View in EDS HTML Full Text PDF Full Text

The Dynamic Experience of Taking an Examination: Ever Changing Cortisol and Expectancy for Success

Saved in:
Bibliographic Details
Title: The Dynamic Experience of Taking an Examination: Ever Changing Cortisol and Expectancy for Success
Language: English
Authors: Graham, Matthew C. (ORCID 0000-0002-0235-5126), Husman, Jenefer, Pekrun, Reinhard (ORCID 0000-0003-4489-3827), Villanueva, Idalis (ORCID 0000-0002-8767-2576), Christensen, Darcie
Source: British Journal of Educational Psychology. Apr 2023 93(1):195-210.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 16
Publication Date: 2023
Sponsoring Agency: National Science Foundation (NSF)
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Undergraduate Students, Test Anxiety, Tests, Expectation, Success, Physiology, Biochemistry, Stress Variables
DOI: 10.1111/bjep.12521
ISSN: 0007-0998
2044-8279
Abstract: Background: This study examined the relations between students' expectancies for success and a physiological component of test anxiety, salivary cortisol, during an authentic testing setting. Aims: The aim of the study was to better understand the connection between shifts in students' control appraisals and changes in the physiological component of test anxiety. Sample: The study comprised 45 undergraduate engineering majors in the United States. Methods: Survey data concerning students' expectancy for success and saliva samples were taken before, during and after the practice midterm examination prior to their actual in-class examination. Results: Students' expectancy for success declined during the examination while cortisol levels declined from the beginning to middle of the examination and began to increase again as a function of time. Although students' initial levels of expectancy for success and cortisol were not correlated, there was a negative relation between change in cortisol and change in expectancy for success. Conclusions: Our study demonstrates a relation between salivary cortisol, a physiological component of test anxiety and students' expectancy for success in an authentic testing context. Most students saw a decrease in cortisol during the examination, suggesting anticipatory anxiety prior to the test and a return to homeostasis as the examination progressed. Some students, however, did not see a declination in cortisol, suggesting they may not have recovered from pre-examination anxiety. The negative relation between change in cortisol and expectancy for success suggests that students who had the greatest decrease in expectancy for success saw the smallest recovery in cortisol.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1374559
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwH_X9NTEsO-lSF0PlBhiueSAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDG31sxzjJwL-BST6swIBEICBmw8yPP6DTU6R5BvMI3Xq7I0bq_sX_tyrW6GNXTBdSwiOGygXEVMu4B4zIYmn77uHp36iuN2sni1kA5k1LeUHfYcjtPLov1SVoGpC-J65mmQ-WENbzs0oEiLKvAHuTovszUn3g36dO4MJAyvUo1TdHtAbeSEYOWGIdLvMkOrzsyizhgwSZjuT1C6Z10yoNPLaTbF-kVyXdcYGpqIp
Text:
  Availability: 1
  Value: <anid>AN0163282825;6kx02apr.23;2023Apr26.05:29;v2.2.500</anid> <title id="AN0163282825-1">The dynamic experience of taking an examination: Ever changing cortisol and expectancy for success </title> <p>Background: This study examined the relations between students' expectancies for success and a physiological component of test anxiety, salivary cortisol, during an authentic testing setting. Aims: The aim of the study was to better understand the connection between shifts in students' control appraisals and changes in the physiological component of test anxiety. Sample: The study comprised 45 undergraduate engineering majors in the United States. Methods: Survey data concerning students' expectancy for success and saliva samples were taken before, during and after the practice midterm examination prior to their actual in‐class examination. Results: Students' expectancy for success declined during the examination while cortisol levels declined from the beginning to middle of the examination and began to increase again as a function of time. Although students' initial levels of expectancy for success and cortisol were not correlated, there was a negative relation between change in cortisol and change in expectancy for success. Conclusions: Our study demonstrates a relation between salivary cortisol, a physiological component of test anxiety and students' expectancy for success in an authentic testing context. Most students saw a decrease in cortisol during the examination, suggesting anticipatory anxiety prior to the test and a return to homeostasis as the examination progressed. Some students, however, did not see a declination in cortisol, suggesting they may not have recovered from pre‐examination anxiety. The negative relation between change in cortisol and expectancy for success suggests that students who had the greatest decrease in expectancy for success saw the smallest recovery in cortisol.</p> <p>Keywords: control‐value theory; cortisol; examination stress</p> <hd id="AN0163282825-2">INTRODUCTION</hd> <p>Examinations are frequent, consequential and often emotion‐filled experiences for students (Stöber & Pekrun, [<reflink idref="bib52" id="ref1">52</reflink>]). An especially frequently occurring emotion during examinations is anxiety. Test anxiety has several components, including cognitive (worry), physiological and affective (combined called 'emotionality' in the test anxiety literature), and behavioural components (von der Embse et al., [<reflink idref="bib58" id="ref2">58</reflink>]). Researchers have argued that measures of physiological arousal can be used to examine the physiological component of test anxiety and allow insights that are not possible utilizing self‐report measures of emotions (Roos et al., [<reflink idref="bib41" id="ref3">41</reflink>]). In this study, we use control‐value theory (CVT, Pekrun, [<reflink idref="bib32" id="ref4">32</reflink>], [<reflink idref="bib33" id="ref5">33</reflink>], [<reflink idref="bib34" id="ref6">34</reflink>]) as a theoretical framework for studying the physiological component of test anxiety.</p> <p>CVT proposes that achievement emotions are dynamic, multi‐component processes that include cognitive, physiological, affective and behavioural components. Test anxiety, one of several major negative emotions considered in CVT, is theorized to be negatively related to students' control appraisals such as their expectancies for success (Pekrun, [<reflink idref="bib32" id="ref7">32</reflink>]). However, the relations between control appraisals and achievement emotions, in the context of examinations, have typically relied on self‐report of students' emotions before and after the examination (e.g., Harley et al., [<reflink idref="bib17" id="ref8">17</reflink>]; Roick & Ringeisen, [<reflink idref="bib40" id="ref9">40</reflink>]) or self‐report of students' trait emotions (von der Embse et al., [<reflink idref="bib58" id="ref10">58</reflink>]). CVT posits that emotions shift as students' appraisals change; however, examinations of in‐the‐moment relations between control appraisals and emotions in examination contexts are less common (Roick & Ringeisen, [<reflink idref="bib40" id="ref11">40</reflink>]). This may be due, in part, to the methodological challenges of asking students to report on the physiological component of anxiety during a cognitively demanding task (Putwain, [<reflink idref="bib38" id="ref12">38</reflink>]).</p> <p>In this study, we use an objective measure of physiological arousal, cortisol, to explore the proposed relation between ongoing cognitive appraisals and the physiological component of test anxiety. Cortisol has been used to measure students' physiological arousal response to performance tasks in both laboratory (e.g., Dickerson & Kemeny, [<reflink idref="bib6" id="ref13">6</reflink>]) and real‐world examination environments (e.g., Ringeisen et al., [<reflink idref="bib39" id="ref14">39</reflink>]). Understanding the relation between control appraisals and changes in cortisol is important as reoccurring momentary rises in cortisol have a negative impact on working memory (Barsegyan et al., [<reflink idref="bib4" id="ref15">4</reflink>]) and adverse health effects (Juster et al., [<reflink idref="bib21" id="ref16">21</reflink>]). Understanding the connection between control appraisals and students' cortisol may assist in developing interventions to reduce students' frequent experience of test anxiety.</p> <hd id="AN0163282825-3">Control appraisals and emotion</hd> <p>CVT proposes that control appraisals, including self‐beliefs about competence, self‐efficacy expectations and expectancies for success, influence students' achievement emotions. As students' progress through a performance activity, their expectancies for success may change due to their ongoing interactions with the environment. Students' emotional experiences change as they begin, engage in, and reflect on a task, and these changes are informed by their expectancies for success (Pekrun, [<reflink idref="bib32" id="ref17">32</reflink>]). As a result, to understand the change of students' emotional state across the task, it is important to consider its relation with expectancy. Researchers using expectancy‐value theory (EVT, e.g., Eccles & Wigfield, [<reflink idref="bib9" id="ref18">9</reflink>], [<reflink idref="bib10" id="ref19">10</reflink>]) have theorized and demonstrated the importance of expectancies for meaningful academic outcomes. However, EVT emphasizes the importance of students' motivational (rather than emotional) experiences during academic performance tasks (Kiuru et al., [<reflink idref="bib24" id="ref20">24</reflink>]).</p> <p>Research on the relations between dynamic expectancies for success and achievement emotions such as anxiety (Goetz et al., [<reflink idref="bib13" id="ref21">13</reflink>]) has been constrained by limitations inherent to self‐report methodologies, including subjectivity of appraisals of emotional states, presentation biases and the emphasis of self‐report measures on cognitive and affective components of emotions rather than physiological components (Linnenbrink‐Garcia et al., [<reflink idref="bib28" id="ref22">28</reflink>]; Pekrun, [<reflink idref="bib32" id="ref23">32</reflink>]; Szafranski et al., [<reflink idref="bib54" id="ref24">54</reflink>]). Furthermore, using self‐report during examinations is problematic because examinations create a high cognitive load, making self‐reflection about one's emotional state difficult (see Roos et al., [<reflink idref="bib41" id="ref25">41</reflink>], for a review). Objective measures of the physiological components of achievement emotions address this concern. Although relations between biological measures and self‐reports of emotions may be modest (e.g., Joseph et al., [<reflink idref="bib20" id="ref26">20</reflink>]), these measures can provide insight into key components of students' emotional stress responses. Based on the multi‐component view of emotion described above, we use change in salivary cortisol as an indicator of the physiological component of test anxiety in our study. Using this measure, we test the hypothesis that students' ongoing expectancies for success are related to their physiological response during the examination.</p> <hd id="AN0163282825-4">Emotional stress and hypothalamic‐pituitary‐adrenal axis activation</hd> <p>Test anxiety is an emotional, physiologically activating response to the threat that students experience in examinations. The physiological component of this stress response includes activation of the hypothalamic‐pituitary‐adrenal (HPA) axis. Activation of the HPA axis during periods of acute threat results in a precipitous excretion of cortisol as the body mobilizes energy stores to aid in response to the perceived threat (Tilbrook, [<reflink idref="bib55" id="ref27">55</reflink>]), supporting the 'flight‐or‐fight' response (Kemeny & Shestyuk, [<reflink idref="bib22" id="ref28">22</reflink>]). Cortisol passively diffuses into saliva proportional to serum‐free cortisol, making salivary cortisol an excellent measure of the physiological component of acute stress responses. However, not all stressful settings are likely to produce an HPA‐axis response and result in a significant increase in cortisol, as shown both in laboratory research and outside of the laboratory (Schlotz, [<reflink idref="bib44" id="ref29">44</reflink>]). The relation between stress and cortisol has been extensively studied in laboratory settings, utilizing stimuli designed to create stress (e.g., the Trier Social Stress Test; Labuschagne et al., [<reflink idref="bib25" id="ref30">25</reflink>]) and in educational settings in a few studies (Adams et al., [<reflink idref="bib1" id="ref31">1</reflink>]). It was found that uncertainty and social‐evaluative tasks that trigger anxiety are the most likely to increase cortisol levels (Dickerson & Kemeny, [<reflink idref="bib6" id="ref32">6</reflink>]). Examinations are a prime example for this type of situation, as they involve social evaluation and uncertainty about the outcome.</p> <p>The expected pattern for a cortisol response to a performance task is an initial rise in cortisol just before the task and a decline following the task (Dickerson & Kemeny, [<reflink idref="bib6" id="ref33">6</reflink>]; Sanz & Villamarín, [<reflink idref="bib43" id="ref34">43</reflink>]; Schlotz, [<reflink idref="bib44" id="ref35">44</reflink>]; Verschoor & Markus, [<reflink idref="bib56" id="ref36">56</reflink>]). In the context of a long examination, initial activation of the HPA axis at the start of the examination and return to baseline, described as cortisol recovery, is expected (Spangler et al., [<reflink idref="bib51" id="ref37">51</reflink>]). The absence of a return to baseline suggests repeated triggering of the HPA axis and is an indicator of a maladaptive physiological arousal (Juster et al., [<reflink idref="bib21" id="ref38">21</reflink>]). An optimal design for capturing maladaptive lack of recovery requires the collection of several saliva samples per participant, including two samples before the performance task and four post‐task samples (Granger et al., [<reflink idref="bib14" id="ref39">14</reflink>]).</p> <p>Research on control appraisals and adaptive/maladaptive physiological arousal have frequently examined the relation between self‐efficacy and physiological arousal. Control‐value theory and social‐cognitive theory both propose that high self‐efficacy (a control appraisal) should have a protective effect, blunting the impact of the performance experience on negative emotions and increasing cortisol (Pekrun, [<reflink idref="bib32" id="ref40">32</reflink>]; Schönfeld et al., [<reflink idref="bib45" id="ref41">45</reflink>]). However, researchers have not found a consistent relation between coping self‐efficacy or academic self‐efficacy and physiological arousal (cortisol response) in clinical and examination settings (Ringeisen et al., [<reflink idref="bib39" id="ref42">39</reflink>]; Schönfeld et al., [<reflink idref="bib45" id="ref43">45</reflink>]). Rather than examining general coping or academic self‐efficacy, in this study we examine the relation between students' momentary, changing expectancies for success and their salivary cortisol, as an indicator of the physiological component of test anxiety, in the context of an examination.</p> <hd id="AN0163282825-5">AIM OF STUDY</hd> <p>Although cortisol is a well‐established indicator of physiological arousal (Dickerson & Kemeny, [<reflink idref="bib6" id="ref44">6</reflink>]; Sanz & Villamarín, [<reflink idref="bib43" id="ref45">43</reflink>]; Schlotz, [<reflink idref="bib44" id="ref46">44</reflink>]; Verschoor & Markus, [<reflink idref="bib56" id="ref47">56</reflink>]), it has been underutilized in the context of authentic academic performance tasks (Ringeisen et al., [<reflink idref="bib39" id="ref48">39</reflink>]). Based on control‐value theory (Pekrun, [<reflink idref="bib32" id="ref49">32</reflink>], [<reflink idref="bib33" id="ref50">33</reflink>], [<reflink idref="bib34" id="ref51">34</reflink>]), we argue that measuring changes in salivary cortisol during and after an examination allows us to better understand the connection between shifts in students' expectancies for success and changes in the physiological component of anxiety during the examination. Specifically, we answer the following questions:</p> <p> <bold>RQ1:</bold> Does students' physiological arousal change over the course of an examination? We hypothesize that students' salivary cortisol will be higher at the start of an examination than at the end.</p> <p> <bold>RQ2:</bold> Is there a relation between students' initial levels and change in expectancy for success and the initial level and change in salivary cortisol? We hypothesize that there will be a negative relation between the initial level of expectancy for success and the initial salivary cortisol level. We also hypothesize a negative relationship between change in expectancy for success and change in cortisol.</p> <hd id="AN0163282825-6">METHOD</hd> <p></p> <hd id="AN0163282825-7">Participants</hd> <p>Participants were 45 undergraduate students recruited using in‐person announcements and an email sign‐up link from an engineering statics course at a research university in the United States Mountain West in Fall Term 2018. In the U.S., statics courses are often taken in the sophomore year as the first required engineering course for engineering majors and are generally considered a challenging course (Suresh, [<reflink idref="bib53" id="ref52">53</reflink>]). The statics course had three midterm examinations and a final examination; students volunteered to participate before the first or second midterm examination. As part of the course students were required to take a practice midterm examination to prepare for their actual midterm. Two weeks before the examination a representative of our research team went to the course to recruit students to take their practice midterm in our testing centre and participate in our research. Participants received 2.5% extra credit and a $5 gift card for participating in the study. They were provided feedback on their performance after the end of the study.</p> <p>A pre‐examination survey included questions about students' health, medication, tobacco use and pregnancy. Of the initial 48 individuals who agreed to participate, three were identified as either having a medical condition or using prescription medication known to potentially influence cortisol concentrations (Foley & Kirschbaum, [<reflink idref="bib12" id="ref53">12</reflink>]). These participants were excluded from the study, resulting in a final sample size of 45 students. No students reported tobacco use or pregnancy. Age ranged from 18 to 32 years (mean = 22.00 years, <emph>SD</emph> = 2.90). Thirty‐four students identified as male, 11 identified as female. Using the U.S. Federal guidelines for reporting race and ethnicity (e.g., Office of Management and Budget, [<reflink idref="bib31" id="ref54">31</reflink>]), 42 identified as White non‐Hispanic, and three indicated another racial and/or ethnic identity. All participants indicated they were engineering majors. The mean self‐reported cumulative university GPA was 3.54 (<emph>SD</emph> = .38).</p> <hd id="AN0163282825-8">Procedures</hd> <p>Following IRB‐approved procedures, participants took the practice midterm examination approximately 1 week before the midterm examination in a testing centre designed to replicate an authentic testing environment (Authors et al., 2019). Participants arrived in the afternoon (3:30–6:30 pm) and were allowed to take as much time as needed to complete the examination. All saliva samples were collected using practices recommended in salivary bioscience (Granger et al., [<reflink idref="bib14" id="ref55">14</reflink>]).</p> <p>Prior to data collection, participants were asked not to consume any foods or beverages with artificial sugar or caffeine 4 h beforehand, not to have a meal 1 h beforehand, not to brush their teeth 45 min beforehand, and not to consume milk, dairy, or high‐acidity foods 20 min beforehand. Upon arrival at the testing centre, participants gave their written consent. They were given 1 oz. of water and prompted to swish the liquid around their oral cavity and swallow. Participants were then led to a workstation where they were oriented towards the salivary collection tools and provided an overview of the study procedures. The time between swishing water in their oral cavity and the first saliva sample varied but, in all cases, exceeded 10 min. Before the examination, participants watched a short video demonstrating the self‐collection of salivary samples. Participants then provided their first saliva sample and reported their pre‐examination expectancy for success. Immediately after reporting expectancy for success, they began the examination. Forty‐five minutes into the examination, participants were directed to collect a second saliva sample and report their mid‐examination expectancy for success. Participants were then allowed as much time as needed to complete the examination. Although all students fully engaged in the examination, some took longer than others. Total time to complete the examination varied from 54 to 210 min; the average time was 118.87 min (<emph>SD</emph> = 33.80). Immediately after the examination, but before receiving their performance scores, participants provided the third saliva sample and reported their post‐examination expectancy for success. Twenty minutes after completing the examination, participants provided the final saliva sample. We present the study timeline in Figure 1.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/02apr23/bjep12521-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12521-fig-0001.jpg" title="1 Timeline for study visit identifying data collection method and timing" /> </p> <p></p> <hd id="AN0163282825-10">Measures</hd> <p></p> <hd id="AN0163282825-11">Expectancy for Success</hd> <p>Before, during and immediately after the examination, expectancy for success was assessed using a single item rated on an 11‐point scale before receiving results. Participants were asked to indicate the highest percent of items correct they expect to earn on the practice examination from 0% to 100% by 10% increments. This one‐item measure is similar to previously used measures of expectancy for success, which ask students about their expected grade or performance on a scale from 0 to 100 (Roick & Ringeisen, [<reflink idref="bib40" id="ref56">40</reflink>]; Shell & Husman, [<reflink idref="bib48" id="ref57">48</reflink>], [<reflink idref="bib49" id="ref58">49</reflink>]). In the U.S., grades (A, B, C) are often determent by the percent of points or items correct: for example, 90–100% = A.</p> <hd id="AN0163282825-12">Salivary cortisol</hd> <p>Before, during, immediately after and 20 min after the examination, participants were prompted to collect whole saliva using Salimetrics Oral Swabs (Salimetrics, State College, PA). All saliva sample vials were immediately collected from participants and stored at −20°C until being centrifuged, vortexed and assayed following established protocols using an enzyme immunoassay with a sensitivity of.007 μg/dl and range from.007 to 1.8 μg/dl (Salimetrics, State College, PA). Inter‐ and intra‐assay coefficients of variation, a measure of reliability, were 6.86 and 6.35, respectively, which is within the accepted range (Hanneman et al., [<reflink idref="bib16" id="ref59">16</reflink>]).</p> <hd id="AN0163282825-13">Statistical analysis</hd> <p>We answered our research questions using Latent Growth Curve Modelling (LGM; Duncan & Duncan, [<reflink idref="bib8" id="ref60">8</reflink>]) with individually varying times of observation (Mehta & West, [<reflink idref="bib29" id="ref61">29</reflink>]) using <emph>MPlus v8.3</emph> (Muthén & Muthén, [<reflink idref="bib30" id="ref62">30</reflink>]–2017). LGM is a statistical technique using a structural equation modelling (SEM) framework to estimate growth trajectories, assuming that an individual's change in a factor across multiple measurement occasions is a function of an underlying predictable growth process (Preacher et al., [<reflink idref="bib37" id="ref63">37</reflink>]). LGM offers advantages over other approaches when analysing repeated measures data in that it allows for the analysis of complex, multivariate models with multiple outcomes (Duncan & Duncan, [<reflink idref="bib8" id="ref64">8</reflink>]). Prior research has found that, even with a smaller sample size, LGM is particularly useful for modelling biomarkers due to its flexibility and ability to model complex data structures (Felt et al., [<reflink idref="bib11" id="ref65">11</reflink>]).</p> <p>LGM with individually varying observation times builds on the foundation of traditional LGM but treats time as a continuous rather than discrete factor. We present a path diagram of a univariate LGM with individually varying times of observations in Figure 2. An individual's predicted value at a specific time point can be expressed as, <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12521:bjep12521-math-0001" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi mathvariant="italic">ϒ</mi><mi>i</mi></msub><mfenced close=")" open="("><msub><mi>t</mi><mi>x</mi></msub></mfenced><mo>=</mo><msub><mi>ξ</mi><mi>i</mi></msub><mfenced close=")" open="("><msub><mi>t</mi><mn>0</mn></msub></mfenced><mo>+</mo><msub><mi>ϴ</mi><mi>i</mi></msub><mfenced close=")" open="("><mrow><msub><mi>t</mi><mi>x</mi></msub><mo>−</mo><msub><mi>t</mi><mn>0</mn></msub></mrow></mfenced><mo>+</mo><msub><mi>ε</mi><mi>i</mi></msub><mfenced close=")" open="("><msub><mi>t</mi><mi>x</mi></msub></mfenced></mrow></math> </ephtml> where an observed score for person <emph>i</emph> at time <emph>t</emph><subs><emph>x</emph></subs>, <emph>ϒ</emph><subs><emph>i</emph></subs>(<emph>t</emph><subs><emph>x</emph></subs>), is the sum of their initial level at time zero, <emph>ξ</emph><subs><emph>i</emph></subs>(<emph>t</emph><subs><emph>0</emph></subs>); the product of rate of change, <emph>ϴ</emph><subs><emph>i</emph></subs>; and elapsed time from the intercept (t<subs>x</subs> – t<subs>0</subs>); and the time‐specific error, <emph>ε</emph><subs><emph>i</emph></subs>(<emph>t</emph><subs><emph>x</emph></subs>) (Mehta & West, [<reflink idref="bib29" id="ref66">29</reflink>]).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/02apr23/bjep12521-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12521-fig-0002.jpg" title="2 Path Diagram of Univariate Quadratic Latent Growth Model with Individually Varying Times of Observation for Salivary Cortisol. Notes. μFactor = mean of latent variable. ΨFactor = variance/covariance of latent variables. C[I] = latent intercept cortisol. C[L] = latent linear slope cortisol. C[Q] = latent quadratic slope cortisol. Tx = time of observation for each wave. Cx = observed cortisol for each wave. εCX = residual variance of manifest variables for each wave" /> </p> <p></p> <p>Using time as a continuous rather than a discrete factor in our LGM allowed us to model inter‐individual differences in the time students took to complete the examination, and to evaluate how expectancy for success and physiological arousal changed as a function of time. However, modelling individually varying times of observations requires the use of numerical integration and definitional variables outside the variance–covariance matrix of observed variables typically used to derive parameter estimates in SEM. As a result, traditional measures of overall model fit (e.g., comparative fit index, Tucker‐Lewis index; Hu & Bentler, [<reflink idref="bib19" id="ref67">19</reflink>]) cannot be obtained. Therefore, to evaluate the overall fit, we tested a series of nested models with an increasing number of latent growth parameters (i.e., intercept‐only, linear and quadratic) and compared model fit using the likelihood ratio test statistic (LRTS; e.g., Lewis et al., [<reflink idref="bib27" id="ref68">27</reflink>]), <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12521:bjep12521-math-0002" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="italic">LRTS</mtext><mo linebreak="goodbreak">=</mo><mn>2</mn><mfenced close=")" open="("><mrow><mi>log</mi><msub><mi>L</mi><mi>B</mi></msub><mo linebreak="goodbreak">−</mo><mi>log</mi><msub><mi>L</mi><mi>A</mi></msub></mrow></mfenced></mrow></semantics></math> </ephtml> where log<emph>L</emph><subs><emph>.B</emph>.</subs> and log<emph>L</emph><subs><emph>.A</emph>.</subs> are the log‐likelihoods for models B and A, and model A is nested in model B. We evaluated the statistical significance of the LRTS assuming a <emph>χ</emph><sups><emph>2</emph></sups> distribution with the difference in the numbers of parameters as the degrees of freedom. Additionally, we compared the Akaike Information Criterion (AIC; Akaike, [<reflink idref="bib3" id="ref69">3</reflink>]), <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12521:bjep12521-math-0003" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi mathvariant="italic">AIC</mi><mi>i</mi></msub><mo linebreak="goodbreak">=</mo><mo linebreak="goodbreak">−</mo><mn>2</mn><mi>log</mi><msub><mi>L</mi><mi>i</mi></msub><mo linebreak="goodbreak">+</mo><mn>2</mn><msub><mi>p</mi><mi>i</mi></msub></mrow></semantics></math> </ephtml> and the Bayesian Information Criterion (BIC; Schwarz, [<reflink idref="bib47" id="ref70">47</reflink>]), <ephtml> <math altimg="urn:x-wiley:00070998:media:bjep12521:bjep12521-math-0004" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi mathvariant="italic">BIC</mi><mi>i</mi></msub><mo linebreak="goodbreak">=</mo><mo linebreak="goodbreak">−</mo><mn>2</mn><mi>log</mi><msub><mi>L</mi><mi>i</mi></msub><mo linebreak="goodbreak">+</mo><msub><mi>p</mi><mi>i</mi></msub><mi>log</mi><msub><mi>n</mi><mi>i</mi></msub></mrow></semantics></math> </ephtml> for model <emph>i</emph>, where L<subs>i</subs> is the likelihood, <emph>p</emph><subs><emph>i</emph></subs> is the number of parameters, and <emph>n</emph><subs><emph>i</emph></subs> is the sample size. For both AIC and BIC, smaller values suggest stronger empirical support for a model.</p> <p>We used a parallel process Latent Growth Model (PP‐LGM; Preacher et al., [<reflink idref="bib37" id="ref71">37</reflink>]) to test the relation of expectancy for success and physiological arousal. The parallel process LGM allows us to simultaneously model growth in the two different factors and evaluate the relation between initial levels and change in both factors. To test the hypothesized relation in the change in expectancy for success and physiological arousal, we allow the intercepts and slopes of both factors to covary.</p> <hd id="AN0163282825-15">RESULTS</hd> <p></p> <hd id="AN0163282825-16">Preliminary analysis</hd> <p>We present correlations, means and standard deviations for all measures in Table 1. Expectancy for success decreased from 8.58 (<emph>SD</emph> = 1.48) prior to the examination to 7.71 (<emph>SD</emph> = 1.99) 45 min into the examination and 6.40 (<emph>SD</emph> = 2.28) at the end. Correlations between all three measures of expectancy were strong, <emph>r</emph>s = .52–.68, <emph>p</emph>s < .01. We observed a similar declination in salivary cortisol from.26 μg/dl (<emph>SD</emph> = .15 μg/dl) pre‐examination to.17 μg/dl (<emph>SD</emph> = .07 μg/dl) during the examination. However, little change was observed from this mid‐examination measure to.15 μg/dl (.07 μg/dl) immediately after and.17 μg/dl (.11 μg/dl) 20 min after the examination. The correlations between cortisol levels between waves were inconsistent with a significant relation between pre‐ and mid‐examination arousal (<emph>r</emph> = .33, <emph>p</emph> = .03) and between the two post‐examination measures (<emph>r</emph> = .42, <emph>p</emph> < .01). However, we observed no other significant relations (<emph>r</emph>s = .16–.25, <emph>p</emph>s > .05).</p> <p>1 TABLE Correlations, means and standard deviations for observed variables and time</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">1</th><th align="left">2</th><th align="left">3</th><th align="left">4</th><th align="left">5</th><th align="left">6</th><th align="left">7</th></tr></thead><tbody valign="top"><tr><td>1. E0</td><td>1.00</td><td /><td /><td /><td /><td /><td /></tr><tr><td>2. E1</td><td>.68**</td><td>1.00</td><td /><td /><td /><td /><td /></tr><tr><td>3. E2</td><td>.52**</td><td>.67**</td><td>1.00</td><td /><td /><td /><td /></tr><tr><td>4. C0</td><td>.05</td><td>.26</td><td>.27</td><td>1.00</td><td /><td /><td /></tr><tr><td>5. C1</td><td>−.09</td><td>.05</td><td>−.01</td><td>.33*</td><td>1.00</td><td /><td /></tr><tr><td>6. C2</td><td>−.34**</td><td>−.03</td><td>.01</td><td>.16</td><td>.25</td><td>1.00</td><td /></tr><tr><td>7. C3</td><td>.11</td><td>.16</td><td>−.05</td><td>.20</td><td>.24</td><td>.42*</td><td>1.00</td></tr><tr><td>Mean</td><td>8.58</td><td>7.71</td><td>6.40</td><td>.26</td><td>.17</td><td>.15</td><td>.17</td></tr><tr><td>SD</td><td>1.48</td><td>1.99</td><td>2.28</td><td>.15</td><td>.07</td><td>.07</td><td>.11</td></tr><tr><td>Time mean</td><td>.00</td><td>.75</td><td>1.98</td><td>.00</td><td>.75</td><td>1.98</td><td>2.31</td></tr><tr><td>Time SD</td><td>—</td><td>—</td><td>.56</td><td>—</td><td>—</td><td>.56</td><td>.56</td></tr></tbody></table> </ephtml> </p> <p>1 Abbreviations: E = Expectancy for success, C = Salivary Cortisol. Time measured in hours. * <emph>p</emph> < .05. ** <emph>p</emph> < .01.</p> <p>As a sensitivity analysis, we conducted a two‐way repeated measures ANOVA with both expectancy and cortisol to identify gender differences. We did not observe any significant main effect of gender on overall expectancy (<emph>F</emph>[<reflink idref="bib1" id="ref72">1</reflink>,<reflink idref="bib43" id="ref73">43</reflink>] = .12, <emph>p</emph> = .73), nor any interaction effect of gender and data collection wave on expectancy (<emph>F</emph>[<reflink idref="bib2" id="ref74">2</reflink>, 86] < .01, <emph>p</emph> > .99). Similarly, we did not observe any significant main effect of gender on overall cortisol (<emph>F</emph>[<reflink idref="bib1" id="ref75">1</reflink>,<reflink idref="bib43" id="ref76">43</reflink>] = 1.02, <emph>p</emph> = .32). There also was no interaction effect of gender and data collection wave on cortisol (<emph>F</emph>[2.20, 94.59] = 1.45, <emph>p</emph> = .95).</p> <hd id="AN0163282825-17">Latent growth curve modelling</hd> <p>We present the fit statistics and parameter estimates for all models in Table 2. To answer our first research question, we compared the model fit for three specifications of the growth trajectory parameters, one with no change (intercept‐only), one with linear change (linear) and one with both linear and quadratic change (quadratic). We found that the quadratic model outperformed both the intercept‐only model, LRTS (<emph>df</emph>) = 34.34 (<reflink idref="bib7" id="ref77">7</reflink>), <emph>p</emph> < .01, ΔAIC = −20.32 and the linear model, LRTS (<emph>df</emph>) = 21.84 (<reflink idref="bib4" id="ref78">4</reflink>), <emph>p</emph> < .01, ΔAIC = −13.84. We present a plot for this best‐fitting quadratic growth curve model in Figure 3. In examining explained and residual variance in our manifest variables, we found the quadratic model better fit the observed data. For the best‐fitting model, participants started high in initial salivary cortisol levels, intercept cortisol (μ<subs>C[I]</subs>) = .252 μg/dl, <emph>SE</emph> = .021, <emph>p</emph> < .01. Cortisol then declined over the course of the examination, linear slope (μ<subs>C[L]</subs>) = −.126 μg/dl/h, <emph>SE</emph> = .036, <emph>p</emph> < .01. This decrease was attenuated by a significant quadratic term, quadratic slope (μ<subs>C[Q]</subs>) = .035 μg/dl/h<sups>2</sups>, <emph>SE</emph> = .013, <emph>p</emph> < .01, such that participants began to experience an increase in salivary cortisol after approximately 1 h and 45 min. We observed significant variance in the intercept, ψ<subs>C[I]</subs> = .017, <emph>SE</emph> = .005, <emph>p</emph> < .01, the linear slope, ψ<subs>C[L]</subs> = . 040, <emph>SE</emph> = .015, <emph>p</emph> < .01 and the quadratic slope, ψ<subs>C[Q]</subs> = .004, <emph>SE</emph> = .002, <emph>p</emph> < .01. Additionally, we observed that initial cortisol level covaried with both the linear slope, ψ<subs>C[I]xC[L]</subs> = −.025, <emph>SE</emph> = .009, <emph>p</emph> < .01 and the quadratic slope, ψ<subs>C[I]xC[Q]</subs> = .008, <emph>SE</emph> = .003 <emph>p</emph> < .01. Linear and quadratic slope were also related, ψ<subs>C[I]xC[Q]</subs> = −.013, <emph>SE =</emph> .005<emph>, p</emph> < .01.</p> <p>2 TABLE Fit Indices and parameter estimates for expectancy for success (E) and salivary cortisol (S) in the latent growth model (LGM) and the combined parallel process latent growth model (PP‐LGC)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Expectancy for success (E) LGM</th><th align="left">Salivary cortisol (C) LGM</th><th align="left">ExC PP‐LGM</th></tr><tr><th align="left">Intercept‐only</th><th align="left">Linear</th><th align="left">Intercept‐only</th><th align="left">Linear</th><th align="left">Quadratic</th><th align="left">PP‐LGM</th></tr></thead><tbody valign="top"><tr><td>Fit indices</td></tr><tr><td>Free parameters</td><td>5</td><td>8</td><td>6</td><td>9</td><td>13</td><td>27</td></tr><tr><td>Log likelihood</td><td>−277.65</td><td>−254.72</td><td>168.76</td><td>175.01</td><td>185.93</td><td>−62.53</td></tr><tr><td>LRTS (df)<xref ref-type="fn" rid="tfn3" /></td><td /><td>45.86** (3)</td><td /><td>12.50** (3)</td><td>21.84** (4)</td><td /></tr><tr><td>AIC</td><td>565.30</td><td>525.43</td><td>−325.51</td><td>−332.01</td><td>−345.85</td><td>179.06</td></tr><tr><td>ΔAIC</td><td /><td>−39.87</td><td /><td>−6.50</td><td>−13.84</td><td /></tr><tr><td>BIC</td><td>574.33</td><td>539.89</td><td>−314.67</td><td>−315.71</td><td>−363.12</td><td>227.84</td></tr><tr><td>Parameters</td></tr><tr><td>Expectancy for success (E)</td><td /><td /><td /><td /><td /><td /></tr><tr><td>Intercept μ<sub>E[I]</sub></td><td>7.95** (.39)</td><td>8.53** (.24)</td><td /><td /><td /><td>8.54** (.24)</td></tr><tr><td>Linear slope μ<sub>E[L]</sub></td><td /><td>−1.06** (.16)</td><td /><td /><td /><td>−1.06** (.16)</td></tr><tr><td>Ψ<sub>E[I]</sub></td><td>1.86** (.71)</td><td>1.61** (.55)</td><td /><td /><td /><td>1.75** (.54)</td></tr><tr><td>Ψ<sub>E[L]</sub></td><td /><td>.22 (.33)</td><td /><td /><td /><td>.32 (.30)</td></tr><tr><td>Ψ<sub>E[I]xE[L]</sub></td><td /><td>.35 (.19)</td><td /><td /><td /><td>.19 (.42)</td></tr><tr><td>ε<sub>E0</sub></td><td>1.44* (.71)</td><td>.77 (.63)</td><td /><td /><td /><td>.54 (.53)</td></tr><tr><td>ε<sub>E1</sub></td><td>1.53 (.86)</td><td>1.29* (.58)</td><td /><td /><td /><td>1.41* (.56)</td></tr><tr><td>ε<sub>E2</sub></td><td>5.41* (2.16)</td><td>2.27 (1.40)</td><td /><td /><td /><td>2.16 (1.28)</td></tr><tr><td>Salivary Cortisol (C)</td></tr><tr><td>Intercept μ<sub>C[I]</sub></td><td /><td /><td>.164** (.009)</td><td>.197** (.013)</td><td>.252** (.021)</td><td>.250** (.022)</td></tr><tr><td>Linear slope μ<sub>C[L]</sub></td><td /><td /><td /><td>−.023** (.008)</td><td>−.126** (.036)</td><td>−.122** (.037)</td></tr><tr><td>Quadratic slope μ<sub>C[Q]</sub></td><td /><td /><td /><td /><td>.035** (.013)</td><td>.034** (.013)</td></tr><tr><td>Ψ<sub>C[I]</sub></td><td /><td /><td>.002* (.001)</td><td>.002 (.002)</td><td>.017** (.006)</td><td>.017** (.006)</td></tr><tr><td>Ψ<sub>C[L]</sub></td><td /><td /><td /><td>.001 (.001)</td><td>.040** (.015)</td><td>.040** (.015)</td></tr><tr><td>Ψ<sub>C[Q]</sub></td><td /><td /><td /><td /><td>.004** (.002)</td><td>.004* (.002)</td></tr><tr><td>Ψ<sub>C[I]xC[L]</sub></td><td /><td /><td /><td>−.001 (.001)</td><td>−.025** (.009)</td><td>−.024** (.009)</td></tr><tr><td>Ψ<sub>C[I]xC[Q]</sub></td><td /><td /><td /><td /><td>.008** (.003)</td><td>.008* (.003)</td></tr><tr><td>Ψ<sub>C[L]xC[Q]</sub></td><td /><td /><td /><td /><td>−.013** (.005)</td><td>−.013* (.005)</td></tr><tr><td>ε<sub>C0</sub></td><td /><td /><td>.027** (.006)</td><td>.022** (.006)</td><td>.004 (.004)</td><td>.005 (.004)</td></tr><tr><td>ε<sub>C1</sub></td><td /><td /><td>.004** (.001)</td><td>.004** (.001)</td><td>.003** (.001)</td><td>.004** (.001)</td></tr><tr><td>ε<sub>C2</sub></td><td /><td /><td>.003** (.001)</td><td>.002 (.001)</td><td>.002 (.001)</td><td>.002 (.001)</td></tr><tr><td>ε<sub>C3</sub></td><td /><td /><td>.009* (.004)</td><td>.009 (.004)</td><td>.007 (.004)</td><td>.008 (.004)</td></tr><tr><td>Covariance</td></tr><tr><td>Ψ<sub>C[I]xE[I]</sub></td><td /><td /><td /><td /><td /><td>.023 (.035)</td></tr><tr><td>Ψ<sub>C[I]xE[L]</sub></td><td /><td /><td /><td /><td /><td>.047* (.019)</td></tr><tr><td>Ψ<sub>E[I]xC[L]</sub></td><td /><td /><td /><td /><td /><td>−.067 (.057)</td></tr><tr><td>Ψ<sub>C[L]xE[L]</sub></td><td /><td /><td /><td /><td /><td>−.075** (.024)</td></tr><tr><td>Ψ<sub>E[I]xC[Q]</sub></td><td /><td /><td /><td /><td /><td>.021 (.020)</td></tr><tr><td>Ψ<sub>C[Q]xE[L]</sub></td><td /><td /><td /><td /><td /><td>.026** (.008)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 Abbreviations: μ<subs>Factor</subs> = mean of latent variable. Ψ<subs>Factor</subs> = variance/covariance of latent variables. [I] = intercept. [L] = linear slope. [Q] = quadratic slope. <emph>ε =</emph> residual variance of manifest variables. E = expectancy for success, C = cortisol. LGM = latent growth model, PP‐LGM = parallel process latent growth model. AIC = Akaike information criterion, BIC = Bayesian information criterion. Higher Log likelihood and lower AIC & BIC suggest better model fit for nested models. Standard errors reported within the parentheses after all parameter estimates. All parameters unstandardized.</item> <item>3 a Likelihood Ratio Test Statistic significance derived using difference in number of parameters and assuming a χ<sups>2</sups> distribution.</item> <item>4 * <emph>p</emph> < .05, ** <emph>p</emph> < .01.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/02apr23/bjep12521-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12521-fig-0003.jpg" title="3 Growth Trajectory for Predicted Change in Student μg/dl Salivary Cortisol (Blue) by Time Overlaid on Top of Observed Change in Salivary Cortisol" /> </p> <p></p> <p>We then estimated two univariate LGMs examining change in expectancy during the examination, one with no slope (intercept‐only) and one with linear change (linear) and compared the model fit. We observed a significant improvement to model fit when comparing the linear with the intercept‐only model, LRTS (<emph>df</emph>) = 45.86 (<reflink idref="bib3" id="ref79">3</reflink>), <emph>p</emph> < .01, ΔAIC = −39.87. We present the best‐fitting linear plot of this growth model in Figure 4. In examining explained and residual variances in the manifest variables, the linear model better fit the observed data at every time point when compared to the intercept‐only model. For the best‐fitting model, participants started high in expectancy for success; intercept expectancy for success (μ<subs>E[I]</subs>) = 8.53, <emph>SE</emph> = .24, <emph>p</emph> < .01. Expectancy for success then declined during the examination; slope expectancy for success (μ<subs>E[L]</subs>) = −1.06/h, <emph>SE</emph> = .16, <emph>p</emph> < .01. Although we observed significant variance in the expectancy intercept, ψ<subs>E[L]</subs> = 1.61, <emph>SE</emph> = .55, <emph>p</emph> < .01, there was less variance in slope, ψ<subs>E[I]</subs> = .22, <emph>SE</emph> = .33, <emph>p</emph> = .51. Additionally, we did not observe a significant covariance between initial level of expectancy and change in expectancy, Ψ<subs>E[I]xE[L]</subs> = .35, <emph>SE</emph> = .19, <emph>p</emph> = .42.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/6KX/02apr23/bjep12521-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjep12521-fig-0004.jpg" title="4 Growth Trajectory for Predicted Change in Expectancy for Success (Blue) by Time Overlaid on Top of Observed Change in Expectancy for Success" /> </p> <p></p> <p>To answer our second research question, we estimated a parallel process LGM examining the relation of initial level and change in expectancies and cortisol during the examination. As we had already determined the best‐fitting growth trajectory for both expectancies and cortisol independently, we fit only a model assuming a linear change in expectancy and a quadratic change in cortisol. We observed no covariance between initial levels of expectancy and cortisol, ψ<subs>E[I]xC[I]</subs> = .023, <emph>SE</emph> = .035 <emph>p</emph> = .50. The initial level of expectancy was also unrelated to either linear change in cortisol, ψ<subs>E[I]xC[L]</subs> = −.067, <emph>SE</emph> = .057, <emph>p</emph> = .24, or quadratic change in cortisol, ψ<subs>E[I]xC[L]</subs> = .021, <emph>SE</emph> = .020, <emph>p</emph> = .30. Initial level of cortisol, however, positively covaried with change in expectancy, ψ<subs>C[I]xE[L]</subs> = .047, <emph>SE</emph> = .019, <emph>p</emph> = .01; individuals with higher cortisol levels were less likely to experience as great a decrease in expectancy for success during the examination. Negative linear change in cortisol inversely covaried with a negative linear change in expectancy, ψ<subs>C[L]xE[L]</subs> = −.075, <emph>SE</emph> = .024, <emph>p</emph> < .01; individuals who experienced a less negative linear change in cortisol experienced a greater decrease in expectancy for success. Individuals who experienced a greater decrease in expectancy also experienced greater quadratic change in cortisol, ψ<subs>C[Q]xE[L]</subs> = .026, <emph>SE</emph> = .008, <emph>p</emph> < .01.</p> <hd id="AN0163282825-20">DISCUSSION</hd> <p>This study used changes in salivary cortisol as an indicator of the physiological component of test anxiety. We examined the relation between change in salivary cortisol and change in students' expectancy for success as proposed by CVT. In the context of a midterm authentic practice examination, students experienced a significant decrease in salivary cortisol during the examination, echoing prior research that has found that individuals often experience spikes in cortisol in anticipation of an examination (e.g., Schoofs et al., [<reflink idref="bib46" id="ref80">46</reflink>]; Spangler et al., [<reflink idref="bib51" id="ref81">51</reflink>]). Additionally, a significant quadratic term attenuated students' declination in salivary cortisol. Prior research on the cortisol response to performance tasks has been conducted using short tasks (e.g., 20 min; Kirschbaum et al., [<reflink idref="bib23" id="ref82">23</reflink>]). In the authentic examination environment, we used for this study, participants were allowed to take as much time as necessary to complete the examination, with one participant taking over 3 h to complete the examination. This additional time allowed some participants to experience a second wave of physiological arousal near the end of the examination, with model‐predicted salivary cortisol levels again beginning to increase 1 h and 45 min into the examination. This second wave of cortisol response occurred near the end of the examination for most participants. However, it is unclear if participants' physiological arousal increased due to the increased difficulty of the last several questions, the anticipation of performance feedback (i.e., receiving the results of the practice examination), a cyclical pattern in which students experience waves of stress‐related HPA‐axis activation separated by intervals where their bodies recover, or some other factor. Future research should be conducted to examine the function of time in relation to physiological arousal during academic examinations.</p> <p>We also found evidence to partially support the hypothesized negative relation between initial level and change in expectancy for success and the indicator of the physiological component of test anxiety, cortisol. CVT proposes that control appraisals such as expectancies for success will be negatively related to negative emotions such as test anxiety. In our study, however, expectancy for success and initial cortisol levels were unrelated. We did find that there was a significant relation between the change in expectancy and the change in cortisol. Students who reported a greater decrease in expectancy for success throughout the examination experienced less recovery from their pre‐examination cortisol levels. The lack of relation between initial expectancies for success and cortisol is inconsistent with our expectations based on the CVT. However, our findings confirm that initial expectancies may frame students' emotions—at least in terms of physiological arousal ‐ throughout the examination.</p> <p>The findings have implications both for CVT and for researchers who examine cortisol in the context of examinations (Lacey et al., [<reflink idref="bib26" id="ref83">26</reflink>]; Verschoor & Markus, [<reflink idref="bib56" id="ref84">56</reflink>]). Researchers have questioned the proposed protective effects of self‐efficacy against both acute and chronic stress‐related physiological arousal (Schönfeld et al., [<reflink idref="bib45" id="ref85">45</reflink>]); other studies have not found relations between self‐efficacy and either the initial rise in cortisol or change in cortisol during authentic oral examinations (Ringeisen et al., [<reflink idref="bib39" id="ref86">39</reflink>]). However, self‐efficacy was measured typically at the beginning of a task, and changes in outcome expectancies were not considered. The findings of our study support the proposition that control appraisals and physiological components of emotions are dynamic processes. To explore these dynamic relations proposed by CVT, research which investigates these phenomena in‐situ is needed (Kiuru et al., [<reflink idref="bib24" id="ref87">24</reflink>]).</p> <p>When thinking about possible applications of this work to practice, it is interesting that students' initial expectancy for success did not predict their level of stress at the start of the examination. Students' revisions of their expectancies after being confronted with the actual examination were related to either regulation or dysregulation of their HPA‐axis. As we think about how to support students, providing them with strategies to address their stress during an examination or to support control appraisals during the examination may be of value. Additionally, these results indicate that researchers who are interested in the relation between control beliefs and biological markers need to look at changes in students' beliefs before, during and after the examination.</p> <p>There were several limitations to the present study. Our sample size was small, particularly for the complexity of the statistical models we used. Due to the structure of our data and the necessary use of individually varying times of observation PP‐LGM, we were not able to use the recommended Bayesian Estimation technique for small sample sizes (Felt et al., [<reflink idref="bib11" id="ref88">11</reflink>]). Future research utilizing a larger sample size will be needed to confirm the patterns of relations observed in this study and to explore the dynamic nature of the relations between control appraisals and physiological responses. We did not capture students' cortisol awakening response, which could better contextualize students' cortisol response during the examination (e.g., Dienes et al., [<reflink idref="bib7" id="ref89">7</reflink>]). Future research should consider capturing morning saliva samples to provide more context for students' responses during the examination.</p> <p>An additional limitation is that we did not include potential moderating and mediating factors such as students' emotion regulation (Gross & John, [<reflink idref="bib15" id="ref90">15</reflink>]) or value appraisals (Pekrun, [<reflink idref="bib32" id="ref91">32</reflink>]). Although much less frequent in examination contexts (Pekrun et al., [<reflink idref="bib36" id="ref92">36</reflink>]), other intense negative emotions can also be related to spikes in cortisol. Future research should add self‐report measures of test anxiety (Hoferichter et al., [<reflink idref="bib18" id="ref93">18</reflink>]), perceptions of stress (Salmela‐Aro et al., [<reflink idref="bib42" id="ref94">42</reflink>]) and other achievement emotions (Pekrun et al., [<reflink idref="bib35" id="ref95">35</reflink>]). Additionally, we were unable to assess the degree to which interrupting students during the performance task to survey them about their expectancy for success and collect saliva samples impacted their arousal response. Future research should consider how these data collection procedures may impact students' experiences during the task. Finally, the participants in this study reflected both the homogeneous population of students at the university from which it came, as well as the broader racial, ethnic and gender demographics of engineering students in the United States more broadly (e.g., Botella et al., [<reflink idref="bib5" id="ref96">5</reflink>]; Villanueva et al., [<reflink idref="bib57" id="ref97">57</reflink>]). The results cannot be generalized beyond this population. Future research should be conducted with a larger, more diverse sample to examine if these relations hold for the broader population.</p> <hd id="AN0163282825-21">CONCLUSIONS</hd> <p>In this study, we found a significant relation between change in an indicator of the physiological component of test anxiety, salivary cortisol and expectancy for success in an authentic examination context. Many students showed anticipatory physiological arousal as indicated by HPA‐axis activation before the examination. Consistent with prior research on cortisol and performance tasks (e.g., Spangler et al., [<reflink idref="bib51" id="ref98">51</reflink>]), most students returned to baseline cortisol level as the examination progressed. However, not all students' cortisol levels declined during the examination; students whose expectancies for success declined did not experience a return towards homeostasis. These findings have implications for those concerned about the possible health effects of examinations. Our study suggests that low expectancies for success on a practice examination in an essential but challenging course are related to lack of cortisol recovery. Research has demonstrated that repeated acute physiological arousal has adverse health effects. Researchers argue that this may have to do with the inflammatory effects of cortisol itself (Sin et al., [<reflink idref="bib50" id="ref99">50</reflink>]). As such, future research should explore the possible health consequences of high stakes examinations, a frequent occurrence, for engineering majors as well as other groups of students. The findings also speak to those seeking to support students in reducing their test anxiety. We argue that physiological arousal is part of many students' examination experiences. Interventions may need to address both objective physiological arousal and subjectively experienced anxiety. One example may be mindfulness‐based interventions, which have demonstrated positive effects on HPA‐axis arousal (Aguilar‐Raab et al., [<reflink idref="bib2" id="ref100">2</reflink>]).</p> <hd id="AN0163282825-22">AUTHOR CONTRIBUTIONS</hd> <p> <bold>Matthew C. Graham:</bold> Conceptualization; Data curation; Formal analysis; Visualization; Writing – original draft. <bold>Jenefer Husman:</bold> Conceptualization; Funding acquisition; Methodology; Writing – original draft. <bold>Reinhard Pekrun:</bold> Conceptualization; Methodology; Writing – original draft. <bold>Idalis Villanueva:</bold> Conceptualization; Funding acquisition; Investigation; Writing – review and editing. <bold>Darcie Christensen:</bold> Data curation; Investigation; Writing – review and editing. All authors reviewed final manuscript.</p> <hd id="AN0163282825-23">ACKNOWLEDGEMENTS</hd> <p>This material is based upon work supported in part by the National Science Foundation in the United States of America (NSF) No. DUE‐1661100, DUE‐1661117, & EEC‐1653140. Any opinions, findings and conclusions or recommendations expressed in this material do not necessarily reflect those of the National Science Foundation.</p> <hd id="AN0163282825-24">CONFLICT OF INTEREST</hd> <p>All authors declare no conflict of interest.</p> <hd id="AN0163282825-25">DATA AVAILABILITY STATEMENT</hd> <p>Data available on request due to privacy/ethical restrictions.</p> <ref id="AN0163282825-26"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref31" type="bt">1</bibl> <bibtext> Adams, R. E., Santo, J. B., & Bukowski, W. M. (2011). The presence of a best friend buffers the effects of negative experiences. Developmental Psychology, 47 (6), 1786 – 1791. https://doi.org/10.1037/a0025401</bibtext> </blist> <blist> <bibl id="bib2" idref="ref74" type="bt">2</bibl> <bibtext> Aguilar‐Raab, C., Stoffel, M., Hernández, C., Rahn, S., Moessner, M., Steinhilber, B., & Ditzen, B. (2021). Effects of a mindfulness‐based intervention on mindfulness, stress, salivary alpha‐amylase and cortisol in everyday life. Psychophysiology, 58 (12), e13937. https://doi.org/10.1111/psyp.13937</bibtext> </blist> <blist> <bibl id="bib3" idref="ref69" type="bt">3</bibl> <bibtext> Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19 (6), 716 – 723. https://doi.org/10.1109/tac.1974.1100705</bibtext> </blist> <blist> <bibl id="bib4" idref="ref15" type="bt">4</bibl> <bibtext> Barsegyan, A., Mackenzie, S. M., Kurose, B. D., McGaugh, J. L., & Roozendaal, B. (2010). Glucocorticoids in the prefrontal cortex enhance memory consolidation and impair working memory by a common neural mechanism. Proceedings of the National Academy of Sciences, 107 (38), 16655 – 16660. https://doi.org/10.1073/pnas.1011975107</bibtext> </blist> <blist> <bibl id="bib5" idref="ref96" type="bt">5</bibl> <bibtext> Botella, C., Rueda, S., López‐Iñesta, E., & Marzal, P. (2019). Gender diversity in STEM disciplines: A multiple factor problem. Entropy, 21 (1), 30. https://doi.org/10.3390/e21010030</bibtext> </blist> <blist> <bibl id="bib6" idref="ref13" type="bt">6</bibl> <bibtext> Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical integration and synthesis of laboratory research. Psychological Bulletin, 130 (3), 355 – 391. https://doi.org/10.1037/0033‐2909.130.3.355</bibtext> </blist> <blist> <bibl id="bib7" idref="ref77" type="bt">7</bibl> <bibtext> Dienes, K., Gartland, N., & Ferguson, E. (2019). The relationship between the cortisol awakening response and cortisol reactivity to a laboratory stressor. British Journal of Health Psychology, 24 (2), 265 – 281. https://doi.org/10.1111/bjhp.12352</bibtext> </blist> <blist> <bibl id="bib8" idref="ref60" type="bt">8</bibl> <bibtext> Duncan, T. E., & Duncan, S. C. (2009). The ABC's of LGM: An introductory guide to latent variable growth curve modeling. Social and Personality Psychology Compass, 3 (6), 979 – 991. https://doi.org/10.1111/j.1751‐9004.2009.00224.x</bibtext> </blist> <blist> <bibl id="bib9" idref="ref18" type="bt">9</bibl> <bibtext> Eccles, J. S., & Wigfield, A. (2002). Motivational Beliefs, Values, and Goals. Annual Review of Psychology, 53 (1), 109 – 132. https://doi.org/10.1146/annurev.psych.53.100901.135153</bibtext> </blist> <blist> <bibtext> Eccles, J. S., & Wigfield, A. (2020). From expectancy‐value theory to situated expectancy‐value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859</bibtext> </blist> <blist> <bibtext> Felt, J. M., Depaoli, S., & Tiemensma, J. (2017). Latent growth curve models for biomarkers of the stress response. Frontiers in Neuroscience, 11, 315. https://doi.org/10.3389/fnins.2017.00315</bibtext> </blist> <blist> <bibtext> Foley, P., & Kirschbaum, C. (2010). Human hypothalamus–pituitary–adrenal axis responses to acute psychosocial stress in laboratory settings. Neuroscience & Biobehavioral Reviews, 35 (1), 91 – 96. https://doi.org/10.1016/j.neubiorev.2010.01.010</bibtext> </blist> <blist> <bibtext> Goetz, T., Preckel, F., Pekrun, R., & Hall, N. C. (2007). Emotional experiences during test taking: Does cognitive ability make a difference? Learning and Individual Differences, 17 (1), 3 – 16. https://doi.org/10.1016/j.lindif.2006.12.002</bibtext> </blist> <blist> <bibtext> Granger, D. A., Fortunato, C. K., Beltzer, E. K., Virag, M., Bright, M. A., & Out, D. (2012). Focus on Methodology: Salivary bioscience and research on adolescence: An integrated perspective. Journal of Adolescence, 35 (4), 1081 – 1095. https://doi.org/10.1016/j.adolescence.2012.01.005</bibtext> </blist> <blist> <bibtext> Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well‐being. Journal of Personality and Social Psychology, 85 (2), 348 – 362. https://doi.org/10.1037/0022‐3514.85.2.348</bibtext> </blist> <blist> <bibtext> Hanneman, S. K., Cox, C. D., Green, K. E., & Kang, D. H. (2011). Estimating Intra‐ and Inter‐Assay Variability in Salivary Cortisol. Biological Research for Nursing, 13 (3), 243 – 250. https://doi.org/10.1177/1099800411404061</bibtext> </blist> <blist> <bibtext> Harley, J. M., Lou, N. M., Liu, Y., Cutumisu, M., Daniels, L. M., Leighton, J. P., & Nadon, L. (2021). University students' negative emotions in a computer‐based examination: the roles of trait test‐emotion, prior test‐taking methods and gender. Assessment & Evaluation in Higher Education, 46 (6), 956 – 972. https://doi.org/10.1080/02602938.2020.1836123</bibtext> </blist> <blist> <bibtext> Hoferichter, F., Raufelder, D., Ringeisen, T., Rohrmann, S., & Bukowski, W. M. (2015). Assessing the multi‐faceted nature of test anxiety among secondary school students: An English version of the German Test Anxiety Questionnaire: PAF‐E. The Journal of Psychology, 1‐23, 450 – 468. https://doi.org/10.1080/00223980.2015.1087374</bibtext> </blist> <blist> <bibtext> Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6 (1), 1 – 55. https://doi.org/10.1080/10705519909540118</bibtext> </blist> <blist> <bibtext> Joseph, N. T., Jiang, Y., & Zilioli, S. (2021). Momentary emotions and salivary cortisol: A systematic review and meta‐analysis of ecological momentary assessment studies. Neuroscience & Biobehavioral Reviews, 125, 365 – 379. https://doi.org/10.1016/j.neubiorev.2021.02.042</bibtext> </blist> <blist> <bibtext> Juster, R.‐P., McEwen, B. S., & Lupien, S. J. (2010). Allostatic load biomarkers of chronic stress and impact on health and cognition. Neuroscience & Biobehavioral Reviews, 35 (1), 2 – 16. https://doi.org/10.1016/j.neubiorev.2009.10.002</bibtext> </blist> <blist> <bibtext> Kemeny, M. E., & Shestyuk, A. (2008). Emotions, the neuroendocrine and immune systems, and health. In M. Lewis, J. M. Haviland‐Jones, & L. F. Barrett (Eds.), Handbook of emotions (pp. 661 – 675). Guilford Press.</bibtext> </blist> <blist> <bibtext> Kirschbaum, C., Pirke, K.‐M., & Hellhammer, D. H. (1993). The "Trier Social Stress Test" – A Tool for Investigating Psychobiological Stress Responses in a Laboratory Setting. Neuropsychobiology, 28 (1–2), 76 – 81. https://doi.org/10.1159/000119004</bibtext> </blist> <blist> <bibtext> Kiuru, N., Spinath, B., Clem, A.‐L., Eklund, K., Ahonen, T., & Hirvonen, R. (2020). The dynamics of motivation, emotion, and task performance in simulated achievement situations. Learning and Individual Differences, 80, 101873. https://doi.org/10.1016/j.lindif.2020.101873</bibtext> </blist> <blist> <bibtext> Labuschagne, I., Grace, C., Rendell, P., Terrett, G., & Heinrichs, M. (2019). An introductory guide to conducting the Trier Social Stress Test. Neuroscience & Biobehavioral Reviews, 107, 686 – 695. https://doi.org/10.1016/j.neubiorev.2019.09.032</bibtext> </blist> <blist> <bibtext> Lacey, K., Zaharia, M. D., Griffiths, J., Ravindran, A. V., Merali, Z., & Anisman, H. (2000). A prospective study of neuroendocrine and immune alterations associated with the stress of an oral academic examination among graduate students. Psychoneuroendocrinology, 25 (4), 339 – 356. https://doi.org/10.1016/S0306‐4530(99)00059‐1</bibtext> </blist> <blist> <bibtext> Lewis, F., Butler, A., & Gilbert, L. (2010). A unified approach to model selection using the likelihood ratio test. Methods in Ecology and Evolution, 2 (2), 155 – 162. https://doi.org/10.1111/j.2041‐210x.2010.00063.x</bibtext> </blist> <blist> <bibtext> Linnenbrink‐Garcia, L., Wormington, S. V., & Ranellucci, J. (2016). Measuring affect in educational contexts: A Circumplex Approach. In Methodological advances in research on emotion and education (pp. 165 – 178). Springer.</bibtext> </blist> <blist> <bibtext> Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5 (1), 23 – 43. https://doi.org/10.1037/1082‐989x.5.1.23</bibtext> </blist> <blist> <bibtext> Muthén, L. K., & Muthén, B. O. (1998 –2017). Mplus user's guide (Eighth ed.). Muthén & Muthén.</bibtext> </blist> <blist> <bibtext> Office of Management and Budget (1997). Revisions to the standards for the classification of federal data on race and ethnicity. 62 (210), 58782 – 58790. Executive Office of the President, Office of Management and Budget (OMB), Office of Information and Regulatory Affairs.</bibtext> </blist> <blist> <bibtext> Pekrun, R. (2006). The control‐value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18 (4), 315 – 341. https://doi.org/10.1007/s10648‐006‐9029‐9</bibtext> </blist> <blist> <bibtext> Pekrun, R. (2018). Control‐value theory: A social‐cognitive approach to achievement emotions. In G. A. D. Liem & D. M. McInerney (Eds.), Big theories revisited 2: A volume of research on sociocultural influences on motivation and learning (pp. 162 – 190). Information Age Publishing.</bibtext> </blist> <blist> <bibtext> Pekrun, R. (2021). Self‐appraisals and emotions: A generalized control‐value approach. In T. Dicke, F. Guay, H. W. Marsh, R. G. Craven, & D. M. McInerney (Eds.), Self – a multidisciplinary concept (pp. 1 – 30). Information Age Publishing.</bibtext> </blist> <blist> <bibtext> Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students' learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36 (1), 36 – 48. https://doi.org/10.1016/j.cedpsych.2010.10.002</bibtext> </blist> <blist> <bibtext> Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students' self‐regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37 (2), 91 – 105. https://doi.org/10.1207/S15326985EP3702_4</bibtext> </blist> <blist> <bibtext> Preacher, K. J., Wichman, A. L., MacCallum, R. C., & Briggs, N. E. (2008). Latent Growth modeling. Sage.</bibtext> </blist> <blist> <bibtext> Putwain, D. (2007). Researching academic stress and anxiety in students: some methodological considerations. British Educational Research Journal, 33 (2), 207 – 219. https://doi.org/10.1080/01411920701208258</bibtext> </blist> <blist> <bibtext> Ringeisen, T., Lichtenfeld, S., Becker, S., & Minkley, N. (2019). Stress experience and performance during an oral exam: the role of self‐efficacy, threat appraisals, anxiety, and cortisol. Anxiety, Stress, & Coping, 32 (1), 50 – 66. https://doi.org/10.1080/10615806.2018.1528528</bibtext> </blist> <blist> <bibtext> Roick, J., & Ringeisen, T. (2017). Self‐efficacy, test anxiety, and academic success: A longitudinal validation. International Journal of Educational Research, 83, 84 – 93. https://doi.org/10.1016/j.ijer.2016.12.006</bibtext> </blist> <blist> <bibtext> Roos, A. L., Goetz, T., Voracek, M., Krannich, M., Bieg, M., Jarrell, A., & Pekrun, R. (2020). Test anxiety and physiological arousal: A systematic review and meta‐analysis. Educational Psychology Review, 33 (2), 579 – 618. https://doi.org/10.1007/s10648‐020‐09543‐z</bibtext> </blist> <blist> <bibtext> Salmela‐Aro, K., Kiuru, N., Leskinen, E., & Nurmi, J.‐E. (2009). School Burnout Inventory (SBI). European Journal of Psychological Assessment, 25 (1), 48 – 57. https://doi.org/10.1027/1015‐5759.25.1.48</bibtext> </blist> <blist> <bibtext> Sanz, A., & Villamarín, F. (2001). The role of perceived control in physiological reactivity: self‐efficacy and incentive value as regulators of cardiovascular adjustment. Biological Psychology, 56 (3), 219 – 246. https://doi.org/10.1016/s0301‐0511(01)00095‐3</bibtext> </blist> <blist> <bibtext> Schlotz, W. (2019). Investigating associations between momentary stress and cortisol in daily life: What have we learned so far? Psychoneuroendocrinology, 105, 105 – 116.</bibtext> </blist> <blist> <bibtext> Schönfeld, P., Preusser, F., & Margraf, J. (2017). Costs and benefits of self‐efficacy: Differences of the stress response and clinical implications. Neuroscience & Biobehavioral Reviews, 75, 40 – 52. https://doi.org/10.1016/j.neubiorev.2017.01.031</bibtext> </blist> <blist> <bibtext> Schoofs, D., Hartmann, R., & Wolf, O. T. (2008). Neuroendocrine stress responses to an oral academic examination: No strong influence of sex, repeated participation and personality traits. Stress, 11 (1), 52 – 61. https://doi.org/10.1080/10253890701453943</bibtext> </blist> <blist> <bibtext> Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6 (2), 461 – 464. https://doi.org/10.1214/aos/1176344136</bibtext> </blist> <blist> <bibtext> Shell, D. F., & Husman, J. (2001). The multivariate dimensionality of personal control and future time perspective beliefs in achievement and self‐regulation. Contemporary Educational Psychology, 26 (4), 481 – 506. https://doi.org/10.1006/ceps.2000.1073</bibtext> </blist> <blist> <bibtext> Shell, D. F., & Husman, J. (2008). Control, motivation, affect, and strategic self‐regulation in the college classroom: A multidimensional phenomenon. Journal of Educational Psychology, 100 (2), 443 – 459. https://doi.org/10.1037/0022‐0663.100.2.443</bibtext> </blist> <blist> <bibtext> Sin, N. L., Ong, A. D., Stawski, R. S., & Almeida, D. M. (2017). Daily positive events and diurnal cortisol rhythms: Examination of between‐person differences and within‐person variation. Psychoneuroendocrinology, 83, 91 – 100.</bibtext> </blist> <blist> <bibtext> Spangler, G., Pekrun, R., Kramer, K., & Hofmann, H. (2002). Students' emotions, physiological reactions, and coping in academic exams. Anxiety, Stress & Coping: An International Journal, 15 (4), 413 – 432. https://doi.org/10.1080/1061580021000056555</bibtext> </blist> <blist> <bibtext> Stöber, J., & Pekrun, R. (2004). Advances in test anxiety research. Anxiety, Stress & Coping, 17 (3), 205 – 211. https://doi.org/10.1080/1061580412331303225</bibtext> </blist> <blist> <bibtext> Suresh, R. (2006). The relationship between barrier courses and persistence in engineering. Journal of College Student Retention: Research, Theory & Practice, 8 (2), 215 – 239.</bibtext> </blist> <blist> <bibtext> Szafranski, D. D., Barrera, T. L., & Norton, P. J. (2012). Test anxiety inventory: 30 years later. Anxiety, Stress, & Coping, 25 (6), 667 – 677. https://doi.org/10.1080/10615806.2012.663490</bibtext> </blist> <blist> <bibtext> Tilbrook, A. J. (2007). Neuropeptides, stress‐related. In G. Fink (Ed.), Encyclopedia of stress (pp. 903 – 908). Academic Press.</bibtext> </blist> <blist> <bibtext> Verschoor, E., & Markus, C. R. (2011). Affective and neuroendocrine stress reactivity to an academic examination: Influence of the 5‐HTTLPR genotype and trait neuroticism. Biological Psychology, 87 (3), 439 – 449. https://doi.org/10.1016/j.biopsycho.2011.06.001</bibtext> </blist> <blist> <bibtext> Villanueva, I., Di Stefano, M., Gelles, L., Osoria, P. V., & Benson, S. (2019). A race re‐imaged, intersectional approach to academic mentoring: Exploring the perspectives and responses of womxn in science and engineering research. Contemporary Educational Psychology, 59, 101786. https://doi.org/10.1016/j.cedpsych.2019.101786</bibtext> </blist> <blist> <bibtext> von der Embse, N., Jester, D., Roy, D., & Post, J. (2018). Test anxiety effects, predictors, and correlates: A 30‐year meta‐analytic review. Journal of Affective Disorders, 227, 483 – 493. https://doi.org/10.1016/j.jad.2017.11.048</bibtext> </blist> </ref> <aug> <p>By Matthew C. Graham; Jenefer Husman; Reinhard Pekrun; Idalis Villanueva and Darcie Christensen</p> <p>Reported by Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib52" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib58" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib41" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib32" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib33" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib34" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib17" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib40" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib38" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib39" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib21" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib10" firstref="ref19"></nolink> <nolink nlid="nl13" bibid="bib24" firstref="ref20"></nolink> <nolink nlid="nl14" bibid="bib13" firstref="ref21"></nolink> <nolink nlid="nl15" bibid="bib28" firstref="ref22"></nolink> <nolink nlid="nl16" bibid="bib54" firstref="ref24"></nolink> <nolink nlid="nl17" bibid="bib20" firstref="ref26"></nolink> <nolink nlid="nl18" bibid="bib55" firstref="ref27"></nolink> <nolink nlid="nl19" bibid="bib22" firstref="ref28"></nolink> <nolink nlid="nl20" bibid="bib44" firstref="ref29"></nolink> <nolink nlid="nl21" bibid="bib25" firstref="ref30"></nolink> <nolink nlid="nl22" bibid="bib43" firstref="ref34"></nolink> <nolink nlid="nl23" bibid="bib56" firstref="ref36"></nolink> <nolink nlid="nl24" bibid="bib51" firstref="ref37"></nolink> <nolink nlid="nl25" bibid="bib14" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib45" firstref="ref41"></nolink> <nolink nlid="nl27" bibid="bib53" firstref="ref52"></nolink> <nolink nlid="nl28" bibid="bib12" firstref="ref53"></nolink> <nolink nlid="nl29" bibid="bib31" firstref="ref54"></nolink> <nolink nlid="nl30" bibid="bib48" firstref="ref57"></nolink> <nolink nlid="nl31" bibid="bib49" firstref="ref58"></nolink> <nolink nlid="nl32" bibid="bib16" firstref="ref59"></nolink> <nolink nlid="nl33" bibid="bib29" firstref="ref61"></nolink> <nolink nlid="nl34" bibid="bib30" firstref="ref62"></nolink> <nolink nlid="nl35" bibid="bib37" firstref="ref63"></nolink> <nolink nlid="nl36" bibid="bib11" firstref="ref65"></nolink> <nolink nlid="nl37" bibid="bib19" firstref="ref67"></nolink> <nolink nlid="nl38" bibid="bib27" firstref="ref68"></nolink> <nolink nlid="nl39" bibid="bib47" firstref="ref70"></nolink> <nolink nlid="nl40" bibid="bib46" firstref="ref80"></nolink> <nolink nlid="nl41" bibid="bib23" firstref="ref82"></nolink> <nolink nlid="nl42" bibid="bib26" firstref="ref83"></nolink> <nolink nlid="nl43" bibid="bib15" firstref="ref90"></nolink> <nolink nlid="nl44" bibid="bib36" firstref="ref92"></nolink> <nolink nlid="nl45" bibid="bib18" firstref="ref93"></nolink> <nolink nlid="nl46" bibid="bib42" firstref="ref94"></nolink> <nolink nlid="nl47" bibid="bib35" firstref="ref95"></nolink> <nolink nlid="nl48" bibid="bib57" firstref="ref97"></nolink> <nolink nlid="nl49" bibid="bib50" firstref="ref99"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1374559
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The Dynamic Experience of Taking an Examination: Ever Changing Cortisol and Expectancy for Success
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Graham%2C+Matthew+C%2E%22">Graham, Matthew C.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0235-5126">0000-0002-0235-5126</externalLink>)<br /><searchLink fieldCode="AR" term="%22Husman%2C+Jenefer%22">Husman, Jenefer</searchLink><br /><searchLink fieldCode="AR" term="%22Pekrun%2C+Reinhard%22">Pekrun, Reinhard</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4489-3827">0000-0003-4489-3827</externalLink>)<br /><searchLink fieldCode="AR" term="%22Villanueva%2C+Idalis%22">Villanueva, Idalis</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8767-2576">0000-0002-8767-2576</externalLink>)<br /><searchLink fieldCode="AR" term="%22Christensen%2C+Darcie%22">Christensen, Darcie</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22British+Journal+of+Educational+Psychology%22"><i>British Journal of Educational Psychology</i></searchLink>. Apr 2023 93(1):195-210.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Science Foundation (NSF)
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Anxiety%22">Test Anxiety</searchLink><br /><searchLink fieldCode="DE" term="%22Tests%22">Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Expectation%22">Expectation</searchLink><br /><searchLink fieldCode="DE" term="%22Success%22">Success</searchLink><br /><searchLink fieldCode="DE" term="%22Physiology%22">Physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Biochemistry%22">Biochemistry</searchLink><br /><searchLink fieldCode="DE" term="%22Stress+Variables%22">Stress Variables</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/bjep.12521
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0007-0998<br />2044-8279
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: This study examined the relations between students' expectancies for success and a physiological component of test anxiety, salivary cortisol, during an authentic testing setting. Aims: The aim of the study was to better understand the connection between shifts in students' control appraisals and changes in the physiological component of test anxiety. Sample: The study comprised 45 undergraduate engineering majors in the United States. Methods: Survey data concerning students' expectancy for success and saliva samples were taken before, during and after the practice midterm examination prior to their actual in-class examination. Results: Students' expectancy for success declined during the examination while cortisol levels declined from the beginning to middle of the examination and began to increase again as a function of time. Although students' initial levels of expectancy for success and cortisol were not correlated, there was a negative relation between change in cortisol and change in expectancy for success. Conclusions: Our study demonstrates a relation between salivary cortisol, a physiological component of test anxiety and students' expectancy for success in an authentic testing context. Most students saw a decrease in cortisol during the examination, suggesting anticipatory anxiety prior to the test and a return to homeostasis as the examination progressed. Some students, however, did not see a declination in cortisol, suggesting they may not have recovered from pre-examination anxiety. The negative relation between change in cortisol and expectancy for success suggests that students who had the greatest decrease in expectancy for success saw the smallest recovery in cortisol.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1374559
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1374559
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/bjep.12521
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 195
    Subjects:
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Test Anxiety
        Type: general
      – SubjectFull: Tests
        Type: general
      – SubjectFull: Expectation
        Type: general
      – SubjectFull: Success
        Type: general
      – SubjectFull: Physiology
        Type: general
      – SubjectFull: Biochemistry
        Type: general
      – SubjectFull: Stress Variables
        Type: general
    Titles:
      – TitleFull: The Dynamic Experience of Taking an Examination: Ever Changing Cortisol and Expectancy for Success
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Graham, Matthew C.
      – PersonEntity:
          Name:
            NameFull: Husman, Jenefer
      – PersonEntity:
          Name:
            NameFull: Pekrun, Reinhard
      – PersonEntity:
          Name:
            NameFull: Villanueva, Idalis
      – PersonEntity:
          Name:
            NameFull: Christensen, Darcie
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 0007-0998
            – Type: issn-electronic
              Value: 2044-8279
          Numbering:
            – Type: volume
              Value: 93
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: British Journal of Educational Psychology
              Type: main
ResultId 1