifically looked experimentally at the influence of mood on self-report Big Five personality measures. Despite the lack of research on the influence of mood on self-reported personality, research has been conducted on the influence of mood on related constructs (for a review, see (Sedikides, 1992). For example, increases in negative affect have been shown to relate to decreases in self-concept clarity (Nezlek & Plesko, 2001) and changes in the level of abstraction in descriptions of the self (Isbell, Lair, & Rovenpor, 2013). Mood has also been shown to change perceptions of self-efficacy across multiple domains (Kavanagh & Bower, 1985), as well as global self-descriptions (Wright & Mischel, 1982) in a mood-congruent fashion. Of particular relevance to the proposed research, (Sedikides, 1995) examined the influence of mood on self-concept by assessing both central and peripheral self-conceptions; central self-conceptions contain detailed autobiographical knowledge, are more positive, and are considered to be certain and ‘true’ by the individual. Peripheral self-conceptions tend to be less detailed, less positive, and held to be less certain and ‘true’ (Sedikides & Green, 2001). In a series of 4 studies, Sedikides (1995) demonstrated that although endorsement of central traits were unaffected by mood state, endorsement of peripheral traits varied by induced mood. 18 Specifically, participants in a negative mood were more likely to endorse ‘negative’ traits and less likely to endorse ‘positive’ traits. These findings indicate that mood state can in fact influence how people report about themselves, which has direct implications for the completion of self-report personality measures. Similar to the research on the self-concept, researchers have examined how the self-appraisal on 16 trait adjectives (e.g. kind, foolish) varied in response to mood and self-esteem in 3 studies (J. D. Brown & Mankowski, 1993). In this study they demonstrated that mood state influenced endorsement of the 16 adjectives in low self-esteem individuals, with those in a negative mood rating themselves more negatively than those in a neutral mood, who in turn rated themselves more negatively than those in a positive mood. This series of studies indicates that mood can have a significant influence on the endorsement of ‘trait’ descriptors, albeit with the moderating influence of self-esteem. The findings again indicate that the relationship of mood on self-reported big personality should be further investigated. 1.4.1 Empirical Support: Summary Although the question of whether current mood could impact self-reported personality was first raised in the 1930s, it has yet to be definitively answered. The few studies that have examined this question directly have suffered from methodological issues which made their results difficult to interpret. More recent research on constructs relevant to self-reported personality have found that mood can change the way individuals think about themselves, often in a valence-congruent fashion. By extension, it is likely that mood may impact self-reported personality in a similar direction: with positive personality traits endorsed more strongly under positive mood conditions, and negative personality traits endorsed more strongly under negative 19 mood conditions. However, studies have yet to demonstrate this effect. The field would benefit from research that investigates this possibility directly and experimentally, using self-report measures of the Big Five factors. 1.5 Additional Exploratory Aims of the Current Research As detailed above, the primary aim of the current work is to determine whether and how mood state at the time of questionnaire completion can influence self-reported personality ratings, given the reasonable theoretical and empirical grounds suggesting that this may be the case. If mood state does influence ratings on self-report personality measures, it raises several additional interesting questions. Firstly, if mood can influence self-reported personality ratings, it has implications for research on the relationship between personality dimensions and psychopathology involving mood dysregulation, such as affective disorders and borderline personality disorder. It would be worthwhile to explore how the potential influence of mood on self-reported personality may differ, or be moderated by, these clinical phenomena. Secondly, it raises the question of whether there are other constructs such as the social desirability of the items being rated which may moderate the impact of mood on self-report ratings of personality traits. Thirdly, it raises the possibility that in addition to influencing personality factor ratings, mood may also influence the relationships among the personality factors themselves. 1.5.1 Affective disorders There exists significant interest in understanding the relationship between personality and psychopathology (Akiskal, Hirschfeld, & Yerevanian, 1983; Widiger, 2011; Widiger, Verheul, & van den Brink, 1999). Specifically, there is much debate as to whether personality and 20 psychopathology influence how each other manifest, whether they exist on a spectrum with psychopathology existing at the points where personality traits become maladaptive, or whether there are causal or etiological relationships between personality traits and psychopathology (aka. where either certain personality traits give rise to certain psychopathologies, or whether certain psychopathologies induce certain personality changes). This has been particularly true in the case of psychological disorders characterized by dysregulated affect such as depression and anxiety disorders (Clark, Watson, & Mineka, 1994; Spinhoven, Does, Ormel, Zitman, & Penninx, 2013), which is unsurprising given the affective components of certain personality traits such as extraversion and neuroticism. Although the current research does not address this topic directly, it does have implications for the measurement of personality traits in the course of studies investigating the relationship between personality and psychopathology. As suggested above, studies have often examined how personality traits are related to affective disorders (Bunevicius, Katkute, & Bunevicius, 2008; Enns & Cox, 1997; Kotov, Gamez, Schmidt, & Watson, 2010; Starcevic, Uhlenhuth, Fallon, & Pathak, 1996) , or how self-reported personality traits change throughout the course of treatment (Jain, Blais, Otto, Hirshfeld, & Sachs, 1999; Noyes, Reich, Suelzer, & Christiansen, 1991; Tang TZ et al., 2009). If current mood state can influence self-report personality ratings, it is possible that differences in mood states at the time of personality measurement may contribute to the differences in personality ratings typically observed between clinical and control groups, or between individuals higher or lower in affective disorder symptoms. This is because it is likely that there would be differences in baseline mood states in affective disorders compared with controls, or pre- to post- treatment, as these are disorders characterized by alterations in affect and mood. 21 Additionally, there is some evidence that individuals with affective disorders respond differently to mood inductions than healthy controls. For example, when individuals with a history of depression are put into a negative mood state, they evidence an increase in dysfunctional attitudes and thinking, such as perfectionistic standards and increased concern about the judgments of others, as compared with controls (Jeanne, Gross, Persons, & Hahn, 1998; Miranda & Persons, 1988). Similar findings have been seen in individuals vulnerable to depression as their mood changes naturalistically in their daily lives, where increases in sadness lead to decreases in self-esteem (Clasen, Fisher, & Beevers, 2015). As the current work aims to examine how mood state influences self-reported personality ratings (i.e. a self-related judgment), it follows that we may see an increased degree of change in individuals who endorse higher levels of affective disorder symptoms (e.g. anxiety and depression), either because they respond more strongly to the negative mood induction, or because negative cognitions are more easily accessible during negative mood states. 1.5.2 Borderline Personality Disorder Borderline personality disorder (BPD) is characterized by instability in a number of broad domains including emotional instability, instability in interpersonal relationships, behavioural instability and impulsivity, cognitive instability, and instability in sense of self (Lieb, Zanarini, Schmahl, Linehan, & Bohus, 2004). Importantly, emotion dysregulation is understood to be a core component of BPD (Glenn & Klonsky, 2009; Linehan, 1993), and many of the other maladaptive behaviours such as self-harm, suicidality, and other impulsive behaviours which can occur in this clinical population are often considered to be consequences of, or attempts to regulate, intense and dysregulated emotion. If current mood state can influence self-reported 22 personality ratings, we might expect that this effect would be exaggerated in individuals with BPD or BPD traits, given the increased intensity and dysregulation of emotional states seen in this population. Furthermore, should we see an exaggerated effect of the mood induction on self-reported personality ratings, it may represent – in a preliminary fashion – one possible pathway through which the emotion dysregulation seen in this population could be leading to the instability in self-concept that is often experienced by individuals with BPD. Specifically, individuals with BPD may have more difficulty creating a coherent sense of self due to the fact that their self-judgments could be fluctuating more drastically or more frequently due to their frequently intense and variable emotional states. As such, it would be of interest to explore whether symptoms of BPD may moderate the possible impact of mood state on self-reported personality ratings. 1.5.3 Social Desirability Discussions about social desirability as a response bias and individual difference variable have a long history in personality assessment (Edwards, 1957; McCrae & Costa, 1983; Wiggins, 1968). Social desirability as a response bias – where individuals are thought to endorse items perceived as more socially acceptable and/or valued – has often been considered a potential threat to the validity of responses to personality measures, particularly if individuals vary in their responsiveness to perceived social desirability. As a result, numerous strategies have been developed in an effort to mitigate the influence of socially desirable responding on personality assessment (Nederhof, 1985). 23 Social desirability can also be considered to be a property of scales or items, where certain items or responses to items are inherently more or less socially desirable (McCrae & Costa, 1983; Wiggins, 1968). When viewed in this way, social desirability can be thought of as an index of how ‘positive’ or ‘negative’ different personality scale items are considered to be, on average. As noted above, one possibility in how mood may be influencing self-reported personality is that current affective state is being used as information when individuals are making their self-report ratings. Presumably, when in a positive mood individuals will be biased toward rating themselves more ‘positively’, and when in a negative mood individuals will be predisposed to ratings themselves more ‘negatively’. As an index of how ‘positive’ versus ‘negative’ a scale item is, the social desirability of personality items could be used in order to explore whether mood state is influencing self-reported personality ratings in this ‘positive’ versus ‘negative’ dimension. 1.5.4 The Influence of Mood on Personality Structure The question of whether the factor structure of the Big Five is consistent has often been investigated. For example, previous research has examined whether variables such as age (Allemand, Zimprich, & Hendriks, 2008) or clinical status (O’Connor, 2002) can change the number or covariation of the Big Five factors. Typically, the Big Five structure is shown to be robust to these differing contexts. However, the question of whether mood state can influence the structure of personality has rarely been investigated, and to the author’s knowledge, never in relation to the Big Five structure which is now considered dominant in the field. If current mood state increases the endorsement of personality factors in a mood-congruent fashion, it is possible that personality factors with similar valences will increase in their covariation under the relevant 24 mood condition (e.g. extraversion and agreeableness increasing in their covariation under a positive mood condition). Relatedly, DeSteno and Salovey (1997) conducted a study to examine whether mood could influence the structure of the self-concept. In this study, participants rated bipolar scales of various trait adjectives under different induced moods (positive, negative, and neutral). The authors found that under the neutral mood condition, the self-concept was organized into three dimensions related to affiliation, achievement, and body size. However, under the positive and negative mood conditions, the self-concept structure changed to be primarily described by a valance dimension, followed by two additional dimensions (body shape and gender). It is possible that current mood state may influence the structure of self-reported personality in a similarly valence-dependent fashion. However, to the author’s knowledge, no study has examined whether current mood state can influence the structure of the Big Five as measured by self-report. It is therefore of interest to investigate this possibility in the current work. 1.6 Summary of the Primary Research Questions In summary, the current research primarily aims to answer two main questions: 1) does mood state influence self-reported personality ratings, and 2) if mood state does influence self-reported personality ratings, is this effect being driven by the positive mood state, the negative mood state, or both. 25 1.6.1 Does Mood State Influence Self-Reported Personality Ratings? Due to the considerable theoretical and empirical work summarized above suggesting a likely impact of mood on self-reported personality, it is hypothesized that self-reported personality traits will significantly differ between the positive and negative mood conditions. 1.6.2 Clarifying the Nature of the Change in Self-Reported Personality Ratings If current mood state does in fact influence self-reported personality ratings, it will be important to understand the direction of the change, and its relation to the valence of the current mood state. More specifically, it will be helpful to clarify whether the changes in personality ratings are being driven by the positive mood state, the negative mood state, or both. By investigating how personality ratings are changing in response to the different mood states, we can clarify whether these changes are occurring in a mood-congruent or mood-incongruent manner. Much of the research on the impact of mood on self-relevant variables has found that mood impacts the self-concept in a mood congruent fashion. Furthermore, both the associative network model and the affect-as-information model predict that the effect of mood on self-reported personality should be mood congruent. Therefore, we hypothesized that a positive mood state should increase ratings of personality factors associated with positive experiences and memories (e.g. extraversion, agreeableness, openness to experience), and that a negative mood state should increase ratings of personality factors associated with negative experiences and memories. This would demonstrate a symmetric effect of mood on self-reported personality, which would be in line with previous related research on the influence of mood on self-related judgments (Bullington, 1990; Sedikides, 1992). 26 1.6.3 Additional Exploratory Aims If current mood state does influence self-report personality ratings, it raises several interesting follow-up questions. Firstly: is the change in self-reported mood state moderated by clinical variables such as symptoms of depression, anxiety, and borderline personality disorder? As discussed previously, one might expect that individuals with higher levels of these clinical symptoms may show an exaggerated influence of mood state on self-reported personality ratings, particularly where clinical disorders are characterized by dysregulated mood. Secondly: using social desirability as an index of the personality items’ overall valence (i.e. the degree to which an item has a positive versus a negative connotation), is the mood-related change in the response to each personality item moderated by the social desirability of that item? In other words, does the influence of mood on self-reported personality trait items vary based on how ‘positive’ or ‘negative’ the item is considered to be? If the valence of current mood is being used as information or a global heuristic during the decision-making process of completing the self-report personality measure, we might expect to see larger changes in items rated as higher and lower on social desirability. Thirdly and finally: the current work will explore whether the factor structure of the Big Five changes in response to changes in current mood state. Although there is little previous research on the effect that current mood might have on personality structure, the research which has been conducted suggests that positive and negative moods may increase the correlations between factors or items of the same valence. As such, we hypothesize that the Big Five factors of similar valence may become more correlated under both positive and negative mood conditions, as compared to a neutral mood condition. 27 Chapter 2: Study 1 The first aim of the current research was to clarify whether current mood state does in fact influence self-reported personality ratings. Study 1 was designed to address this question directly. In order to isolate the influence of current mood state on self-reported personality ratings, an experimental, within-person design was employed. Current mood state was manipulated using a positive and negative mood induction, so as to be able to draw causal inferences about the relationship between mood and any observed changes in personality ratings. A within-person design was used so as to decrease the likelihood that ideographic response sets or response styles could influence results, and to thereby increase the power to detect small changes in personality ratings. 2.1 Methods 2.1.1 Study Sample and Recruitment A power analysis was conducted using the software program G*Power (Faul, Erdfelder, Lang, & Buchner, 2007) to determine an appropriate sample size for Study 1. As there is little prior research on this topic, we estimated that the effect size of the influence of mood on self-reported personality ratings would likely represent a Cohen’s d between small and medium. We determined that a sample size of N = 147 would provide .95 power to detect a Cohen’s d of 0.3, and elected to collect additional data in order to compensate for attrition across the 2 visits. Study 1 involved 185 undergraduate students from the University of British Columbia who were recruited online through the UBC Psychology Research Participation System as part of a larger study. Data from the initial 4 participants were discarded due to technical issues in data collection, and so they were excluded from all analyses. All participants received course credit 28 for their participation and were debriefed as to the purpose of the research at the end of their participation in the study. The sample was primarily female (81.2%), of East Asian descent (54.1%), with a mean age of 20.5 (SD=3.1). For additional sample characteristics, see Table 1. Table 1 - Study 1 Sample Demographics Demographic variable Age (years) M (SD) Range 20.5 (3.1) 17 – 41 Gender (n) Female Male 147 34 Race/Ethnicity (n) East Asian descent European descent Middle Eastern descent South Asian descent Latin American descent African descent Other/mixed descent 98 42 10 7 1 1 21 Sexual Orientation (n) Heterosexual Bisexual Gay/Lesbian Other 163 10 5 3 2.1.2 Procedure Participants visited the laboratory individually at two time-points, approximately 2 weeks apart. We chose a 2-week window in order to minimize the chances of participants recalling their responses from prior visits, while also avoiding the potential for actual changes in personality traits which may occur over longer periods of time. After providing informed consent, each 29 participant was seated in front of a computer in a private room. The Self-Assessment Manikin (SAM) was then introduced by a research assistant. Following an explanation of the SAM scales and how to use them, the research assistant and the participant practiced using the SAM scales together, in order to ensure that the participant understood the SAM dimensions and how to complete the measure. The research assistant then left the room, and the participant completed a computerized demographics questionnaire. Each participant also completed several other questionnaires on the computer as part of an unrelated study. Participants were then presented with a computerized ‘imagination task’, adapted from a mood induction used by C.L. Rusting and T. DeHart (Rusting & DeHart, 2000), designed to induce either positive or negative mood. In this ‘imagination task’, participants were asked to recall and write for 5 minutes about a recent instance where they felt either ‘upset’ (negative affect induction) or ‘happy’ (positive mood induction). Broad positive and negative mood states were elicited rather than specific emotional states in order to increase the likelihood that we would capture any differences which may occur between positive and negative mood states. The order of the mood inductions was counterbalanced across visits, and the SAM was administered both before and after each mood induction in order to assess change in affective state in response to the mood induction. Participants were subsequently asked to complete the 44-item Big Five Inventory, or BFI-44, (Benet-Martínez & John, 1998; John et al., 1991) to assess their self-reported agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. 30 2.1.3 Measures 2.1.3.1 Demographics Information about participant characteristics was collected using a standard demographics form which captured date of birth, gender, race/ethnicity, and sexual orientation. Other demographic characteristics were collected as part of a larger study (e.g. occupation, marital status, number of individuals in the household), but were not deemed relevant to the current research question and so were not used to describe the sample. 2.1.3.2 Self-Reported Personality Self-reported personality was assessed using the 44-item version of the Big Five Inventory (BFI-44). The BFI-44 (Benet-Martínez & John, 1998) is a self-report inventory designed to measure the Big Five personality dimensions. Each item is rated on a 5-point Likert-type scale, ranging from 1 (“Disagree Strongly”) to 5 (“Agree strongly”). Specifically, it was created to measure agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. Cronbach’s alpha for the five factors of the 44-item BFI range from .75-.90 (John, Naumann, & Soto, 2008). 2.1.3.3 Current Affective State A computerized version of the Self-Assessment Manikin (Bradley & Lang, 1994), or SAM, was used to assess participants’ affective state before and after each mood induction. The SAM measures current affective state on three dimensions (pleasure/valence, arousal, and dominance) using three pictorial scales. Each pictorial scale consists of 5 separate figures 31 illustrating the range of each dimension. Participants indicate where their current affective state best fits on the continuum of images using a 9-point scale (with each figure representing 1 point on the scale, and each space between the figures also representing 1 point on the scale). 2.1.4 Missing Data and Data Cleaning Out of the 185 individuals who took part in the study, 24 participants completed only one visit and therefore took part in only one mood induction. Additionally, two participants accidentally took part in the same mood induction across their two laboratory visits, and so the data from their second visit were excluded from all analyses. The individuals with data from only one study visit/mood induction were not significantly different from individuals who completed both visits in their SAM ratings either before or after the positive or negative mood induction (see Table 2), or on their mean scores on any of the personality factors after the positive and negative mood inductions (see Table 3). Table 2 - SAM ratings of study completers vs. non-completers pre- and post-induction SAM Dimension Completers (SD) Non-completers (SD) t p Pre-positive induction Valence 5.50 (1.64) 5.07 (2.20) -0.90 0.37 Arousal 3.91 (1.82) 3.07 (2.06) -1.63 0.10 Dominance 5.62 (1.90) 5.86 (2.18) 0.43 0.67 Post-positive induction Valence 6.69 (1.67) 7.07 (1.86) 0.80 0.42 Arousal 4.82 (1.94) 4.07 (2.70) -1.34 0.18 Dominance 6.17 (1.92) 6.86 (1.56) 1.30 0.20 Pre-negative induction Valence 5.52 (1.42) 5.50 (1.35) -0.05 0.96 Arousal 3.93 (1.92) 4.40 (1.58) 0.76 0.45 Dominance 5.71 (2.03) 5.40 (1.58) -0.47 0.64 32 SAM Dimension Completers (SD) Non-completers (SD) t p Post-negative induction Valence 4.14 (1.67) 4.00 (1.76) -0.25 0.81 Arousal 4.74 (1.97) 5.30 (2.00) 0.86 0.39 Dominance 5.01 (2.09) 4.90 (1.37) -0.168 0.87 Note: n completers = 155; n non-completers = 26. Table 3 - Comparison of study completers vs. non-completers on BFI ratings post-induction BFI Dimension Completers (SD) Non-completers (SD) t p Post-positive induction Agreeableness 3.81 (0.61) 3.84 (0.47) 0.18 0.86 Conscientiousness 3.46 (0.65) 3.20 (0.60) -1.46 0.15 Extraversion 3.10 (0.84) 3.28 (0.87) 0.76 0.45 Neuroticism 3.16 (0.81) 3.29 (0.78) 0.61 0.54 Openness to experience 3.46 (0.61) 3.55 (0.64) 0.54 0.59 Post-negative induction Agreeableness 3.72 (0.64) 3.71 (0.51) -0.03 0.98 Conscientiousness 3.41 (0.66) 3.31 (0.55) -0.45 0.65 Extraversion 3.05 (0.82) 3.26 (0.42) 0.82 0.41 Neuroticism 3.24 (0.85) 3.00 (0.98) -0.85 0.40 Openness to experience 3.41 (0.61) 3.29 (0.65) -0.61 0.55 Note: n completers = 155; n non-completers = 26. 2.2 Results 2.2.1 Manipulation Check As a manipulation check, the SAM ratings after each induction were compared in order to verify that the mood inductions were effective at inducing differing mood states within individuals. Paired-samples t-tests comparing SAM ratings pre- and post- mood inductions indicated that the mood inductions were significantly impacting mood state (see Table 4). Paired-samples t-tests were conducted comparing SAM ratings after the positive and negative mood inductions, with results supporting the efficacy of the mood induction at eliciting positive versus 33 negative moods (see Table 5). Most importantly, the valence of participants’ reported affect was significantly lower following the negative mood induction, as compared to the positive mood induction. Table 4 – Paired samples t-tests comparing SAM ratings pre- and post-mood induction Pre-induction (SD) Post-induction (SD) t p Cohen’s d Happy Valence 5.46 (1.69) 6.73 (1.69) -8.67 <.01 0.75 Arousal 3.84 (1.85) 4.76 (2.01) -5.69 <.01 0.48 Dominance 5.64 (1.92) 6.23 (1.90) -4.51 <.01 0.31 Sad Valence 5.52 (1.42) 4.13 (1.67) 11.36 <.01 -0.90 Arousal 3.96 (1.90) 4.78 (1.97) -6.05 <.01 0.42 Dominance 5.69 (2.00) 5.01 (2.05) 5.04 <.01 -0.34 Note: N = 171; Cohen’s d was calculated using methods shown to be appropriate for use in repeated measures designs (Dunlap, Cortina, Vaslow, & Burke, 1996). Table 5 - Paired samples t-tests comparing SAM ratings post-mood induction SAM Scale Post-Happy (SD) Post-Sad (SD) t p (2-tailed) Cohen’s d Valence 6.70 (1.68) 4.14 (1.67) 14.10 <.001 1.53 Arousal 4.81 (1.95) 4.75 (1.97) 0.32 .75 0.03 Dominance 6.17 (1.93) 5.01 (2.09) 6.47 <.001 0.57 Note: N = 155; Cohen’s d was calculated using methods shown to be appropriate for use in repeated measures designs (Dunlap et al., 1996). 2.2.2 The Influence of Mood on Mean Personality Ratings Paired samples t-tests were conducted to compare the mean scores for each of the five personality factors following the positive and negative mood inductions. Results indicated that there were small but significant differences between the mood conditions (see Table 6). Specifically, when in a positive mood (versus a negative mood), participants rated themselves as 34 higher in extraversion, agreeableness, conscientiousness, and lower in neuroticism. Ratings of openness to experience did not appear to change in response to the mood inductions. Table 6 - Paired Samples t-tests of BFI Scales Comparing Happy versus Sad Mood Inductions BFI Scale Happy (SD) Sad (SD) t p (2-tailed) Cohen’s d Extraversion 3.10 (0.84) 3.05 (0.82) 2.30 .02 0.06 Agreeableness 3.81 (0.61) 3.72 (0.64) 3.46 <.01 0.14 Conscientiousness 3.47 (0.65) 3.41 (0.66) 2.17 .03 0.09 Neuroticism 3.16 (0.81) 3.24 (0.85) -2.39 .02 -0.10 Openness to Experience 3.45 (0.61) 3.41 (0.61) 1.63 .11 0.07 Note: N = 155; Cohen’s d was calculated using methods shown to be appropriate for use in repeated measures designs (Dunlap et al., 1996). Although the SAM manipulation check detailed above demonstrated that the overall effect of the mood induction on mood valance was robust (see Table 5), on an individual level certain participants could be classified as ‘non-responders’ – meaning that there was either no difference in their valence ratings between the mood induction conditions, or their valence ratings were counter to what was expected (aka. mood ratings were more positive following the negative mood induction than the positive mood induction). This may have been the result of within-person differences in their baseline mood at each visit (e.g. having a very positive mood on the day of the negative mood induction, or vice versa) or to idiosyncratic emotional responses to the mood inductions (e.g. feeling sadness/sense of loss when reflecting on a past instance of happiness). Given the small effect sizes, additional t-tests were performed with these ‘non-responders’ removed (see Table 7), in order to verify that ‘non-responders’ were not greatly altering or impacting the results. The results from these additional t-tests were largely in line with the analyses 35 conducted using the whole sample, except that the difference in ratings of conscientiousness between mood conditions was no longer significant. Table 7 - Paired Samples t-tests of BFI scales (‘non-responders’ removed) BFI Scale Happy (SD) Sad (SD) t p (2-tailed) Cohen’s d Extraversion 3.07 (0.85) 3.01 (0.82) 2.91 <.01 0.07 Agreeableness 3.83 (0.56) 3.71 (0.61) 4.08 <.001 0.20 Conscientiousness 3.46 (0.67) 3.40 (0.66) 1.83 .07 0.09 Neuroticism 3.18 (0.84) 3.26 (0.87) -2.28 .02 -0.09 Openness to Experience 3.45 (0.62) 3.41 (0.61) 1.39 .17 0.07 Note: N = 120; Cohen’s d was calculated using methods shown to be appropriate for use in repeated measures designs (Dunlap et al., 1996). 2.2.3 The Influence of Mood State on Personality Structure Pearson correlations were conducted for each of the personality factors across and within mood conditions, in order to visualize the degree of similarity in ratings across mood conditions, and to gauge whether the relationships among the personality factor ratings appear to differ between mood conditions. The correlations between the mood conditions for each personality factor ranged from .87 to .95 (see Table 8), indicating a high degree of correlation in the ratings of the same personality factor across mood conditions. Additionally, the pattern of correlations among the personality factors did not appear to greatly differ between the mood conditions. 36 Table 8 – Correlations among personality ratings across and within mood conditions in study 1 Happy Sad E A C N O E A C N Happy E A 0.02 C 0.10 .25** N -.31** -.34** -.36** O 0.05 0.06 0.06 -.16* Sad E .95** 0.05 .19* -.32** 0.09 A 0.03 .87** .20* -.34** 0.06 0.08 C 0.13 0.16 .87** -.31** 0.03 .19* .19* N -.31** -.31** -.41** .89** -.21** -.34** -.34** -.37** O 0.06 0.02 0.06 -.17* .87** 0.12 0.06 0.01 -.21** Note: E = Extraversion; A = Agreeableness; C = Conscientiousness; N = Neuroticism; O = Openness to Experience; *p<.05 (two-tailed); ** p<.01 (two-tailed). Maximum likelihood exploratory factor analyses were conducted at the item level, using promax rotation. Six factors were identified under both the positive and the negative mood conditions using the scree plots. Based on the loadings of the items on the 6 factors, it appeared as though the conscientiousness items had been divided between two factors. The other 4 factors generally loaded as expected and appeared to correspond to extraversion, neuroticism, agreeableness, and openness to experience. We then used Tucker’s congruence coefficient to characterize how similar these factor loadings were between the mood conditions. Tucker’s congruence coefficients ranged from 0.93 to 0.98 across the 6 factors, indicating that the factors were highly similar between the mood conditions. For context, congruence coefficients between .85-.94 are considered to represent fair similarity, and coefficients above .95 indicate that the factors being compared can be considered equal (Lorenzo-Seva & ten Berge, 2006). 37 2.3 Summary of Study 1 Results Study 1 was designed to investigate whether self-reported personality ratings are impacted by an individual’s mood state. The results from Study 1 suggest that mood state can in fact impact self-report ratings of a number of personality dimensions. More specifically, self-report ratings of extraversion and agreeableness were increased when in a positive (versus a negative) mood state. Self-report ratings of neuroticism showed the opposite effect, where ratings were decreased when in a positive (versus a negative) mood state. Self-reported openness to experience did not appear to be influenced by mood state in Study 1. It remained somewhat unclear in Study 1 as to whether self-reported conscientiousness is impacted by mood state. Although preliminary results suggested that self-reported ratings of conscientiousness may be increased during positive (versus negative) mood states, this initial finding did not hold when excluding ‘non-responders’. It could be that ‘non-responders’ were somehow influencing the results and creating a spurious finding. However, in the sample with ‘non-responders’ removed, there was still a marginally significant change in self-report conscientiousness ratings. It is therefore also possible that the reduction in sample size and power which occurred when the ‘non-responders’ were removed was responsible for the change in findings. A replication of Study 1 with greater power was needed in order to clarify whether or not self-reported conscientiousness ratings are influenced by mood state. Finally, the results from Study 1 suggested that there was very little difference between the factor structure of the personality ratings between the mood conditions. It is possible that we may detect differences with greater power. However, the strength of the congruence coefficients in Study 1 suggest that this is unlikely to be the case. It is possible that the factor structure of the personality ratings during the experience of any kind of valenced mood may differ from the 38 structure of ratings made in a neutral mood, and so the design which includes a neutral condition would be of help in investigating this possibility. 39 Chapter 3: Study 2 Study 1 was conducted to investigate whether mood state can impact self-reported personality ratings. The results from Study 1 suggested that yes, an individual’s mood state impacts self-reported ratings of extraversion, agreeableness, and neuroticism. Specifically, self-report ratings of extraversion and agreeableness are higher when individuals are in a positive mood state (versus a negative mood state). The reverse appears to be true for neuroticism, where self-report ratings of neuroticism are lower when individuals are in a positive mood state (versus a negative mood state). The results regarding changes in self-reported conscientiousness were less clear, although seemed to suggest that ratings may be increased when in a positive mood state (versus a negative mood state). What the results from Study 1 do not reveal is what is driving the changes in self-report ratings of these personality dimensions: the positive mood state, the negative mood state, or both. There are several different ways that differing mood states could be creating the changes in self-report ratings observed in Study 1. Firstly, mood state could be amplifying the self-report personality ratings in a mood-congruent fashion, where a positive mood state leads to increased endorsement of more favourable or ‘positive’ personality traits, and a negative mood state leads to increased endorsement of ‘negative’ personality traits. If this were the case, we might expect self-reported extraversion and agreeableness ratings to be increased when individuals are in a positive mood state, whereas self-reported neuroticism ratings would be increased when individuals are in a negative mood state. Secondly, mood state could be inhibiting ratings in a mood-incongruent fashion. If this were the case, we might expect self-reported extraversion and agreeableness ratings to be lower when individuals are in a negative mood state, whereas self-reported neuroticism ratings would be decreased when individuals are in a positive mood state. 40 Thirdly, it is possible that both amplification and inhibition are occurring in each mood state, whereby in the positive mood state self-reported ratings of extraversion and agreeableness are increased while self-report ratings of neuroticism are decreased, and vice versa in the negative mood state1. In addition to simply understanding the phenomenon further for its own sake, understanding how self-reported personality ratings are changing under the influence of positive and negative mood states in this level of detail has implications for understanding how these changes do or do not reflect what would be predicted by the various theories discussed in Chapter 1. In order to actually investigate how the changes observed in Study 1 are related to the positive and negative mood states, a “neutral” or “baseline” mood state was needed as a comparison. As such, Study 2 was designed as a replication of Study 1, with the addition of a neutral condition into the imagination task. The results from Study 1 established that mood state can in fact influence self-reported personality trait ratings. In addition to replicating these effects, and clarifying which mood states are driving the changes in self-reported personality ratings, Study 2 had several exploratory aims. As previously discussed, it is possible that the impact of mood on self-reported personality ratings may be heightened in certain clinical populations, such as individuals with borderline personality disorder or depression. It is also possible that some of the changes in ratings may be related to the social desirability of the personality traits being rated. Study 2 was designed with these exploratory aims in mind. 1 Technically a fourth option exists, where only one of the mood states is responsible for all of the effects (i.e. either the positive mood state is increasing self-report ratings of extraversion and agreeableness while decreasing self-report ratings of neuroticism, or the negative mood state is decreasing ratings of extraversion and agreeableness while increasing self-report ratings of neuroticism. However, there is no theoretical reason why only one of the mood states would have an influence on ratings when another would not, as none of the theories discussed previously are specific to only positive or negative affective states. 41 3.1 Methods 3.1.1 Study Sample and Recruitment A power analysis was again conducted using the software program G*Power (Faul et al., 2007) in order to determine an appropriate sample size for Study 2. We based the analysis on the mean effect size of the main analyses from Study 1 (Cohen’s d = .09). We determined that a sample size of N = 307 would provide .95 power to detect a Cohen’s d of 0.09, and elected to collect additional data in order to compensate for possible attrition across the 3 visits. Study 2 involved 431 undergraduate students from the University of British Columbia. As in As in Study 1, participants were recruited online through the UBC Psychology Research Participation System. All participants received course credit for their participation and were debriefed about the purpose of the study at the end of their study participation. Similar to Study 1, the sample was primarily female (83.1%), of East Asian descent (45.0%), and had a mean age of 23.7 (SD = 3.9). See Table 9 below for further sample demographic information. Table 9 - Study 2 Sample Demographics Demographic variable Age (years) M (SD) Range 23.7 (3.9) 19 – 54 Gender (n) Female Male Other 358 70 3 42 Demographic variable Race/Ethnicity (n) East Asian descent European descent Middle Eastern descent South Asian descent Latin American descent African descent Other/mixed descent 194 117 18 24 21 4 53 3.1.2 Procedure Participants visited the laboratory individually at three time-points, with 9-14 days between each study visit. As in Study 1, after providing informed consent, each participant was seated in front of a computer in a private room and the Self-Assessment Manikin (SAM) was then introduced and explained by a research assistant. The research assistant and the participant practiced using the SAM scales together, in order to ensure that the participant understood the SAM dimensions and how to complete the measure. The research assistant then left the room, and the participant completed a computerized demographics questionnaire, as well as the clinical screening measures. Each participant also completed several other questionnaires which were unrelated to the current research question. Participants were then presented with the computerized “imagination task” (Rusting & DeHart, 2000), where they were asked to recall and write for 5 minutes about a recent instance where they felt either “upset” (negative mood condition), “happy” (positive mood condition), or “neutral” (neutral mood condition). The order of the mood inductions was counterbalanced across the three visits, and the SAM was administered both before and after each mood induction in order to assess change in affective state in response to the mood induction. Participants were then asked to complete the 44-item 43 Big Five Inventory (BFI-44; (Benet-Martínez & John, 1998; John et al., 1991) to assess their self-reported agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. 3.1.3 Measures 3.1.3.1 Demographics Information about basic participant demographic characteristics was collected using a computerized questionnaire which captured date of birth, gender and race/ethnicity. 3.1.3.2 Self-Reported Personality Traits Self-reported personality traits were again assessed using the 44-item version of the Big Five Inventory (BFI-44; Benet-Martínez & John, 1998). 3.1.3.3 Current Affective State As in Study 1, the Self-Assessment Manikin (Bradley & Lang, 1994) was used to assess participants’ affective state before and after each mood induction. 3.1.3.4 Clinical Screening Measures In order to investigate whether certain clinical phenomena are associated with greater changes in self-reported personality ratings between the mood conditions, participants completed two self-report measures which together assessed individuals on four kinds of psychopathological symptoms: depressive symptoms, physical anxiety symptoms, general anxiety and stress symptoms, and borderline personality disorder symptoms. 44 3.1.3.4.1 Depression, Anxiety and Stress Symptoms The Depression Anxiety Stress Scales, or DASS, (S. H. Lovibond & Lovibond, 1995) were used in order to assess individuals’ self-reported symptoms of depression, physical anxiety, and stress. The DASS is a 42-item self-report measure which has shown to perform particularly well in distinguishing between symptoms of depression, physiological hyperarousal/panic symptoms, and generalized anxiety symptoms, as compared to other measures of anxiety and depression in both clinical and community samples (Antony, Bieling, Cox, Enns, & Swinson, 1998; P. F. Lovibond & Lovibond, 1995). 3.1.3.4.2 Borderline Personality Disorder Symptoms Borderline personality disorder symptoms were assessed using the McLean Screening Instrument for Borderline Personality Disorder, or MSI-BPD (Zanarini et al., 2003). The MSI-BPD has been used in research with both clinical and community samples (Patel, Sharp, & Fonagy, 2011; Zanarini et al., 2003) to screen for the presence of borderline personality disorder. 3.1.4 Missing Data and Data Cleaning Of the 431 individuals who took part in Study 2, 67 participants did not complete all three study three study visits (27 participants dropped out after the first visit and 40 participants dropped out after the second study visit). The individuals who dropped out after visit 1 or 2 were not significantly different from individuals who completed all three study visits in the majority of their SAM ratings before or after the mood inductions (see Table 10), or on their personality ratings following the three mood inductions (see 45 Table 11). Non-completers were significantly lower than completers in SAM dominance ratings following the neutral induction, and significantly lower in SAM arousal ratings following the negative induction. Given the small size of the non-completer groups in these analyses and the large number of analyses conducted, it is unlikely that these differences are meaningful. Table 10 - SAM ratings of study completers vs. non-completers pre- and post-induction SAM Dimension Completers (SD) Non-completers (SD) t p Pre-positive induction Valence 5.28 (1.53) 4.84 (1.46) -1.56 .12 Arousal 3.70 (1.83) 3.58 (2.08) -0.35 .73 Dominance 5.07 (1.95) 4.81 (2.26) -0.71 .48 Post-positive induction Valence 6.92 (1.46) 6.90 (1.56) -0.07 .94 Arousal 4.00 (1.96) 3.71 (2.24) -0.78 .43 Dominance 5.98 (1.95) 5.65 (2.11) -0.90 .37 Pre-neutral induction Valence 5.20 (1.53) 5.37 (1.79) 0.68 .49 Arousal 3.75 (1.91) 3.72 (1.96) -0.10 .92 Dominance 5.14 (1.93) 4.70 (2.20) -1.40 .16 Post-neutral induction Valence 5.49 (1.33) 5.70 (1.58) 0.94 .35 Arousal 3.50 (1.82) 3.74 (2.14) 0.82 .42 Dominance 5.64 (1.86) 4.86 (2.19) -2.54 .01 Pre-negative induction Valence 5.30 (1.44) 5.36 (1.75) 0.19 .85 Arousal 3.77 (1.76) 3.71 (2.26) -0.15 .88 Dominance 5.21 (2.03) 5.93 (1.96) 1.80 .07 Post-negative induction Valence 4.18 (1.62) 4.54 (1.90) 1.11 .27 Arousal 4.61 (1.94) 3.75 (1.96) -2.27 .02 Dominance 4.72 (2.02) 5.46 (2.28) 1.86 .06 Note: total n of completers = 364; total n of non-completers = 67; 31 non-completers took part in the positive induction, 43 non-completers took part in the neutral induction, and 28 non-completers took part in the negative induction. 46 Table 11 - Comparison of study completers vs. non-completers on BFI ratings post-induction BFI Dimension Completers (SD) Non-completers (SD) t p Post-positive induction Agreeableness 3.69 (0.63) 3.80 (0.65) 0.92 .36 Conscientiousness 3.33 (0.64) 3.30 (0.69) -0.24 .81 Extraversion 3.11 (0.79) 3.15 (0.88) 0.29 .77 Neuroticism 3.17 (0.76) 3.21 (0.91) 0.25 .80 Openness to experience 3.53 (0.64) 3.45 (0.74) -0.64 .52 Post-neutral induction Agreeableness 3.67 (0.61) 3.64 (0.69) -0.37 .71 Conscientiousness 3.33 (0.63) 3.25 (0.82) -0.77 .44 Extraversion 3.04 (0.77) 3.27 (0.84) 1.80 .07 Neuroticism 3.20 (0.75) 3.19 (0.81) -0.08 .93 Openness to experience 3.49 (0.68) 3.46 (0.65) -0.25 .81 Post-negative induction Agreeableness 3.63 (0.62) 3.69 (0.72) 0.42 .68 Conscientiousness 3.31 (0.63) 3.40 (0.77) 0.64 .52 Extraversion 3.06 (0.79) 3.17 (0.95) 0.64 .52 Neuroticism 3.24 (0.76) 3.08 (0.92) -1.07 .29 Openness to experience 3.49 (0.66) 3.59 (0.70) 0.81 .42 Note: total n completers = 364; total n non-completers = 67; 31 non-completers took part in the positive induction, 43 non-completers took part in the neutral induction, and 28 non-completers took part in the negative induction. 3.1.5 General Statistical Method Statistical analyses for Study 2 were conducted with the use of R 3.3.3 (R Core Team, 2017). Data were fit using R’s multilevel model package “lme4” (Bates, Maechler, Bolker, & Walker, 2015). Where applicable, p-values were obtained using R’s multilevel model package “lmerTest” (Kuznetsova, Brockhoff, & Christensen, 2016). Linear mixed modelling was used to analyze the data in Study 2 in part in order to prevent the loss of power due to listwise deletion which would occur when conducting repeated measures ANOVAs in SPSS. Additionally, one of the questions that Study 2 was designed to 47 address was whether scores on clinical measures moderate the relationship between mood state and self-reported personality. Given the three conditions involved in Study 2, this moderation analysis was best addressed through a linear mixed modelling framework. 3.2 Results 3.2.1 Manipulation Check The SAM ratings after each induction were compared in order to confirm that the mood inductions were effective at inducing differing mood states within individuals. Initial paired-samples t-tests were conducted to confirm that the mood inductions were significantly impacting mood state (see Table 13). Since Study 2 involved three mood conditions, SAM ratings post-induction were compared using linear mixed modeling. For each of the SAM dimensions separately (valence, arousal, and dominance), we entered the mood induction condition as a fixed effect and the intercept for subjects as a random effect. Results showed a significant effect of condition on all SAM dimensions (valence: F(2, 788.60) = 390.45, p<.001; arousal: F(2, 784.61) = 40.03, p<.001; dominance: F(2, 781.41) = 58.26, p<.001). Follow up t-tests showed that the mood inductions were having the expected impact on valence, with the positive mood induction leading to significantly higher valence ratings as compared to the neutral induction, and the negative mood induction leading to significantly lower valence ratings as compared to the neutral induction. Dominance ratings showed a similar pattern. Arousal ratings following each induction were also significantly different from each other, although arousal ratings were highest following the negative mood induction, and lowest following the neutral mood induction. (See Table 12 for the SAM means, and Table 14 for the contrasts.) 48 Table 12 - Mean SAM ratings pre- and post-mood inductions Table 13 – Paired Samples t-tests of SAM ratings pre- to post-mood induction 95% C.I. Mean Difference Lower Bound Upper Bound t P d Valence Positive 1.67 1.52 1.83 20.91 <.01 1.12 Neutral 0.29 0.16 0.43 4.40 <.01 0.20 Negative -1.10 -1.26 -0.94 -13.47 <.01 -0.71 Arousal Positive 0.29 0.10 0.47 3.01 <.01 0.15 Neutral -0.22 -0.39 -0.06 -2.61 .01 -0.12 Negative 0.79 0.61 0.96 8.84 <.01 0.42 Dominance Positive 0.90 0.74 1.06 10.87 <.01 0.46 Neutral 0.46 0.31 0.62 5.86 <.01 0.12 Negative 0.90 -0.67 -0.32 10.87 <.01 0.44 95% C.I. 95% C.I. Pre-induction SAM Rating (SE) Lower Bound Upper Bound Post-induction SAM Rating (SE) Lower Bound Upper Bound Valence Positive 5.26 (0.08) 5.11 5.41 6.93 (0.07) 6.78 7.07 Neutral 5.21 (0.07) 5.07 5.36 5.51 (0.07) 5.37 5.66 Negative 5.25 (0.08) 5.15 5.45 4.20 (0.08) 4.06 4.35 Arousal Positive 3.69 (0.09) 3.51 3.88 3.97 (0.10) 3.78 4.16 Neutral 3.74 (0.09) 3.56 3.92 3.51 (0.10) 3.33 3.70 Negative 3.77 (0.09) 3.58 3.95 4.56 (0.10) 4.37 4.75 Dominance Positive 5.06 (0.10) 4.86 5.25 5.95 (0.10) 5.75 6.14 Neutral 5.12 (0.10) 4.92 5.31 5.57 (0.10) 5.38 5.76 Negative 5.24 (0.10) 5.05 5.44 4.75 (0.10) 4.55 4.94 49 Table 14 - SAM contrasts post-mood induction only 95% C.I. Mean Difference Lower Bound Upper Bound t p d Valence Positive - Negative 2.72 2.53 2.91 27.94 <.001 1.75 Positive - Neutral 1.41 1.22 1.60 14.65 <.001 1.00 Neutral - Negative 1.31 1.12 1.50 13.55 <.001 0.87 Arousal Positive - Negative -0.59 -0.82 -0.35 - 4.96 <.001 -0.30 Positive - Neutral 0.46 0.23 0.69 3.94 <.001 0.24 Neutral - Negative -1.05 -1.28 0.82 -8.93 <.001 -0.55 Dominance Positive - Negative 1.20 0.97 1.42 10.55 <.001 0.31 Positive - Neutral 0.38 0.16 0.59 3.35 <.001 0.20 Neutral - Negative 0.82 0.60 1.04 7.31 <.001 0.41 3.2.2 The Impact of Mood State on Personality Ratings Using linear mixed modelling, we explored the effect of condition on personality factor ratings, using separate models for each personality factor. In these models, mood condition was entered as a fixed effect and subject intercept was allowed to vary randomly. Results suggested that there were significant differences between mood state conditions on self-reported personality ratings for agreeableness (F(2, 770.95) = 5.39, p<.01), extraversion (F(2, 765.55) = 3.76, p=.02), neuroticism (F(2,767.70) = 5.44, p<.01), and openness to experience (F(2, 765.96) = 4.34, p=.01). Self-report ratings of conscientiousness did not appear to significantly differ among mood conditions (F(2, 766.77) = 0.24, p=.79). Follow-up pairwise comparisons were conducted to examine the differences between the mean personality factor ratings following each mood condition (see Table 15 for the mean personality factor ratings and 50 Table 16 for the pairwise comparisons). These results are summarized below for each personality factor separately. 3.2.2.1 Agreeableness As noted above, the results showed that agreeableness ratings were significantly higher following the positive mood induction as compared to the negative mood induction. The difference between agreeableness ratings following the negative and neutral mood inductions was not significant. Similarly, the difference between agreeableness ratings following the positive and neutral mood inductions was not significant. 3.2.2.2 Conscientiousness Conscientiousness ratings did not significantly differ among any of the mood conditions. 3.2.2.3 Extraversion Extraversion ratings were significantly higher following the positive mood induction, as compared to both the negative mood induction and the neutral mood condition. The difference between the extraversion ratings following the neutral and negative mood inductions did not significantly differ from each other. 3.2.2.4 Neuroticism Neuroticism ratings were significantly lower following both the neutral and the positive mood inductions, as compared to the negative mood induction. However, the ratings following the neutral and positive mood inductions did not significantly differ from each other. 51 3.2.2.5 Openness to Experience Openness to experience ratings were significantly higher following the positive mood induction, as compared to both the negative and the neutral mood inductions. The ratings following the negative and the neutral mood inductions were not significantly different from each other. Table 15 - Mean BFI personality factor ratings by condition 95% CI Condition Mean Rating SE df Lower Bound Upper Bound Agreeableness Positive 3.70 0.03 541.83 3.64 3.76 Neutral 3.67 0.03 535.04 3.61 3.73 Negative 3.64 0.03 543.76 3.58 3.70 Conscientiousness Positive 3.33 0.03 526.76 3.27 3.39 Neutral 3.33 0.03 520.63 3.27 3.39 Negative 3.32 0.03 528.50 3.26 3.38 Extraversion Positive 3.11 0.04 496.91 3.04 3.19 Neutral 3.07 0.04 492.63 2.99 3.14 Negative 3.08 0.04 498.15 3.00 3.15 Neuroticism Positive 3.17 0.04 532.51 3.10 3.24 Neutral 3.20 0.04 526.08 3.12 3.27 Negative 3.24 0.04 534.33 3.17 3.31 Openness Positive 3.53 0.03 505.05 3.46 3.59 Neutral 3.48 0.03 500.27 3.42 3.55 Negative 3.49 0.03 506.43 3.42 3.55 52 Table 16 - Pairwise comparisons of mean ratings across conditions Comparison Mean Difference SE t p d Agreeableness Positive - Neutral 0.03 0.02 1.57 0.12 0.05 Negative - Neutral -0.03 0.02 -1.75 0.08 -0.05 Positive - Negative 0.06 0.02 3.28 <0.01 0.10 Conscientiousness Positive - Neutral 0.00 0.02 -0.15 0.88 0.00 Negative - Neutral -0.01 0.02 -0.66 0.51 -0.02 Positive - Negative 0.01 0.02 0.50 0.62 0.02 Extraversion Positive - Neutral 0.05 0.02 2.60 0.01 0.06 Negative - Neutral 0.01 0.02 0.50 0.61 0.01 Positive - Negative 0.04 0.02 2.07 0.04 0.05 Neuroticism Positive - Neutral -0.03 0.02 -1.30 0.19 -0.04 Negative - Neutral 0.04 0.02 2.01 0.04 0.05 Positive - Negative -0.07 0.02 -3.27 <0.01 -0.09 Openness Positive - Neutral 0.04 0.02 2.59 0.01 0.06 Negative - Neutral 0.00 0.02 -0.05 0.96 0.00 Positive - Negative 0.04 0.02 2.52 0.01 0.06 3.3 Moderation by Clinical Variables A secondary aim of Study 2 was to examine whether the influence of mood state on self-reported personality ratings is moderated by certain clinical variables (physical anxiety symptoms, generalized stress symptoms, depressive symptoms, borderline personality disorder symptoms; see Table 17). To answer this question, we used linear mixed modelling to investigate the impact of various clinical variables on the relationship between condition and personality rating, for each personality factor. We constructed models predicting personality ratings (for 53 each personality factor separately) from condition (dummy coded so that the neutral condition was considered “baseline”), the clinical variable, the interaction term between the condition and clinical variable, and allowed for individuals to vary randomly in their intercept on the personality ratings. Condition, clinical variable, and the interaction term were entered as fixed effects. The subject intercept term was entered as random effects. The scores on the clinical measures were grand mean centered. Across all of the models tested, results suggested that there was no significant moderation by any of the four clinical variables examined (see Table 18 for a list of the results from each interaction term). Table 17 – Descriptive Statistics of Clinical Variables Clinical Variable Mean (SD) Cronbach’s α Anxiety Symptoms 7.58 (6.26) .86 Depression Symptoms 8.75 (8.58) .94 Stress Symptoms 12.24 (8.14) .91 BPD Symptoms 3.61 (2.68) .77 54 Table 18 - Moderation effects across all clinical variables Moderator Variable Personality Factor Interaction Term b SE df t p Anxiety Agreeableness Negative*Anxiety 0.00 0.00 770.35 0.76 0.45 Agreeableness Positive*Anxiety 0.00 0.00 769.52 0.81 0.42 Conscientiousness Negative*Anxiety 0.00 0.00 765.28 -0.48 0.63 Conscientiousness Positive*Anxiety 0.00 0.00 764.47 -0.52 0.60 Extraversion Negative*Anxiety 0.00 0.00 764.34 -0.04 0.97 Extraversion Positive*Anxiety 0.00 0.00 763.86 -0.91 0.37 Neuroticism Negative*Anxiety 0.00 0.00 770.14 0.11 0.92 Neuroticism Positive*Anxiety 0.00 0.00 769.07 0.54 0.59 Openness to Experience Negative*Anxiety 0.00 0.00 764.42 -0.58 0.56 Openness to Experience Positive*Anxiety 0.00 0.00 763.90 0.64 0.52 Depression Agreeableness Negative*Depression 0.00 0.00 773.20 -0.34 0.74 Agreeableness Positive*Depression 0.00 0.00 769.81 0.26 0.80 Conscientiousness Negative*Depression 0.00 0.00 767.70 -0.22 0.82 Conscientiousness Positive*Depression 0.00 0.00 764.47 0.11 0.92 Extraversion Negative*Depression 0.00 0.00 766.91 -0.55 0.58 Extraversion Positive*Depression 0.00 0.00 764.64 -1.48 0.14 Neuroticism Negative*Depression 0.00 0.00 774.45 0.09 0.93 Neuroticism Positive*Depression 0.00 0.00 770.27 0.16 0.87 Openness to Experience Negative*Depression 0.00 0.00 766.54 0.85 0.39 Openness to Experience Positive*Depression 0.00 0.00 764.24 -0.07 0.94 55 Moderator Variable Personality Factor Interaction Term b SE df t p Stress Agreeableness Negative*Stress 0.00 0.00 773.11 0.43 0.67 Agreeableness Positive*Stress 0.00 0.00 770.66 0.25 0.80 Conscientiousness Negative*Stress 0.00 0.00 767.35 -0.91 0.36 Conscientiousness Positive*Stress 0.00 0.00 765.16 -0.29 0.78 Extraversion Negative*Stress 0.00 0.00 766.13 -0.70 0.48 Extraversion Positive*Stress 0.00 0.00 764.63 -0.70 0.48 Neuroticism Negative*Stress 0.00 0.00 775.45 1.02 0.31 Neuroticism Positive*Stress 0.00 0.00 772.28 1.07 0.28 Openness to Experience Negative*Stress 0.00 0.00 766.48 -0.24 0.81 Openness to Experience Positive*Stress 0.00 0.00 764.83 -0.61 0.54 BPD Agreeableness Negative*BPD 0.00 0.01 771.06 0.02 0.99 Agreeableness Positive*BPD -0.01 0.01 771.36 -0.79 0.43 Conscientiousness Negative*BPD -0.01 0.01 765.25 -0.93 0.35 Conscientiousness Positive*BPD 0.00 0.01 765.50 0.30 0.77 Extraversion Negative*BPD 0.00 0.01 764.64 0.31 0.76 Extraversion Positive*BPD 0.01 0.01 764.81 1.11 0.27 Neuroticism Negative*BPD 0.00 0.01 770.08 0.34 0.73 Neuroticism Positive*BPD 0.00 0.01 770.41 -0.05 0.96 Openness to Experience Negative*BPD 0.00 0.01 764.65 0.20 0.84 Openness to Experience Positive*BPD 0.00 0.01 764.84 -0.36 0.72 56 3.3.1 Social Desirability Findings Exploratory analyses were performed in order to investigate whether and how social desirability may be related to the changes in self-report personality ratings under positive and negative mood conditions. To examine this question, we obtained a dataset which had been previously collected at the University of British Columbia by researchers in Dr. Biesanz’s Social Accuracy Lab. In this dataset, 62 undergraduate UBC students had rated each of the BFI-44 questionnaire items on the social desirability of the item on a 7-point rating scale (1 corresponding to “disagree strongly” that the personality descriptor assessed by the item was socially desirable, and 7 corresponding to “agree strongly”). The mean social desirability ratings of the BFI-44 items from Dr. Biesanz’s dataset were positively correlated overall with item ratings in our dataset in each of the three mood conditions. However, the correlations differed substantially at the level of personality factor (see Table 19). Table 19 - Correlation between mean item social desirability and trait ratings, by mood condition Condition Personality Factor r p 95% CI Lower Bound Upper Bound Positive Agreeableness .54 <.01 0.52 0.56 Conscientiousness .24 <.01 0.21 0.28 Extraversion .01 .52 -0.02 0.05 Neuroticism -.04 .02 -0.08 -0.01 Openness to Experience .23 <.01 0.20 0.26 Overall .24 <.01 0.23 0.26 Neutral Agreeableness .53 <.01 0.51 0.55 Conscientiousness .24 <.01 0.21 0.27 Extraversion -.03 .14 -0.06 0.01 Neuroticism -.06 <.01 -0.09 -0.02 Openness to Experience .21 <.01 0.18 0.24 Overall .22 <.01 0.21 0.24 57 Condition Personality Factor r p 95% CI Lower Bound Upper Bound Negative Agreeableness .50 <.01 0.48 .53 Conscientiousness .24 <.01 0.21 0.27 Extraversion -.02 .39 -0.05 0.01 Neuroticism -.09 <.01 -0.12 -0.05 Openness to Experience .22 <.01 0.19 0.25 Overall .21 <.01 0.20 0.23 In order to investigate mean item social desirability as a potential moderator of the change in item ratings between mood conditions, we created a linear mixed model predicting item rating from the following fixed effect variables: mood condition (dummy coded with “neutral” as baseline), the mean social desirability of the item (from Dr. Biesanz’s previously collected dataset), the item’s corresponding personality factor (dummy coded with “agreeableness” as baseline), as well as all two- and three-way interaction terms among these three variables. We also allowed subjects to vary randomly on their mean item ratings, and on the slope of the relationship between the social desirability of the items and the item ratings themselves. We initially attempted to allow the slope of the relationship between mood condition and item rating to vary randomly between individuals. However, likely due to not all participants having data for all three mood conditions, the models tested failed to consistently converge. As such, we elected not to allow the slope of the relationship between mood condition and item ratings to vary randomly between individuals. When we ran the model described above, none of the 3-way interaction terms between mood condition, social desirability of the item, and the item’s corresponding personality factor were significant (ps >.25). As such, we removed the item’s corresponding personality factor as a 58 variable from the model. The simplified model included mood condition and social desirability of the item, as well as the interaction between these two variables, as fixed effects. The random effects in the simplified model included mean item ratings, and the slope of the relationship between the social desirability of the items and the item ratings. The results from the simplified model (see Table 20) indicated that there was a significant effect of the positive mood condition on item rating, where BFI items were rated higher in the positive mood condition as compared with the neutral mood condition, t(52067.13) = 2.75, p=.01. Table 20 - Results from the linear mixed model Model Term b SE df t p (Intercept) 3.43 0.01 722.16 262.29 <0.01 Condition: Negative 0.01 0.01 52082.01 0.52 0.60 Condition: Positive 0.03 0.01 52067.13 2.75 0.01 Social Desirability 0.17 0.01 524.48 12.67 <0.01 Condition: Negative * Social Desirability -0.01 0.01 52000.39 -1.66 0.10 Condition: Positive * Social Desirability 0.01 0.01 52014.01 1.81 0.07 As an final exploratory analysis of the potential relationship between social desirability and personality ratings, we elected to identify which personality items were more or less responsive to the mood manipulations (i.e. had the highest variance across mood conditions) and examined whether the variance of the personality questionnaire items was related to the items’ social desirability (see Table 21 for the social desirability and variance of each item). We calculated the variance across the three ratings for each of the 44 BFI items, and then correlated the item variances with the social desirability of the items. The results suggested that the higher 59 the social desirability of the BFI-44 item, the lower the variance of the rating across the three mood conditions, r = -0.51 [95% C.I = -0.70, -0.25]. Table 21 – Social desirability and variance of the BFI items BFI Item Factor Social Desirability BFI Item Variance 7 Agreeableness 6.29 0.87 32 Agreeableness 6.15 0.85 9 Neuroticism 6.00 1.14 38 Conscientiousness 6.00 1.06 22 Agreeableness 5.95 1.03 24 Neuroticism 5.94 1.17 42 Agreeableness 5.94 0.87 13 Conscientiousness 5.89 0.79 34 Neuroticism 5.84 1.06 33 Conscientiousness 5.82 0.95 36 Extraversion 5.79 1.13 11 Extraversion 5.68 1.08 16 Extraversion 5.68 1.06 17 Agreeableness 5.66 1.14 3 Conscientiousness 5.66 0.85 5 Openness to Experience 5.60 1.06 10 Openness to Experience 5.53 0.91 20 Openness to Experience 5.50 1.02 28 Conscientiousness 5.50 1.00 40 Openness to Experience 5.35 0.99 15 Openness to Experience 5.24 1.00 25 Openness to Experience 5.23 1.03 30 Openness to Experience 5.21 1.06 26 Extraversion 4.94 1.14 44 Openness to Experience 4.77 1.22 1 Extraversion 4.74 1.15 35 Openness to Experience 3.71 1.13 31 Extraversion 3.71 1.11 41 Openness to Experience 3.50 1.27 21 Extraversion 3.50 1.18 6 Extraversion 3.48 1.10 8 Conscientiousness 2.97 1.05 14 Neuroticism 2.90 0.97 43 Conscientiousness 2.82 1.02 39 Neuroticism 2.63 1.11 18 Conscientiousness 2.47 1.29 60 BFI Item Factor Social Desirability BFI Item Variance 19 Neuroticism 2.47 1.10 29 Neuroticism 2.35 1.08 27 Agreeableness 2.29 1.21 23 Conscientiousness 2.26 1.15 2 Agreeableness 2.23 1.15 37 Agreeableness 1.98 1.15 12 Agreeableness 1.68 1.03 4 Neuroticism 1.56 1.24 Note: social desirability ratings were on a 7-point scale. 3.3.2 The Influence of Mood State on Personality Structure As in Study 1, we used Pearson correlations to examine the relationships among the personality factors both across and within mood conditions. The correlations for the same personality factors across mood conditions ranged from .84 to .91 (see Table 22), again suggesting that there was a high degree of correspondence between ratings made for each personality factor across mood conditions. The pattern of correlations also appeared quite similar across mood conditions. We also conducted maximum likelihood exploratory factor analyses at the item level, using promax rotation. Six factors were again identified across all three mood conditions. Based on the item loadings, the five factors (extraversion, agreeableness, neuroticism, conscientiousness, and openness to experience) were recovered. The sixth factor appeared to possibly be related to reverse-scored items, as a majority of the factor items loading on this factor were reverse-scored. However, there were some minor differences observed. In the positive mood condition, the sixth factor appeared to consist largely of extraversion items which were reverse-scored. In the neutral and negative mood conditions, the sixth factor consisted of primarily of reverse-scored agreeableness items. 61 Tucker’s congruence coefficients indicated that four of the five factors were consistently recovered (corresponding to the extraversion, neuroticism, conscientiousness and openness to experience; all congruence coefficients above 0.95). Additionally, the agreeableness factor was also highly similar across the negative and neutral mood conditions (congruence coefficient of 0.95). The agreeableness factor and the sixth factor in the positive mood condition appeared to differ most from their corresponding factors in the other mood conditions, with congruence coefficients ranging from 0.73 to 0.86).62 Table 22 – Correlations among personality ratings across and within mood conditions in study 2 Positive Mood State Neutral Mood State Negative Mood State A C E N O A C E N O A C E N Positive Mood State A C .25** E .17** .16** N -.32** -.34** -.37** O .14** .09 .32** -.14** Neutral Mood State A .85** .26** .20** -.30** .19** C .21** .86** .15** -.32** .08 .24** E .17** .14** .91** -.34** .33** .24** .18** N -.27** -.33** -.36** .85** -.18** -.31** -.37** -.37** O .15** .09 .28** -.13* .90** .21** .10 .28** -.14** Negative Mood State A .84** .27** .14** -.26** .15** .86** .26** .19** -.28** .20** C .26** .86** .11* -.30** .05 .26** .85** .14** -.35** .08 .32** E .19** .14** .90** -.35** .32** .24** .18** .90** -.34** .30** .21** .16** N -.31** -.31** -.35** .86** -.12* -.33** -.30** -.34** .85** -.10 -.34** -.32** -.39** O .22** .05 .26** -.13* .88** .26** .08 .27** -.15** .90** .23** .05 .30** -.12* Note: Pearson correlations conducted using pairwise deletion; E = Extraversion; A = Agreeableness; C = Conscientiousness; N = Neuroticism; O = Openness to Experience ; *p<.05; **p<.01. 63 Of note, we did also investigate the Tucker’s congruence coefficients when we constrained the models to five factors. When we constrained the models to include only 5 factors, the congruence coefficients across all three mood conditions ranged from .93 to .98, indicating that the factors were generally equal (Lorenzo-Seva & ten Berge, 2006). 3.4 Summary of Study 2 Results The primary aim of Study 2 was to replicate the findings from Study 1 with respect to the impact of mood state on self-reported personality ratings, and to clarify in which direction mood states were influencing self-report personality trait ratings (e.g. mood congruent vs. mood incongruent). The results from Study 2 supported the findings from Study 1 in that there were again small but significant differences in self-report ratings of agreeableness, extraversion, and neuroticism between the positive and negative mood state conditions. Unlike in Study 1, Study 2 found no differences in conscientiousness ratings between the mood conditions, but did find that openness to experience ratings were higher in the positive mood condition. Study 2 also aimed to clarify which mood conditions were driving the changes in personality ratings. Results suggested that personality ratings were generally changing in a mood-congruent fashion. Specifically, when in a positive mood individuals’ extraversion and openness to experience ratings increased. Conversely, when in a negative mood individuals’ neuroticism ratings increased. The results for agreeableness were less clear, as there was a significant difference between agreeableness ratings in the positive and negative mood conditions. However, neither the positive nor the negative mood conditions significantly differed from the neutral mood condition. 64 Study 2 also had several secondary aims, including exploring how symptoms of psychopathology moderate the change in personality ratings between the mood conditions, as well as investigating how the social desirability of the items may be related to the changes in ratings between the mood conditions. The results from these exploratory analyses suggested that there is no relationship between the change in self-reported personality traits between mood states and clinical symptoms of depression, panic, generalized anxiety, or borderline personality disorder. In terms of the social desirability findings, the results from Study 2 suggested that social desirability is related to overall item endorsement, in that individuals are more likely to rate socially desirable items higher, regardless of the specific personality factor assessed. Interestingly, social desirability was moderately and negatively related to the variance in ratings across mood conditions, with items higher in social desirability tending to show smaller changes in ratings between mood conditions. Finally, unlike in Study 1 we did find some minor differences in personality structure in Study 2. Specifically, the agreeableness factor loaded somewhat differently in the positive mood condition as compared to the negative and neutral conditions when we allowed for a 6-factor solution. Additionally, the “sixth factor” (which largely appeared to consist with reverse-scored items) also had different factor loadings in the positive mood condition versus the negative mood condition. However, when we constrained the analyses to consist of only 5 factors, as would be most consistent with the Big Five theory, all of the factors demonstrated a high degree of similarity across mood conditions. 65 Chapter 4: Conclusion The current research was designed to investigate whether current mood state can influence self-reported personality ratings by contrasting self-reported personality ratings under experimentally-induced positive and negative mood states. Once this phenomenon was established, we sought to clarify whether the observed changes were occurring in response to the positive mood state, the negative mood state, or both, by including a neutral mood condition into the original experimental design. In term of more exploratory aims, we also examined whether symptoms of anxiety, depression, or borderline personality disorder moderated the influence of current mood on changes in personality ratings. Additionally, we investigated whether the social desirability of the personality measure items moderated the changes in personality item endorsement. 4.1 Overall Summary of Research Findings The main finding from Study 1 was that current mood state does influence ratings of extraversion, agreeableness, and neuroticism, albeit to a very small extent. Participants rated themselves as more extraverted, agreeable, and less neurotic when in a positive (versus a negative) mood state. Participants in Study 1 also rated themselves as more conscientious when in a positive mood state, although this finding was slightly less robust. In Study 2 we replicated the findings from Study 1 that extraversion, agreeableness and neuroticism are influenced by current mood state, and again replicated the very small effect size. This finding fits with prior work which has found that the scale of ‘harm avoidance’ on the Tridimensional Personality Questionnaire (S. L. Brown et al., 1992; Svrakic, Przybeck, & Cloninger, 1992) is the personality dimension most impacted by mood states. ‘Harm avoidance’ 66 has been shown to correlate negatively and positively with the Big Five dimensions of extraversion and neuroticism, respectively (De Fruyt, Van De Wiele, & Van Heeringen, 2000). This would seem to suggest that the current findings are not necessarily constrained by the specific personality measure that we used, and might apply across personality measures and models. Conscientiousness ratings in Study 2 were not influenced by current mood state, whereas openness to experience ratings significantly differed between the mood conditions, with participants rating themselves as more open to experience when in a positive mood state, as compared to a negative or neutral mood state. It is not immediately clear why the conscientiousness and openness to experience findings were inconsistent across Study 1 and 2. However, there are several possible reasons for this discrepancy. One possibility is that the larger sample size in Study 2 provided us with more power to detect true changes in personality ratings. This possibility is supported by the fact that the actual mean differences in openness to experience ratings between the positive and the negative mood conditions in Study 1 and Study 2 were very similar. Extending this logic, the finding in Study 1 that conscientiousness ratings differed between the positive and negative mood conditions could have been a Type I error, and the replication using a larger sample size clarified that in fact there is no real difference between the two mood conditions. Another possibility is that self-report ratings of openness to experience and conscientiousness may not be as responsive to current mood state as extraversion, agreeableness, and neuroticism. Interestingly, whereas the BFI-44’s extraversion, agreeableness, and neuroticism scales all include items which include or imply affective content (e.g., extraversion: “generates a lot of enthusiasm”; agreeableness: “starts quarrels with others”; neuroticism: “gets nervous easily”), few if any of the conscientiousness and openness to experience items even 67 imply the presence of affect in their items (e.g., conscientiousness: “does a thorough job”; openness to experience: “is sophisticated in art, music, or literature”). This is generally consistent with research that has found that only certain personality traits such as extraversion and neuroticism are strongly associated with positive and negative affect, respectively (Watson & Clark, 1992). From the perspective of the associative network model, it is possible then that the item content referenced by the conscientiousness and openness to experience items are not as closely or strongly linked to affective ‘nodes’, and so recall related to these traits is less influenced by current mood states. In Study 2 we also attempted to clarify whether the changes in self-reported personality ratings were attributable to the effect of the positive mood state, the negative mood state, or both. We found that extraversion and openness to experience ratings increased in response to the positive mood induction. Neuroticism showed the opposite pattern, where neuroticism ratings increased in response to the negative mood induction. We were unable to clarify whether agreeableness ratings were changing in response to the positive or negative mood inductions, as the agreeableness ratings in the positive and negative mood conditions significantly differed from each other, but did not significantly differ from the neutral mood condition. Overall, the extraversion, openness to experience, and neuroticism findings from Study 2 support a mood-congruent shift in self-reported personality ratings. The agreeableness findings suggest that there is a possibility that agreeableness may be demonstrating a different pattern, but given the lack of statistical significance this remains speculative. Study 2 also assessed a number of potential moderators of the mood-induced change in personality ratings. None of the potential clinical moderators (symptoms of anxiety/stress, depression, or borderline personality disorder) significantly moderated the change in personality 68 ratings between the mood conditions. This was somewhat unexpected given prior research which has found that individuals with affective disorders can show increased negative cognitive biases which arise in response to negative mood states (Solis, Antypa, Conijn, Kelderman, & Van der Does, 2017; Van der Does, 2002). It is possible that our undergraduate sample did not represent the clinical severity necessary to detect significant moderation in our clinical variables, and so the lack of moderation in the current study should be interpreted with caution. It is also possible that the lack of moderation by clinical variables indicates that the influence of current mood state on certain self-reported personality traits is a universal phenomenon, rather than something which is impacted by clinical state per se. In Study 2 we also aimed to identify whether social desirability might moderate the change in ratings between mood conditions. The results in Study 2 suggested that social desirability was moderately and negatively correlated with the variance of ratings across the three mood conditions. Although not a measure of how strongly participants responded to the mood induction individually, the variance of the item responses was an indication of how much variability in general was seen at the item level. These results suggest that the social desirability of the items may actually diminish the influence of any extraneous source of variability on ratings, presumably including the effect of mood on self-reported personality ratings. These findings are interesting in light of prior work which found that ‘central’ traits are less susceptible to the influence of mood on self-reported personality (Sedikides, 1995). In addition to being more ‘central’ to the self-concept, ‘central’ traits also tended to be more positive. Although the current study cannot tease apart whether the relationship between item variance and social desirability was due to the more ‘positive’ nature of the socially desirable items (possibly reflecting a greater likelihood of motivated processing), or whether these items are more likely to 69 be considered ‘central’ to the participants’ self-concept (possibly reflecting a greater likelihood of direct access processing), it does lend support to the possibility that participants may be employing more than one type of the AIM processing strategies even within the same study visit, depending on the target item. Finally, in both Study 1 and Study 2 we investigated whether the factor structure of the personality ratings differed between the mood conditions. No evidence of any difference in factor structure was observed in Study 1, which compared a positive and a negative mood condition. In Study 2 there were some minor differences which were observed between the positive versus the negative and neutral mood conditions, specifically in the loadings on the agreeableness factor and the “sixth” factor. However, it should be noted that this difference in factors loadings disappeared when we constrained the model to include only 5 factors. 4.2 Implications of the Current Findings The findings from the current research are novel in that they are, to the author’s knowledge, the only experimental investigation into the possible influence of current mood state on self-reported personality ratings. The results from the current research are interesting in that they demonstrate how certain self-reported personality trait ratings are impacted by current emotional state, as would be expected from prior empirical and theoretical work. The current work also suggests that the more affectively-laden personality traits (such as extraversion and neuroticism) may be the most susceptible to the impact of current mood state, which represents a novel contribution to the literature on the relationship between mood and temperament/personality. 70 However, the current findings are also helpful in that they illustrate that, by and large, researchers using self-report personality measures do not need to be overly concerned about the mood states of individuals when they are taking part in the study. Although current mood state does impact self-reported personality ratings, the magnitude of that influence is very small – both in terms of the impact on item ratings, and on the factor structure of the Big Five. The size of the effect was in fact smaller than might have been expected given prior work on the influence of mood on other self-related judgments, such as subjective wellbeing (Schwarz & Clore, 1983). However, these small effect sizes are in line with more recent and rigorous replications of the impact of mood on self-related judgments (Yap et al., 2017). The fact that current mood state impacts self-reported personality traits only to a small degree increases confidence in the validity of the BFI-44, and by extension self-report personality measures more broadly. The current work supports the assertion that the proportion of variance attributable to current mood state is likely to be too small to be concerned about in most research paradigms, unless the effect sizes under study are also very small or the paradigm is likely to elicit very strong affect at some point in the protocol. Given that there is a significant effect of mood on ratings, albeit a small one, researchers might be encouraged to have participants complete personality self-report measures prior to any activities or questionnaires which are likely to elicit strong mood changes (e.g. a Trier Stress Test, asking about highly aversive or positive memories or traumatic events). In terms of exploratory questions in the current work, none of the clinical variables moderated the changes in self-reported personality traits. The lack of significant findings related to the exploratory moderator analyses makes it difficult to speak to the implications of those results. However, the overall finding that mood has a small impact on self-reported personality traits has implications for clinical research that looks at the relationship between disorders 71 characterized by dysregulated mood (e.g. affective disorders, borderline personality disorder). As described previously, there is a significant interest in the relationship between affective disorders and personality traits (Clark et al., 1994; Kotov et al., 2010; Watson & Clark, 1992), with researchers often assessing differences in self-reported personality traits before and after treatment, or contrasting personality ratings during the course of a disorder and after recovery. The influence of current mood state on self-reported personality ratings has the potential to be an important confound in these lines of research, because individuals’ mood states are likely to be more negative when they are experiencing an affective disorder. Furthermore, the findings from these studies tend to demonstrate increases in neuroticism and decreases in extraversion during periods of illness (Karsten et al., 2012) which mirrors the changes that we observed in the current study during negative mood states. It is likely that current mood state is contributing to the differences or changes in self-reported extraversion and neuroticism typically observed in these studies. However, given the small size of the effects in the current study and the lack of moderation by clinical variables, it is unlikely that current mood state is the sole driver of the differences in extraversion and neuroticism seen in clinical versus non-clinical samples. The results related to social desirability as a moderator of the influence of mood on self-reported personality were not entirely straightforward. We expected to see social desirability moderate the relationship between mood state and personality ratings, in so far as social desirability might index the “affective valence” of the personality items. In other words, participants would be expected to increase their ratings of socially desirable items when in a more positive mood state, versus increasing their ratings of less socially desirable items when in a more negative mood state, either due to increased accessibility of mood-congruent information, or due to using their current mood state as a heuristic for how they ‘felt’ about themselves. We in 72 fact found that social desirability of an item may actually decrease the likelihood that ratings would change in response to the mood inductions. This finding, although unexpected given our hypotheses which focused on mood-congruent changes in ratings, could still be conceptualized as supporting the affect infusion model. It is possible that the social desirability of the personality items, rather than indexing the overall “affective valence” of the items, could be considered to reflect how likely an individual might be to engage in motivated processing when answering the item. In other words, individuals are likely motivated to view themselves in a socially desirable fashion, and according to the affect infusion model, motivated processing is a “low affect infusion” strategy whereby current mood state is less likely to influence judgments. It is also possible that socially desirable items may more likely to be central to individuals’ self-concept. As noted above, prior research has demonstrated how traits or characteristics that individuals consider to be more central to their self-concept are often judged to be more positive, and less susceptible to the influence of current mood state (Sedikides, 1995). Although the social desirability of a personality item is not necessarily a measure of how central a construct may be to an individual’s self-concept, social desirability does index how “positive” an item is likely to be considered, and so may be related to the centrality of the trait to an individual’s self-concept indirectly. 4.3 Limitations and Future Directions The current research had a number of strengths. Firstly, the experimental, within-person design built on a number of limitations of prior work purporting to investigate the influence of mood state on responses to personality measures. Secondly, the replication of a number of findings between Study 1 and Study 2, as well as the relatively large sample size of Study 2, 73 allows for increased confidence in the main findings. However, the current research also had a number of limitations. Firstly, the current research employed only one personality measure, the BFI-44. Although it is unlikely that the influence of current mood on other self-report personality measures would be drastically different, it would be helpful to demonstrate that this phenomenon occurs across other self-report personality measures, to increase confidence in the generalizability of the current findings. Additionally, by employing personality measures like the Revised NEO Personality Inventory (Costa & McCrae, 1992), which also assesses facets of the Big Five personality traits, we would be able to further explore whether there is differentiation between the facets in terms of the influence of mood on self-reported personality, and whether these differences are also between the more- and less- affectively laden facets. Secondly, the current research was conducted in undergraduate samples, which have inherent limitations with respect to generalizability (Henrich, Heine, & Norenzayan, 2010). Although it is hard to anticipate why the influence of mood on self-reported personality might function differently in a more representative sample, replicating the current research in other populations would strengthen the conclusions drawn here. For example, given that the samples used in the current work consisted of predominantly female students, it is not clear to what extent these results would generalize to male undergraduate students; future work could aim to capture a more balanced sample with respect to gender. Additionally, extending the current work into certain clinical populations (e.g. individuals with a diagnosis of borderline personality disorder) might be a particularly important future direction, as it is still possible that the lack of moderation by clinical variables observed in the current study was attributable to a lack of clinical severity in the sample. 74 Thirdly, in the current research we used the average social desirability of items, as rated by an undergraduate sample. However, the social desirability of different personality traits might vary between individuals. For example, several items assessing openness to experience have been shown to have a high degree of variability in how socially desirable they are perceived to be (Wood & Wortman, 2012). In order to further investigate whether the social desirability of personality traits or items moderates the influence of current mood on self-reported personality trait ratings, it would be helpful to have the same individuals making the social desirability ratings and completing the personality measures under differing mood conditions. Finally, the current study was not designed to compare and contrast theories of how current mood might influence personality ratings, as the focus was on determining and characterizing the phenomenon. To a certain extent, support was seen for all three of the theories outlined in the introduction, as they all predicted that there would be an effect of current mood state on self-report personality ratings. The fact that the most robust mood-congruent changes occurred in personality traits associated with positive and negative affect (extraversion and neuroticism, respectively) could indicate that the changes in item endorsement is occurring because associative networks associated with the current mood state are being activated and thereby biasing recall. The mood congruent changes could also be interpreted to support the affect as information model and the AIM, in that the mood congruent changes could be due to heuristic processing. However, the fact that the social desirability of the items – which we used as index of how ‘positive’ or ‘negative’ the trait items were considered to be – did not clearly moderate the changes in personality ratings between mood conditions, could be interpreted as evidence that ‘positive’ or ‘negative’ affect is not being used as a simple ‘positive’ or ‘negative’ heuristic when participants are making their personality ratings. Given that the affect-as-75 information model was built not only to incorporate the associative network model and the affect as information theory, but to also integrate these theories with situations where affect is unlikely to influence judgments, it is unsurprising that the findings are perhaps best explained within the context of this model. Specifically, only the affect as information model is able to explain why more socially desirable items were less likely to demonstrate variance in their ratings. The affect as information model allows for the possibility that the “affective valence” of a personality item may be less of a determinant of how susceptible a personality item is to being influenced by current mood state, compared to how motivated an individual might be to see themselves a certain way, or how central or ‘pre-stored’ information about the trait may be for a given to an individual. It could be interesting for future work to use the fact that current mood state can influence certain personality trait ratings, to test out predictions from some of the theories about how mood state can influence judgments. For example, the affect-as-information theory predicts that individuals will only use their current affective state in forming their judgments when they are unaware of the source of their affect. Future studies could examine whether the influence of mood on self-reported personality ratings is eliminated when participants are cued to the source of their current mood state. Alternately, it would be interesting to try and clarify whether the relationship between variance in item responses and social desirability is reflective of motivated processing or direct access processing – both of which could be possible according to the affect as information model. If future studies could capture how central various traits and/or personality items are to each participant, as well as have them rate their perceived social desirability of each item, we would be able to identify which explanation better accounts for the decreased variance observed in the current study. 76 4.4 Summary In conclusion, the current research demonstrated that mood state at the time of personality questionnaire completion can influence self-report ratings of certain personality traits in predictable ways. The most robust findings were that extraversion ratings increase when in a positive mood state, and neuroticism ratings increase when in a negative mood state. However, it is important to note that across the studies the effect size of this influence remained very small. This small size of the effect supports the validity of self-report personality measures, and indicates that under typical conditions researchers do not need to be overly concerned about their participants’ mood states when completing self-report personality questionnaires. 77 References Adelstein, J. S., Shehzad, Z., Mennes, M., DeYoung, C. G., Zuo, X.-N., Kelly, C., … Milham, M. P. (2011). Personality Is Reflected in the Brain’s Intrinsic Functional Architecture. PLOS ONE, 6(11), e27633. https://doi.org/10.1371/journal.pone.0027633 Akiskal, H. S., Hirschfeld, R. M. A., & Yerevanian, B. I. (1983). The Relationship of Personality to Affective Disorders: A Critical Review. Archives of General Psychiatry, 40(7), 801–810. https://doi.org/10.1001/archpsyc.1983.01790060099013 Allemand, M., Zimprich, D., & Hendriks, A. A. J. (2008). Age differences in five personality domains across the life span. Developmental Psychology, 44(3), 758–770. https://doi.org/10.1037/0012-1649.44.3.758 Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998). Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychological Assessment, 10(2), 176–181. https://doi.org/10.1037/1040-3590.10.2.176 Back, M. D., Schmukle, S. C., & Egloff, B. (2009). Predicting actual behavior from the explicit and implicit self-concept of personality. Journal of Personality and Social Psychology, 97(3), 533–548. https://doi.org/10.1037/a0016229 Barrick, M. R., Mount, M. K., & Judge, T. A. (2001). Personality and Performance at the Beginning of the New Millennium: What Do We Know and Where Do We Go Next? International Journal of Selection and Assessment, 9(1–2), 9–30. https://doi.org/10.1111/1468-2389.00160 78 Benet-Martínez, V., & John, O. P. (1998). Los cincos grandes across cultures and ethnic groups: Multitrait multimethod analyses of the big five in spanish and english. Journal of Personality and Social Psychology, 75(3), 729–750. Block, J. (1995). A contrarian view of the five-factor approach to personality description. Psychological Bulletin, 117(2), 187–215. https://doi.org/10.1037/0033-2909.117.2.187 Bower, G. H. (1981). Mood and memory. American Psychologist, 36(2), 129–148. https://doi.org/10.1037/0003-066X.36.2.129 Bower, G. H., & Forgas, J. P. (2000). Affect, memory, and social cognition. In E. Eich, J. F. Killstrom, G. H. Bower, J. P. Forgas, & P. M. Niedenthal (Eds.), Cognition and Emotion (pp. 87–168). New York, NY: Oxford University Press. Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49–59. https://doi.org/10.1016/0005-7916(94)90063-9 Brown, J. D., & Mankowski, T. A. (1993). Self-esteem, mood, and self-evaluation: Changes in mood and the way you see you. Journal of Personality and Social Psychology, 64(3), 421–430. https://doi.org/10.1037/0022-3514.64.3.421 Brown, S. L., Svrakic, D. M., Przybeck, T. R., & Cloninger, R. C. (1992). The relationship of personality to mood and anxiety states: A dimensional approach. Journal of Psychiatric Research, 26(3), 197–211. https://doi.org/10.1016/0022-3956(92)90023-H Bullington, J. C. (1990). Mood Congruent Memory: A Replication of Symmetrical Effects for Both Positive and Negative Moods. Journal of Social Behavior and Personality, 5(4), 123–132. 79 Bunevicius, A., Katkute, A., & Bunevicius, R. (2008). Symptoms of Anxiety and Depression in Medical Students and in Humanities Students: Relationship With Big-Five Personality Dimensions and Vulnerability To Stress. International Journal of Social Psychiatry, 54(6), 494–501. https://doi.org/10.1177/0020764008090843 Campbell, J. B. (2008). Modern personality theories: What have we gained? What have we lost? In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The SAGE Handbook of Personality Theory and Assessment: Personality Theories and Models (Vol. 1, pp. 190–212). London: SAGE Publications Ltd. Clark, L. A., Watson, D., & Mineka, S. (1994). Temperament, personality, and the mood and anxiety disorders. Journal of Abnormal Psychology, 103(1), 103–116. https://doi.org/10.1037/0021-843X.103.1.103 Clasen, P. C., Fisher, A. J., & Beevers, C. G. (2015). Mood-Reactive Self-Esteem and Depression Vulnerability: Person-Specific Symptom Dynamics via Smart Phone Assessment. PLoS One; San Francisco, 10(7), e0129774. http://dx.doi.org.ezproxy.library.ubc.ca/10.1371/journal.pone.0129774 Cloninger, R. C., Przybeck, T. R., & Svrakic, D. M. (1991). The Tridimensional Personality Questionnaire: U.S. Normative Data. Psychological Reports, 69(3), 1047–1057. https://doi.org/10.2466/pr0.1991.69.3.1047 Cloninger, S. (2009). Conceptual issues in personality theory. In P. J. Corr & G. Mathews (Eds.), The Cambridge handbook of personality psychology (pp. 3–26). Cambridge, U.K. ; New York: Cambridge University Press. Clore, G. L., Gasper, K., & Garvin, E. (2001). Chapter 6. Affect as Information. In J. P. Forgas (Ed.), Handbook of Affect and Social Cognition. Mahwah, NJ: L. Erlbaum Associates. 80 Costa, P. T. ., & McCrae, R. R. (1985b). The NEO Personality Inventory Manual. Odessa, FL: Psychological Assessment Resources. Costa, P. T., & McCrae, R. R. (1992). Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO FFI): Profesional manual. Odessa, FL: Psychological Assessment Resources. De Fruyt, F., Van De Wiele, L., & Van Heeringen, C. (2000). Cloninger’s Psychobiological Model of Temperament and Character and the Five-Factor Model of Personality. Personality and Individual Differences, 29(3), 441–452. https://doi.org/10.1016/S0191-8869(99)00204-4 DeSteno, D. A., & Salovey, P. (1997). The Effects of Mood on the Structure of the Self-concept. Cognition and Emotion, 11(4), 351–372. https://doi.org/10.1080/026999397379836 Digman, J. M. (1990). Personality Structure: Emergence of the Five-Factor Model. Annual Review of Psychology, 41(1), 417–440. https://doi.org/10.1146/annurev.ps.41.020190.002221 Dunlap, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measures designs. Psychological Methods, 1(2), 170–177. https://doi.org/10.1037/1082-989X.1.2.170 Edwards, A. L. (1957). The social desirability variable in personality assessment and research. Ft Worth, TX, US: Dryden Press. Enns, M. W., & Cox, B. J. (1997). Personality dimensions and depression: Review and commentary. Canadian Journal of Psychiatry, 42(3), 274–284. 81 Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146 Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological Bulletin, 117(1), 39–66. https://doi.org/10.1037/0033-2909.117.1.39 Forgas, J. P., & Koch, A. S. (2013). Mood effects on cognition. In M. D. Robinson, E. R. Watkins, & E. Harmon-Jones (Eds.), Handbook of Cognition and Emotion (pp. 213–251). New York, NY: The Guilford Press. Forgas, J. P., & Moylan, S. (1987). After the Movies Transient Mood and Social Judgments. Personality and Social Psychology Bulletin, 13(4), 467–477. https://doi.org/10.1177/0146167287134004 Funder, D. C., Kolar, D. C., & Blackman, M. C. (1995). Agreement among judges of personality: Interpersonal relations, similarity, and acquaintanceship. Journal of Personality and Social Psychology, 69(4), 656–672. https://doi.org/10.1037/0022-3514.69.4.656 Gaddy, M. A., & Ingram, R. E. (2014). A meta-analytic review of mood-congruent implicit memory in depressed mood. Clinical Psychology Review, 34(5), 402–416. https://doi.org/10.1016/j.cpr.2014.06.001 Glenn, C. R., & Klonsky, E. D. (2009). Emotion dysregulation as a core feature of borderline personality disorder. Journal of Personality Disorders, 23(1), 20–28. https://doi.org/10.1521/pedi.2009.23.1.20 82 Goldberg, L. R. (1990). An alternative “description of personality”: The Big-Five factor structure. Journal of Personality and Social Psychology, 59(6), 1216–1229. https://doi.org/10.1037/0022-3514.59.6.1216 Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83. https://doi.org/10.1017/S0140525X0999152X Isbell, L. M., Lair, E. C., & Rovenpor, D. R. (2013). Affect-as-Information about Processing Styles: A Cognitive Malleability Approach. Social and Personality Psychology Compass, 7(2), 93–114. https://doi.org/10.1111/spc3.12010 Jain, U., Blais, M. A., Otto, M. W., Hirshfeld, D. R., & Sachs, G. S. (1999). Five-factor personality traits in patients with seasonal depression: treatment effects and comparisons with bipolar patients. Journal of Affective Disorders, 55(1), 51–54. https://doi.org/10.1016/S0165-0327(98)00206-7 Jeanne, M., Gross, J. J., Persons, J. B., & Hahn, J. (1998). Mood Matters: Negative Mood Induction Activates Dysfunctional Attitudes in Women Vulnerable to Depression. Cognitive Therapy and Research, 22(4), 363–376. https://doi.org/10.1023/A:1018709212986 John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The big five inventory - Versions 4a and 54. Berkeley, CA: University of California, Berkeley, Institute of Personality and Social Research. John, O. P., & Srivastava, S. (1999). Chapter 4. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In Handbook of personality: Theory and research (2nd ed., pp. 102–138). New York, NY: The Guilford Press. 83 Johnson, W. B. (1934). The effect of mood on personality traits as measured by Bernreuter. The Journal of Social Psychology, 5, 515–522. https://doi.org/10.1080/00224545.1934.9921619 Karsten, J., Penninx, B. W. J. H., Riese, H., Ormel, J., Nolen, W. A., & Hartman, C. A. (2012). The state effect of depressive and anxiety disorders on big five personality traits. Journal of Psychiatric Research, 46(5), 644–650. https://doi.org/10.1016/j.jpsychires.2012.01.024 Kavanagh, D. J., & Bower, G. H. (1985). Mood and self-efficacy: Impact of joy and sadness on perceived capabilities. Cognitive Therapy and Research, 9(5), 507–525. https://doi.org/10.1007/BF01173005 Kotov, R., Gamez, W., Schmidt, F., & Watson, D. (2010). Linking “big” personality traits to anxiety, depressive, and substance use disorders: A meta-analysis. Psychological Bulletin, 136(5), 768–821. https://doi.org/10.1037/a0020327 Lieb, K., Zanarini, M. C., Schmahl, C., Linehan, M. M., & Bohus, M. (2004). Borderline personality disorder. The Lancet, 364(9432), 453–461. https://doi.org/10.1016/S0140-6736(04)16770-6 Linehan, M. (1993). Cognitive-behavioral Treatment of Borderline Personality Disorder. New York, NY: Guilford Press. Loeffler, S. N., Myrtek, M., & Peper, M. (2013). Mood-congruent memory in daily life: Evidence from interactive ambulatory monitoring. Biological Psychology, 93(2), 308–315. https://doi.org/10.1016/j.biopsycho.2013.03.002 Lorenzo-Seva, U., & ten Berge, J. M. F. (2006). Tucker’s congruence coefficient as a meaningful index of factor similarity. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 2(2), 57–64. https://doi.org/10.1027/1614-2241.2.2.57 84 Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour Research and Therapy, 33(3), 335–343. https://doi.org/10.1016/0005-7967(94)00075-U Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales (2nd ed.). Sydney, Australia: Psychology Foundation Monograph. Matt, G. E., Vázquez, C., & Campbell, W. K. (1992). Mood-congruent recall of affectively toned stimuli: A meta-analytic review. Clinical Psychology Review, 12(2), 227–255. https://doi.org/10.1016/0272-7358(92)90116-P Mayer, J. D., McCormick, L. J., & Strong, S. E. (1995). Mood-Congruent Memory and Natural Mood: New Evidence. Personality and Social Psychology Bulletin, 21(7), 736–746. https://doi.org/10.1177/0146167295217008 McCrae, R. R. (2009). The Five-Factor Model of personality traits: Consensus and controversy. In The Cambridge Handbook of Personality Psychology (pp. 148–161). Cambridge, U.K. ; New York: Cambridge University Press. McCrae, R. R., & Costa, P. T. (1983). Social desirability scales: More substance than style. Journal of Consulting and Clinical Psychology, 51(6), 882–888. https://doi.org/10.1037/0022-006X.51.6.882 McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52(1), 81–90. https://doi.org/10.1037/0022-3514.52.1.81 85 McCrae, R. R., Costa, P. T., & Busch, C. M. (1986). Evaluating comprehensiveness in personality systems: The California Q-Set and the five-factor model. Journal of Personality, 54(2), 430–446. https://doi.org/10.1111/j.1467-6494.1986.tb00403.x McCrae, R. R., & Costa, P. T. J. (1997). Personality trait structure as a human universal. American Psychologist, 52(5), 509–516. https://doi.org/10.1037/0003-066X.52.5.509 Mehl, M. R., & Pennebaker, J. W. (2003). The sounds of social life: A psychometric analysis of students’ daily social environments and natural conversations. Journal of Personality and Social Psychology, 84(4), 857–870. https://doi.org/10.1037/0022-3514.84.4.857 Miranda, J., & Persons, J. B. (1988). Dysfunctional attitudes are mood-state dependent. Journal of Abnormal Psychology, 97(1), 76–79. https://doi.org/10.1037/0021-843X.97.1.76 Nederhof, A. J. (1985). Methods of coping with social desirability bias: a review. European Journal of Social Psychology, 15(3), 263–280. Nezlek, J. B., & Plesko, R. M. (2001). Day-to-Day Relationships among Self-Concept Clarity, Self-Esteem, Daily Events, and Mood. Personality and Social Psychology Bulletin, 27(2), 201–211. https://doi.org/10.1177/0146167201272006 Noyes, R., Reich, J. H., Suelzer, M., & Christiansen, J. (1991). Personality traits associated with panic disorder: Change associated with treatment. Comprehensive Psychiatry, 32(4), 283–294. https://doi.org/10.1016/0010-440X(91)90076-O O’Connor, B. P. (2002). The search for dimensional structure differences between normality and abnormality: A statistical review of published data on personality and psychopathology. Journal of Personality and Social Psychology, 83(4), 962–982. https://doi.org/10.1037/0022-3514.83.4.962 86 Patel, A. B., Sharp, C., & Fonagy, P. (2011). Criterion Validity of the MSI-BPD in a Community Sample of Women. Journal of Psychopathology and Behavioral Assessment; New York, 33(3), 403–408. http://dx.doi.org.ezproxy.library.ubc.ca/10.1007/s10862-011-9238-5 Paulhus, D. L. (1986). Self-Deception and Impression Management in Test Responses. In A. Angleitner & J. S. Wiggins (Eds.), Personality Assessment via Questionnaires (pp. 143–165). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-70751-3_8 Paulhus, D. L., & Vazire, S. (2009). The Self-Report Method. In R. W. Robins, R. C. Fraley, & R. F. Krueger (Eds.), Handbook of Research Methods in Personality Psychology. Guilford Press. Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135(2), 322–338. https://doi.org/10.1037/a0014996 Rusting, C. L., & DeHart, T. (2000). Retrieving positive memories to regulate negative mood: Consequences for mood-congruent memory. Journal of Personality and Social Psychology, 78(4), 737–752. https://doi.org/10.1037/0022-3514.78.4.737 Schmukle, S. C., Back, M. D., & Egloff, B. (2008). Validity of the five-factor model for the implicit self-concept of personality. European Journal of Psychological Assessment, 24(4), 263–272. https://doi.org/10.1027/1015-5759.24.4.263 Schnabel, K., Asendorpf, J. B., & Greenwald, A. G. (2008). Using implicit association tests for the assessment of implicit personality self-concept. In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The SAGE Handbook of Personality Theory and Assessment - Volume 2: Personality Measurement and Testing (Vol. 2, pp. 508–528). Thousand Oaks, CA: Sage Publications, Inc. 87 Schnabel, K., Banse, R., & Asendorpf, J. B. (2006). Assessment of implicit personality self-concept using the implicit association test (IAT): Concurrent assessment of anxiousness and angriness. British Journal of Social Psychology, 45(2), 373–396. https://doi.org/10.1348/014466605X49159 Schwarz, N., & Clore, G. L. (1983). Mood, misattribution, and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45(3), 513–523. https://doi.org/10.1037/0022-3514.45.3.513 Sedikides, C. (1992). Changes in the valence of the self as a function of mood. Review of Personality and Social Psychology, 13, 271–311. Sedikides, C. (1995). Central and peripheral self-conceptions are differentially influenced by mood: Tests of the differential sensitivity hypothesis. Journal of Personality and Social Psychology, 69(4). https://doi.org/10.1037/0022-3514.69.4.759 Sedikides, C., & Green, J. D. (2001). Chapter 7. Affective influences on the self-concept: Qualifying the mood-congruency principle. In J. P. Forgas (Ed.), Handbook of Affect and Social Cognition (pp. 146–161). Mahwah, NJ: L. Erlbaum Associates. Smeding, A., Dompnier, B., & Darnon, C. (2017). Individual differences in perceived social desirability of openness to experience: A new framework for social desirability responding in personality research. Personality and Individual Differences, 113, 155–160. https://doi.org/10.1016/j.paid.2017.03.028 Soldz, S., & Vaillant, G. E. (1999). The Big Five Personality Traits and the Life Course: A 45-Year Longitudinal Study. Journal of Research in Personality, 33(2), 208–232. https://doi.org/10.1006/jrpe.1999.2243 88 Solis, E., Antypa, N., Conijn, J. M., Kelderman, H., & Van der Does, W. (2017). Psychometric properties of the Leiden Index of Depression Sensitivity (LEIDS). Psychological Assessment, 29(2), 158–171. https://doi.org/10.1037/pas0000326 Spinhoven, P., Does, W. van der, Ormel, J., Zitman, F. G., & Penninx, B. W. (2013). Confounding of Big Five Personality Assessments in Emotional Disorders by Comorbidity and Current Disorder. European Journal of Personality, 27(4), 389–397. https://doi.org/10.1002/per.1885 Starcevic, V., Uhlenhuth, E. H., Fallon, S., & Pathak, D. (1996). Personality dimensions in panic disorder and generalized anxiety disorder. Journal of Affective Disorders, 37(2), 75–79. https://doi.org/10.1016/0165-0327(95)00058-5 Steffens, M. C., & König, S. S. (2006). Predicting spontaneous big five behavior with implicit association tests. European Journal of Psychological Assessment, 22(1), 13–20. https://doi.org/10.1027/1015-5759.22.1.13 Svrakic, D. M., Przybeck, T. R., & Cloninger, R. C. (1992). Mood states and personality traits. Journal of Affective Disorders, 24(4), 217–226. https://doi.org/10.1016/0165-0327(92)90106-G Tang TZ, DeRubeis RJ, Hollon SD, Amsterdam J, Shelton R, & Schalet B. (2009). Personality change during depression treatment: A placebo-controlled trial. Archives of General Psychiatry, 66(12), 1322–1330. https://doi.org/10.1001/archgenpsychiatry.2009.166 Tobin, S. J., & Tidwell, J. (2013). The role of task difficulty and affect activation level in the use of affect as information. Journal of Experimental Social Psychology, 49(2), 250–253. https://doi.org/10.1016/j.jesp.2012.11.011 89 Tupes, E. C., & Christal, R. E. (1961). Recurrent personality factors based on trait ratings (No. ASD-TR-61-97). PERSONNEL RESEARCH LAB LACKLAND AFB TX. Tupes, E. C., & Christal, R. E. (1992). Recurrent Personality Factors Based on Trait Ratings. Journal of Personality, 60(2), 225–251. https://doi.org/10.1111/j.1467-6494.1992.tb00973.x Van der Does, W. (2002). Cognitive reactivity to sad mood: structure and validity of a new measure. Behaviour Research and Therapy, 40(1), 105–119. https://doi.org/10.1016/S0005-7967(00)00111-X Watkins, P. C., Vache, K., Verney, S. P., & Mathews, A. (1996). Unconscious mood-congruent memory bias in depression. Journal of Abnormal Psychology, 105(1), 34–41. https://doi.org/10.1037/0021-843X.105.1.34 Watson, D., & Clark, L. A. (1992). On Traits and Temperament: General and Specific Factors of Emotional Experience and Their Relation to the Five-Factor Model. Journal of Personality, 60(2), 441–476. https://doi.org/10.1111/j.1467-6494.1992.tb00980.x Widiger, T. A. (2011). Personality and psychopathology. World Psychiatry, 10(2), 103–106. https://doi.org/10.1002/j.2051-5545.2011.tb00024.x Widiger, T. A., Verheul, R., & van den Brink, W. (1999). Personality and Psychopathology. In L. A. Pervin & O. P. John (Eds.), Handbook of Personality (2nd ed., pp. 347–366). Elsevier. Wiggins, J. S. (1968). Personality Structure. Annual Review of Psychology, 19(1), 293–350. https://doi.org/10.1146/annurev.ps.19.020168.001453 Wiggins, J. S., & Pincus, A. L. (1992). Personality: Structure and Assessment. Annual Review of Psychology, 43(1), 473–504. https://doi.org/10.1146/annurev.ps.43.020192.002353 90 Wood, D., & Wortman, J. (2012). Trait Means and Desirabilities as Artifactual and Real Sources of Differential Stability of Personality Traits. Journal of Personality, 80(3), 665–701. https://doi.org/10.1111/j.1467-6494.2011.00740.x Wright, J., & Mischel, W. (1982). Influence of affect on cognitive social learning person variables. Journal of Personality and Social Psychology, 43(5), 901–914. https://doi.org/10.1037/0022-3514.43.5.901 Yap, S. C. Y., Wortman, J., Anusic, I., Baker, S. G., Scherer, L. D., Donnellan, M. B., & Lucas, R. E. (2017). The effect of mood on judgments of subjective well-being: Nine tests of the judgment model. Journal of Personality and Social Psychology, 113(6), 939–961. https://doi.org/10.1037/pspp0000115 Zanarini, M. C., Vujanovic, A. A., Parachini, E. A., Boulanger, J. L., Frankenburg, F. R., & Hennen, J. (2003). A screening measure for BPD: The McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD). Journal of Personality Disorders, 16, 568–573. https://doi.org/10.1521/pedi.17.6.568.25355 Zuckerman, M. (1998). Psychobiological Theories of Personality. In D. F. Barone, M. Hersen, & V. B. V. Hasselt (Eds.), Advanced Personality (pp. 123–154). Springer US. https://doi.org/10.1007/978-1-4419-8580-4_6
Thesis/Dissertation
eng
Psychology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
The influence of mood on self-reported personality
Text