When do students ask why? Examining the precursors and outcomes of causal search among first-year college students

Contemporary Educational Psychology 36 (2011) 201–211

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Contemporary Educational Psychology journal homepage: www.elsevier.com/locate/cedpsych

When do students ask why? Examining the precursors and outcomes of causal search among first-year college students Robert H. Stupnisky a,*, Tara L. Stewart b, Lia M. Daniels c, Raymond P. Perry b a

University of North Dakota, Grand Forks, ND, USA University of Manitoba, Winnipeg, Canada c University of Alberta, Edmonton, Canada b

a r t i c l e

i n f o

Article history: Available online 26 June 2010 Keywords: Causal search Precursors Attribution Emotion Academic achievement

a b s t r a c t It has been theorized that students are most likely to ask why following unexpected, negative, and/or important events (Weiner, 1985); however, the unique contribution of these precursors to causal search and the resultant cognitions, emotions, and behaviors remain largely unclear. In the current study we examined causal search regarding test outcomes among 371 first-year college students. Responses to hypothetical scenarios indicated that unexpected events, and unexpected/negative events in combination, would elicit the most causal search. Based on performance on an actual test, precursors measured prior to the test indicated negative test outcomes elicited the greatest causal search. Alternatively, precursors measured following the test indicated a similar pattern to the scenarios. In each instance, event importance was also found to positively predict casual search. Overall, the results suggest that the exclusion of relevant precursors, self-serving biases, and divergent methodologies may have resulted in the discrepancies of previous research on causal search precursors. Finally, students who engaged in more causal search made more ability, test difficulty, and luck attributions, fewer effort attributions, experienced less pride and more shame, guilt, regret, and anger, and received poorer grades. The internal/ uncontrollable attributional pattern suggests that first-year college students who are at-risk of de-motivating cognitions, emotions, and behaviors could be supported with cognitive interventions such as attributional retraining. Ó 2010 Elsevier Inc. All rights reserved.

1. Introduction Many of the outcomes that students experience during their first year of college, most notably receiving a test grade, may result in them asking ‘‘why did this happen?” In Weiner’s attribution theory (1985, 2006) this initial process of searching for an attribution is called causal search.1 According to Weiner, attributions resulting from causal search can be classified into three orthogonal dimensions: locus of causality (internal/external), stability (stable/unstable), and controllability (controllable/uncontrollable). The unique combination of these dimensions determines the emotional, motiva-

* Corresponding author. Address: University of North Dakota, 231 Centennial Drive Stop 7189, Dakota Hall – Teaching and Learning Department, Grand Forks, ND 58202, USA. Fax: +1 701 777 3246. E-mail addresses: [email protected], [email protected] (R.H. Stupnisky), [email protected] (T.L. Stewart), [email protected] (L.M. Daniels), [email protected] (R.P. Perry). 1 Causal search has also been referred to as attributional activity (Keinan & Sivan, 2001) and causal reasoning (Bohner, Bless, Schwarz, & Strack, 1988), and similar cognitive processes have been studied that are highly comparable to causal search, including counterfactual thinking (Sanna & Turley, 1996) and skepticism (Ditto, Munro, Apanovitch, Scepansky, & Lockhart, 2003). 0361-476X/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.cedpsych.2010.06.004

tional, and behavioral consequences for a given attribution. Although numerous empirical studies have demonstrated that people spontaneously engage in causal search (see Weiner, 1983 for a review), important questions remain regarding both the determinants and consequences of causal search. Understanding when college students engage in causal search is critical because the attributional process that unfolds afterward can determine future academic success. Regarding the determinants of causal search, cognitive psychologists have long contended that people lack the mental capacity to perform an exhaustive search for attributions following every event, outcome, or circumstance (Broadbent, 1958; Deutsch & Deutsch, 1963; Treisman, 1969). Given this, only a select number of events having certain characteristics will trigger individuals to engage in causal search. Weiner (1985) suggested three event characteristics that serve as precursors to causal search: (1) unexpected, (2) negative, and/or (3) important events. Numerous studies have examined whether these event characteristics initiate causal search; however, inconsistent results have prevented firm conclusions. Therefore, one objective of the current study was to test the capacity of Weiner’s three precursors to elicit causal search while accounting for several factors that may have led to past irregularities. Regarding the consequences of causal search, only

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a limited amount of research has examined whether causal search is associated with beneficial or detrimental patterns of attributions, emotions, and behaviors. Understanding the outcomes associated with causal search was a second objective of the current study because these consequences may determine student success. 1.1. The precursors to causal search Heider (1958) posited that we undertake a search for causal attributions in order to make sense of our daily lives. Although many factors may influence when individuals engage in causal search, the three precursors suggested by Weiner (1985; unexpected, negative, important events) have absorbed the majority of researchers’ attention and consequently are the focus of the current study.2 The previous research on Weiner’s three precursors to causal search are outlined below, followed by a discussion of possible reasons for past empirical inconsistencies. Unexpected events are believed to elicit causal search because they are a deviation from a norm that requires an explanation in order to avoid future inconsistencies with expectations. To test this premise, Kanazawa (1992) asked participants to listen to and retell a story as if they were explaining it to a friend who had not heard the story before. Participants spontaneously added more causal statements to those stories that contained unexpected events compared to stories that contained expected events. Similarly, Pyszczynski and Greenberg (1981) found that individuals were more likely to engage in a causal search after observing an unexpected than an expected helping act. These and other studies support the proposition that unexpected events prompt causal search (see also Clary & Tesser, 1983; Ditto et al., 2003; Gendolla & Koller, 2001; Hastie, 1984; Sanna & Turley, 1996). Alternatively, other empirical evidence suggests that unexpected events do not lead to greater amounts of causal search. For example, Schoeneman, van Uchelen, Stonebrink, and Cheek (1986) found that college undergraduates remembered asking themselves no greater number of attributional questions (e.g., ‘‘What caused this event”) when recalling unexpected versus expected past events. Similarly, Bohner et al. (1988) found that participants who received an unexpected result on a professional skills test did not engage in more causal search than those who received an expected result (see also Gilovich, 1983; Lau, 1984; Moeller & Koeller, 2000). In order to shed light on these empirical inconsistencies, this study examined the role of event unexpectedness in the initiation of causal search. Event valence refers to the positive or negative quality of an event and has been a heavily debated precursor to causal search. Weiner (1985) contends that negative events instigate causal search via the Law of Effect: organisms are motivated to terminate or prevent a negative state of affairs (Thorndike, 1905). Furthermore, the quantity of processing view asserts that people are more likely to accept information that is consistent with preferred judgments, thereby initiating less cognitive analyses for positive events (Ditto & Lopez, 1992). Indeed, Ditto et al. (2003) found that participants who were given unfavorable medical results were more skeptical of the validity of the results and were more likely to recheck them than those who received favorable results. The 2 Several event characteristics other than those specified in Weiner’s (1985) theory have been examined as precursors to causal search; however most can be subsumed under those posited by Weiner. For example, a surprising event (Gendolla & Koller, 2001) is analogous to an unexpected event, although the term surprising does have a slightly more positive connotation. Similarly, a stress-causing event (Keinan & Sivan, 2001) could be a negative, unexpected, and/or important event. Also, a serious event (Hall, French, & Marteau, 2003) is highly similar to an important event, although ‘serious’ has a more negative undertone than importance. Event novelty has also been suggested as a precursor (Wong & Weiner, 1981), however, was not examined in the current study to reduce complexity and maintain consistency with previous studies.

capacity of negative events to elicit causal search has been found across a range of domains, including academic test results (Moeller & Koeller, 1999), athletic events (Lau, 1984), gambling (Gilovich, 1983), and marriage problems (Holtzworth-Munroe & Jacobson, 1985). Alternatively, some researchers have contended that positively and negatively valenced events elicit similar levels of causal search. In direct rebuttal to Weiner’s (1985) assertion, Kanazawa (1992) cited the second half of the Law of Effect: organisms are equally motivated to continue or to increase a positive state of affairs (Thorndike, 1905). Kanazawa provided empirical evidence for this position: participants in his study made the same number of causal attributions when retelling negative and positive stories. Hastie (1984) also found that participants were equally likely to complete partial sentences describing positive or negative events with explanations or attributions. Due to these discrepancies, the current study also assessed the effect of event valence to clarify the impact of this potential precursor on causal search. Finally, event importance has often been overlooked as a precursor to causal search. This is surprising given that the logic underpinning importance as a precursor is reasonably straightforward: individuals will focus more cognitive energy on events that are personally valued and less attention on events that are less relevant. Although event importance has received very little direct empirical attention, evidence for its effectiveness as a precursor can be seen indirectly. In Weiner’s (1983) review of the causal search literature, for example, he cited numerous studies that analyzed written material from newspaper articles, business reports, letters, and personal journals to determine whether unexpected and/or negative events led to more causal search. Interestingly, none of these studies accounted for the impact of event importance, even though it seems doubtful that any of the articles and letters would have been written unless they were about important events. Similarly, Wong and Weiner (1981) asked students to imagine that they had expectedly or unexpectedly succeeded on or failed a test, and then inquired, ‘‘What questions, if any, would you most likely ask yourself?” It seems unlikely that the same number of questions (i.e., causal search) would occur following a highly important test such as a final exam, compared to a less important test such as one given only for practice; however, this was not tested. Other studies failed to account for event importance by asking participants to retell stories that lack personal meaning (Kanazawa, 1992), presenting uncommon hypothetical scenarios (e.g., controlling an oil company; Moeller & Koeller, 2000), and giving small compensation for participation (e.g., $1.50; Hastie, 1984). Gendolla and Koller (2001) conducted one of the few studies that included event importance as a precursor, and did in fact find importance to be positively associated with causal search. Based on the limited empirical evidence for importance as a precursor, we also sought to test this variable as a predictor of causal search among college students. 1.2. Potential reasons for past inconsistencies Clearly, previous results regarding the effectiveness of event unexpectedness, valence, and importance to elicit causal search have been inconclusive. Several possible explanations for these inconsistencies are offered here. First, the frequent testing of only one or two of these precursors, and not all three of the precursors concurrently, may play a role. In several past studies, precursors have been found to interact or work together to predict causal search. For example, Wong and Weiner (1981) noted a significant unexpectedness by valence interaction whereby unexpected/negative events elicited the greatest amount of causal search; however, they did not test if importance also contributed to this effect. Only

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recently were all three of these factors tested together when Gendolla and Koller (2001) used a hypothetical scenario to demonstrate that causal search was most strongly predicted with important, surprising (i.e., unexpected), and negative events. This result suggests that testing only one or two of these precursors may lead to an incomplete picture of when causal search is likely to occur. A second possible reason for past inconsistencies in this literature is that individuals may adjust their expectations, perceived level of success, and reported value of events after they have taken place. For instance, after a test students may indicate that a particular grade was more unexpected, less negative, and/or less important in order to maintain their self-esteem and reduce their anxiety about a poor result. One way to test if such a self-serving bias affects self-reported causal search would be to compare precursor effects on causal search measured before and after an event. A third potential reason for inconsistencies across previous studies is the use of divergent research methods. In the past, two distinct methods of empirically testing causal search have been prominent: (1) studies based on hypothetical events, and (2) studies based on real-life events (i.e., in vivo). The use of hypothetical scenarios stems from the criticism that the process of measuring causal search is reactive, meaning the scale used to assess causal search was more responsible for eliciting causal search than the actual event itself (Weiner, 1983). To deflect this criticism, hypothetical scenarios were implemented because they are believed to be less reactive, and additionally allow for easier manipulation of the independent variables. However, hypothetical scenarios suffer from reduced ecological validity because they typically measure participants’ anticipated beliefs or intuitions, instead of their actual reactions. Measures of causal search following actual events have the potential for greater ecological validity. Several studies have compared how differences between hypothetical scenarios and real-life events influence causal search. Ditto et al. (2003) found that different precursors elicited causal search depending on whether they were assessed following scenarios (unexpected effect, no valence effect) or real-life events (unexpected effect and valence effect). Alternatively, several studies have found that the same precursors elicit causal search across both methodologies (unexpected effect and valence effect; Sanna & Turley, 1996; Wong & Weiner, 1981). However, none of the studies comparing hypothetical and real-life outcomes have contrasted the methodologies within a single group of participants, nor have they assessed the impact of event importance as a precursor to causal search. To test these explanations for the inconsistencies of past research, the current study examined: (1) the effectiveness of all three of Weiner’s (1985) precursors to initiate a causal search, (2) measured before and after an actual course test, and (3) using both hypothetical test scenarios and reported causal search regarding an actual test. 1.3. The consequences of causal search for students Causal search is believed to be a common and critical process for first-year college students because they are in an environment filled with potential triggers of causal search. For example, students are faced with numerous novel tasks that can lead to unexpected results, such as selecting their major, taking a broad range of new courses, using different studying strategies and technologies, having to secure financial resources, finding new housing, and changes to social circles and friends. Also, a great deal of data suggests that negative experiences for incoming students are common. Specifically, a review of the literature found first-year college attrition in Canada and the United States to be approximately 20– 25%, while over the long term only about 60% of students beginning their studies are expected to graduate (Grayson & Grayson, 2003). Similar data from the Canadian Youth in Transition Study

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found only 58% of 18–20 years old participate in post-secondary education (21% in university, 26% college, 11% other), and two years later 15% of those students have dropped out (Lambert, Zeman, Allen, & Bussière, 2004). Finally, college outcomes are highly important as they represent the pursuit of academic and professional aspirations at the risk of costly tuition. With so many opportunities to engage in causal search at this time, the attributions, emotions, and behaviors that follow are key determinants of students’ future academic success. Two plausible alternatives exist concerning the cognitive and behavioral outcomes of first-year college students engaged in causal search. First, causal search is beneficial. This would be the case if students who sought out explanations for their performances typically chose adaptive attributions (internal, controllable, unstable, such as lack of effort), which resulted in positive emotions (e.g., hope, pride) and subsequent academic success. This alternative has an evolutionary basis whereby causal search is a spontaneous mechanism leading to perseverance and self-improvement. A second alternative is that high levels of causal search promote detrimental outcomes among college students. This would be the case if students engaged in causal search commonly settle on attributions less conducive to motivation (external, uncontrollable, stable, such as low professor quality), resulting in negative emotions (e.g., anger, helplessness), and subsequently suffered poor academic performances. Wong and Weiner (1981) suggested that this pattern of attributions following causal search would reflect defensiveness and an attempt to preserve self-esteem. Unfortunately this alternative may be more common as the first year of college often results in a ‘‘paradox of failure” wherein many intelligent, talented, successful high school students struggle with the transition to college (Perry, 2003). These students have difficulties adjusting to the increased demands for academic autonomy, and subsequently display a maladaptive pattern of cognitions, emotions, and achievement. The extent to which students engage in causal search may relate to how well students are adjusting to their new college environment. Previous empirical research on the relationship between causal search and resulting cognitions, emotions, and behaviors is sparse. Wong and Weiner (1981) found that causal search leads to internal attributions following failure, but toward external attributions following success. This suggests an adaptive pattern of attributions for students; however, the reverse finding of the oft-reported hedonic bias suggests that the hypothetical method may have resulted in response bias. Schoeneman et al. (1986) found that causal search following failure prompted participants to make more external, unstable, and uncontrollable attributions, which is a very de-motivating pattern. Additionally, research on rumination (i.e., focusing passively and repetitively on one’s symptoms of distress and on the meanings of those symptoms without taking action to correct the problems) has found that dysphoric individuals who engage in rumination are prone to make pessimistic attributions, show diminished concentration, and to perform poorly on academic achievement related tasks (Lyubomirsky, Kasri, & Zehm, 2003; Lyubomirsky & Nolen-Hoeksema, 1995). Due to limited and conflicting previous findings, a more thorough test of the outcomes associated with causal search is needed. The current study examined the outcomes related to causal search among a sample of first-year college students, and extended previous studies by examining a wide range of cognitions and emotions, as well as academic achievement. 1.4. Research objectives and hypotheses Fig. 1 outlines our hypothesized model of causal search following a course test. The first research objective involved examining the degree to which Weiner’s (1985) three precursors, namely

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Attributions

Causal search precursors: Course Test

If test outcome is… - unexpected - negative - and/or important

Causal Search

Emotions

Course %, GPA

Fig. 1. Hypothesized model of causal search precursors and outcomes.

unexpectedness, valence, and/or importance of a test outcome, elicit causal search. The predictive effects of each of these precursors on causal search were examined individually (i.e., first-order effects) and in combination (i.e., interactions). Furthermore, each precursor was measured both before and after the course test, and assessed using both real-life (in vivo) and hypothetical scenario methods. Based on Weiner’s theory and previous empirical findings (e.g., Ditto et al., 2003; Gendolla & Koller, 2001; Kanazawa, 1992), it was hypothesized that all three precursors would elicit causal search; however, it was expected that some inconsistencies in the effects would appear due to self-serving bias and methodological differentiation. The second research objective was designed to examine the academic outcomes associated with causal search. Three categories of student outcomes were examined based on Weiner’s attribution theory, specifically attributions, emotions, and academic achievement. Based on previous research (Lyubomirsky & Nolen-Hoeksema, 1995; Lyubomirsky et al., 2003; Schoeneman et al., 1986), it was hypothesized that high levels of causal search would be associated with detrimental attributions (i.e., uncontrollable and stable), less positive and more negative emotions, and poorer academic performance. If this hypothesis was supported, it could be inferred that causal search is linked to the high attrition rate of first-year college students (Grayson & Grayson, 2003; Lambert et al., 2004) because of its association with detrimental outcomes. The remaining causal paths in the hypothesized model linking students’ attributions, emotions, and academic achievement are based on Weiner’s (1985) theory, have been established in previous empirical research (e.g., Perry, Stupnisky, Daniels, & Haynes, 2008; Van Overwalle, Mervielde, & De Schutyer, 1995), and thus were not tested in the current study. 2. Methods 2.1. Participants Participants were first-year college students at a Canadian Midwestern research intensive university enrolled in a two-semester Introductory Psychology course. To be included in the study each student was required to complete two questionnaires near the beginning of the academic year, namely a pre-test questionnaire before their first Introductory Psychology test and a post-test questionnaire shortly after receiving their grades from that first test, and consent to release their grades to the researchers. In exchange participants received research credits for their course. The sample consisted of 371 students, 243 females and 126 males (two did not indicate gender), most of whom were between the ages of 17 and 22 (86.8%). 2.2. Measures 2.2.1. Hypothetical scenarios Prior to their first Introductory Psychology course test, participants responded to a series of hypothetical scenarios that involved

receiving a grade on a test. This procedure, similar to that used by Wong and Weiner (1981), involved eight scenarios that each presented a unique combination of Weiner’s (1985) three precursors to causal search: expected/unexpected  positive/negative  highly important/less important (2  2  2). After reading each scenario, students rated the amount of time they would spend thinking about why they received that grade (1 = No time, 7 = Lots of time). Two of the eight scenarios are as follows, with italics added here to highlight the terms that varied between conditions in order to manipulate valence and unexpectedness: ‘‘You did very well on the test, and it was expected because you usually do well in that subject” (positive-expected scenario), ‘‘You failed the test, and it was unexpected because you usually do well in that subject” (negative-unexpected scenario). To manipulate importance, each of the four valence  expectedness combinations was presented for a test ‘‘worth 40%” or ‘‘worth only 5%” of students’ final grades. It was believed that students would regard a test worth more of their final course grade as more important than a test worth less.3 2.2.2. Event unexpectedness For the in vivo (i.e., real-life) measure of pre-test unexpectedness, prior to students’ first course test they were asked, ‘‘What percentage do you expect to get on your first Introductory Psychology test?” (1 = 50% or less, 2 = 51–55%, 3 = 56–60%, 4 = 61–65%, 5 = 66–70%, 6 = 71–76%, 7 = 76–80%, 8 = 81–85%, 9 = 86–90%, 10 = 91–100%). The mean expected first test percentage was 7.58 (SD = 1.64; 95% CI = 7.41–7.75) or approximately 80%. Students’ average on the actual first test was 70.83% (SD = 15.89; 95% CI = 69.21–72.45). The actual test percentages were recoded into the same categories as students’ responses on the expected test percentage item (M = 5.71, SD = 2.88; 95% CI = 5.42–6.00). Students’ actual scores were subtracted from their expected scores to create a difference score that reflected unexpectedness: positive numbers indicated an unexpected positive test score (i.e., students did better than expected) and negative numbers indicated an unexpected negative test score (i.e., students did worse than expected). Based on the mean (M = 1.88, SD = 2.91, range = 9.00– 6.00), most students received a test score less than what they were expecting. The absolute value of the difference score was then taken in order to eliminate the valence component. For the final measure, low scores indicated an expected event and high scores indicated an unexpected event: M = 2.70, SD = 2.16, range = 0.00– 9.00. For the post-test in vivo unexpectedness measure, following students’ test they were asked, ‘‘How unexpected was your grade on your first Introductory Psychology test?” (1 = Exactly what I 3 Two limitations of the causal search scenarios are acknowledged. First, due to a restriction in the manner with which the data were collected, it was not possible to counterbalance the order in which the scenarios were presented. Thus, item order effects may be present. Second, the scenarios were only assessed prior to the course test and not afterwards. Therefore, conclusions regarding the effect of self-serving bias on the inconsistencies of previous studies using scenarios to assess the precursors of causal search were not possible. Nevertheless, the hypothetical scenario results were presented because they are an opportunity to contrast the results based on the actual test.

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expected, 7 = Very unexpected). The mean of the post-test unexpectedness measure indicated that most students received a score that was somewhat unexpected: M = 4.01, SD = 1.68, range = 1.00–7.00. 2.2.3. Event valence For the pre-test in vivo valence measure, prior to students course test they were asked: ‘‘With regards to your first Introductory Psychology test, for you personally doing well means a grade of _____ or more” (1 = 50%, 2 = 55%, 3 = 60%, 4 = 65%, 5 = 70%, 6 = 75%, 7 = 80%, 8 = 85%, 9 = 90%, 10 = 95%). Responses were recoded into the respective percentage categories (1 = 50, 2 = 55, etc.): M = 81.19, SD = 7.20, range = 55–95, 95% CI = 80.46–81.92. Students’ responses to the perception of success item were then subtracted from their first test scores. For the final pre-test valence measure, high positive numbers indicated that the first test was a successful (positive) event, and high negative numbers indicated that the test was an unsuccessful (negative) event. Based on the mean (M = 10.43, SD = 14.86, range = 56.11–39.30), most of the students received a score less than what they would consider ‘‘doing well” on their first test. For the post-test in vivo valence measure, following students’ course test they were asked: ‘‘How successful do you feel you are in your Introductory Psychology course this year?” (1 = Very unsuccessful, 10 = Very successful). The mean of the post-test valence item indicated that most students felt that their first test was somewhat positive: M = 6.57, SD = 1.88, range = 1.00–10.00. 2.2.4. Event importance For the pre-test in vivo importance measure, four items measuring importance were administered prior to students’ first course test. The first two items were measured on a seven-point scale (1 = Not at all important, 7 = Very important): ‘‘How important do you consider your first Introductory Psychology test grade?” (M = 6.35, SD = .86, range = 2–7), and ‘‘How important do you consider your Introductory Psychology course?” (M = 5.77, SD = 1.05, range = 2–7). The third and fourth items were measured on a different seven-point scale (1 = Much less important, 7 = Much more important): ‘‘Compared to your first test grade in other courses, how important do you consider your first Introductory Psychology test grade?” (M = 4.44, SD = 1.03, range = 2–7), and on the same scale ‘‘Compared to other courses you are taking, how important do you consider your Introductory Psychology course” (M = 4.30, SD = 1.13, range = 2–7). The four importance items were summed to create the pre-test importance measure: M = 20.88, SD = 2.84, range = 12–28, Cronbach’s a = .64. For the post-test in vivo importance measure, the same four importance items were assessed after students’ first course test. Much like the pre-test measure the post-test importance measure indicated that most students felt their test to be important, although to a slightly lesser extent: M = 19.04, SD = 3.78, range = 5–28, Cronbach’s a = .75. 2.2.5. Causal search As part of the post-test questionnaire, students responded to the item, ‘‘I have spent a great deal of time and effort searching for the reason(s) why I received the mark I did on my first Introductory Psychology test” (1 = Not at all true of me, 4 = Moderately true of me, 7 = Very true of me): M = 3.22, SD = 1.67, range = 1–7. The item utilizes a retrospective, critical incident method (Schoeneman et al., 1986) to assess causal search by having an easy-to-read item about a past event which allowed students to quickly reflect, respond, and limit reactivity. Higher scores indicated higher levels of causal search. As a convergent validity check, the causal search measure was correlated with several self-report behavioral measures believed to be indicative of individuals engaged in causal search. Causal search was in fact found to have a significant (p < .01) positive correlation with students

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wanting to talk to the professor about the test (r = .20), to check over their class notes and text (r = .30), to ask other students how they did (r = .16), and to review strategies used in preparing for the test (r = .30). 2.2.6. Causal attributions In the post-test questionnaire students were asked, ‘‘When your performance on a test or assignment is poor, or less than you were expecting, to what extent do each of the following explain your performance?” Six attributions for academic performance in college followed and students rated them on a Likert-style scale (1 = Not at all, 7 = Very much so): lack of ability (M = 2.80, SD = 1.59), lack of effort (M = 5.15, SD = 1.73), lack of a strategy (M = 4.24, SD = 1.54), just bad luck (M = 2.34, SD = 1.42), poor quality of teaching (M = 3.29, SD = 1.46), and difficulty level of the test or assignment (M = 4.11, SD = 1.48). These specific attributions were selected due to their demonstrated relevance in achievement settings (Perry et al., 2008; Van Overwalle, 1989). 2.2.7. Academic emotions In the post-test questionnaire students were asked to ‘‘Rate the extent to which each of the following emotions describes how you feel about your performance in your Introductory Psychology course so far this year”. Seven course-specific emotions outlined in Weiner’s (1985) attribution theory were assessed using a Likertstyle scale (1 = Not at all, 10 = Very much so): hope (M = 5.39, SD = 1.34), pride (M = 4.12, SD = 1.73), shame (M = 2.25, SD = 1.58), guilt (M = 2.51, SD = 1.57), helpless (M = 2.19, SD = 1.47), anger (M = 2.39, SD = 1.56), and regret (M = 2.86, SD = 1.82). 2.2.8. Academic performance Students’ final Introductory Psychology course percentage was calculated by averaging students’ scores on all course tests completed after their first test (M = 68.46, SD = 14.94). Test 1 was not included in the course percentage variable in order to measure academic performance after students indicated their level of causal search. Additionally, students’ cumulative grade point averages (GPAs) were obtained from institutional records after the conclusion of the academic year. GPA constitutes students’ average grade attained in all courses completed during their first year of college and is expressed as a numeric value based on the following scale: 0 = F, 1 = D, 2 = C, 2.5 = C+, 3 = B, 3.5 = B+, 4 = A, 4.5 = A+ (M = 2.76, SD = 0.93). Finally, students’ self-reported overall final high school percentage was assessed to be used as a covariate to control for academic aptitude in students’ college academic performance measures (M = 72.27, SD = 21.56; Perry, Hladkyj, Pekrun, & Pelletier, 2001). High school academic performance was used instead of SAT and ACT scores because these standardized tests are not generally administered in Canadian high schools or used as admission criteria to Canadian universities. 2.3. Procedure A pre-test questionnaire was administered early in the first semester approximately one week prior to students’ first Introductory Psychology course test. The questionnaire contained a series of hypothetical achievement scenarios followed by several pre-test measures of the precursors to causal search (unexpectedness, valence, and importance). Several days afterward students completed their first course test and received their grade approximately one week later. The post-test questionnaire was administered several days after students’ feedback on their first course test and contained post-test measures of the three precursors to causal search, a measure of causal search relating to students’ actual course test, as well as attributions and achievement-related emotions. After the conclusion of the academic year, students’ test grades from

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their Introductory Psychology course were collected from their professors and their GPAs were obtained from institutional records.

3.1. Predicting causal search: hypothetical scenarios The impact of the three precursors on students’ causal search was assessed using an expectedness (unexpected/expected) by valence (negative/positive) by importance (low/high) 2  2  2 within-subjects ANOVA with Type III (marginal or orthogonal) sums of squares. The ANOVA grouped the scenarios according to a specific precursor when examining main effects, or according to different combinations of precursors when examining interactions, and compared the groups of scenarios on their mean levels of causal search. For example, when testing for a main effect of unexpectedness, the four scenarios containing an expected event were compared to the four scenarios containing an unexpected event on how much causal search they elicited. Partial-eta squared (partial-g2) was included as a measure of effect size to gauge the practical significance of each effect. Three significant main effects revealed that each of the three precursors predicted causal search (see Table 1). Unexpectedness had the largest effect on causal search, as students reported that they would think more about a test result if it was unexpected than if it was expected (Ms = 4.25 vs. 3.25, respectively). Event importance had the second largest effect, as students believed they would spend more time thinking about the result of a test that was worth 40% of their final grade (i.e., highly important) compared to a test worth only 5% (i.e., less important, Ms = 4.05 vs. 3.44, respectively). Event valance had the smallest main effect, as students said they would think more about a failure than a success (Ms = 4.03 vs. 3.46, respectively). Several interactions emerged among the three precursors, the strongest of which was between event unexpectedness and valence (see Fig. 2). This interaction was probed using pairedsamples t-tests, assessed at a Bonferroni adjusted alpha level (a = .05/4 = .01). Simple main effect tests revealed that expected events led to the same low levels of causal search regardless of valence (MExp/Neg = 3.14 vs. MExp/Pos = 3.35), t(369) = 1.52, p > .01. However, unexpected/negative events were found to result in significantly more time devoted to causal search than unexpected/ positive events (MUnexp/Neg = 4.92 vs. MUnexp/Pos = 3.58), t(362) = 13.31, p < .001. Although the other two-way interactions and the three-way were statistically significant (p < .001), the meaningfulness of these effects were minimal relative to the unexpectedness/ valence interaction and are not further discussed here. 3.2. Predicting causal search: in vivo test 3.2.1. Correlations Each of the respective precursors showed a moderate positive correlation between the pre- and post-test measurements (see Table 1 F table for scenario main effects and interactions on causal search. Effect

MSE

MSW

F

p

Unexpectedness (UNEXP) Valence (VAL) Importance (IMP) UNEXP  VAL UNEXP  IMP VAL  IMP UNEXP  VAL  IMP

4.03 4.62 1.89 3.97 0.86 0.85 0.71

724.00 231.05 266.19 433.15 9.75 7.36 6.02

179.66 49.97 141.20 109.00 11.37 8.67 8.48

.000 .000 .000 .000 .000 .000 .000

Note: Numerator df = 1 and denominator df = 361 for all tests.

Partial-g2 .33 .12 .28 .23 .03 .02 .02

Negative 5.00

Causal Search

3. Results

5.50 4.92

Positive

4.50

4.00 3.58 3.50

3.35 3.14

3.00

2.50 Unexpected

Expected

Fig. 2. Causal search scenario unexpectedness by valence interaction.

Table 2). The moderate, rather than large, size of the pre- and post-test correlations was anticipated because this was the first university test for many of the students and they would have had little experience on which to base their pre-test responses. Nevertheless, the positive correlations were strong enough to provide support for the concurrent validity of the precursor measures. Several noteworthy patterns emerged when comparing the intercorrelations among the pre- and post-test precursor measures and were subsequently tested for statistical significance. First, event unexpectedness and valence were negatively correlated when measured both prior to and after the test, indicating that unexpected events tended to be negative. However, this correlation was much smaller among the post-test measures (Dr = .36, ZPF = 7.88, p < .001; confirmed using the procedure outlined by Raghunathan, Rosenthal, & Rubin, 1996). This change suggests that, after the test, students reinterpreted unexpected events to be less negative than they originally reported they would be. Second, pretest importance had a positive correlation with unexpectedness and a negative correlation with valence. However, the post-test importance precursor did not significantly relate to unexpectedness and had a positive correlation with valence, and both of these changes were statistically significant (Dr = .16, ZPF = 2.48, p < .05; Dr = .34, ZPF = 5.43, p < .001, respectively). The shift in the importance/valence correlation from negative (pre-test) to positive (post-test) suggests that after the test result was known, many students altered their perceived value of their test to be in line with their received grade (i.e., better performance = more important test, poorer performance = less important test). The correlations of the precursors with causal search were quite consistent from pre- to post-test, but again differences were found. Among the pre-test measures, valence had the largest correlation with causal search suggesting that causal search occurs most strongly following negative events. Unexpectedness had the next strongest correlation as more causal search occurred among students experiencing unexpected events. Finally, importance had a small positive correlation with causal search. However, when the post-test measures were examined, unexpectedness had the largest correlation with causal search, followed by importance, and valence had the smallest relationship. This was attributed to a significant decrease in the correlation between valence and causal search from the pre- to post-test measurement (Dr = .14, z = 2.57, p < .01), whereas no significant pre- to post-test changes were found for unexpectedness or importance (confirmed using the procedure outlined by Meng, Rosenthal, & Rubin, 1992). Taken together, the differences among the pre- and post-test correlations suggest that relationships among the precursors are different prior to versus after an outcome.

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R.H. Stupnisky et al. / Contemporary Educational Psychology 36 (2011) 201–211 Table 2 Zero-order correlations among demographics, precursors to causal search, and causal search.

*

Variable

1

2

3

4

5

6

7

1. 2. 3. 4. 5. 6. 7. 8. 9.

– .07 .05 .08 .19* .01 .02 .16* .01

– .01 .02 .03 .06 .01 .13* .09

– .72* .22* .41* .45* .03 .27*

– .21* .30* .52* .00 .29*

– .13 .02 .49* .19*

– .36* .06 .32*

– .13* .15*

Age Gender (being male) Pre-test unexpectedness Pre-test valence (pos) Pre-test importance Post-test unexpectedness Post-test valence (pos) Post-test importance Causal search

8

9

– .23*

–

p  .01 (2-tailed).

first course test grade predicted causal search. Regression analyses were chosen instead of ANOVA in order to preserve the continuous nature of the variables (MacCallum, Zhang, Preacher, & Rucker, 2002). In Step 1, the individual effects of the three precursors on causal search were tested. In Step 2, two-way multiplicative interaction terms were created using centered variables to reduce multicollinearity (Cohen, Cohen, West, & Aiken, 2003) and were entered to determine if a particular combination of precursors predicted causal search. In Step 3, a three-way multiplicative interaction term was added. The pre-test precursors were examined first, followed by a separate analysis of the post-test precursors. For the pre-test precursors of causal search, in Step 1 event valence was the strongest predictor of causal search, indicating that negative events led students to engage in significantly more causal search (see Table 3). Event importance also had a significant effect, showing that more causal search was experienced by students who considered their first test to be important. Event unexpectedness did not significantly relate to causal search. Together the pre-test precursor first-order effects explained a small yet significant amount of the variance in causal search (10%). With the inclusion of the interaction terms in Steps 2 and 3, the effects of the individual precursors remained the same and none of the interactions were found to be significant. A different pattern of results was found for the post-test precursors of causal search. In Step 1, event unexpectedness was found to

3.2.2. Comparing pre- and post-test precursor responses The means of the pre- and post-test precursors to causal search were compared in order to examine potential differences in students’ responses before and after the test outcome. For unexpectedness, the post-test measure was first transformed to be on an equal scale to the pre-test item: M = 4.51, SD = 2.52, range = 0.00– 9.00. A paired-samples t-test revealed that students considered their test outcome to be significantly more unexpected when asked after the test than their pre-test expectations indicated it would be: t(354) = 13.29, p < .001. For valence, the pre-test unexpectedness measure was transformed to be on an equal scale with the post-test measure: M = 4.84, SD = 1.54, range = 1.00–10.00. A paired-samples t-test indicated that students felt significantly less negative about their performance after their test than their pre-test responses indicated they would feel: t(361) = 19.22, p < .001. Finally, for event importance a paired-samples t-test indicated that students’ felt that their first test was significantly less important afterwards: t(360) = 10.11, p < .001. Overall, after the test students reported their performance was significantly more unexpected, less negative, and less important than they had anticipated before the test. 3.2.3. Multiple regressions A three-step multiple regression determined how the pre- and post-test unexpectedness, valence, and importance of students’

Table 3 Regression coefficients and R2s for regressions on causal search. Variable

Pre-test precursors Unexpectedness Valence Importance UNEXP  VAL UNEXP  IMP VAL  IMP UNEXP  VAL  IMP R2 Post-test precursors Unexpectedness Valence Importance UNEXP  VAL UNEXP  IMP VAL  IMP UNEXP  VAL  IMP R2

Step 1

Step 2

B

SE B

b

.09 .02 .08

.06 .01 .03

.11 .18* .13*

Step 3

B

SE B

b

.09 .02 .08 .00 .01 .00

.06 .01 .03 .00 .02 .00

.11 .17* .14* .02 .05 .01

.10 .28 .09 .10

.05 .05 .02

.29*** .10* .22***

.16 2

B

SE B

b

.09 .02 .08 .00 .02 .00 .00

.06 .01 .04 .00 .02 .00 .00

.11 .21** .13* .02 .06 .02 .06 .10

.32 .06 .09 .06 .02 .02 .01

.05 .05 .02 .03 .01 .01 .01

.32*** .06 .21*** .12* .09 .11* .05 .17

.10 .30 .07 .09 .05 .02 .02

.05 .05 .02 .03 .01 .01

.31*** .07 .22*** .11* .08 .11* .17

Note: For pre-test precursors, adjusted R was significant at p < .001 in Step 1, however, it did not significantly increased in Steps 2 or 3. For post-test precursors, adjusted R2 was significant at p < .001 at Step 1, significantly increased in Step 2 (DR2 = .018, p = .055), however, did not increase in Step 3. * p < .05. ** p < .01. *** p = .001.

R.H. Stupnisky et al. / Contemporary Educational Psychology 36 (2011) 201–211

be the biggest predictor of causal search, as unexpected events predicted the most causal search. Event importance was the second strongest predictor as more important events were again found to be related to more causal search. Finally, event valence had a small negative effect, as negative events were associated with higher levels of causal search. Together the post-test precursor first-order effects explained a significant amount of the variance in causal search (16%). In Step 2, two significant two-way interactions were found; specifically, an unexpectedness by valence interaction and an importance by valence interaction. In Step 3 the three-way interaction was not significant. To examine the unexpectedness by valence interaction, two simple slopes were tested using the centered, unstandardized regression coefficients (Cohen et al., 2003). Two values of unexpectedness were chosen, one to represent the line for an unexpected test grade (one standard deviation above the mean = 1.68) and one to represent the line for an expected test grade (one standard deviation below the mean = 1.68). Two values of valence were also chosen to plot the lines, one representing a positive test grade (one standard deviation above the centered mean = 1.88) and one representing a negative test grade (one standard deviation below the centered mean = 1.88). The simple slope of the expected line was not significant, t(342) = 0.57, ns, revealing that students who received an expected test grade typically engaged in the same low level of causal search regardless of the valence of the grade (see Fig. 3). Alternatively, the unexpected line had a slope significantly different from zero, t(342) = 2.69, p < .01, indicating that students who received an unexpected test grade tended to engage in more causal search when the event was negative than when it was positive. The same simple slopes procedure was utilized to probe the importance by valence interaction. First, values of importance were chosen to represent two separate lines, one for high importance (one standard deviation above the centered mean = 3.78) and one for low importance (one standard deviation below the centered mean = 3.78). The same values of valence used in the previous analysis were chosen to plot positive ( 1.88) and negative (1.88) test grades. The simple slope of the high importance line was not significant, t(342) = 0.38, ns, indicating that students who felt their test was important engaged in high levels of causal search regardless of the test outcome valence (see Fig. 4). The simple slope of the low importance line was significant, t(342) = 2.42, p < .05, revealing that among students who reported the test outcome to be of

4.00 Unexpected Expected

Causal Search

3.50

3.00

2.50

2.00 Positive

Negative

Fig. 3. Post-test precursors to causal search unexpectedness by valence interaction.

4.00 High Importance Low Importance

3.50

Causal Search

208

3.00

2.50

2.00 Positive

Negative

Fig. 4. Post-test precursors to causal search importance by valence interaction.

lesser importance, those who experienced a negative test result engaged in more causal search than those who received a positive test result. Together, the significant unexpectedness by valence and importance by valence interactions resulted in a small yet significant increase in the amount of variance explained in causal search, R2 = .17, DR2 = .018, p = .055. 3.3. The outcomes of causal search following an actual test The association of causal search following an actual test performance with attributions, emotions, and academic performance was examined using correlations and partial correlations. For the partial correlations, the pre-test precursor measures of valence, unexpectedness, and importance of students’ first test in their Introductory Psychology course were included to control for the effects of these factors on causal search and on cognitions, emotions, and performance. The pre-test precursor measures, as opposed to the post-test measures, were selected as covariates because these measures were believed to be less affected by selfserving bias as they were taken before students knew their grades and engaged in self protection. Also, students’ high school grades were included as a covariate when predicting course percentage and GPA in order to account for potential differences in academic aptitude upon entering college that may affect the causal search/ academic achievement relationships. The full and partial correlations revealed a consistent pattern. Specifically, causal search positively correlated with attributions to lack of ability, test difficulty, and to a lesser extent luck (see Table 4). According to Weiner’s (1985) attribution theory, these attributions are commonly considered uncontrollable and are associated with avoidance of blame, reduced motivation, and negative emotions. The external attributions to test difficulty and luck could also be seen as self-protective, and perhaps students use these explanations to deflect responsibility and to preserve motivation. Causal search was also negatively related to effort attributions, which is an internal, controllable, unstable attribution associated with motivating cognitions and emotions. The correlations between causal search and emotions echoed the pattern observed among the attributions. Specifically, causal search correlated negatively with pride and positively with shame, which commonly occurs following increased external/uncontrollable attributions (e.g., lack of ability, bad luck) and decreased

R.H. Stupnisky et al. / Contemporary Educational Psychology 36 (2011) 201–211 Table 4 Partial correlations of causal search with cognitions, emotions, and achievement. Causal search Full correlations

Causal search Partial correlationsa

Attributions Ability Effort Strategy Luck Prof quality Test difficulty

.14** .15** .08 .11* .00 .12*

.16** .10* .12* .05 .03 .17**

Emotions Hope Pride Shame Guilt Helplessness Anger Regret

.08 .20** .27** .21** .22** .27** .28**

.04 .13* .19** .14** .17** .22** .17**

Achievement Course percent GPA

.12* .09

.02 .09

*

p < .05 (2-tailed). p < .01 (2-tailed). Partial correlations among attributions and emotions controlled for Test 1 valence, unexpectedness, and importance, and partial correlations among achievement measures additionally controlled for final high school percentage. **

a

internal/controllable attributions (e.g., lack of effort). Causal search was positively related to anger, which is an other-directed emotion related to external/controllable attributions (e.g., test difficulty). Interestingly, causal search also positively correlated with guilt and regret, which are control-related attributions and could motivate students to try harder in the future. Finally, the full correlations revealed that causal search was negatively related to students’ final Introductory Psychology course percentage, and to a lesser extent GPA. This effect became non-significant in the partial correlations due to the performance dependent pre-test precursors included as covariates being strongly related to achievement. Overall, the results of the correlations suggest that students engaged in higher levels of causal search are at risk of detrimental cognitive, emotional and performance outcomes. 4. Discussion 4.1. The precursors to causal search: exploring sources of past inconsistencies A primary aim of the current study was to examine the event characteristic precursors that most strongly predict causal search among first-year college students, and furthermore to take into account several factors that may have led to discrepancies among previous studies. Overall, the results were consistent with Weiner’s (1985) theory and conceptual logic, to the extent that unexpected, negative, and important events were each found to be predictors of more causal search. In addition, consistencies and discrepancies in the results provide new information about the precursors to causal search based on the inclusion of all three of Weiner’s precursors simultaneously, whether the precursors were measured before or after the causal search inducing event, and the use of hypothetical versus in vivo methodologies. The need to account for all three precursors when predicting causal search was corroborated. First, the zero-order correlations between the individual precursors and causal search were noticeably different than the regression results that included all of the precursors simultaneously. This may have in part been due to the

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high correlation between unexpectedness and valence. Among the pre-test precursors, for example, unexpectedness was significantly related to causal search; however, when unexpectedness was compared to valence and importance the effect became nonsignificant. Indeed, research on counterfactual reasoning suggests that in general, individuals usually expect to do well; thus, when they do poorly it is typically unexpected (Roese, 1997). Applied to the current study, this principle suggests that negative events will also typically be considered unexpected, hence explaining the substantial correlation. The second indication that all three precursors need to be considered when predicting causal search was that several interactions suggested certain combinations of precursors yield unique levels of causal search. Specifically, individuals who experienced unexpected/negative events or low importance/negative events were found to have higher levels of causal search than any single event characteristic would have identified. Third, a noteworthy consistency among the results was that event importance had a significant positive effect on causal search. The value of importance as a precursor may be due to its uniqueness from the other precursors. Specifically, importance showed considerably lower correlations with valence and unexpectedness (pre-test r = .22, .22, post-test r = .13, .06, respectively) than valence and unexpectedness showed with each other. Therefore, the variance in causal search explained by importance makes a more distinct contribution than the overlapping variance explained by valence and unexpectedness. This steadfast finding strongly reinforces event importance as a precursor to causal search, and draws attention to the limitations of previous research that did not include importance as a precursor (for an exception see Gendolla & Koller, 2001). These results suggest that in order to get an accurate sense of which precursors elicit the greatest levels of causal search, all three precursors should be examined. Comparisons of the precursors measured prior to and following an actual test outcome also revealed important information about predicting causal search. Specifically, the pre-test measures indicated that negative valence was the most powerful precursor, while the post-test measures suggested unexpectedness was the strongest. It therefore appears that individuals adjusted their reported expectations and perceived level of success after receiving their test results. Mean comparisons of the pre- and post-test measures supported this premise, as students indicated after the test they felt the result was more unexpected and less negative than their pre-test responses would have suggested. This pattern of change implies that students engage in self-serving or self-protective bias during post-test responses. Students may do this to protect their self-esteem and reduce their anxiety about the outcome (e.g., after a poor grade students may think ‘‘It wasn’t that bad, just less than I was expecting”), therefore preserving motivation for the future. This result also indicates that individuals typically underestimate the psychological and emotional impact of experiencing negative events (guilt, shame, helplessness, etc.). In other words, initially the cognitive activation of causal search due to unexpectedness is often outweighed by the more affective impact of event valence. The bias effect was also seen when the scenario and actual test results were compared. The hypothetical test scenarios showed a nearly identical pattern of results to the post-test precursors following an actual test. Specifically, unexpectedness had the largest individual effect followed by importance and valence, and an unexpectedness by valence interaction wherein unexpected/negative events were associated with the greatest causal search. This parallel pattern implies that students’ responses to hypothetical beliefs (scenario results) are more similar to responses following an actual test (post-test precursor results) than their anticipated reactions to the actual test (pre-test precursor results). This greater similarity

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between the hypothetical scenarios and the post-test results may have occurred because both measures were collected at a time when responses could be adjusted in line with self-serving bias; specifically, students described the test result as merely unexpected as opposed to negative in order to protect self-worth, which in turn predicted more causal search. In contrast, students were not able to adjust their pre-test responses regarding the actual test, and thus the predictive effect of experiencing a negative event on causal search was more clearly seen. To summarize, our results support previous studies on the precursors to causal search. Findings based on precursors measured prior to an actual event, and therefore least affected by self-serving bias, suggest that causal search occurs most often following negative (Ditto et al., 2003; Gilovich, 1983; Holtzworth-Munroe & Jacobson, 1985; Lau, 1984; Moeller & Koeller, 1999) and important events (Gendolla & Koller, 2001). Event unexpectedness was also found to elicit causal search (Clary & Tesser, 1983; Hastie, 1984; Kanazawa, 1992; Pyszczynski & Greenberg, 1981; Sanna & Turley, 1996), however, this precursor may be more powerful in circumstances where students seek to protect their self-worth. The precursors were also found to interact and result in unexpected/negative (Wong & Weiner, 1981) and low important/ negative events to yield greater causal search. 4.2. After causal search: educational implications and applications Entering a new academic achievement setting such as the firstyear of college is a critical time for establishing an adaptive pattern of cognitions, emotions, motivation, and performance. Unfortunately, first-year college students engaged in higher levels of causal search made more attributions to ability, fewer attributions to effort, felt less pride and more shame and helplessness. According to Weiner’s (1985) attribution theory, this indicates an internal/ uncontrollable mindset that leads to decreased motivation. Additionally, students engaged in greater levels of causal search were making more attributions to test difficulty and luck, and experiencing greater other-directed anger. This external and uncontrollable pattern harkens back to the self-protective bias observed in the precursors, whereby students attempt to avoid the blame and decreased self-esteem that follows negative events. Causal search was also positively related to guilt and regret, which are considered somewhat adaptive as they are tied to internal and controllable attributions; however, the associated attributions (e.g., increased attributions to lack of effort and poor strategy following failure) were not observed and the motivational value may have been lost. Ultimately, students engaged in greater causal search were receiving no better or slightly lower grades; thus, it appears that high levels of causal search are not generally associated with benefits for students. One explanation for this result is that students do not use causal search adaptively; that is, they ask ‘‘why” but do not select motivating attributions (e.g., choose lack of ability versus lack of effort). Furthermore, it was surprising to see that the correlations of causal search with the emotions were consistently stronger than with the attributions. This pattern suggests that students with high levels of causal search had stronger emotional than cognitive responses, and perhaps this affectivity interferes with choosing adaptive attributions to explain the outcome. Another explanation for why those students who were engaged in a large amount of causal search performed poorly is that they may be too distracted by ‘‘why” questions to properly focus on their studies. In some cases, incessant searching for causal explanations may result in a state of rumination, which has well-documented detrimental effects on cognitions and behaviors (Lyubomirsky & Nolen-Hoeksema, 1995; Lyubomirsky et al., 2003). Perhaps because these students recently arrived from an

environment where fewer negative, unexpected, and important events occurred (i.e., high school), the act of simply searching for attributions in an academic environment may be a novel and draining experience that contributes to more negative outcomes. A third reason for the poor cognitive and achievement outcomes associated with causal search is that students are not being guided towards adaptive attributions. Fortunately, because these students are actively searching for reasons to explain their performances they should also be well positioned to accept guidance in finding an explanation. Therefore, interventions designed to assist struggling students may be particularly useful for high causal search students. Attributional Retraining (AR; Perry, Hechter, Menec, & Weinberg, 1993) is a cognitive intervention based on Weiner’s (1985) theory that encourages adaptive attributions such as lack of effort or poor strategy. Indeed, the effectiveness of AR among high causal search students is intuitive because these students are already primed to receive suggestions for which attributions to make. Studying the relationship between causal search and AR treatments would be a strategic objective for future research. 4.3. Limitations and future directions Several potential limitations should be considered when interpreting the current results and in designing future research. First, the current study did not experimentally manipulate the independent variables; therefore causal statements cannot be made. However, the current results can still inform the literature on causal search for several reasons. Specifically, a field setting was chosen for the study to make the importance precursor more valid, as many previous studies chose only hypothetical or trivial tasks (e.g., retelling a story; Kanazawa, 1992). And furthermore, throughout the study we attempted to place constraints on our analyses that made results more meaningful. For example, the inclusion of the pre-test precursors as covariates in the partial correlations made for a highly conservative test. Second, the method of measuring causal search following an actual event requires further reflection. Although Weiner (1983) convincingly demonstrated that causal search spontaneously occurs, many past studies have focused on measuring causal search as unobtrusively as possible to limit reactivity. With the previous criticisms in mind, the current causal search measure was designed to reduce reactivity by using a retrospective, critical incident method (Schoeneman et al., 1986). Furthermore, because Weiner has shown that spontaneous causal search exits, we feel confident that our assessment only measured causal search and did not cause it. Causal search was also measured by a single item, a process that remains common despite associated psychometric issues. In using single-item measures, researchers have argued that, both empirically and conceptually, validity is more about the appropriateness of interpretations than about the measure itself (McMillan, 2008; Messick, 1995). Empirically, convergent evidence emerged in the correlation between the single causal search item and several behavioral indicators of causal search. Conceptually, the act of causal search has a clear experiential factor, which is thought to be captured by a single-item measure (Ainley & Patrick, 2006). In these ways, causal search may be like several other concepts that have been adequately measured by single-items, including selfesteem (Robins, Hendin, & Trzesniewski, 2001), course interest (Ainley & Patrick, 2006), quality of life (Zimmerman et al., 2006), self-reported health (Menec, Chipperfield, & Perry, 1999), and job satisfaction (Wanous, Reichers, & Hudy, 1997). Finally, using the same sample of first-year college students throughout the study may be a limitation. It is possible that students’ responses to items on the post-test questionnaire may have been influence by exposure to the same or similar items on

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pre-test questionnaire. To reduce this, the scales and items presented in each questionnaire were carefully ordered. For example, the scenarios appeared in the pre-test questionnaire several pages earlier than the measures of the precursors regarding the actual test. Similarly, in the post-test questionnaire the causal search scale was presented before the measures of the attributions and emotions. Interestingly, the results themselves suggested no method effect was present, as the outcome of the pre- and post-test questionnaires were noticeably different, although in a conceptually logical way. In conclusion, the current study offers some increased understanding of causal search, however, there are still many unknowns about this important process. For example, because the precursors of this study explained approximately 10–16% of the variance in causal search, other event characteristics and personality traits may be powerful predictors. Other questions include how long does causal search last? Does causal search continue until an attribution is decided upon? And what if no attribution is decided upon, when and how does causal search end? With these questions in mind, researchers interested in causal search should continue to study the precursors in real-world settings to better understand this critical aspect in attribution theory. If researchers and educators can better predict when attributions are going to be made, they can better understand the attributional process including the critical cognitions, emotions, and behavior that follow causal search. Acknowledgments This study was supported by a SSHRC Postdoctoral Fellowship to the second author and a SSHRC research Grant (410-20072225) to the fourth author. Parts of this research were presented at the American Educational Research Association annual meeting in Chicago, Illinois in April, 2007. References Ainley, M., & Patrick, L. (2006). Measuring self-regulated learning processes through tracking patterns of student interaction with achievement activities. Educational Psychology Review, 18, 267–286. Bohner, G., Bless, H., Schwarz, N., & Strack, F. (1988). What triggers causal attributions? The impact of valence and subjective probability. European Journal of Social Psychology, 18, 335–345. Broadbent, D. E. (1958). Perception and communication. London: Pergamon. Clary, E. G., & Tesser, A. (1983). Reactions to unexpected events: The naive scientist and interpretive activity. Personality and Social Psychology Bulletin, 9(4), 609–620. Cohen, J., Cohen, P., West, S., & Aiken, L. (2003). Applied multiple regression/ correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. Deutsch, J. A., & Deutsch, D. (1963). Attention: Some theoretical considerations. Psychological Review, 20(1), 80–90. Ditto, P. H., & Lopez, D. F. (1992). Motivated skepticism: Use of differential decision criteria for preferred and nonpreferred conclusions. Journal of Personality and Social Psychology, 63, 568–584. Ditto, P. H., Munro, G. D., Apanovitch, A. M., Scepansky, J. A., & Lockhart, L. K. (2003). Spontaneous skepticism: The interplay of motivation and expectation in responses to favorable and unfavorable medical diagnoses. Personality and Social Psychology Bulletin, 29(9), 1120–1132. Gendolla, G. H. E., & Koller, M. (2001). Surprise and motivation of causal search: How are they affected by outcome valence and importance? Motivation and Emotion, 25(4), 327–349. Gilovich, T. (1983). Biased evaluation and persistence in gambling. Journal of Personality and Social Psychology, 44(6), 1110–1126. Grayson, J. P., & Grayson, K. (2003). Research on retention and attrition. Montreal: Canada Millennium Scholarship Foundation. Hall, S., French, D. P., & Marteau, T. M. (2003). Causal attributions following serious unexpected negative events: A systematic review. Journal of Social and Clinical Psychology, 22(5), 515–536. Hastie, R. (1984). Causes and effects of causal attribution. Journal of Personality and Social Psychology, 45(1), 44–56. Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.

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