Measures Among Current Nondonors. A dissertation presented to. the faculty of. the College of Arts and Sciences of Ohio University

Transcription

1 An Examination of the Use of Implicit Blood Donation Attitude and Social Identity Measures Among Current Nondonors A dissertation presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Regina M. Warfel December Regina M. Warfel. All Rights Reserved.

2 2 This dissertation titled An Examination of the Use of Implicit Blood Donation Attitude and Social Identity Measures Among Current Nondonors by REGINA M. WARFEL has been approved for the Department of Psychology and the College of Arts and Sciences by Christopher R. France Professor of Psychology Robert A. Frank Dean, College of Arts and Sciences

3 3 Abstract WARFEL, REGINA M., Ph.D., December 2013, Experimental Psychology An Examination of the Use of Implicit Blood Donation Attitude and Social Identity Measures Among Current Nondonors (151 pp) Director of Dissertation: Christopher R. France To expand our prior research and address the limitation of full reliance on selfreport measures within the blood donation literature, the present study examined the ability of three novel blood donation implicit measures to predict reported intention to donate blood, immediate decision to sign-up to donate blood, and confirmation of actual behavior among a sample of nondonors. A total of 225 undergraduate Psychology students with no history of blood donation agreed to participate in a 60-minute testing session, in which they sat at a computer to complete three implicit measures (image and word versions of implicit attitudes and implicit social-identity) followed by a series of explicit measures (donation attitudes, donation anxiety, self-efficacy, anticipated regret, subjective norm, descriptive norm, personal moral norm, and donation intention). After completing the computerized portion of the experiment, participants were given an opportunity to sign-up for a local blood drive that took place one to three weeks after the testing session. Finally, participants were contacted 30 days post session to confirm whether or not they donated blood. Results revealed that the image and word implicit measures demonstrated stronger internal consistency and construct validity than the social-identity implicit measure. Further, only the image implicit measure significantly predicted donation intention, explaining 1.7% of the variability. None of the implicit measures was shown to contribute variance over and above their explicit counterparts.

4 4 Likewise, level of decisiveness and consideration did not moderate the relationship between implicit measures and donation intention, sign-up behavior, or 30-day behavior. These findings suggest that, while the implicit attitude measures may be valid in this context, they appear to be weak predictors of nondonor intentions and behavior, especially when tested alongside their explicit counterparts.

5 5 To my husband, Greg Moeller, for his wonderful patience and support

6 6 Acknowledgements I am grateful to all of those who have contributed to this project. I would like to especially thank my advisor, Dr. Chris France, for his excellent feedback and guidance throughout all phases of this project. Much thanks to all members of my committee for their fantastic suggestions to make this an even higher quality study. I would also like to express my gratitude to the research assistants I had the pleasure to work with, including Katrina Hamilton, Anthony Hess, Emily Jones, Jake Mosher, Mary Mushaben, Markie Ruggeri, and Jessica Solano. Finally, I am grateful for the support of my family and friends and look forward to the time I ll have to spend with them when I am finished.

7 7 TABLE OF CONTENTS Page Abstract...3 Dedication...5 Acknowledgements...6 List of Tables...10 List of Figures...12 Introduction...13 Overview of Blood Donation...14 Needs and Trends...14 Theoretical Background...14 General Theory of Planned Behavior Findings...18 Important Constructs for Nondonors...20 Overview of Implicit Measurement...27 Implicit Measures...28 Superiority of the ST-IAT and SC-IAT...35 Theoretical Background...37 Dual-Process Models...37 Implicit-Explicit Relationship...39 Consumer and Voting Behaviors...40 Consumer-Related Behavior...41 Voting Behavior...55

8 8 Use of Implicit Measures in the Blood Donation Context...60 Pilot Study...62 The Current Study...63 Specific Aims and Hypotheses...64 Methods...68 Overview...68 Participants...68 Implicit Measures...69 Explicit Measures...73 Behavioral Measures...78 Procedures...78 ST-IAT Scoring and Data Reduction...88 Statistical Analyses...90 Results...95 Sample Characteristics...94 Data Preparation...96 Overall Findings...96 Predictive Validity Analyses...97 Incremental Predictive Validity Analyses Moderation of Decisiveness Moderation of Consideration Exploring Incremental Validity in the Presence of Multiple Explicit Predictors...112

9 9 Discussion References Appendix A: Demographic and Brief History Questionnaire Appendix B: Blood Donation Attitude Appendix C: Self-Efficacy Appendix D: Social Norm, Personal Moral Norm, and Donation Regret Appendix E: Donation Anxiety and Anticipated Regret Appendix F: Behavioral Intention Appendix G: Blood Donation Decisiveness and Consideration Appendix H: Blood Donation Sign-up & 30-day Follow-up of Behavior Appendix I: Informed Consent Appendix J: Screening Criteria Appendix K: Debriefing Form...160

10 10 LIST OF TABLES Page Table 1. Single Target Implicit Measures...32 Table 2. Consumer Behavior...43 Table 3. Voting Behavior...56 Table 4. Depiction of all 12 Images used in the Image Version of the Implicit Attitude Measure, including Four from each Attribute Category (Pleasant and Unpleasant) and Four from the Target Category (Blood Donation)...70 Table 5. List of the 12 Selected Words for the Word Version of Implicit Attitudes, Including Four from Each Attribute Category (Pleasant and Unpleasant) and Four from the Target Category (Blood Donation)...71 Table 6. List of the 12 Selected Words for the Implicit Social Identity Measure, Including Four from Each Attribute Category (Self and Other) and Four from the Target Category (Blood Donation)...73 Table 7. Overview of the Two Phases for the Image and Word Implicit Attitude Measures (Pleasant versus Unpleasant)...84 Table 8. Overview of the Two Phases for Implicit Social Identity (Self versus Other).85 Table 9. Correlations among Implicit (Image, Word, and Social-Identity ST-IATs) and Explicit Measures (Attitude, Anxiety, Self-Efficacy, Anticipated Regret, Descriptive Norm, Personal Moral Norm, and Intention)...98 Table 10. Descriptives and T-tests Comparing Males and Females for both Explicit and Implicit Measures...99 Table 11. Logistic Regression Analyses Testing the Ability of the Image, Word, and Social-Identity ST-IATs to Predict Blood Donation Sign-up and Self-reported Donation Attempt at 30-day Follow-up Table 12. Hierarchical Linear Regression Analyses to Predict Intention Using Decisiveness, Explicit Attitude, and Image and Word Implicit Attitude and their Interaction Terms...103

11 Table 13. Hierarchical Logistic Regression Analyses to Predict Sign-up Behavior using Decisiveness, Explicit Attitude, and Image and Word Implicit Attitude and their Interaction Terms Table 14. Hierarchical Logistic Regression Analyses to Predict 30-day Follow-up Behavior Using Decisiveness, Explicit Attitude, and Image and Word Implicit Attitude and their Interaction Terms Table 15. Hierarchical Linear Regression Analyses to Predict Donation Intention Using Consideration, Explicit Attitude, and Image and Word Implicit Attitude and their Interaction Terms Table 16. Hierarchical Logistic Regression Analyses to Predict Sign-up Behavior Using Consideration, Explicit Attitude, and Image Implicit Attitude and their Interaction Terms Table 17. Hierarchical Logistic Regression Analyses to Predict 30-day Follow-up Behavior Using Consideration, Explicit Attitude, and Image Implicit Attitude and their Interaction Terms Table 18. Correlations Between Implicit (Image and Word ST-IATs) and Explicit Measures (Attitude, Anxiety, Self-Efficacy, Anticipated Regret, Descriptive Norm, Personal Moral Norm, and Intention), Among Individuals with Low and High Perceived Social Pressure to Donate

12 12 LIST OF FIGURES Page Figure 1. Basic Constructs of the Theory of Planned Behavior (Ajzen, 1985)...16 Figure 2. Replication of Final Model Proposed by Robinson et al. (2008) with Standardized Path Coefficients...25 Figure 3. Flowchart of Study Procedures...80 Figure 4. Sample Screens of (a) Instructions (Indicating Key Assignment) and (b) Combined Block Trials During Phase 1 of the Word Version of Implicit Attitudes. For this Example, the Correct Response is the E Key...86 Figure 5. Sample Screens of (a) Instructions (Indicating Key Assignment) and (b) Combined Block Trial During Phase 2 of the Word Version of Implicit Attitudes. For this Example, the Correct Response is the I key, as the Word Gloom is Associated with the Unpleasant Category...87 Figure 6. Graphical Representation of Significant Interaction Between the Binary Variable Decisiveness and Explicit Attitude to Predict Intention, in the Presence of the Main Effects of these Variables and Image Implicit Attitude Figure 7. Graphical Representation of Significant Interaction Between the Binary Variable Decisiveness and Explicit Attitude to Predict Intention, in the Presence of the Main Effects of these Variables and Word Implicit Attitude...105

13 13 Introduction To address the ongoing need to recruit and retain blood donors, behavioral scientists have closely examined psychosocial factors involved in predicting key motivations underlying blood donation behavior. Based solely on the use of self-reported indices of these motivations, researchers have reliably predicted between 31-86% of the variability in donation intentions or actual behavior (Armitage & Conner, 2001a; Giles & Cairns, 1995; France, France & Himawan, 2007, 2008; Lemmens et al., 2005; Lemmens et al., 2009; Masser et al., 2009; Veldhuizen et al., 2011). Although these results are promising, a more accurate prediction of donation behavior may be limited by a reliance on self-report (i.e. explicit) measures to assess individual differences in emotions, cognitions, efficacy, and perceived social pressures toward blood donation. Consistent with this notion, behavioral scientists contend that all human behavior is a result of the interplay of conscious and unconscious processing (Baumeister, Masicampo, & Vohs, 2011). Further, in a recent meta-analysis Greenwald and colleagues (2009) found that the stronger the relationship between implicit and explicit measures, the greater the predictive validity of each, suggesting the importance of both forms of measurement. Therefore, while researchers have solely relied on explicit self-report measures in the past, implicit measures in this context may also be fruitful. We recently conducted the first study designed to assess the validity of implicit measurement within the blood donation context (Warfel, France & France, 2012). Preceding a detailed account of our pilot study results and the specific aims of the current project, the following introduction will review existing research regarding the prediction

14 14 of blood donation intention and behavior among those who have not previously donated. This will be followed by a thorough description of relevant implicit measurement theory and relevant techniques, along with a review of studies utilizing implicit measurement to predict behaviors that are analogous to blood donation. Overview of Blood Donation Needs and Trends. In the coming years, the demand for blood will rise substantially. This is precipitated by an anticipated 36% growth of the population of individuals over 65 during the next decade (The Federal Interagency Forum on Aging- Related Statistics, 2010), coupled with the fact that older adults utilize approximately 43% of the blood supply (Zuck et al, 1995). Thus, efforts to prevent future shortages are crucial. According to the most recent National Blood Collection and Utilization Survey (2009), only 5.4% of the eligible blood donor population (ages 16 to 64) donated in Among them, almost one-third (29.3%) were newly recruited first-time donors. Because it is estimated that only 8% of new donors will go on to become repeat donors (Schreiber et al., 2005), efforts to enhance recruitment and retention of new donors are crucial to meet the growing demands for blood and blood products. In order to address this need, behavioral scientists have applied models of behavioral motivation, such as the theory of planned behavior, to the blood donation context in order to gain knowledge about what motivates individuals to donate blood. Theoretical Background. The theory of reasoned action (TRA; Fishbein & Ajzen, 1975; 1980) proposes that one s stated intention to partake or not partake in a behavior is the direct determinant and therefore best predictor of a given action. The

15 15 theory of reasoned action is interested in understanding behaviors, not just predicting them, and thus two determinants of intention were also identified: a personal factor, attitude, and social factor, subjective norm. Intention is presumed to be guided by variations in attitude toward the behavior (i.e. positive or negative evaluation of the behavior) and subjective norm (i.e. perception of social pressures to perform the behavior). The TRA presumes that individuals intend to perform behaviors that they evaluate favorably and perceive that important others would like them to partake in. The theory presumes that for some intentions attitude may be the more important factor, whereas for other intentions normative beliefs may hold more weight; the influence of these two factors may also vary between individuals. To account for this, relative weights of these two parameters are incorporated such that intention is directly proportional to the weighted sum of the attitude and subjective norm of the behavior. While individuals are generally expected to act in accordance with their intentions, unforeseen circumstances may influence behavioral outcomes and become increasingly likely as the time interval between intention and observation of the behavior lengthens. While for many situations the theory of reasoned action may be sufficient, the TRA performs poorly for behaviors in which individuals have less volitional control over the outcome (i.e. a behavior which requires opportunities, resources, or skills). To account for this, the theory of reasoned action was modified by its own creator to include the construct perceived behavioral control (i.e. perception of factors that may facilitate or impede the behavior) in a newer model named the theory of planned behavior (TPB; Ajzen, 1985).

16 16 Figure 1. Basic constructs of the Theory of Planned Behavior (Ajzen, 1985) The TPB, as depicted in Figure 1, is currently the most widely used model to explain behavioral motivations, and has consistently demonstrated an ability to predict blood donation intention and behavior (e.g. Giles & Cairns, 1995; Armitage & Conner, 2001a; France, France & Himawan, 2007; Giles, McClenahan, Cairns & Mallet, 2004). In the context of blood donation, attitude represents a basic evaluative judgment of giving blood (i.e. good vs. bad), subjective norm refers to the perception of social pressure to donate blood, and perceived behavioral control is the perceived level of volitional control an individual has over donating blood. To better explain donor behavior, modifications and extensions of the TPB model have been made. For instance, perceived behavioral control (PBC) is similar to, and has often been interchanged with, Bandura s (1997) concept of self-efficacy (the perceived ease or difficulty of donating blood). Early TPB papers (Ajzen, 1985, 1987)

17 17 conceptualized PBC as perceived internal or external factors that may interfere with an individual s intended behavior. Although other behavioral scientists interpreted PBC narrowly as comprising of control factors only, and as discriminant from self-efficacy (Manstead & van Eekelen, 1998; Terry & O Leary, 1995), Ajzen (2006) later clarified that perceived self-efficacy (internal) and perceived control factors (external) are convergent constructs that together define the unitary construct of PBC. He illustrates this conceptualization of PBC through a hierarchical model, in which self-efficacy and controllability are two separate but compatible components that together comprise the higher order construct PBC. Of these two constructs, self-efficacy has surfaced as a stronger correlate and dominant overall predictor of donation intention and behavior than controllability (Armitage & Conner, 2001a; Giles & Cairns, 1995; Giles, McClenahan, Cairns, & Mallet, 2004). Other components that have been added to blood donation behavior models include moral norm (perceived moral correctness or incorrectness associated with the act of donating blood; Armitage & Conner, 2001; Godin et al, 2005; Robinson et al., 2008), descriptive norm (whether or not family and friends donate blood; Robinson et al., 2008), anticipated regret (negative feelings associated with failing to act in accordance with ones intention to donate blood; Richard, de Vries & Van Der Pligt, 1998; Robinson et al., 2008), donation anxiety (how anxious one expects they would feel when donating blood; Robinson et al., 2008) and anticipated affect (how one expects they will feel after donating blood; Godin et al., 2005; Lemmens et al., 2009). Studies incorporating these factors in order to improve our ability to predict nondonors intention to donate blood in the future will be examined in the next sections.

18 18 General Theory of Planned Behavior (TPB) Findings. Meta-analytic evidence provides strong general support for the basic TPB model, as Armitage and Conner (2001) reported that the TPB accounted for an average of 39% of the variability in intention and 27% of the variability in behavior. Researchers have also noted that the TPB is particularly well suited to the blood donation context (e.g. Giles & Cairns, 1995), and consistent with this notion average variability explained within this context is generally higher than for other behaviors. Through a variety of extended or augmented TPB models, blood donation researchers have reliably predicted 43-72% of the variability in intentions to donate blood (France, France & Himawan, 2007, 2008; Lemmens et al., 2005; Lemmens et al., 2009; Masser et al., 2009; Robinson et al., 2008; Veldhuizen et al., 2011). In addition, these studies have shown that donation experience influences the type and strength of motivational factors involved in deciding to donate blood in the future. When all donors (experienced and novice) were sampled, variations of the TPB model accounted for 52-76% (Armitage & Conner, 2001a; Giles & Cairns, 1995; Giles et al., 2004) of the variance in donation intention. Amount of variance explained remains high when only experienced donor populations are sampled (France, France, & Himawan, 2007; Masser et al., 2009), accounting for 65-86% of the variance in donor intention (France, France, & Himawan, 2008). However, when nondonors are exclusively considered, TPB-based models account for a relatively lower (41-70%) proportion of the variance (Lemmens et al., 2005; Lemmens et al., 2009; Robinson et al., 2008). One possible reason for the lower variance accounted for among nondonors is that individuals who, for example, have not even contemplated the idea of donating blood, may likewise

19 19 have lower explicit self-awareness regarding their attitudes toward the process of blood donation. If true, this suggests that measurement of automatic responses toward blood donation the goal of the present study may tap underlying attitudes where self-report cannot. The studies introduced thus far have been discussed in terms of self-reported intention as their main criterion variable. However, notwithstanding the fact that intention is a significant predictor of blood donation behavior (Ferguson, 1996), it is important to recognize that there is an intention-behavior gap. That is, intentions do not account for approximately 72% of the variability in behavior (e.g. Giles & Cairns, 1995); hence, reported intentions to donate blood may often be overestimated due to unanticipated factors such as forgetfulness, procrastination, or simply changing one s mind. For these reasons, use of a behavioral outcome measure is important to provide a truer test of the predictor variables. Only three of the eleven studies mentioned above used a behavioral-based criterion measure to test a TPB-based model, and they account for 54-70% of the variability in behavioral-based measures of donation (Armitage & Conner, 2001a; Giles & Cairns, 1995; Masser et al., 2009). Most recently, in a sample of experienced donors only, Masser et al. (2009) determined that an extended TPB model accounted for 70% of the variability of a self-reported measure of donor behavior three months after the lab session. Two other studies that incorporated both novice and experienced donors demonstrated that TPB-based models accounted for 56% of the variability in a one-week follow-up of donation behavior (Giles & Cairns, 1995) and 54% of the variability in a

20 20 hypothetical behavioral enactment scenario, in which participants were given a hypothetical situation of an opportunity to give blood and asked how likely it is that they would donate in that scenario (Armitage & Conner, 2001a). All three studies produced impressive values relative to TPB meta-analytic values for a variety of behavioral domains (27% on average; Armitage & Conner, 2001). However, the latter two studies used less than ideal measures of behavior (e.g. only one week follow-up and hypothetical scenario, respectively), and none of the studies tested a sample composed solely of nondonors. Thus, follow-up studies using improved behavioral measures focused on nondonor populations are needed. In sum, whether a criterion of reported intention or actual behavior is used, studies involving nondonor samples are not only scarce, but (based on the currently used predictive measures) these studies provide poorer prediction than those using donor samples. Important Constructs for Nondonors. Although most research has focused on the motivations of established blood donors or mixed samples, it has been argued that the motivations of donors and nondonors are distinct and should be examined separately (Ferguson & Bibby, 2002). Not surprisingly, individuals without a history of donation generally report higher levels of fear and anxiety than established donors (Bednall & Bove, 2011), lapsed donors (Duboz & Cuneo, 2010), or young donors (Giles & Cairns, 1995; Giles et al., 2004). Fears of blood, needles, and/or vasovagal reactions are among the most cited reasons for not donating (e.g., Beerli-Palacio & Martin-Santana, 2009; Bednall & Bove, 2011; Olatunji, Etzel & Ciesielski, 2010; France, Rader, & Carlson,

21 ), and these negative factors, along with fear of catching an infection or worry about the loss of time (e.g. Giles et al., 2004), are more common for nondonors (e.g. McMahon & Byrne, 2008). In addition, it is well documented that fears of blood, needles, or physical reactions are often the factors that lead many individuals to associate blood donation with anxiety and negative affect (e.g., Duboz & Cuneo, 2010; Olatunji, Etzel & Ciesielski, 2010). Many researchers have attempted to incorporate these types of concerns in extended TPB models to predict donation intentions for both new and experienced donors; predictors that have been shown to be important for nondonors will be reviewed below. Many earlier studies used combined samples of novice and experienced donors without testing their models on each population separately; an exception was one study by Godin and colleagues (2005), as they sampled enough Quebec community members (N = 1116) to conduct additional tests of their model on ever versus never donors separately. These researchers merged concepts from several theoretical frameworks to assess an augmented model of behavior. Overall, among the factors that were tested (perceived behavioral control, facilitating factors, anticipated regret, moral norm, and attitude), all of the factors except moral norm contributed significantly to the prediction of intentions among nondonors. On the other hand, for donors, all of the factors except attitude contributed toward the prediction of intention. When the strength of each of the five factors was directly compared between donors and nondonors, attitude was a significantly stronger predictor (p < 0.05) of intention for nondonors than donors,

22 22 whereas perceived behavioral control (p < 0.01) and moral norm (p < 0.01) were significantly stronger predictors for donors. As previously noted, perceived behavioral control may have been significantly stronger for those with donation experience due to greater confidence and accuracy in donors perception of their own capability of carrying out the behavior. Similarly, moral norm may have been stronger for donors because items representing this factor were very specific to one s personal feelings of the degree to which the act of blood donation has moral significance (e.g. it is in accordance with my principles to give blood), and those who have donated may be more likely to have spent time processing the behavior deeply at both a personal and moral level. Of note, other studies represent moral norm in a way that can be more easily interpretable for both donors and nondonors (e.g. I feel a personal responsibility to give blood; Lemmens et al., 2009). Finally, the finding that attitude was a stronger predictor for nondonors is expected given that donation anxiety may be best represented within the attitude construct. In sum, results of the Godin et al. (2005) study suggest that nondonors may be more motivated by their perception of how pleasant or unpleasant the process of donation is, whereas donors may be more likely to personalize blood donation as a moral responsibility and may feel more certain of their ability to carry out the behavior. Lemmens and colleagues (2005) also examined the predictive capability of a TPB-based model on a sample of 284 young (mean age = 19.7) undergraduate nondonors. These researchers tested the ability of several TPB-based (attitude, selfefficacy, and subjective norm) and extended factors (personal moral norm, anticipated

23 23 affective consequences, perceived knowledge, and knowledge) to contribute unique variance toward predicting intention to donate blood. Using hierarchical regression analyses, the researchers first individually entered the three TPB factors (self-efficacy, attitudes, and subjective norm), followed by individual entry of the extended variables, to determine whether they contributed additional variance. From this analysis, they found that self-efficacy predicted 12% of the variance in reported donations, attitude (cognitive and affective combined) contributed an additional 15%, subjective norm contributed yet an additional 5%, and finally personal moral norm contributed an additional 10% of the variability. These factors contributed a total of 42% of the variability in intention, while anticipated affective consequences, perceived knowledge and knowledge did not contribute significantly to the model. Stepwise regression was used to test whether specific behavioral beliefs about blood donation explained variation in attitude, and it was shown that fear of blood and/or needles accounted for 40% of its variance, and anxiety contributed an additional 5% of the variance in attitude. These data confirm that fear of blood/needles influences individual differences in nondonor attitudes toward donation. Overall, these data suggest that attitude (cognitive and affective), self-efficacy, and personal moral norm are three strong predictors of nondonor intentions to donate blood, whereas the relative importance of subjective norm, knowledge, and anticipated affective consequences may be overestimated for young nondonors. It is important to note, however, that because selfefficacy was the first variable entered into the model and the authors did not mention

24 24 which variables remained significant in the final model there is no way of knowing whether this variable remained important in the presence of the other factors. Robinson et al. (2008) used structural equation modeling to test a TPB-based extended model on a self-selected community sample of Australian nondonors (N = 195). In addition to TPB factors (attitude, subjective norm, perceived behavioral control), extended factors (descriptive norm, moral norm, anticipated regret, donation anxiety) were simultaneously included in a model of potential predictors of intention to donate blood for the first time. As illustrated in Figure 2, subjective norm was the only variable excluded from the final model because it did not contribute significant variance; the remaining six components accounted for 70% of the variability toward the intention to become a blood donor. These data confirm prior evidence suggesting that attitudes and other affect-laden factors such as donation anxiety and moral and descriptive norm may play a particularly important role in the formation of one s intentions to donate blood for the first time (e.g. Giles, McClenahan, Cairns, & Mallet, 2004; Giles & Cairns, 1995; Lemmens et al., 2005; McVittie, Harris, & Tiliopoulos, 2006). Most recently, Lemmens et al. (2009) conducted two studies designed to examine the motivations of nondonors using extended TPB models. The first study involved a self-selected online undergraduate student sample of 246 Dutch nondonors (mean age = 37.1), and they conducted a hierarchical regression analysis to test both TPB (cognitive attitude, affective attitude, subjective norm, and self-efficacy) and extended (descriptive norm, moral norm, anticipated affect, and altruism) predictors of intention. Results revealed a total of 46% of the variance in intention was predicted in a final model, which

25 25 Figure 2. Replication of final model proposed by Robinson et al. (2008), with standardized path coefficients. Note: *p < 0.05; **p < included affective attitude, subjective norm, descriptive norm, and moral norm; selfefficacy dropped out as a contributor once affective attitude was added, and cognitive attitude dropped out once moral norm was added. Anticipated affect and altruism were also non-contributing factors in the final model. The researchers noted that self-efficacy and affective attitude shared a fairly large amount of variance (r = 0.57), hence this may have accounted for the failure of both constructs to enter in the final model. Their second study used a larger (N = 823), younger sample (mean age = 23.1) of Dutch nondonors

26 26 with two additions: (1) subjective norm was expanded to include family and colleagues and (2) fear of blood and needles was added to the model. Hierarchical regression results paralleled study 1, except that self-efficacy remained a significant predictor in the final model, and this final model predicted a total of 41% of the variance in intention. Although blood/needle fears did not emerge as one of the direct contributing factors like self-efficacy in the first study it had high overlap with affective attitude (r = -0.43) and was shown to influence intention through both affective attitude and self-efficacy. Combined, these two studies suggest that the most important factors in a core model to predict nondonor intentions include affective attitude, descriptive norm, subjective norm, moral norm, and self-efficacy (study 2 only). A discussion of the most important predictors for nondonors is complicated by two issues that make it difficult to compare results between studies: (1) definitions and items used to measure individual constructs differ between studies, and (2) the factors included and analyses used to test the final models is unique for each study. Despite these restrictions, as a whole the TPB-based studies reviewed above point to donation attitude, donation anxiety, and descriptive and moral norm as the strongest predictors of whether younger individuals without any donation history intend to donate blood in the future. Additionally, self-efficacy and subjective norm were sometimes shown to be contributing factors. However, at least one study found that self-efficacy was overshadowed by attitude (e.g. Lemmens et al., 2009), and another study demonstrated that self-efficacy is not as strong a predictor for nondonors relative to donors (e.g. Godin et al., 2005). Further, personal moral norm, descriptive norm, and subjective norm have been viewed

27 27 as overlapping concepts (see Lemmens et al., 2009), as these three factors are related in that they each involve personal and social pressures to either donate blood or see the blood donation process as a morally important behavior. Likewise, at least one study demonstrated that blood/needle fear and anxiety towards donation accounts for much of the variability in attitude (Lemmens et al., 2005), and this evidence shows that attitude and anxiety are also overlapping constructs. After merging these respective factors, the strongest identified predictors of blood donation intentions among nondonors are descriptive/moral/subjective norm and attitude/anxiety. Interestingly, each of these constructs have potential implicit counterparts that may contribute additional variance to the prediction of nondonor intentions and behavior. The following sections will introduce implicit measurement strategies and discuss their potential role within the context of blood donation. Overview of Implicit Measurement In order to gain a complete picture of an individual s behavior, both external situational variables and internal psychological factors must be considered. To accomplish this ideal, within the past decade psychologists have introduced implicit measures (De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009). Despite its growing popularity, the term implicit measure has rarely been defined. Some have argued that the term implicit is synonymous with the term automatic (De Houwer, 2006; De Houwer & Moors, 2007). From this standpoint, De Houwer et al. (2009) define an implicit measure as a set of reactions that reflect processes operating in the absence of certain goals, awareness, a substantial amount of cognitive resources, or substantial time. The main

28 28 purpose of using implicit measurement is to capture and separate the automatic component of evaluations from the more deliberate attitudes assessed by self-report (Wittenbrink & Schwarz, 2007). Three necessary components to design a tool for this purpose have been well articulated in a review by Petty, Fazio, & Brinol (2009), which states that implicit measurement must be: (1) indirect (as opposed to direct self-report; Petty, Wheeler, & Tormala, 2003), (2) automatic (comes to mind spontaneously; Fazio, Jackson, Dunton, & Williams, 1995), and (3) unconscious (quality of unawareness; Kihlstrom, 2004). With a clear definition in mind, the next section will introduce the types of implicit measurement tools that will be implemented in the present study. Implicit Measures. Until fairly recently, self-report measures were by far the most common way to measure attitudes (see Greenwald & Banaji, 1995). In theory, explicit attitude scales are useful insofar as individuals are able to accurately report their attitudes. Often, however, attitudes are not available to introspective access, and thus implicit measures have been developed to overcome this barrier and to more fully capture the conceptual richness of attitudes. Because implicit measurement outcomes are thought to be outside the conscious control of the respondent, they may at times even offer a more accurate reflection of a person s attitudes (Prislin & Crano, 2008). Among the paradigms used, the Implicit Association Test (IAT, Greenwald, McGhee, & Schwartz, 1998) and variations such as the Single Target Implicit Association Test (ST-IAT; Wigboldus, Holland, & van Knippenberg, 2004) are most prevalent, and have been used in hundreds of studies (see Greenwald, Poehlman, Uhlmann, & Banaji, 2009; Nosek & Smyth, 2007 for reviews).

29 29 Recent reviews have deemed the IAT the most dependable implicit instrument for providing evidence of unique insights into behavior beyond traditional explicit measures (De Houwer et al., 2009; Greenwald, Poehlman, Uhlmann, & Banaji, 2009). Operationally, implicit measurement is based on a combination of the participant s response time and accuracy over a series of trials (i.e. De Houwer, 2003; Nosek & Banajii, 2001). To illustrate this, in a typical IAT (Greenwald et al., 1998) a single stimulus is presented that belongs in one of four categories, and participants are asked to categorize the stimuli in a particular way by pressing one of two keys as quickly as possible. Two of the categories are assigned to one key, and two are assigned to the second key. In a flower versus insect IAT, for example, the four categories are: (1) flowers (i.e. photo of a rose), (2) insects (i.e. photo of a grasshopper), (3) positive words (i.e. summer), and (4) negative words (i.e. gloom). During the flower-positive task, flowers and positive stimuli are linked to one key, whereas insects and negative stimuli are assigned the second key. The reverse keys are linked in the insect-positive task, such that insects and positive words are assigned to the same key, while flowers and negative words are linked to the second key. If participants respond faster, on average, during the flower-positive task than the insect-positive task, then they are thought to have greater ease of association with the flower-positive pairing than the insect-positive pairing (or more difficulty with the flower-negative association than the insect-negative). One could argue that this difference in association translates into an index of attitude difference towards flowers versus insects.

30 30 While the IAT is a strong measure for comparing two opposing target items, one limitation of the original form of the IAT is that there is sometimes no logical counterpart to a given topic (e.g. blood donation). Additionally, due to the forced paired evaluation of the IAT, interpretation of IAT scores are always based on a comparison category. To address the limitations of the IAT, researchers suggest the use of tools designed for measuring single-concept attitudes, such as the single-target variant of the IAT (ST-IAT; Wigboldus, Holland, & van Knippenberg, 2004), single-category IAT (SC-IAT; Karpinski & Steinman, 2006), Go/No-Go Association Task (GNAT; Nosek & Banaji, 2001), or Extrinsic Affective Simon Task (EAST; De Houwer, 2003). A brief review of each of these measures is written in parallel of Table 1 below, which gives a quick comparison of the reliability of six single target implicit measures. Single Target Implicit Association Test (ST-IAT). Essentially, the Single Target Implicit Association Test (ST-IAT; Wigboldus, et al., 2004) is exactly like the original IAT, except with the removal of a simultaneously measured counter-category. The ST- IAT is perhaps a better alternative to using the original IAT with a neutral comparison stimulus such as the word neutral, <blank>, non-words, a negative form of the target (i.e. John vs. not John), or a neutral object (i.e. tree). The ST-IAT has demonstrated predictive validity of various criterion (e.g. Bluemke & Friese, 2008; Dotsch & Wigboldus, 2008; Richetin, Perugini, Adjali, & Hurling, 2007; Warfel, France, & France, 2012). Friese, Bluemke, & Wanke (2008) demonstrated both sufficient reliability (adjusted r = ) and validity of the ST-IAT in a comparison of attitudes toward multiple distinct German political parties. Results of our pilot study (Warfel, France &

31 31 France, 2012) provide additional evidence of the ST-IAT s acceptable internal consistency (Image ST-IAT, Cronbach s α = 0.64; Word ST-IAT, Cronbach s α = 0.68) and ability to predict intention (Image ST-IAT, r = 0.19; Word ST-IAT, r = 0.15). Although single target tasks solve the issue of paired category comparisons, the fact that the number of total categories is uneven requires twice the number of responses from one key than the other key, and the key with the majority of responses switches from one phase to the next. This introduces an additional source of error variability in single category variants that the traditional IAT does not have, thus explaining the slightly lower reliability of the ST-IAT (Friese, Bluemke, & Wanke, 2007: adjusted r = ; Warfel, France, & France, 2012: Cronbach s α = ) relative to the traditional IAT (mean of 0.79 across 50 studies; e.g. Bosson, Swann, & Pennebaker, 2000; Dasgupta & Greenwald, 2001; Greenwald & Farnham, 2000; Greenwald & Nosek, 2001). Despite this limitation, we chose the ST-IAT for use in ourpilot study (Warfel, France, & France, 2012) due to its generally sound psychometrics and the extensive use of its most similar counterpart, the IAT. Single Category Implicit Association Test (SC-IAT). The Single Category IAT (SC-IAT; Karpinski & Steineman, 2006) was developed to incorporate a sense of response urgency to the ST-IAT. Specifically, a maximum 1500 to 2000 millisecond latency was incorporated to force quick responses. The scoring procedures of the SC-IAT and ST-IAT are also slightly different: only trials with the target category are typically incorporated for the ST-IAT, but the SC-IAT uses both the target and the attribute trials in its calculation of an attitude index. When the ST-IAT has been calculated using both

32 Table 1. Single Target Implicit Measures Measure Authors Reliability Description Single Target Implicit Wigboldus, Holland, & Single target variant of the IAT: involves only one Association Test (ST-IAT) van Knippenberg (2004) target category, rather than two. Single Category Implicit Association Test (SC-IAT) Karpinski & Steinman (2006) Similar to the ST-IAT, except each trial only lasts milliseconds in order to create force quicker responses. Go/No-Go Association Task (GNAT) Category Focus Implicit Association Test (CF-IAT) Extrinsic Affective Simon Task (EAST) Evaluative Priming Nosek & Banaji (2001) Average = 0.20 Within a set period of time, participants respond by either pressing a single key ( go ) or not pressing the key ( no-go ). Siebler et al. (2010) Complicated procedure originally designed as an effort to merge the strengths of the GNAT and the ST-IAT. De Houwer (2003) Similar to the IAT, but the EAST uses colored words to evaluate target categories and was designed to allow measurement of either single or multiple target concepts. Fazio et al., (1986); Fazio et al., (1995) No data Based on response time of attribute judgments when the attribute precedes the target concept

33 target and attribute stimuli, the SC-IAT was found to have sufficient reliability 33 (Cronbach s α = ; Karpinski & Steinman, 2006) that is comparable to the ST- IAT s reliability. Both the ST-IAT and SC-IAT have successfully exhibited the ability to correct the one shortcoming of the IAT: to directly measure the evaluative assessments of a single item rather than using comparative-based scores. Go/No-Go Association Task (GNAT). The Go/No-Go Association Task (GNAT; Nosek & Banaji, 2001) is another reaction time task designed to measure implicit attitudes toward a single target object. The structure is similar to the SC-IAT, such that a response deadline is incorporated into the task. Within a set period of time, participants respond by either pressing a single key ( go ) or not pressing the key ( no-go ). Due to the fact that only a single-key is required for a response, the GNAT was designed in attempt to solve the issue of error introduced from unbalanced response key frequencies in the ST-IAT and SC-IAT. However, there is scarce evidence of predictive validity of the GNAT (see Spence & Townsend, 2007; Zogmaister, Arcuri, Castelli, & Smith, 2008), perhaps in part because its reliability tends to be low (average split-half reliability is 0.20; Nosek & Banaji, 2001). Thus, weak psychometrics deems the GNAT less desirable than the ST-IAT and SC-IAT. Category Focus Implicit Association Test (CF-IAT). Siebler and colleagues (2010) recently introduced a new implicit attitude task, the Category-Focus Implicit Association Test (CF-IAT), in an effort to create a more reliable task that would merge the strengths of the GNAT and the ST-IAT. However, the CF-IAT did not solve the problem of low reliability (r = ). In addition, the task had several

34 34 methodological issues, as the procedure is both over-complicated and a large proportion of the recorded latencies were not even included in the scoring procedure. Evaluative Priming. Basic sequential priming procedures (Fazio et al., 1986; 1995) have been practiced in a large body of cognitive psychology research (for review, see Neely, 1991). Measurement of implicit attitudes through priming is based on response time of attribute judgments when the attribute precedes the target concept (Wittenbrink & Schwartz, 2007). In a classic example, replicated by several experiments, participants are faster when butter is paired with the prime bread compared to an unrelated word such as doctor. In a basic form of the priming procedure, an evaluative category is presented as a prime (i.e. pleasant or awful) or other concept dimension (i.e. healthy or unhealthy) and participants press keys to evaluate the subsequent target object (i.e. spinach). The assumption is that the shorter the response time for a given attitude prime-target pair, the stronger the association between them. No data are available on the reliability of these procedures, and relatively few studies have measured its predictive validity (Bessenoff & Sherman, 2000; Lambert, Payne, Ramsey & Shaffer, 2005). Due to the lack of available psychometric data, evaluative priming is not a preferred implicit instrument. Extrinsic Affective Simon Task (EAST). The Extrinsic Affective Simon Task (EAST; De Houwer, 2003) is fairly similar to the original IAT, with one key change. Rather than using semantic evaluative attributions, the EAST involves the use of colored words to evaluate target categories and was design to allow measurement of either single or multiple target concepts. However, even with data supporting the predictive validity of behavior through results from the EAST (e.g. Ellwart, Becker & Rinck, 2005; Houben, Gijsen, Peterson, & de Jong, 2005), others, including the creators of the EAST, have

35 35 raised strong doubt as to the reliability and validity of the measure (De Houwer, 2008; De Houwer & De Bruycker, 2007a, 2007b; Schmukle & Egloff, 2006; Teige, Schnabel, Banse, & Asendorpf, 2004). More specifically, the IAT has outperformed the EAST in direct comparisons (De Houwer & De Bruycker, 2007b; Nosek & Smyth, 2007) of construct validity (IAT was significantly related to self-reported attitudes and behavior whereas the EAST was not) and split-half reliability (EAST: range r = ; IAT: r = 0.88). Finally, the IAT and EAST were not significantly related to each other, demonstrating lack of concurrency between the two measures. Overall, the EAST has relatively poor psychometrics and is not a preferred implicit measurement tool. Superiority of the ST-IAT and SC-IAT. The preceding review of six different single target implicit measurement techniques (ST-IAT, SC-IAT, GNAT, CF-IAT, Evaluative Priming, and EAST) points to both the ST-IAT and SC-IAT as having relatively superior psychometric properties. This is clear in Table 1, as both the ST-IAT and SC-IAT rank highest in terms of reliability. Between these two, the ST-IAT is the chosen measure for the present study because it is more easily comparable to other studies, which predominantly use the IAT. The underlying assumption of this single target implicit measure is that individual differences in evaluations of a target concept influence the speed of response to evaluative stimuli and target stimuli presented in the task. Thus, differences in reaction times are presumed to infer implicit attitudes toward a single target concept. In an example of an ST-IAT designed to assess implicit attitudes toward one political candidate (target), stimuli are presented one at a time on a computer monitor. Three categories of stimuli are presented: 1) candidate stimuli (e.g., pictures of candidate), 2) pleasant stimuli (e.g., pictures of happy, friendly faces), and 3) unpleasant

36 stimuli (e.g., pictures of unhappy, unfriendly faces). Participants are asked to sort the 36 stimuli into the correct category by pressing one of two computer keys as quickly as possible. During one phase of the task, correct responses to both candidate pictures and pleasant stimuli are assigned to one key, whereas correct responses to unpleasant stimuli are assigned to a second key. In the second phase of the task the key assignment is reversed, such that correct responses to the candidate pictures and unpleasant stimuli are assigned to one key, while correct responses to pleasant stimuli are assigned to the second key. If participants respond faster when correct responses to the candidate stimuli are paired with correct responses to the pleasant stimuli (rather than when paired with the unpleasant stimuli), it is assumed that the faster performance reflects a stronger association of the candidate as pleasant rather than unpleasant. This difference in strength of association is interpreted as an index of implicit attitudes toward the candidate. The current project will use two paradigms that are similar to the political candidate ST-IAT example above: one implicit attitude paradigm that was used in our pilot study (Warfel, France, & France, 2012) and one measure that resembles a selfidentity implicit measure, termed the social-identity implicit measure. For both, the target category candidate will be replaced with blood donation. However, the socialidentity paradigm will also use different evaluative categories: instead of pleasant versus unpleasant, blood donation stimuli will be paired with self versus other categories. Keeping in mind the measurement and scoring methods of implicit tools, a discussion of the theoretical basis for use of implicit measurement will follow.

37 Theoretical Background 37 Social-cognitive dual-process models (Smith & DeCoster, 2000; Strack & Deutsch, 2004) postulate two distinct types of processes involved in all decision-making and behavior: (1) implicit/underlying processes and (2) reflective processes. From an evolutionary perspective, reflective processes are seen to be a relatively recent concept, serving to enhance goal-directed behavior against impulsive processes (Hofmann, Friese, & Wiers, 2008). The reflective system involves higher order operations, such as executive functioning, designed to provide flexibility and control over behavior (Wiers et al., 2010). Implicit processes are thought to play a role in both reflective processes as well as impulsive or reactive processes (Prislin & Crano, 2008). Researchers have attempted to explain the notion of dual processes in determination of behavior in greater detail. The next section will more closely examine this distinction. Dual-Process Models. Although social cognitive research has evolved toward a conceptual distinction between implicit and explicit processes, there is still no generally agreed upon answer of what, precisely, the difference is between an explicit versus implicit phenomenon (Deutsch & Strack, 2010). However, most researchers agree on two assumptions: (1) indirect measures are less susceptible to correction due to unwanted biases or demand characteristics and (2) implicit and explicit measures differ in a psychologically meaningful way. To elaborate on this second assumption, some theorists contend implicit measures capture attitudes in memory, whereas direct measures assess a representation of these attitudes after deliberate reasoning (e.g., Fazio & Olson, 2003; Olson & Fazio, 2009). Within a blood donation context, this may mean that indirect measures are more likely to capture one s unbridled or unrestrained affective associations

38 that exist in memory, and for nondonors this would likely involve more salient stimuli 38 that are related to blood donation (e.g. affective responses to blood or needles) or in the way nondonors associate blood donation with their in-group (e.g. what their friends and family have shared with them about the process). These uninhibited affective responses toward blood donation are impossible to capture through measures that allow more time for reasoned interpretation of these attitudes (e.g. self-report measurement) and may be better measured using implicit measurement tools that are based on quick responses to relevant stimuli associations. In addition to understanding how implicit versus deliberate processes are captured, it is important to understand when, or under what circumstances, they are typically activated. The next section will detail the circumstances in which overt versus implicit processing are thought to drive behavior. Motivation and Opportunity as Determinants of Behavior (MODE) Model. In order to explain the circumstances most likely to utilize deliberate versus automatic processing, Fazio (1990) proposed the parsimonious MODE (motivation and opportunity as determinants of behavior) model. MODE theory posits that the level of motivation and opportunity are what determine whether involuntary or controlled processes facilitate behavior. To explain this in the context of blood donation, if motivation (i.e. strong desire to donate blood) and opportunity for deliberate processing (i.e. plenty of time to decide whether or not to donate blood) are high, it is expected that controlled processes will drive behavior. However, when motivation is low (i.e. haven t given much thought to blood donation) and opportunity for deliberate processing is limited (i.e. a friend asks if you want to donate blood with them right now), it is more probable that automatic

39 39 processes will prompt behavior. For instance, first-time donors who decide to give blood can do so either in the context of making a quick, spontaneous decision (e.g. walking past a collection site) or through a controlled, planned decision (e.g. deciding well in advance when and where to donate). Given this theoretical implication that implicit processing may be more predictive of a spontaneous form of blood donation behavior, the present study will use both a more spontaneous as well as a controlled behavioral measure. In sum, dual-process theory helps to explain both the conceptual (i.e. spontaneous versus reflective processing) and practical (i.e. circumstances under the respective processes are activated) differences between implicit versus deliberate processing. With these distinctions in mind, the next several sections will review evidence that supports and/or explains theoretical concepts regarding implicit processes. Implicit-Explicit Relationship. Some theorists contend that implicit and explicit attitudes are distinct (Devine, 1989; Greenwald & Banaji, 1995). Other perspectives posit that the two forms of measurement tap the same construct, and differ only in terms of levels of processes (e.g. deliberate versus automatic; Fazio, 2001). Studies utilizing structural equation modeling techniques generally conclude that the two forms of evaluation are distinct, yet related constructs, and that the level of distinction depends on the type of behavior (e.g. Cunningham, Nezlek, & Banaji, 2004). To explain, a recent meta-analysis based on 155 studies (Greenwald, Poehlman, Uhlmann & Banaji, 2009) reported a low average correlation (r = 0.21) between explicit and implicit measures of attitudes. This overall modest relationship may be expected, particularly under the assumption that the two types of measures are tapping distinct constructs. What is surprising is the extreme variability of this relationship; for instance, topics dealing with

40 race or prejudice have a much lower implicit-explicit correlation (average r = 0.12) 40 relative to political preferences (average r = 0.54). The authors note that this is likely due to little or no social desirability concern over the explicit reporting of political preferences relative to prejudice. Likewise, reporting of blood donation attitudes are generally not highly susceptible to impression management concerns, and thus higher overlap between explicit and implicit measures would be expected within the blood donation context. Overall, the data indicate greater implicit-explicit correlations are more typical within topics that evoke fewer social desirability concerns. To extend this to predictive validity, Greenwald and colleagues (2009) found that, generally speaking, the higher the magnitude of the implicit-explicit relationship, the higher the predictive power for both types of measures. Thus, for studies involving behaviors that involve low impression management concerns such as consumer preferences (40 samples) and political preferences (11 samples), implicit and self-report measures exhibited comparable predictive validity. Although independent prediction of explicit and implicit measures is stronger in such situations, because of the greater correlation between measures there is also decreased likelihood that incremental validity will be demonstrated. In the following sections I will review relevant studies that included (1) a behavioral criterion measure that is analogous to blood donation, (2) populations with little experience and/or knowledge about the behavior of interest, and/or (3) implicit attitude measures and/or implicit self-identity measures. Consumer and Voting Behaviors According to the most recent meta-analysis conducted by Greenwald, Poehlman, Uhlmann, and Banaji (2009), socially sensitive topics such as race, gender, sexual

41 41 orientation, and other intergroup behaviors share relatively low implicit measurebehavior correlations (average r = 0.21). In contrast, less socially sensitive topics such as consumer and political behavior demonstrate stronger relationships between implicit measures and behavioral criterion measures (e.g., r = 0.32 for consumer behavior and r = 0.48 for voting behavior). These non-sensitive behavior topics are what drive Greenwald et al. s overall finding that stronger explicit-implicit relationships are indicative of greater independent predictive potential of each form of measurement. The logic here is that greater implicit-explicit correlations imply that the two measures share greater overlap which suggests the measures are valid, thus making each of them stronger independent predictors of behavior. On the other hand, as explained above the stronger the implicitexplicit correlation, the greater their overlap and thus lower likelihood incremental validity will be demonstrated. Given that blood donation is a relatively less socially sensitive behavior, and that our pilot is the only study that has examined implicit measures in a blood donation context, a review of studies in the context of other relatively less socially sensitive behaviors (i.e. consumer and voting behavior) will follow. Consumer-Related Behavior. Consumer behavior is analogous to blood donation in a number of respects. Most importantly, as indicated above, admitting consumer preferences is generally not a socially sensitive subject: when individuals have a strong preference for a specific product they are often willing to report it. Consumerrelated behavior is sometimes spontaneous (e.g. impulse buy or quick comparison) and other times more deliberate (e.g. using a grocery list or researching a product), just as blood donation decision-making can likewise be either a spontaneous or reflective

42 42 process. In addition, while some consumer behaviors are relatively common (e.g. eating behavior), other consumer behaviors occur less frequently (e.g. purchasing cleaning products and condom use). Blood donation behavior is akin to the latter, less common consumer behaviors in that it is a behavior that occurs relatively infrequently. Further, research indicates that individuals often hold ambivalent attitudes toward blood donation (e.g. positive cognitive attitudes about the act but negative affective attitudes toward blood and needles), and ambivalent attitudes are likewise common in many consumerrelated behaviors, especially regarding eating healthy foods (e.g. positive cognitive attitudes toward eating healthy foods but negative affective attitudes regarding their taste). Due to some of their similarities to blood donation behavior, a review of studies utilizing implicit measures in consumer-related contexts follows (see Table 2). Karpinski and Hilton (2001) conducted a relatively early study designed to test the construct and predictive validity of a candy bar versus apple implicit attitude measure. To avoid the potential issue of implicit and explicit order effects, eighty-five participants completed either candy bar versus apple implicit and explicit attitude measures or just the implicit measures, as well as a behavioral choice measure. Univariate logistic regression analyses revealed that explicit, but not implicit measures predicted subsequent choice of a Snicker s bar versus a Red Delicious apple; also, when the measures were entered simultaneously, the implicit measure did not contribute significant variance but two explicit measures marginally predicted behavior. For the group that did not complete explicit measures, implicit attitudes likewise did not significantly predict choice of Snickers bar or Red Delicious apple. Unfortunately, the limited choice behavioral measure used in this study does not allow sufficient variability of responses for this

43 Table 2. Consumer Behavior Category Target(s); Implicit Topic Authors Assessment Food Karpinski & Apple versus choice Hilton (2001) Candy bar; pleasant- Food choice Condom Use Maison, Greenwald, & Bruin (2001) Marsh, Johnson, & Scott-Sheldon (2001) unpleasant IAT Study 1: Fruit juice versus Soda; pleasantunpleasant IAT. Study 2:Healthy versus Unhealthy food; pleasantunpleasant IAT. Condom versus Non-condom; positive-negative IAT and selfother IAT Behavioral Measure(s) Choice of Red Delicious apple or Snickers bar. Study 1: Self-report measure of soda and juice consumption. Study 2: Self-report dieting activity and guilt questionnaires Self-reported condom use with steady or casual partners. Implicit- Explicit Correlation (Y/N) N Study 1: Y Study 2: Y Evaluative: N Self-identity: N Results Both implicit (f 2 = 0.04) and explicit attitude measures showed a preference for apples. Explicit, but not implicit (f 2 = 0.02) attitudes predicted behavior. Study 1: Implicit attitude was significantly related to self-reported drink consumption behavior (r = 0.2). Study 2: More favorable implicit attitudes toward healthy or low calorie foods predicted self-reported dieting activities (r = 0.34) and guilt about eating unhealthy, high calorie foods (r =0.31). The evaluative IAT was not correlated with condom use with a steady partner (r = -0.09) or at last occasion of sex (r = 0.06) but it was associated with condom use with casual partners (r = 0.36). Selfidentity IAT was not associated with condom use with steady (r = -0.08), casual, or last partners (r = 0.04). But, when me-not condom was paired first, the IAT was positively correlated with condom use with casual partners (r = 0.00), whereas when me-condom was paired first, the association was negative. 43

44 Table 2 (Continued) Brand Maison, choice Greenwald, & Bruin (2004) Brand choice Selfconcept Brunel, Tietje, & Greenwald (2004) Pleasantunpleasant IATs Study 1: Two leading yogurt brands; Study 2: McDonald s versus Milk Bar. Study 3: Coca- Cola versus Pepsi Mac versus PC computers; pleasantunpleasant and self-other IAT Study 1: self-reported consumption of each brand; Study 2: present at the restaurant during study recruitment; Study 3: screened for strong preference and whether they drank their preferred beverage at least several times per week; at pre-screen also gave recognition accuracy test Self-report ownership and frequency of use. Study 1: Y Study 2: Y Study 3: n/a Evaluative: Y Self- Concept: Y Study 1: Implicit attitudes marginally predicted self-reported behavior ( = 0.26; d = 0.68) in the presence of explicit attitudes. Study 2: Implicit-preferred location was positively correlated. In the presence of explicit attitudes, the IAT no longer predicted location preference ( = 0.12; d = 0.27). Study 3: Even in the presence of explicit attitudes, implicit attitudes significantly predicted reported consumption ( = 0.36; d = 1.01) as well as accurate identification of their preferred brand ( = 0.39; d = 0.99). Overall, whether loyalty was measured based on ownership or usage, both the evaluative (r = 0.47, and r = 0.69, respectively) and self-concept IATs (r = 0.41, and r = 0.54, respectively) were significantly related to loyalty. Further, Mac loyalists had a stronger evaluative as well as self-concept IAT-behavior effects compared to PC loyalists with respect to both ownership and usage measures. 44

45 45 Table 2 (Continued) Food Perugini choice (2005) Brand choice Vantomme, Geuens, De Houwer, & De Pelsmacker (2005) Snacks versus Fruit; pleasantunpleasant IAT Study 1: Fictitious and real green versus traditional cleaning products; positive-negative IAT. Study 2: Only real green versus traditional cleaning products; positive-negative IAT. Spontaneous: choice of one snack or fresh fruit among a variety. Deliberate: self-report measure of typical snack and fruit consumption. Study1: Purchase intention measures in which the green products were displayed as more expensive. Study 2: Purchase intention measures in which both green and traditional products were the same price. N Study 1: N Study 2: Y Using structural equation modeling, there was excellent fit for a doubledissociation pattern (CFI = 1.00), such that the IAT was a predictor of spontaneous (d = 0.45), but not deliberate behavior (d = 0.32), while explicit attitude was a predictor of deliberate, but not spontaneous behavior. Study 1: Those intending to purchase real ecological products likewise held a positive implicit preference for them (d =.051), but no implicit difference was shown for fictitious products (d = 0.39). Whereas those intending to purchase fictitious ecological products likewise held a positive explicit preference for them, but no explicit difference was shown for real products. Study 2: Those intending to purchase green products likewise held a positive implicit (d = 0.50) as well as explicit preference for them.

46 46 context (i.e. fruit and candy come in many different forms). Further, the researchers used an older method to score the IAT (Greenwald et al., 1998 compared to Greenwald et al., 2003), resulting in potentially less valid implicit attitude scores. Finally, curiously there was no mention of any of the IAT measures reliability, and thus it is possible that the older scoring method produced internal consistency values that were too low to reliably predict behavior. The main weakness of this study, however, is the use of a limited criterion measure for a context that involved great variety. Blood donation behavior can be measured in a variety of forms (e.g. intention, sign-up behavior, and actual behavior) to help solve the potential issue of limited variability. Rather than a single-choice behavioral measure, Maison, Greenwald and Bruin (2001) conducted two studies that used self-report measures of general behavior patterns as their criterion measure. The studies used two different IATs: a fruit juice versus soda IAT and a high calorie/unhealthy food versus low calorie/healthy food IAT, respectively. In study 1, seventy-one male and female participants completed self-reported behavioral measures of drink consumption before completing the fruit juice versus soda IAT. Findings indicated the fruit juice versus soda implicit attitude measure was significantly related to self-reported consumption of the respective drinks. However, not surprisingly explicit attitudes were even more highly related to self-reported consumption habits, given these measures are psychometrically related (i.e. both paper-and-pencil self-report measures). Fifty-one females were tested in study 2 and completed self-reported dieting behavior before the high calorie/unhealthy food versus low calorie/healthy food IAT. Results demonstrated implicit attitudes predicted self-reported dieting activities and higher reported guilt toward eating high calorie foods. Explicit attitude effects were not

47 47 discussed. Combined, these two studies demonstrate implicit measures may be predictive of various related behaviors (e.g. reported consumption, dieting behavior and guilty eating). Unfortunately, however, none of the studies discussed thus far tested incremental validity, or the ability of implicit measures to predict variance in a criterion over and above self-report measures. These analyses are necessary to conclude that implicit measures predicted unique variance of behaviors. In addition, the studies reviewed thus far have only used evaluative implicit measures, but the next study also used a selfconcept form of implicit measurement. Marsh, Johnson and Scott-Sheldon (2001) designed a study in which ninety-seven participants completed a self-concept IAT and a positive-negative evaluative IAT to predict self-reported condom use with both steady and casual partners. Self-concept measures are identical to implicit attitude measures except that the pleasant versus unpleasant evaluative categories in implicit attitude measures are replaced with self versus other categories. Results revealed that the self-concept IAT was not associated with condom use with steady, casual, or most recent partners. Pairing order moderated this effect, such that when the me-not condom categories were paired first, the IAT was positively related to condom use with casual partners, whereas when the me-condom was paired first, the association was negative. The evaluative IAT was significantly associated with condom use with casual partners only. Finally, an interesting serial position analysis was conducted that suggested evaluative IAT effects decreased significantly over the course of the 38 trials. Results of this relatively early study should be interpreted with caution, as older IAT scoring procedures were used (Greenwald et al., 1998 versus Greenwald et al., 2003). Nonetheless, the finding that implicit attitudes was shown to be

48 related to a relatively spontaneous behavior (condom use with casual partners) lends 48 support to the MODE theory, in which implicit measurement is thought to be more predictive of spontaneous behavior. Further, this study raises potentially important statistical issues concerning IAT effects, including pairing order effects as well as changes in strength of effects over the course of trials. Where possible, counterbalancing should be used to eliminate any order effects. Finally, the self-concept IAT may not be valid in this context, as it is unusual for individuals to associate themselves with condoms. In sum, while these earlier studies provide some evidence that implicit measures can independently predict food consumption and condom use behaviors, none demonstrated incremental validity, or implicit and explicit measures ability to simultaneously predict behavior. Demonstration of implicit measures incremental validity is key in establishing utility beyond that of explicit attitude measures; accordingly, a review of some studies that specifically test the incremental validity of implicit measures will follow. Maison, Greenwald and Bruin (2004) tested the incremental validity of implicit attitudes in three studies, all within the context of various consumer preferences. Study 1 compared attitudes of frequent yogurt consumers toward two popular Polish yogurt brands (Danone and Bakoma). Forty participants completed explicit attitude, selfreported consumption, and implicit attitude of the two yogurt brands. Results demonstrated that implicit preference was significantly related to reported frequency of consumption. Further, in the presence of explicit attitudes, implicit attitudes marginally predicted consumption behavior. In a second study, twenty participants were recruited at

49 two target fast food restaurants (McDonald s and Milk Bar) and they completed both 49 implicit and explicit attitude measures. Presence at the restaurant during recruitment, selfreported preference, and attendance were used as behavioral measures. Results revealed that implicit attitude was significantly related to reported preference, reported attendance and location at time of recruitment, but in the presence of explicit attitudes, the IAT scores no longer predicted location at time of recruitment. Unfortunately, a test of incremental validity using self-reported attendance as the criterion measure was not reported. In a third study, Maison and colleagues recruited 103 high school students with a strong preference for either Coke or Pepsi and who reported at least moderate consumption of their preferred beverage ( several times per week or greater). At the prescreen, participants were also tested for accurate identification of the product in a blind taste test. Next, participants completed measures of explicit and implicit attitudes of the two brands. Interestingly, accurate identifiers of their preferred brand were found to show a stronger implicit preference (but not explicit preference) than inaccurate identifiers. In addition, tests of incremental validity demonstrated that in the presence of explicit attitudes, implicit attitudes provided unique variance toward predicting reported frequency of consumption and accurate identification of their preferred brand. Overall, these three studies provide some support for the ability of implicit measures not only to predict a variety of behaviors, but also to provide incremental validity of self-reported frequency of consumption. Thus, even for common behaviors that individuals are perfectly willing to report accurately, incremental validity was demonstrated. Interestingly, the third study includes evidence that implicit measures may provide unique variance toward predicting behaviors that individuals cannot explicitly predict:

50 50 accurate identification of Coke versus Pepsi. Thus, implicit measures may be particularly useful for providing unique variance toward predicting blood donation behavior among individuals who are explicitly uncertain what their future behavior will be. In a study utilizing two types of implicit measures, Brunel, Tietje and Greenwald (2004) recruited eighty-eight participants to complete Mac versus PC implicit attitude, implicit self-concept, and explicit attitude measures to determine whether the implicit measures differentially predicted reported frequency of use of the computer brands. Results indicated that both types of implicit measures were highly correlated with explicit attitudes, reported ownership, and reported usage frequency. Interestingly, Mac users and owners also demonstrated significantly stronger implicit preferences and self-associations with Macs relative to PCs. This evidence suggests that there is a stronger implicit preference and self-connection among Mac loyalists than PC loyalists. Interestingly, Brunel and colleagues explain this finding as a result of both the stronger sense of community Mac users exhibit as well as the stronger loyalty stemming from the years that Mac computers held a smaller share of the computer market. Unfortunately incremental validity was not tested in this study, but it nonetheless provides support for the construct validity of a self-concept implicit measurement. A tool similar to this could be particularly valuable for distinguishing levels of personal and social pressures to donate blood among nondonors. In an innovative study specifically designed to test the MODE theory of behavioral prediction, Perugini (2005) recruited fifty participants to complete fresh-fruit versus unhealthy snack implicit and explicit attitude measures. Within this single study, Perugini used both a spontaneous form of behavior (choice of either one snack or fruit

51 among variety) as well as a more deliberate form of behavior (self-reported typical 51 consumption of snacks and fruit). Explicit versus implicit measurement order was counterbalanced, and the food choice behavior occurred at the very end of the study, when the experimenter pointed towards two bowls on a nearby table with a variety of snacks and fruit, directing the participant to choose one free snack before debriefing. Findings indicated the IAT scores were significantly related to the spontaneous behavior (behavioral choice) whereas explicit attitudes were significantly related to the more deliberate measure of behavior (self-reported consumption), but the cross-relations were not significant. Structural equation modeling analyses revealed that this model of prediction is an excellent fit for a double-dissociation pattern (implicit attitudes predict spontaneous behavior whereas explicit attitudes predict deliberate behavior), but not an additive (implicit and explicit attitudes each explain unique variance in the behavior; i.e. incremental validity) or interactive pattern (implicit and explicit attitudes interact to predict behavior). On the one hand, it is difficult to know why support for an additive behavioral pattern (incremental validity) was not found. On the other hand, Greenwald et al. s (2009) meta-analysis suggests that incremental validity is more difficult to find for behaviors that individuals are willing and able to accurately report. Further, it is possible that explicit and implicit measures may share greater overlap for very common behaviors that individuals are better able to report accurately via self-report (e.g. food consumption) relative to more novel behaviors (e.g. blood donation), which may make it more difficult to find an additive pattern for consumer behaviors. In addition, this study lends support to the MODE model, and the importance of measuring various types of behaviors (e.g. spontaneous versus deliberate) in predictive studies. Interestingly, the spontaneous food

52 choice behavioral measure is not unlike some blood donation studies that give 52 participants an opportunity to sign-up for an upcoming blood drive at the end of a study (e.g. France, France, Kowalsky & Cornett, 2010). Rather than self-reported past behavior, a better measurement form of deliberate behavior would be to use a follow-up measure of actual behavior. In two studies that used a behavioral choice measure, Vantomme, Geuens, De Houwer, and De Pelsmacker (2005) sought to improve understanding of consumer behavior and explicit and implicit attitudes toward environmentally friendly cleaning products. In the first study, sixty participants completed measures of implicit attitudes, explicit attitudes and purchase intentions toward traditional versus green real and fictitious cleaning products. During the behavioral choice measure in an attempt to create a more realistic measure green products were displayed as slightly more expensive than traditional products. Results indicated that those intending to purchase real ecological products likewise held a significantly more positive implicit preference for them, but no implicit difference was shown for fictitious products. Whereas those intending to purchase fictitious ecological products likewise held a positive explicit preference for them, no explicit difference was shown for real products. One potential interpretation of the finding that implicit measures were only predictive of real but not fictitious products is that implicit measurement primarily taps memory associations. In their second study of seventy-two participants, only real, and not fictitious products were used. Further, both green and traditional products were portrayed as equal in cost, which should resolve the potential issue of higher cost green products counteracting the general preference for them. Those intending to purchase green products likewise held a positive

53 53 implicit as well as explicit preference for them. Unfortunately, incremental validity was not reported in either of these two studies, and thus it cannot be determined whether implicit measures provided unique variance in addition to explicit measures toward predicting behavior. Overall, studies involving consumer-related behaviors either demonstrate predictive validity or incremental validity of implicit measurement in the context of behaviors that have low social sensitivity. To summarize, implicit measurement was shown to be predictive of all forms of behavior except one relatively early study that forced participants to select between only two choices (Snickers bar versus Red Delicious apple; Karpinski & Hilton, 2001), thus greatly limiting the variability of this criterion measure. Further, these studies showed implicit measures may be better than explicit measures at predicting less direct forms of behavior (e.g. blind taste test identification of preferred brand, dieting, guilty eating; Maison, Greenwald, & Bruin, 2001, 2004) as well as less common behaviors (e.g. choosing eco-friendly products, condom use; Marsh, Johnson, & Scott-Sheldon, 2001; Vantomme et al., 2005). For less common behaviors, such as blood donation, individuals have spent less time in conscious reflection of behaviors they are not as familiar with, and may therefore be less certain of or accurate in their explicit reports of the behavior. In addition, two of the consumer-related studies reviewed above (Marsh, Johnson, & Scott-Sheldon, 2001; Perugini, 2005) support MODE theory, or a double-dissociation pattern of prediction, such that implicit measures are stronger predictors of more spontaneous behavior (e.g. choice among snacks, condom use with casual partners), while explicit measures are stronger predictors of deliberate behavior (e.g. self-reported consumption, condom use with steady partners). Finally, only

54 one lab tested incremental validity (Maison, Greenwald, & Bruin, 2004), and these 54 researchers found implicit measures contributed unique variance toward predicting both self-reported frequency of consumption and taste test identification, but not presence at one of two restaurants during recruitment (a very limited behavioral measure). In sum, this set of studies demonstrates that it is important to choose criterion measures of behavior wisely, such that they have sufficient variability and tap multiple forms of decision-making (spontaneous and deliberate). In addition, for less common behaviors (e.g. blood donation) and for individuals who may have spent less time reflecting about a given behavior (e.g. nondonors), implicit measures may be very useful for assessing attitudes. To continue, self-concept implicit measures were used in two consumer-related studies to predict use of Mac versus PCs (Brunel, Tietje, & Greenwald, 2004) and condom use (Marsh, Johnson, & Scott-Sheldon, 2001). Self-concept or self-identity implicit measurement was shown to be a valid predictor of Mac versus PC brand loyalty, but did not predict condom use with either steady or casual partners. This is likely due to the fact that individuals often identify with their choice of computer brand, whereas it might be unusual to identify oneself with condoms. Regarding blood donation, although self-identification as a blood donor has typically been discussed as a factor for established donors (Masser et al., 2009), social factors (e.g. subjective norm, moral norm) are also important predictors of whether nondonors will give blood. Thus, a slightly different form of a self-concept measure perhaps one termed a social-identity measure may be particularly relevant to measure the level of both personal and social identification that nondonors have with blood donation.

55 Consumer behavior studies have helped inform the ways various types of 55 behaviors and implicit measurement tools may be useful in the blood donation context. Voting behavior studies in the next section will highlight additional resons why the nondonor population may be of particular interest. Voting Behavior. Like blood donation and consumer behavior, voting may involve both reflective behavior (i.e. planning to carry out the behavior and make a final decision) as well as spontaneous behavior (i.e. making a selection in the moment). In addition, similar to consumer and blood donation behaviors, individuals are typically willing to report their political opinions, even when this completely and directly conflicts with what someone has just said (Nosek, Graham, & Hawkins, 2010). It is interesting to note that voters vary tremendously in their level of decisiveness about their upcoming voting decision, and undecided voters often determine the outcome of an election. As a parallel, there are varying levels of certainty among nondonors desire to donate blood. Level of certainty will be a special focus in the following review of voting behavior studies that are also listed in Table 3. In a study conducted by Karpinski, Steinman and Hilton (2005), 176 undergraduate students who were eligible to vote in the 2000 election completed a Bush- Gore IAT as well as explicit attitude and behavioral intention measures. It was found that for Democrats, Republicans, and Independents/Other, there was a strong correlation between implicit and explicit attitude measures. Further, although the implicit attitude measure predicted intention on its own, it was no longer significant in the presence of explicit attitude measures. Results were different in a larger study conducted by Friese, Bluemke and Wanke (2007), in which 1,548 undergraduate students completed a series of

56 Table 3. Voting Behavior Topic Voting Voting Voting Study Karpinski, Steinman, & Hilton (2005) Study 1 Friese, Bluemke, & Wanke (2007) Arcuri, Castelli, Galdi, Zogmaister, & Amadori (2008) Category Target(s); Implicit Assessment Bush versus Gore; pleasantunpleasant IAT. Pre and post election, each of 5 German political parties; pleasantunpleasant ST- IATs Study 1: Focus on sample that clearly sided with one party. Rutelli vs Berlusconi; Behavioral Measure(s) Party affiliation and which candidate they would vote for today. Self-reported voting intentions (preelection) and selfreported actual voting behavior (post-election) Studies 1 & 2: Selfreported voting intentions (preelection) and selfreported actual Implicit- Explicit Correlation (Y/N) Democrats: Y Republicans: Y Indepen-dents: Y Y not applicable Results The IAT was a significant predictor of intended voting choice (WALD = 33.52, = 4.45) however, it did not emerge as a predictor over and above explicit attitude measures (WALD = 1.35, = 1.72). All five ST-IATs showed evidence of incremental validity over explicit attitude when predicting voting intentions (range WALD = , range = ) and voting behavior (range WALD = , range = ). Even further, all five ST-IATs showed incremental validity over and above voting intention when predicting voting behavior (range WALD = , range = ). Study 1: The implicit measures predicted voter choice, both for those who were decided (WALD = 25.68) and undecided (WALD = 5.23) at Time 1. The small sample of 56

57 Political choice Galdi, Arcuri, & Gawronski (2008) negative-positive IAT. Study 2: Sample of undecided voters. Galan vs Carraro; negative-positive IAT U.S. Military base; positivenegative SC-IAT voting behavior (post-election). Completion of all measures occurred twice, one week apart. Self-reported item of those in favor of U.S. Military base enlargement, against, or undecided. not reported undecided voters in Study 1 prompted Study 2. Study 2: For those who were undecided at Time 1, the implicit measures predicted voter choice (WALD = 6.33). For decided participants, reported beliefs at Time 1 predicted (d = 0.64) reported choice at Time 2, whereas automatic associations at Time 1 did not predict choice at Time 2. The reverse was true for undecided participants: automatic associations predicted choice at Time 2 (d = 1.03), whereas selfreported beliefs at Time 1 did not predict choice at Time 2. 57

58 web-based questionnaires, including explicit and implicit attitude measures of five 58 different German political parties. In a follow-up after the election, participants indicated by self-report if they actually voted and, if so, which party they voted for. The authors found that all five political party single-target implicit attitude measures demonstrated incremental validity when predicting voting intentions and voting behaviors. Further, all five single-target implicit attitude measures also predicted voting behavior over and above voting intention. The fact that Karpinski et al. (2005) did not find incremental validity but Friese et al. (2007) did may be, in part, due to the fact that Friese and colleagues used maximum latencies at 3,000 milliseconds, whereas Karpinski and colleagues used a much higher cutoff at 10,000 milliseconds. Both have been recommended in past guidelines (Greenwald et al., 1998 and Greenwald et al., 2003), but a 10,000 millisecond max cutoff per trial allows participants ten seconds to make a response that typically takes them under two seconds. Using a cutoff value that is so high would likely create additional error and ultimately lower the overall task reliability. Friese et al. reported reliability values of their five single-target implicit attitude measures as ranging from , but unfortunately Karpinski and colleagues (2005) did not report their task reliability and thus a comparison cannot be made on this level. Combined, these two studies demonstrate the validity of implicit measures in the voting behavior context, and Friese et al. s study demonstrates the potential for implicit measures to provide incremental variance toward predicting a behavioral domain with such strong implicit-explicit variance overlap.

59 Whereas the prior two studies did not consider level of decisiveness among 59 voters, Arcuri, Castelli, Galdi, Zogmaister and Amadori (2008) focused two studies on the whether the predictive capability of implicit measurement differs for decided and undecided Italian voters. In the first study, the implicit attitude measure predicted report of actual voting decisions for both decided (n = 44) and undecided (n = 30) participants. The small sample of undecided participants in the first study prompted a second study, which also found that the implicit attitude measure predicted voting decision among undecided voters (n = 51). Unfortunately, explicit attitude was not measured in these two studies, and thus incremental validity could not be tested. However, a focus on undecided voters makes these results unique, as they suggest that for individuals who are explicitly indecisive implicit measures might have the ability to predict behavior. Likewise, implicit measures may be useful to predict the behavior of nondonors who are explicitly indecisive about whether to donate blood in the future. The next study further examines this possibility. In a similar study within the same lab regarding voting for a military base enlargement, Galdi, Arcuri and Gawronski (2008) also tested the ability of implicit attitude measures to predict voting among Italian participants who were either decided (n = 96) or undecided (n = 33). Participants completed implicit and explicit attitude measures twice, once before voting and a second time after they voted one week later. Findings indicated that there were different predictive patterns for decided versus undecided participants. For those who were decided, self-reported attitudes predicted voting decision, but implicit attitudes did not. Interestingly, the reverse was true for undecided participants: implicit attitudes predicted voting decision, but self-reported

60 attitudes did not. This study extends their previous work in that it indicates implicit 60 measures may be able to predict the behavior of indecisive individuals even better than self-report measures. This is a key strength of implicit measurement that can be utilized in the present study to determine whether implicit measures are likewise more useful than explicit measures for predicting the behavior of indecisive nondonors. Overall, the four voting behavior studies reviewed above contribute to the present research in two major ways: (1) predictive validity and incremental validity of implicit attitude measures is supported in the context of a behavior that typically requires considerable reflection and planning to carry out and (2) implicit attitude measures may be better suited than explicit measures to predict the voting decisions of undecided voters. Regarding the first contribution, like voting, donating blood is a behavior that typically involves reflection and planning, and thus demonstration of predictive and incremental validity of another reflective behavior is important. As for the second contribution, undecided voters have some similarity to nondonors who are unsure whether or not they plan to donate blood in the future. Therefore, implicit measures may likewise be best suited for predicting donor behavior among the less decisive sub-population of nondonors. Use of Implicit Measures in the Blood Donation Context Initially, implicit measures were primarily used in research dealing with socially sensitive topics (i.e. prejudice), due to the potential for these measures to circumvent the problem of impression management concerns motivating self-serving responses. More recently, researchers have found implicit measures provide useful information even in situations where individuals are generally willing to accurately report their opinion. That

61 61 is, rather than serving only as evidence of hidden attitudes, implicit measures reinforce explicit ones in determining behavior. As previously discussed, implicit voting preferences have been shown to provide additional predictive value beyond explicit reports (Arcuri, Castelli, Galdi, Zogmaister & Amadori, 2008; Friese, Bluemke & Wanke, 2007). Thus, even for seemingly transparent, reflective, and deliberate behaviors such as voting or blood donation, implicit processes are thought to influence a generation of attitudes at a stage that is inaccessible to conscious observation (Perkins & Forehand, 2010). In addition, both theory and research suggest that implicit attitude measures at least partially represent affective associations (e.g. Associated Systems Theory; Carlston, 1994; Zajonc, 1980; Strack, Martin, & Stepper, 1988). Along these lines, Masser et al. (2008) pointed out in a recent examination of the current state of psychological blood donation research that the TPB does not consider how donor decisions may be influenced by anticipated affective reactions that impede the rational decision-making process. These researchers also noted that the decision to donate involves an inherent discrepancy between positive cognitive evaluations (e.g. how rewarding or worthwhile it is to donate) and negative affective evaluations (e.g. potential unpleasant experiences such as feeling pain, feeling embarrassment). They assert that the latter response anticipation of affective reactions is particularly important when examining blood donation behavior, especially for nondonors. A few studies that have incorporated self-report measures of anxiety have confirmed its importance in both the blood donation context (Farley & Stasson, 2003; Lemmens et al., 2009) and in predicting blood donor return (France, France Roussos, & Ditto, 2004; Meade, France, & Peterson, 1996). Based on this

62 reasoning, we conducted a pilot study to determine whether affective-based implicit 62 attitude measures are indeed valid for use in the blood donation context. Discussion of the pilot study along with how the present project will extend its results will follow. Pilot Study. Our lab recently conducted a pilot study (Warfel, France & France, 2012) to introduce and validate two forms of blood donation implicit attitude measures. Undergraduate donors (n = 112) and non-donors (n = 115) performed image and word versions of a Single Target Implicit Association Test (ST-IAT) before completing selfreport measures of donation attitudes, blood and needle fears, and donation intention. Results provided evidence that the implicit measures are both reliable and valid assessments of underlying affective reactions toward blood donation. Reliability was demonstrated based on acceptable internal consistency values (Image ST-IAT, Cronbach s α = 0.64; Word ST-IAT, Cronbach s α = 0.68). In addition, construct validity was supported in two ways: (1) nondonors implicit attitudes were significantly more negative than those of donors, and (2) implicit attitudes were negatively related to blood and needle fears and positively related to explicit attitudes and donation intentions. Further, although implicit attitude measures were not found to predict stated intentions over and above explicit attitudes, the Image ST-IAT significantly enhanced prediction of intentions in the presence of self-reported needle fears and marginally enhanced prediction of intentions in the presence of self-reported blood fears. This latter finding suggests that participants implicit affective reactions toward blood donation might contribute unique variance toward predicting overtly stated intentions to donate. The current project is designed to extend these findings in a follow-up study focused on nondonors. In doing so, a second implicit measurement tool which I have termed

63 63 implicit social-identity will be designed to capture a construct deemed important for this population, namely descriptive/moral/subjective norm. With this in mind, a summary of the present study s purpose along with the specific aims and hypotheses will follow. The Current Study Past research indicates first-time donors make up approximately one-third of the total blood supply (National Blood Collection and Utilization Survey, 2009) and understanding their motivations to decide whether to donate blood is crucial to continuing effective recruitment and retention techniques. In addition, behavioral scientists have suggested that donors and nondonors have unique motivations that should be examined separately (Ferguson & Bibby, 2002). Thus, after close examination of the existing literature that focuses on nondonors, as a whole the factors that emerge as the strongest predictors of intention to donate blood in the future are attitude, anxiety, and descriptive, subjective, and moral norms. Evidence suggests that attitude and anxiety are overlapping constructs, as blood/needle fear and anxiety towards donation accounts for much of the variability in attitude (Lemmens et al., 2005). Likewise, personal moral norm, descriptive norm, and subjective norm have been viewed as converging concepts (see Lemmens et al., 2009), as these three factors each involve personal and social pressures to either donate blood or view the blood donation process as a morally important behavior. Thus, among these factors, two general constructs emerge: attitude/anxiety and descriptive/subjective/moral norm. Based on their similarities to blood donation behavior, studies focusing on implicit measurement in the context of consumer and voting choice were examined. Overall, implicit measures have demonstrated predictive and incremental validity within

64 these contexts. Additionally, after a careful review of this literature, three important 64 research elements became apparent: (1) if possible, criterion measures should have sufficient variability and multiple types of behavior should be captured (e.g. spontaneous and deliberate), (2) self-concept or self-identity measures are valid within contexts that individuals are likely to personally identify with, and (3) implicit measures may be better than explicit measures at predicting the behavior of those who report being undecided and those who have spent relatively little time considering whether to donate blood. This third point suggests that implicit measures may likewise be best suited for predicting donor behavior among the less decisive and low consideration sub-populations of nondonors. Further, the pilot study we conducted has demonstrated that implicit attitude measures are valid for use in the blood donation context, although this study was limited in that self-reported intention was the only criterion measure used. Accordingly, the proposed study was designed to expand on prior work by introducing a social-identity implicit measure to capture an important construct for nondonors: descriptive/subjective/moral norm, and by including multiple behavioral outcome measures of blood donation. Specifically, this study examined the utility of two forms of implicit measurement (implicit attitude and implicit social-identity) in predicting unique variance toward nondonors future blood donation intentions, immediate decision to signup to donate blood, and confirmation of their actual behavior. Three specific aims were proposed. Specific Aims and Hypotheses Aim 1: Predictive Validity of the Implicit Measures. Determine whether implicit measures significantly predict behavioral criterion measures.

65 65 Hypothesis 1a. Image and word versions of implicit attitudes were hypothesized to be significant predictors of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Hypothesis 1b. Implicit social-identity was hypothesized to be a significant predictor of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Aim 2: Incremental Validity of the Individual Explicit Predictors. Determine whether implicit measures predict variance in behavioral criterion measures over and above their respective explicit measures. Hypothesis 2a. In the presence of explicit attitudes toward blood donation, both image and word versions of implicit attitudes were hypothesized to predict unique variance toward behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Hypothesis 2b. In the presence of explicit norm measures (subjective norm, descriptive norm, and personal moral norm), implicit social-identity was hypothesized to predict unique variance toward behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Aim 3: Moderation of Decisiveness and Consideration Levels. Determine whether decisiveness and/or consideration moderates the relationship between implicit attitudes and behavioral criterion measures. Hypothesis 3a. For nondonors low in decisiveness, image and word versions of implicit attitudes were hypothesized to be stronger predictors than explicit attitudes of behavioral intention, immediate sign-up behavior, and follow-up

66 66 report of donation behavior. For nondonors high in decisiveness, explicit attitudes were hypothesized to be a stronger predictor than either the image or word version of implicit attitudes of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Hypothesis 3b. For nondonors low in consideration, image and word versions of implicit attitudes were hypothesized to be stronger predictors than explicit attitudes of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. For nondonors high in consideration, explicit attitudes were hypothesized to be a stronger predictor than either the image or word version of implicit attitudes of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Exploratory Aim: Exploration of Incremental Validity in the Presence of Multiple Explicit Predictors. Determine whether each implicit measure predicted unique variance in the presence of multiple explicit measures that were deemed important predictors for nondonors by Robinson et al. (2008; refer to Figure 2 for a depiction of their final model). Exploratory Hypothesis 1a. In the presence of the explicit measures included in Robinson et al. s (2008) final model (donation attitudes, donation anxiety, selfefficacy, anticipated regret, descriptive norm, and personal moral norm), image and word versions of implicit attitudes were examined as predictors of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Exploratory Hypothesis 1b. In the presence of the explicit measures included in Robinson et al. s (2008) final model (donation attitudes, donation anxiety, self-

67 67 efficacy, anticipated regret, descriptive norm, and personal moral norm), implicit social-identity was examined as a predictor of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior.

68 Methods 68 Overview In the present study, multiple explicit (donation attitudes, donation anxiety, selfefficacy, anticipated regret, subjective norm, descriptive norm, and personal moral norm) and implicit (image and word versions of implicit attitudes and implicit social-identity) independent variables were measured along with several behavioral-based criterion measures (behavioral intention, blood donation sign-up, and self-reported donation behavior). To avoid self-selection bias based on a dislike for blood donation, this study was advertised in a vague manner as A Study of Implicit Attitudes and Social Identity through an online experiment management system for undergraduate psychology students. The description of the study noted that the experiment involved answering questionnaires and completing a computerized categorization task. Initially, participants signed-up for a single 60-minute testing session during which the implicit and explicit measures were completed, in that order. One month later, participants were contacted to inquire about whether or not they donated blood since their laboratory testing session. Participants A total of 229 participants with no history of blood donation were recruited for the study. This sample size was determined a priori based on appropriate power needed to conduct a one-tailed test of Hypothesis 1 using (1) an alpha level of 0.05, (2) 80% power, and (3) a small effect size based on the relationship between implicit attitudes and intention (r = 0.18) among nondonors within our pilot study (Warfel, France, & France, 2012).

69 Implicit Measures 69 Image version of implicit attitudes. The Image ST-IAT (see Table 4) included 12 images in total: four blood donation pictures, four pleasant pictures, and four unpleasant pictures. All pleasant and unpleasant images were selected from the International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 2001). The IAPS is an image database with normative valence ratings (1-9 scale; higher scores indicate more positive images) that includes positive, negative and neutral rated images (see Table 4). Four pictures with valence ratings greater than seven were used for the pleasant category (item # 2091, 2340, 2345, and 2550) and four pictures with valence ratings less than three were used for the unpleasant category (item # 2205, 3160, 9041, and 9331). The selected images were matched in terms of arousal ratings from the IAPS database (Lang, Bradley, & Cuthbert, 2001). All of the pleasant and unpleasant pictures included at least one person in the image. Both the present study (Cronbach s = 0.72) and our pilot study (Warfel, France, & France, 2012; Cronbach s = 0.64) demonstrated acceptable internal consistency relative to other ST-IAT measures (e.g. Cronbach s = ; Karpinski & Steinman, 2006; adjusted r = ; Bluemke & Friese, 2008). Further, construct validity was demonstrated by (1) expected group differences (i.e., nondonors implicit attitudes were significantly more negative than those of donors) and (2) significant relationships in expected directions with relevant explicit measures (i.e., implicit attitudes were negatively related to blood and needle fears and positively related to explicit attitudes and donation intentions). Word version of implicit attitudes. For the Word ST-IAT (see Table 5), four

70 70 Table 4. Depiction of all 12 images used in the image version of the implicit attitude measure, including four from each attribute category (pleasant and unpleasant) and four from the target category (blood donation). Attribute Categories Target Category Pleasant Unpleasant Blood Donation

71 71 Table 5. List of the 12 selected words for the word version of implicit attitudes, including four from each attribute category (pleasant and unpleasant) and four from the target category (blood donation). Attribute Categories Target Category Pleasant Unpleasant Blood Donation Beauty Defeated Blood Joy Gloom Needle Laughter Failure Red Cross Love Lonely Vein words were used for the blood donation target category (Blood, Needle, Red Cross, Vein; see Warfel, words were chosen from a database of normative affective ratings known as the Affective Norms for English Words (ANEW; Bradley & Lang, 1999). The ANEW database uses normative valence ratings (1-9 scale; higher scores indicating more positive words) that includes positive, negative and neutral rated words. Four words with valence ratings greater than seven were used for the pleasant category (Beauty, Joy, Laughter, Love), and four words with valence ratings less than three were used for the unpleasant category (Defeated, Failure, Gloom, Lonely). The selected words were matched in terms of arousal ratings from the ANEW database (Bradley & Lang, 1999). Acceptable internal consistency has been found by both the present study (Cronbach s = 0.74) and our pilot study (Cronbach s = 0.68; Warfel, France, & France, 2012), which is comparable to internal consistency values of comparable measures (e.g. Cronbach s = ; Karpinski & Steinman, 2006; adjusted r = ; Bluemke & Friese, 2008). Construct validity has also been established for this measure as demonstrated by (1) expected group

72 differences (i.e., nondonors implicit attitudes were significantly more negative than 72 those of donors; Warfel, France, & France, 2012), and (2) significant relationships in expected directions with relevant explicit measures (i.e., implicit attitudes were negatively related to blood and needle fears and positively related to explicit attitudes and donation intentions; Warfel, France, & France, 2012). Implicit Social-identity. For the social-identity ST-IAT (see Table 6), attribute categories were labeled Self and Other and the target dimension was labeled Blood Donation. Four target word sets were used for the blood donation target category: Donate Blood, Blood Donor, Give Blood, and Blood Bank. In addition, four words were used for the self category (Me, Myself, Family, Friends), and four words were used to represent the other category (You, Your, Stranger, Them). Most of these terms have been used in prior studies that used a self-identity implicit measure (e.g. Brunel, Tietje, & Greenwald, 2004; Perkins & Forehand, 2006). The terms Family, Friends, and Stranger were incorporated to create a social element in parallel to the self-reported blood donation norm measures. The present study found the internal consistency of this measure to be somewhat lower (Cronbach s = 0.58) than comparable measures have generally been reported as acceptable (e.g. Cronbach s = ; Karpinski & Steinman, 2006; adjusted r = ; Bluemke & Friese, 2008).

73 73 Table 6. List of the 12 selected words for the implicit social-identity measure, including four from each attribute category (self and other) and four from the target category (blood donation). Attribute Categories Target Category Self Other Blood Donation Me You Donate Blood Myself Your Blood Donor Family Stranger Give Blood Friends Them Blood Bank Explicit Measures Demographic and Brief History Questionnaire (Appendix A). Basic demographic information and a brief assessment of blood donation history were obtained. Blood Donation Attitude (Appendix B). Self-reported attitude toward blood donation was obtained using five items pertaining to the idea of donating within the next 4 weeks (Giles, McClenahan, Cairns & Mallet, 2004; France, France, & Himawan, 2007, 2008). Each item was rated on a seven-point scale, and thus total possible scores can range from 5 to 35. Anchor pairs for the items are bad/good, unpleasant/pleasant, dissatisfying/satisfying, sad/happy, and attractive/repulsive (reverse scored item). Strong internal consistency has been reported for this measure (Cronbach s = ; Giles, McClenahan, & Mallet, 2004; France, France, & Himawan, 2007; Warfel, France, & France, 2012), which is comparable to the present study (Cronbach s = 0.89). Construct validity has also been demonstrated through significant correlations in expected directions with relevant measures (i.e., attitude is positively related to satisfaction and negatively related to donation anxiety, negative reactions, and blood and needle fears;

74 e.g. France et al, 2007, 2008; Robinson et al., 2008; Warfel, France, & France, 2012). 74 Finally, criterion-related validity has been established through significant positive relationships between attitudes and intention (e.g. Giles, McClenahan, Cairns & Mallet, 2004; France et al, 2007, 2008; Warfel, France, & France, 2012). Self-efficacy (Appendix C). Four items, each rated on a seven-point scale, were used to measure self-efficacy (Giles, McClenahan, Cairns & Mallet, 2004; France, France & Himawan, 2007, 2008), including If it were entirely up to me, I am confident that I would be able to give blood in the next 4 weeks (1 = strongly disagree; 7 = strongly agree), How confident are you that you will be able to give blood in the next 4 weeks? (1 = not confident at all; 7 = very confident), To what extent do you see yourself as capable of giving blood in the next 4 weeks? (1 = extremely incapable; 7 = extremely capable), and I believe I have the ability to give blood in the next 4 weeks (1 = definitely do not; 7 = definitely do). Total scores can range from 4 to 28. Good internal consistency reliability of this measure has been demonstrated by the present study (Cronbach s = 0.89), which is comparable to past research (Cronbach s = ; Giles, McClenahan, Cairns & Mallet, 2004; France, France, & Himawan, 2007, 2008). In addition, criterion-related validity has been established through significant positive relationships found between self-efficacy and intention (e.g. Giles, McClenahan, Cairns & Mallet, 2004; France et al, 2007, 2008; Warfel, France, & France, 2012). Subjective Norm (Appendix D). Subjective norm was measured using the following three items used by Godin and colleagues (2005): The people who are most important to me think I should give blood (1 = Strongly disagree; 7 = Strongly agree), Most people who are important to me would recommend I give blood (1 = Strongly

75 disagree; 7 = Strongly agree) and If I were to give blood, most of the people who are 75 important to me would (1 = Strongly disapprove; 7 = Strongly approve). Total scores on this measure can range from 3 to 21. Good internal consistency was reported for these three items both in the present study (Cronbach s = 0.74) and prior research (see Godin et al., 2005; Cronbach s = 0.78). Personal Moral Norm (Appendix D). This construct was assessed using three items: I feel a moral obligation to give blood, I feel a personal responsibility to give blood, and It is a social obligation to give blood (Lemmens et al., 2005; France, France, Himawan, 2007, 2008). Each of the three items were rated on a seven-point scale (1 = strongly disagree; 7 = strongly agree), and as such total scores on this scale can range from 3 to 21. Internal consistency was observed to be strong for this measure in the present study (Cronbach s = 0.87) as well as past studies (Cronbach s = ; Lemmens et al., 2005; France, France, & Himawan, 2007, 2008). Construct validity of this measure has been demonstrated through significant positive relationships between subjective norm and attitudes (e.g. Lemmens et al., 2005; France, France, & Himawan, 2007, 2008). In addition, criterion-related validity has been established through significant positive relationships found between personal norm and intention (e.g. France, France, & Himawan, 2007, 2008). Descriptive Norm (Appendix D). This construct was assessed by participants responses to two items assessing perceptions of how likely it is that the following groups of people will donate blood in the next 3 months : 1) family and 2) friends and colleagues. These items were adapted from a descriptive norm measure developed by Robinson et al. (2008). Each item was rated on a seven-point scale from 1 = very likely to

76 76 7 = very unlikely. Total scores on this scale can range from 2 to 14. Internal consistency of these two items was found to be Cronbach s = 0.50 in the present study. Construct validity of this measure was established through significant positive relationships with attitudes, personal moral norm, and subjective norm (Robinson et al., 2008). In addition, criterion-related validity has been established through significant positive relationships found between descriptive norm and intention (e.g. Robinson et al., 2008). Donation Anxiety (Appendix E). Two items were used to assess blood donation anxiety: In the future if I donate blood, I would feel: 1) distressed and 2) anxious (see Robinson et al., 2008). These two items were rated on a scale from 1 = not at all to 7 = very much. Total scores on this scale can range from These two items were shown to exhibit good internal consistency in both the present study (Cronbach s = 0.78) and prior research (r = 0.81; Robinson et al., 2008). In addition, construct validity of this measure was established through a significant negative relationship reported between donation anxiety and attitudes (e.g. Robinson et al., 2008). In addition, criterion-related validity has been established through significant negative relationships found between donation anxiety and intention (e.g. Robinson et al., 2008). Anticipated Regret (Appendix E). Three items were used to assess this construct, using the following stem: If I do not donate blood in the next 4 weeks: 1) I will regret it, 2) It will bother me, and 3) I will be disappointed (Godin et al., 2005; Robinson et al., 2008). Each of these three items were rated on a scale from 1 = very unlikely to 7 = very likely. Total scores on this scale can range from 3 to 21. Good internal consistency for this measure has been shown by the present research (Cronbach s = 0.82) and was also reported by prior research (Cronbach s = 0.87; Godin et al.,

77 2005). Construct validity of this measure was established through significant positive 77 relationships with attitudes (Robinson et al., 2008), and criterion-related validity has been established through significant positive relationships found between anticipated regret and intention (Robinson et al., 2008). Behavioral Intention (Appendix F). An individual s intention to donate blood in the next 4 weeks was assessed using three items on a seven-point scale (Giles, McClenahan, Cairns & Mallet, 2004; France, France, & Himawan, 2007, 2008). The items are I intend to give blood (unlikely/likely), I have decided to give blood (disagree/agree), and I will try to give blood (improbable/probable). Scores on this scale range from 3 to 21. Strong internal consistency has been reported for this measure by both present (Cronbach s = 0.90) and past research (Cronbach s = ; France, France, & Himawan, 2007, 2008; Warfel, France, & France, 2012), and construct validity has been established through positive significant relationships found between intention and attitudes (e.g. France, France, & Himawan, 2007, 2008; Warfel, France, & France, 2012). Blood Donation Decisiveness (Appendix G). As a measure of decisiveness, participants responded to the extent they agreed with the single statement I am definitely going to donate blood at some point in the future on a seven-point scale, with 1 = strongly disagree and 7 = strongly agree. Individuals who chose any of the four extreme options (1, 2, 6 or 7) were deemed decisive, while those who chose the three moderate options (3, 4 or 5) were deemed indecisive. This item was developed for the specific purpose of distinguishing between these populations in order to test the present hypotheses.

78 78 Blood Donation Consideration (Appendix G). As a measure of consideration, participants responded to the single question How much time have you spent considering whether or not to donate blood? on a six-point scale, with 0 = none and 5 = a very large amount. Similarly to the Decisiveness measure described above, this item was developed for the purpose of distinguishing between individuals who have spent a relatively small versus large amount of time considering whether or not to donate blood, in order to test the present hypotheses. Behavioral Measures Sign-up Behavior (Appendix H). Participants were offered an opportunity for immediate sign-up at a blood drive in locations either on the Ohio University campus or within walking distance from campus. Two dates, with multiple time-slots, within 1-3 weeks from the testing session were made available. For individuals who did sign-up for a blood drive, an reminder was sent 48 hours prior to this date (see Appendix H) to remind them of their sign-up date, time, and drive location. 30-day Follow-Up of Blood Donation Behavior (Appendix H). Self-reported blood donation behavior was measured using a single yes or no question: Within the last 30 days, did you attend a blood drive with the intention to donate blood? Procedures A flowchart of the procedures is depicted in Figure 3. Upon arrival to the main laboratory room, participants were greeted in small groups and allowed the opportunity to consent (Appendix I) to the study. Next, experimenters explained that a critical component to the study involved having a voluntary opportunity to sign-up to donate blood at a local blood drive. It was further explained that participants in the study were

79 79 required to meet minimal eligibility criteria to donate blood, and they were given a list of criteria that would deem them ineligible (Appendix J). Experimenters then asked participants to carefully review the list, and if any of the criteria applied to them they would still receive a research credit, but would not be eligible to continue with the study. After allowing the opportunity for participants who did not meet minimal eligibility to leave, experimenters briefly described the implicit measures participants would later complete as computerized sorting tasks and explained the importance of working both quickly and accurately during this portion of the experiment. Each participant was then seated at a desk with a Dell 2.66 Gz Intel Core 2 Duo processor desktop computer and flat screen monitor in one of two quiet rooms. Inquisit 3 software (Millisecond Software, Seattle, WA) was used to administer all three implicit measures, and Qualtrics software (Qualtrics, Inc.) was used to administer all other questionnaires.

80 80 Figure 3. Flowchart of study procedures. To begin, participants completed a basic demographic questionnaire and blood donation history questionnaire. Next, participants performed the image and word versions of implicit attitudes as well as the implicit social-identity measure (administered in

81 counterbalanced order across participants). Next, participants completed a series of 81 computerized self-report questionnaires, including measures of behavioral intention, blood donation attitudes, donation anxiety, self-efficacy, anticipated regret, subjective norm, descriptive norm, personal moral norm, and blood donation decisiveness. Participants were then given an opportunity to sign-up for a local blood drive that took place one to three weeks after the testing session. After completing all computer-based questionnaires, participants were asked to provide contact info ( address, cell phone number) so that they could receive a single follow-up question 30 days after the testing session. It was explained that if they did not respond to the initial within three days, they would receive the question in the form of a text message. If they did not respond to the text, they would receive a phone call. Further, we explained that once they answered the follow-up question, they would automatically be entered into a lottery for a chance to win one of two $50 Amazon gift cards. Finally, participants were thanked and fully debriefed (Appendix K). For participants who signed up for an upcoming blood drive, the date, time, and location of the blood drive was written directly on their debriefing forms to serve as an appointment reminder card (see Appendix K). Participants earned research credit toward undergraduate psychology course requirements in exchange for their time in the testing session. Implicit Measurement Procedure. During the testing session, each of the three implicit measures consisted of two phases administered in counterbalanced order. Tables 7 and 8 detail the organization of a total of six blocks across the two phases, including 24 attribute practice trials (blocks 1 and 4), 24 combined practice trials (blocks 2 and 5), and 48 combined test trials (blocks 3 and 6). For the image and word ST-IATs (Table 7),

82 during all trials pleasant was assigned to one response key (the letter E on the 82 keyboard), and unpleasant was assigned to a different response key (the letter I on the keyboard). For combined trials during phase one (e.g. Blood Donation + Pleasant), blood donation and pleasant was assigned to the same response key and unpleasant was assigned to a different response key. For combined trials during phase two (e.g. Blood Donation + Unpleasant), blood donation and unpleasant was assigned to the same response key and pleasant was assigned to a different response key. The social-identity ST-IAT (Table 8) was similar except that the attribute categories were self versus other instead of pleasant versus unpleasant. All attribute and blood donation stimuli were presented in random order, without replacement. Each ST-IAT task included two phases of reaction time trials, which are detailed in Tables 7 and 8. Each phase began with participants learning to sort the pleasant/self and unpleasant/other images or words into their correct categories. During all trials, the correct response for pleasant stimuli was to press the letter E on the computer keyboard, while the correct response for unpleasant stimuli was to press the letter I on the keyboard. The category labels were displayed throughout the task in the upper corners of the computer screen as a reminder for the participants, while the stimuli appeared one at a time in the middle of the screen. After 24 practice trials including only pleasant and unpleasant stimuli, blood donation images or words were added to the task. Participants were given an additional 24 trials to correctly sort the three categories of stimuli. In phase 1, correct blood donation responses involved pressing the E (or pleasant or self ) key (see Figure 4), and in phase 2, a correct response to the blood donation stimuli required pressing the I (or unpleasant or other ) key (see Figure 5). After

83 completion of the practice trials, each phase concluded with 48 additional trials where 83 participants correctly sorted stimuli from the three categories. All stimuli were presented in random order, without replacement. Each of the two phases began with basic ST-IAT task instructions, and brief reminder instructions regarding the categorization and key assignments of the upcoming set of trials were given throughout the task. Particular emphasis was placed on reminding participants that the task was timed and that the objective was to work both quickly and accurately. Participants were informed that going too slowly or making too many errors would result in an uninterpretable score. The inter-stimulus interval after each correct response was set to 400 milliseconds. If participants answered incorrectly, a red X appeared in the lower part of the screen. Participants were unable to go on to the next trial until a correct response is given. Thus, the time latency recorded for each trial included the total time taken to make a correct response. As discussed in detail below, ST-IAT scores were determined by computing difference scores between trials in the first phase (e.g. Blood Donation + Pleasant or Self) and the second phase (e.g. Blood Donation + Unpleasant or Other), such that higher scores indicate stronger associations during the first phase (when blood donation is paired with pleasant or self stimuli).

84 Table 7. Overview of the two phases for the image and word implicit attitude measures (pleasant versus unpleasant). 84 Phase Block Trials Left key ( E ) Right key ( I ) Description Pleasant Unpleasant Practice trials during which the correct response keys for pleasant versus unpleasant stimuli are learned Pleasant and Blood Donation 3 48 Pleasant And Blood Donation Unpleasant Unpleasant Scored trials during which the correct response for pleasant and blood donation stimuli is the E key, while the correct response for the unpleasant stimuli is the I key. Scored trials during which Block 2 key assignments are repeated Pleasant Unpleasant Practice trials during which the correct response keys for pleasant versus unpleasant stimuli are repeated Pleasant Unpleasant and Blood Donation 6 48 Pleasant Unpleasant And Blood Donation Scored trials during which the correct response for pleasant stimuli is the E key, while the correct response for blood donation and unpleasant stimuli is the I key. Scored trials during which Block 5 key assignments are repeated.

85 Table 8. Overview of the two phases for implicit social identity (self versus other). 85 Phase Block Trials Left key ( E ) Right key ( I ) Description Self Other Practice trials during which the correct response keys for self versus other stimuli are learned Self and Blood Donation 3 48 Self And Blood Donation Other Other Scored trials during which the correct response for self and blood donation stimuli is the E key, while the correct response for the other stimuli is the I key. Scored trials during which Block 2 key assignments are repeated Self Other Practice trials during which the correct response keys for self versus other stimuli are repeated Self Other and Blood Donation 6 48 Self Other And Blood Donation Scored trials during which the correct response for self stimuli is the E key, while the correct response for blood donation and other stimuli is the I key. Scored trials during which Block 5 key assignments are repeated.

86 (a) PHASE ONE 86 (b) Figure 4. Sample screens of (a) instructions (indicating key assignment) and (b) a combined block trial during Phase 1 of the word version of implicit attitudes. For this example, the correct response is the E key, as the word Needle is associated with the blood donation category.

87 (a) PHASE TWO 87 (b) Figure 5. Sample screens of (a) instructions (indicating key assignment) and a (b) combined block trial during Phase 2 of the word version of implicit attitudes. For this example, the correct response is the I key, as the word Gloom is associated with the unpleasant category.

88 88 ST-IAT Scoring and Data Reduction Consistent with recommended IAT methodology (Greenwald, Nosek, & Banaji, 2003), participants were excluded from the analyses if their response times were less than 300 milliseconds on more than 10% of the trials [1(0.4%) Word ST-IAT], their error rates exceeded 20% for that measure [3(1.3%) Image ST-IAT, 4(1.7%) Word ST-IAT, 15(6.5%) Social-Identity ST-IAT], or both [1(0.4%) Image ST-IAT, 2(0.9%) Word ST- IAT, 3(1.3%) Social-Identity ST-IAT]. For all three ST-IATs, overall scores were computed according to the following Greenwald, Nosek, and Banaji (2003) algorithm: D = First, because the ST-IAT task required a correct response before proceeding to the next trial, any trial lasting longer than 3,000 milliseconds was replaced with the mean withinparticipant response time to avoid skewing the computations (Greenwald, McGhee & Schwartz, 1998). Next, the average response times of blocks 2 and 3 (e.g. Blood Donation + Pleasant or Self) were subtracted from the average response times of blocks 5 and 6 (e.g. Blood Donation + Unpleasant or Other), respectively. Each difference score was then divided by its associated inclusive standard deviation of all response items within practice blocks 2 and 5 and likewise test blocks 3 and 6. Finally, an ST-IAT D-

89 score was computed by obtaining an average of these two values. Higher D-scores 89 indicate faster responses when blood donation was paired with pleasant or self stimuli relative to unpleasant or other stimuli.

90 Statistical Analyses 90 Predictive Validity of the Implicit Measures Hypothesis 1a. Image and word versions of implicit attitudes were each hypothesized to be significant predictors of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Linear regression analyses were performed to test the relationship between each of these implicit measures (word version of implicit attitudes and image version of implicit attitudes) and each of the behavioral measures (behavioral intention, sign-up behavior, and donation behavior). The latter two behavioral measures are dichotomous variables, and thus binomial logistic regression was used. Hypothesis 1b. Implicit social-identity was hypothesized to be a significant predictor of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Linear regression analyses were performed to test the relationship between implicit social-identity and each of the behavioral measures (behavioral intention, sign-up behavior, and donation behavior). The latter two behavioral measures are dichotomous variables, and thus binomial logistic regression was used. Incremental Validity of the Individual Explicit Predictors Hypothesis 2a. In the presence of explicit attitudes toward blood donation, both image and word versions of implicit attitudes were hypothesized to predict unique variance toward behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior.

91 91 Hierarchical regression and logistic regression analyses were conducted such that, initially, self-reported attitudes were entered into the model, followed by each of the respective individual implicit measures (word version of implicit attitudes and image version of implicit attitudes). Behavioral measures (behavioral intention, sign-up behavior, and behavior follow-up) served as the criterion or dependent variable. Logistic regression was used for the latter two dichotomous behavioral measures. Hypothesis 2b. In the presence of explicit norm measures (subjective norm, descriptive norm, and personal moral norm), it was hypothesized that implicit socialidentity would predict unique variance toward behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. Hierarchical regression and logistic regression analyses were conducted such that, initially, the relevant explicit measures (subjective norm, descriptive norm, and personal moral norm) were entered into the model individually as well as simultaneously, followed by implicit social-identity. Each of the behavioral measures (behavioral intention, sign-up behavior, and behavior follow-up) served as the criterion or dependent variable. Logistic regression was used for the latter two dichotomous behavioral measures. Moderation of Decisiveness and Consideration Hypotheses 3a and 3b. For nondonors low in decisiveness or consideration, it was hypothesized that image and word versions of implicit attitudes would be stronger predictors than explicit attitudes of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. For nondonors high in decisiveness or consideration, it was hypothesized that explicit attitudes would be a stronger predictor

92 than either the image or word versions of implicit attitudes of behavioral intention, 92 immediate sign-up behavior, and follow-up report of donation behavior. To test Hypothesis 3a, groups were first split based on their response to the Blood Donation Decisiveness measure (see Appendix G). Specifically, participants who responded with 3, 4, or 5 (1-7 scale, with 1 being strongly disagree and 7 being strongly agree ) to the statement I am definitely going to donate blood at some point in the future were considered low in decisiveness; whereas participants who selected 1, 2, 6, or 7 were considered high in decisiveness. Hierarchical multiple regression and logistic regression analyses were conducted such that the binary decisiveness variable, explicit attitude, and one of the implicit attitude measures (word implicit attitude or image implicit attitude) were entered in the first step. In the second step, both the decisiveness by implicit attitude and the decisiveness by explicit attitude interactions were entered. Each of the behavioral measures (behavioral intention, sign-up behavior, and behavior follow-up) served as the criterion or dependent variable. Logistic regression was used for the latter two dichotomous behavioral measures. To test Hypothesis 3b, groups were first split based on their response to the Blood Donation Consideration measure (see Appendix G). Specifically, participants who responded with 0, 1, or 2 (0-5 scale, with 0 being none and 5 being a very large amount ) to the statement How much time have you spent considering whether or not to give blood? were deemed low in consideration; whereas participants who selected 3, 4, or 5 were deemed high in consideration. Hierarchical multiple regression and logistic regression analyses were conducted such that the binary consideration variable, explicit attitude, and one of the implicit attitude measures (word implicit attitude or image

93 93 implicit attitude) were entered in the first step. In the second step, both the decisiveness by implicit attitude and the decisiveness by explicit attitude interactions were entered. Each of the behavioral measures (behavioral intention, sign-up behavior, and behavior follow-up) served as the criterion or dependent variable. Logistic regression was used for the latter two dichotomous behavioral measures. Exploration of Incremental Validity in the Presence of Multiple Explicit Predictors Exploratory Hypothesis 1a. In the presence of the explicit measures included in Robinson et al. s (2008) final model (donation attitudes, donation anxiety, self-efficacy, anticipated regret, descriptive norm, and personal moral norm), image and word versions of implicit attitudes were examined as predictors of behavioral intention, immediate signup behavior, and follow-up report of donation behavior. Exploratory Hypothesis 1b. In the presence of the explicit measures included in Robinson et al. s (2008) final model (donation attitudes, donation anxiety, self-efficacy, anticipated regret, descriptive norm, and personal moral norm), implicit social-identity was examined as predictors of behavioral intention, immediate sign-up behavior, and follow-up report of donation behavior. To test the Exploratory Hypotheses, hierarchical regression and logistic regression analyses were conducted such that, initially, all of the relevant explicit measures (donation attitudes, donation anxiety, self-efficacy, anticipated regret, descriptive norm, and personal moral norm) were simultaneously entered into the model, followed by each of the respective individual implicit measures (word version of implicit attitudes, image version of implicit attitudes, and implicit social-identity). Each of the behavioral measures (behavioral intention, sign-up behavior, and behavior follow-up) served as the

94 criterion or dependent variable. Logistic regression was used for the latter two 94 dichotomous behavioral measures.

95 Results 95 Sample Characteristics The complete sample consisted of 229 undergraduate students with no prior history of blood donation. However, consistent with recommended IAT methodology (Greenwald, Nosek, & Banaji, 2003), and as noted above, a few participants were excluded specific to each implicit measure either because their response times were less than 300 milliseconds for more than 10% of the trials or their error rates exceeded 20%. Additionally, four participants experienced computer issues causing interruptions and thus are missing data for some tasks, but these participants were included in analyses where possible. In total, 225 participants (156 female, 69 male) were included in the analyses involving the Image ST-IAT; 222 participants (155 female, 68 male) were included in analyses involving the Word ST-IAT; and 210 participants (151 female, 61 male) were included in analyses involving the Social Identity ST-IAT. For the largest sample (Image ST-IAT; N = 225), participants mean age was 19.2 (SD = 1.4) and the sample was predominantly White (80.4% White, 11.1% Asian, 6.7% Black, and 1.8% Other) and Non-Hispanic (2.7% Hispanic). Blood Donation Sign-up and 30-day Follow-up Behavior. All 225 participants used in the present analyses indicated whether or not they were willing to sign-up for an upcoming local blood drive. Of these, 8.9% (n = 20) did sign-up for a campus blood drive taking place one to three weeks after the testing session, while the remaining 205 (91.1%) did not. Further, a total of 184 of the original 225 (81.8%) responded to the follow-up question 30 days post-session: Within the last 30 days, did you attend a blood drive with the intention to donate blood? Of these 184, 14 (6.2%) participants replied yes whereas

96 170 (92.4%) replied no. Among the 20 participants who signed-up for an upcoming 96 blood drive, 14 responded to the 30 day follow-up and only six of these 14 (42.8%) indicated showing up to a blood drive intending to donate. As expected, there is an obvious imbalance in the proportion of participants who donated blood and/or made attempts to donate versus those who did not. Data Preparation Distributions of all non-nominal independent and dependent variables were examined for normality and extreme data. All variables were observed to be normally distributed with no extreme values with the exception of intention, which was positively skewed (Skewness = 0.788, SE = 0.162). In an attempt to normalize the distribution, square root, inverse and log transformations were conducted; however, each of these transformations proved unsuccessful in approximating a normal distribution. Therefore, analyses were run using the original distribution given that linear regression and logistic regression analyses are robust to violations of normality. Overall Findings An examination of the overall sample revealed participants responded significantly faster during trials when the unpleasant and blood donation stimuli were paired to the same response key, for both the Image (M = -0.18, SD = 0.29, t(224) = 9.01, p < , d = 1.2) and Word (M = -0.19, SD = 0.29, t(224) = 9.83, p < , d = 1.3) ST-IATs. This indicates that participants more easily associated blood donation with unpleasant stimuli relative to stimuli that are pleasant. Similarly, for the Social Identity ST-IAT, participants responded significantly faster during trials when non-self (e.g. You, Them, Stranger) and blood donation words were paired to the same response key (M = -

97 0.04, SD = 0.29, t(223) = 2.06, p < 0.05, d = 0.28), indicating that participants more 97 easily associated blood donation with non-self stimuli relative to stimuli that are related to the self. Additionally, as can be seen in Table 9, both the Image and Word ST-IATs were significantly related in a positive direction to explicit attitude and self-efficacy, and in a negative direction to donation anxiety. The Social-Identity ST-IAT showed a significant positive relationship with personal moral norm only. Further, Table 10 demonstrates that when gender differences were considered, males and females generally did not differ significantly in their responses to both explicit and implicit measures. The only exception was that females responded significantly faster to donation-unpleasant pairings on the Word ST-IAT than males. This suggests females had more negative automatic associations with blood donation stimuli than males. Predictive Validity Analyses Predicting Intention (Hypothesis 1a). Correlation analyses were conducted to test each of the three implicit measures relationship with blood donation intention. As shown in the last column of Table 9, results indicated the Image ST-IAT was positively correlated (r = 0.13, p < 0.05) with intention, explaining 1.7% of the variability. However, neither the Word ST-IAT (r = 0.06, p = 0.17) nor the Social-Identity ST-IAT (r = 0.07, p = 0.14) contributed significant variance toward the prediction of intention. Predicting Sign-up and 30-day Follow-up Behavior (Hypothesis 1b). Binomial logistic regression analyses were conducted to test the ability of each of the three implicit measures to predict sign-up behavior and 30-day follow-up behavior. As can be seen in

98 Table 11, absence of any significant effects failed to support the first hypothesis of the predictive validity of these three measures. 98 Table 9. Correlations among implicit (image, word, and social-identity ST-IATs) and explicit measures (attitude, anxiety, self-efficacy, anticipated regret, descriptive norm, personal moral norm, and intention). (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (1) Image ST-IAT * ** 0.27** 0.23** 0.13* (2) Word ST-IAT * 0.11* -0.19** 0.21** 0.06 (3) Social Identity ST-IAT (4) Attitude ** 0.57** 0.31* 0.58** 0.73** (5) Self-Efficacy ** 0.15* 0.37** 0.67** (6) Anxiety ** 0.36** (7) Anticipated Regret ** 0.16* 0.43** (8) Descriptive Norm * 0.09 (9) Personal Moral Norm ** (10) Donation Intention --- Note. *p < 0.05, **p < 0.01; one-tailed

99 Table 10. Descriptives and t-tests comparing males and females for both explicit and implicit measures. 99 Variable Explicit Measures Females (n = 150) M (SD) Males (n = 60) M (SD) t p (1-tailed) Attitude 18.3 (7.5) 18.5 (6.4) Self Efficacy 14.7(7.1) 15.2(6.4) Donation Anxiety 7.9(4.0) 7.0(3.8) Anticipated Regret 7.01(4.2) 7.43(4.1) Descriptive Norm 17.2 (3.0) 17.3(3.0) Personal Moral Norm 10.7(4.2) 11.0(4.3) Donation Intention 7.7(4.8) 7.8(3.9) Implicit Measures Image ST-IAT -0.19(0.3) -0.13(0.3) Word ST-IAT -0.23(0.3) -0.10(0.3) Social-Identity ST-IAT -0.07(0.3) -0.00(0.3) D

100 100 Table 11. Logistic regression analyses testing the ability of the Image, Word, and Social-Identity ST-IATs to predict blood donation sign-up and self-reported donation attempt at 30 day follow-up. Criterion ST-IAT Predictor N Β SE β Wald X 2 P Image Donation Sign-up Word Social-Identity Image Donation Attempt Word Social-Identity

101 Incremental Predictive Validity Analyses 101 Predicting Intention (Hypothesis 2a). Given that only the Image ST-IAT significantly predicted intention, this was the only measure that would be appropriate to test in the presence of explicit attitudes. Results of a hierarchical linear regression analysis revealed explicit attitude was a significant predictor of donation intention (R 2 = 0.54, F(2, 225) = 130.6, p < 0.001); however, after controlling for explicit attitude the Image ST-IAT did not significantly enhance prediction of donation intention (ΔR 2 = 0.00, β = 0.016, p = 0.83). Predicting Sign-up and 30-day Follow-up Behavior (Hypothesis 2b). Given that the three implicit measures were not significant predictors of either donation sign-up or 30-day follow-up of donation behavior, it was not appropriate to test their ability to predict behavior in the presence of explicit measures. Moderation of Decisiveness Predicting Intention (Hypothesis 3a). Hierarchical multiple linear regression analyses were conducted to determine whether level of decisiveness moderated the prediction of intention. In the first step, decisiveness, explicit attitude, and one of the implicit attitude measures (image or word) were entered; in the second step, the decisiveness by explicit attitude and decisiveness by implicit attitude interactions were entered into the model. As can be seen in Table 12, absence of any significant effects of the implicit measures failed to support the hypothesis that decisiveness moderates the relationship between implicit attitude and intention; however, decisiveness does appear to moderate the relationship between explicit attitude and intention. In

102 102 the presence of the main effects of decisiveness, explicit attitude, images implicit attitude, and the decisiveness by images implicit attitude interaction, the decisiveness by explicit attitude interaction was significant (β = 0.44, p < 0.01), contributing 1.7% additional variance over and above the main effects. This interaction effect is depicted in Figure 6, and shows that increases in explicit donation attitude are associated with stronger donation intentions among those who are high versus low on decisiveness. Similar results were obtained for both the Image and the Word ST-IAT (see Figure 7). Predicting Behavior (Hypothesis 3a). Hierarchical multiple logistic regression analyses were conducted to determine whether decisiveness moderated the prediction of sign-up and follow-up behavior. In the first step, decisiveness, explicit attitude, and one of the implicit attitude measures were entered; in the second step, the decisiveness by explicit attitude and decisiveness by implicit attitude interactions were entered into the model. As can be seen in Tables 13 and 14, absence of any significant effects of the implicit measures failed to support the hypothesis of that decisiveness moderates the relationship between implicit attitude and either sign-up or follow-up behavior; additionally, the explicit attitude by intention interactions were not significant. In fact, only the main effect of explicit attitude was significant (sign-up: β = 0.21, p < 0.01; follow-up: β = 0.15, p < 0.01) in the presence of decisiveness and image implicit attitude. Similar results were obtained for the Word ST-IAT, as only the main effect of implicit attitude was significant (sign-up: β = 0.20, p < 0.01; follow-up: β = 0.13, p < 0.01) in the presence of decisiveness and word implicit attitude.

103 Table Hierarchical linear regression analyses to predict intention using decisiveness, explicit attitude, and image and word implicit attitude and their interaction terms. Variable β t ΔF ΔR 2 Step ** Decisiveness ** Explicit Attitude ** Image Implicit Attitude Step * Decisiveness x Explicit Attitude ** Decisiveness x Image Implicit Attitude Step ** 0.53 Decisiveness * Explicit Attitude ** Word Implicit Attitude Step * Decisiveness x Explicit Attitude ** Decisiveness x Word Implicit Attitude Note. *p < 0.05, **p < 0.01

104 Figure 6. Graphical representation of significant interaction between the binary variable decisiveness and explicit attitude to predict intention, in the presence of the main effects of these variables and image implicit attitude. 104

105 Figure 7. Graphical representation of significant interaction between the binary variable decisiveness and explicit attitude to predict intention, in the presence of the main effects of these variables and word implicit attitude. 105

106 106 Table 13. Hierarchical logistic regression analyses to predict sign-up behavior using decisiveness, explicit attitude, and image and word implicit attitude and their interaction terms. Predictors Β SE β Wald X 2 Odds Ratio 95% CI Step 1 Decisiveness Explicit Attitude ** Image Implicit Attitude Step 2 Decisiveness x Explicit Attitude Decisiveness x Image Implicit Attitude Step 1 Decisiveness Explicit Attitude ** Word Implicit Attitude Step 2 Decisiveness x Explicit Attitude Decisiveness x Word Implicit Attitude Note. *p < 0.05, **p < 0.01

107 Table 14. Hierarchical logistic regression analyses to predict 30-day follow-up behavior using decisiveness, explicit attitude, and image and word implicit attitude and their interaction terms. 107 Predictors Β SE β Wald X 2 Odds Ratio 95% CI Step 1 Decisiveness Explicit Attitude ** Image Implicit Attitude Step 2 Decisiveness x Explicit Attitude Decisiveness x Image Implicit Attitude Step 1 Decisiveness Explicit Attitude ** Word Implicit Attitude Step 2 Decisiveness x Explicit Attitude Decisiveness x Word Implicit Attitude Note. *p < 0.05, **p < 0.01

108 Moderation of Consideration 108 Predicting Intention (Hypothesis 3b). Hierarchical multiple linear regression analyses were conducted to determine whether level of consideration moderated the prediction of intention. In the first step, consideration, explicit attitude, and one of the implicit attitude measures (image or word) were entered; in the second step, the consideration by explicit attitude and consideration by implicit attitude interactions were entered into the model. As can be seen in Table 15, absence of any significant effects of the implicit measures failed to support the hypothesis of that consideration moderates the relationship between implicit attitude and intention; additionally, the explicit attitude by intention interactions were not significant. In fact, only the main effect of explicit attitude was significant (β = 0.72, p < 0.01) in the presence of decisiveness and image implicit attitude. Similar results were obtained for the Word ST-IAT, as only the main effect of implicit attitude was significant (β = 0.71, p < 0.01) in the presence of decisiveness and word implicit attitude. Predicting Behavior (Hypothesis 3b). Hierarchical multiple logistic regression analyses were conducted to determine whether decisiveness moderated the prediction of behavior. In the first step, decisiveness, explicit attitude, and one of the implicit attitude measures were entered; in the second step, the decisiveness by explicit attitude and decisiveness by implicit attitude interactions were entered into the model. As can be seen in Tables 16 and 17, absence of any significant effects of the implicit measures failed to support the hypothesis of that decisiveness moderates the relationship between implicit attitude and intention; additionally, the explicit attitude by intention interactions were not significant. In fact, only the main effect of explicit attitude was significant (sign-up: β =

109 109 Table 15. Hierarchical linear regression analyses to predict donation intention using consideration, explicit attitude, and image and word implicit attitude and their interaction terms. Variable β t ΔF ΔR 2 Step ** 0.52 Consideration Explicit Attitude ** Image Implicit Attitude Step Consideration x Explicit Attitude Consideration x Image Implicit Attitude Step ** 0.51 Consideration Explicit Attitude ** Word Implicit Attitude Step Consideration x Explicit Attitude Consideration x Word Implicit Attitude Note. *p < 0.05, **p < 0.01

110 Table 16. Hierarchical logistic regression analyses to predict sign-up behavior using consideration, explicit attitude, and image implicit attitude and their interaction terms. 110 Predictors Β SE β Wald X 2 Odds Ratio 95% CI Step 1 Consideration Explicit Attitude ** Image Implicit Attitude Step 2 Consideration x Explicit Attitude Consideration x Image Implicit Attitude Step 1 Consideration Explicit Attitude ** Word Implicit Attitude Step 2 Consideration x Explicit Attitude Consideration x Word Implicit Attitude Note. *p < 0.05, **p < 0.01

111 111 Table 17. Hierarchical logistic regression analyses to predict 30-day follow-up behavior using consideration, explicit attitude, and image implicit attitude and their interaction terms. Predictors Β SE β Wald X 2 Odds Ratio 95% CI Step 1 Consideration Explicit Attitude ** Image Implicit Attitude Step 2 Consideration x Explicit Attitude Consideration x Image Implicit Attitude Step 1 Consideration Explicit Attitude * Word Implicit Attitude Step 2 Consideration x Explicit Attitude Consideration x Word Implicit Attitude Note. *p < 0.05, **p < 0.01

112 , p < 0.01; follow-up: β = 0.14, p < 0.01) in the presence of decisiveness and image implicit attitude. Similar results were obtained for the Word ST-IAT, as only the main effect of implicit attitude was significant (sign-up:β = 0.21, p < 0.01; follow-up: β = 0.11, p < 0.05) in the presence of decisiveness and word implicit attitude. Exploring Incremental Validity in the Presence of Multiple Explicit Predictors Predicting Intention. Given that the three implicit measures were not shown to be significant predictors of donation intention in the presence of explicit attitudes, it was not appropriate to test their ability to predict intention in the presence of all of the explicit measures in Robinson et al. s (2008) final model (donation attitudes, donation anxiety, self-efficacy, anticipated regret, descriptive norm, and personal moral norm). Predicting Behavior. Given that the three implicit measures were not shown to be significant predictors of either donation sign-up or subsequent donation attempts, it was not appropriate to test their ability to predict behavior in the presence of all of the explicit measures in Robinson et al. s (2008) final model (donation attitudes, donation anxiety, self-efficacy, anticipated regret, descriptive norm, and personal moral norm). To explore whether social pressure may have affected results, a median split was used to identify those who were high versus low in social norm (see Appendix D for Social Norm items). Correlational analyses were conducted separately for those deemed high versus low in perceived social pressure to donate blood. As shown in Table 18, correlation coefficients between the Image ST-IAT and explicit measures are generally stronger among those indicating lower perceived social pressure.

113 113 Table 18. Correlations between implicit (image and word ST-IATs) and explicit measures (attitude, anxiety, self-efficacy, anticipated regret, descriptive norm, personal moral norm, and intention), among individuals with low and high perceived social pressure to donate. Attitude Low Social Pressure Image ST-IAT (n = 113) Word ST-IAT (n = 114) High Social Pressure Image ST-IAT (n = 112) Word ST-IAT (n = 109) Note. *p < 0.05, **p < 0.01; one-tailed Self- Efficacy Anxiety Anticipated Regret Descriptive Norm Personal Moral Norm Donation Intention 0.16* 0.17* -0.25* 0.19* ** ** * *

114 Discussion 114 This study is the first to investigate the ability of novel implicit measures to predict blood donation intention and behavior among individuals with no prior donation history. To this end, three implicit measures were designed to tap constructs shown to be important for predicting nondonor behavior, including two attitude-based measures (Image and Word ST-IATs) and a social norm-based measure (Social-Identity ST-IAT). Overall, analyses indicate that both the Image and Word ST-IATs are reliable and demonstrate construct validity, but the Social-Identity ST-IAT is less strong in these areas. All three measures appear to be weak predictors of nondonor intentions and behavior, especially when tested alongside their explicit counterparts. To summarize the present reliability and construct validity findings, internal consistency reliability values were acceptable for the Image and Word ST-IATs (Cronbach s α = 0.72 and 0.74, respectively), but somewhat lower for the Social Identity ST-IAT (Cronbach s α = 0.58). Further, an examination of the overall sample indicated that participants responded significantly faster when blood donation was paired with unpleasant and non-self stimuli, compared to pleasant stimuli and stimuli related to the self, respectively. Construct validity was demonstrated for both the Image and Word ST- IATs, as supported by significant relationships with explicit measures in expected directions. Specifically, both the Image and Word ST-IATs shared positive correlations with attitude and self-efficacy, and negative correlations with donation anxiety, whereas the Social-Identity ST-IAT only showed a significant positive relationship with personal moral norm. Interpretation of these findings is elaborated on in the next section.

115 Implicit Attitudes and Affective Responding 115 Past blood donation research shows that while individuals uniformly have positive cognitive evaluations about blood donation, these positive attitudes are coupled with negative affective associations about the act of donation itself, especially among nondonors (Breckler & Wiggins, 1989). Consistent with this notion and with our pilot study results (Warfel, France, & France, 2012), overall the present sample displayed negative implicit attitudes toward donation because they more readily associated donation images and words with an unpleasant category. These findings suggest the Image and Word ST-IATs are capturing automatic negative affective responses toward the act of blood donation, which is consistent with prior research suggesting that implicit attitude measures are highly related to affective reactions (Shiv & Fedorikhin, 2002; Wilson & Schooler, 1991). Although the Social-Identity ST-IAT showed lower internal consistency than the Image and Word ST-IATs, the measure still demonstrated the tendency for individuals to associate blood donation with non-self words. Similar to the Image and Word ST-IATs, this is in line with the fact that individuals generally hold negative affective evaluations of the concept of donating blood. Thus, this suggests that the Social-Identity ST-IAT is tapping the automatic tendency for individuals to disassociate themselves and close others with the blood donation, a generally aversive process. Proposed Hypotheses Regarding the proposed hypotheses, hypothesis 1 indicated that each of the three implicit measures would predict donation intention, donation sign-up, and 30-day selfreported follow-up. This hypothesis was generally not supported, but the Image ST-IAT

116 116 was shown to significantly predict donation intention, explaining 1.7% of the variability. Hypothesis 2 predicted that the three implicit measures would contribute incremental variance, above and beyond self-reported attitudes. Not surprisingly, hypothesis 2 was not supported. Further, hypothesis 3 expected that implicit attitude would be better predictors for indecisive individuals and those who have spent little time considering blood donation, whereas explicit attitude would be a stronger predictor for decisive individuals and those high in consideration. This hypothesis was not supported, although explicit attitude was shown to be associated with stronger donation intentions for decisive relative to indecisive participants. Finally, given that none of the implicit measures predicted significant variance in the presence of explicit attitudes, it was not appropriate to conduct a test to explore incremental validity in the presence of six explicit measures (donation attitudes, donation anxiety, self-efficacy, anticipated regret, descriptive norm, and personal moral norm). Taken together, the results of the present study parallel our pilot study results (Warfel, France, & France, 2012), and are partially in line with past implicit measurement studies regarding consumer and voting behaviors. In the sections to follow, potential reasons for and interpretations of the null effects and trends in the results will be discussed. Hypothesis One Hypothesis one was generally not supported particularly in terms of predicting behavioral outcome measures and there are several possibilities for why this hypothesis was disconfirmed. Most critically, the present study s behavioral measures were limited due to a lack of both variability and power; specifically, both the sign-up and selfreported follow-up behavioral measures were dichotomous, and representation of those

117 117 who did attempt to donate was inadequate. Only 20 out of 233 (8.6%) participants signed up to donate at a local blood drive, and a comparable proportion (14 out of 186; 7.5%) reported showing up to a blood drive in an attempt to donate. Despite successful efforts to attain a large proportion (80%) of total responses to the 30-day follow-up, a relatively small representation (n = 14) of those who attempted donation precludes assurance of sufficient power for analyses involving the behavioral measures. Donation intention, on the other hand, included sufficient variability given that it is a Likert-type scale measure. Despite this, the present results demonstrated the ability of the Image ST-IAT alone to predict donation intention. The general lack of predictive capability of the implicit measures and the small variability explained (1.7%) in the case of the significant finding suggests the implicit measures are fairly weak predictors for nondonors. Thus, while the present implicit attitude measures have demonstrated construct validity due to significant expected relationships with self-report measures, they may simply not be strong enough to demonstrate predictive power in this context, particularly among nondonors. Limited predictive findings in this study may have been compounded by the overall greater difficulty predicting the behavior of individuals who have never donated blood before compared to those who have donated in the past. Past research testing predictive models based on the theory of planned behavior (TPB) has shown that studies of nondonors predict less overall variance (41-70%; Lemmens et al., 2005; Lemmens et al., 2009; Robinson et al., 2008) than studies focusing on past and current donors (65-86%; France, France, & Himawan, 2007, 2008; Masser et al., 2009). One study that sampled enough participants to test both populations in separate models (Godin et al.,

118 2005) found that attitude was a stronger predictor for nondonors, but perceived 118 behavioral control and moral norm were each stronger for donors. This evidence points to two possible reasons theory of planned behavior models are less accurate for nondonors: (1) by virtue of having no experience, nondonors are simply less accurate in estimating their own ability and likelihood to engage in blood donation, and (2) nondonors are less likely to have had conversations with their close friends and family about blood donation and are therefore less accurate in their assessment of how these important others feel about the topic. Individuals who are less familiar with blood donation may have been even more prevalent in our sample given advertisement for the study was ambiguous and thus participants did not know the study topic beforehand. Given that nondonors have less experience and knowledge about blood donation, measures of donation attitude, selfefficacy, social norm and the like are inherently less reliable and generally less likely to demonstrate predictive power; this is one possible reason why implicit measures in the present study were likewise shown to be weak predictors. Data from our pilot study (Warfel, France, & France, 2012) confirm that implicit measures may be slightly better at predicting combined donor and nondonor samples compared to nondonors only. In particular, the Images ST-IAT was shown to predict donation intention in the pilot study with a stronger effect size (r = 0.19, p < 0.01, N = 253) than the present nondonor sample (r = 0.13, p < 0.05, N = 225); further, the combined samples demonstrated greater variability explained ( %) than the present study (1.7%). These data reaffirm a likely explanation for the present limited predictive findings: nondonors are less decisive about whether or not they will donate

119 blood in the future, likewise making their intention measure less accurate and more 119 difficult to predict than that of donors. The present findings, however, were somewhat inconsistent with past consumer and voting behavior studies, which may reflect the limited similarity of these behavioral measures to blood donation. Most notably, blood donation behavior involves making an appointment to do something that is generally thought of as unpleasant, takes considerable planning, is inherently deliberate in nature, and may require considerable motivation. Both voting and consumer behaviors, on the other hand, involve a simple decision between comparable choices, such as that made between products (e.g. fruit versus candy) or political candidates (e.g. Obama versus Romney); both of these behaviors are measures of preferences, as opposed to carrying out a deliberate, difficult behavior such as blood donation. Additionally, we sampled individuals who had never engaged in the behavior of interest, whereas individuals in consumer and voting studies were highly experienced with these respective behaviors. Thus, while consumer and voting behavior studies are the closest existing comparison, the behavioral measures in these studies are not an ideal match with blood donation behavior. Despite this, generalizations can still be made from these studies, and it was shown that the implicit measures in consumer behavior studies were more likely to predict spontaneous forms of behavior (i.e. condom use with casual partners, choosing a snack on the spot) rather than more deliberate forms of behavior (i.e. condom use with steady partners, self-reported typical snack consumption). Even with our best efforts to include both spontaneous (sign-up) and deliberate (30 day follow-up) forms of blood

120 120 donation behavior, it s possible that both forms of behavior were inherently deliberate and thus more difficult for implicit measures to predict. Interestingly, the Social-Identity ST-IAT was generally weaker than the Image and Word ST-IATs, as it did not even show significant relationships with any of the predominant self-report blood donation measures (i.e. attitude, self-efficacy, and anxiety). One possible reason for this is that it was simply more difficult for participants to perform this task, thus creating more error variance. To illustrate, the distinction between pleasant versus unpleasant words (e.g. Joy versus Gloom) is likely clearer than the distinction between identity versus non-identity constructs (e.g. Myself versus Them) constructs. Indeed, a greater number of participants (n = 15) were eliminated for making more than 20% errors for the Social-Identity ST-IAT when compared to the Image (n = 3) and Word (n = 4) ST-IATs. Further support for this notion comes from differential internal consistency reliability observed for the Image and Word ST-IATs (α = ) relative to the Social-Identity ST-IAT (α = 0.58). Thus, the present Social-Identity ST- IAT may have been less predictive due to greater error and lower reliability of the task itself. Nonetheless, the measure appears to be a valid, albeit weak, construct given its significant correlation with moral norm. This significant relationship would be expected, as it suggests that those who are more likely to implicitly personally identify with blood donation are also more likely to feel a moral obligation to donate blood, both of which indicate personalization of the blood donation process. Hypothesis Two Given that hypothesis one was largely disconfirmed, it was not surprising that hypothesis two implicit prediction in the presence of self-reported attitudes was also

121 disconfirmed. This was in line with our pilot study (Warfel, France, & France, 2012) 121 results, which also failed to find incremental validity in the presence of self-reported attitudes for combined donor and nondonor samples. Taking into account results of both samples, it appears that implicit measures simply do not contribute additional variance beyond well-established, strong self-report predictors. Previous research in the consumer and voting behavior literature rarely reported, let alone demonstrated, incremental validity of implicit measures. Only four implicit measurement studies previously reviewed tested incremental validity (Karpinski & Hilton, 2001; Maison, Greenwald, & Bruin, 2004; Karpinski, Steinman, & Hilton, 2005; Friese, Bluemke, & Wanke, 2007), and only one of those four consistently demonstrated incremental validity over and above self-report measures (Friese, Bluemke, & Wanke, 2007). This only positive study, however, had a large sample (N = 1,548), suggesting that this may be a weak effect. From the present and past results of implicit measurement studies, it is clear that while implicit measurement is clearly a valid measure in contexts that are relatively non-socially sensitive, they simply do not perform well against traditional self-report measures. To explore whether social pressure may have affected results, correlational analyses were conducted separately for those deemed high versus low in perceived social pressure to donate blood. Results indicated the relationship between the Image ST-IAT and explicit measures are generally stronger among those indicating lower perceived social pressure. Likewise, the Image ST-IAT significantly predicts the intention of those with lower social pressure (r = 0.24, p < 0.01) but not those with higher pressure (r = 0.05, p = 0.31).This suggests those with more perceived social pressure to donate may be

122 inflating their intentions to donate; in support of this possibility, a t-test revealed 122 significantly higher (t(227) = 3.23, p < 0.01) intentions among those reporting higher perceived social pressure to donate (M = 8.90, SD = 4.88) relative to those reporting lower pressure to donate (M = 6.97, SD = 4.14). Interestingly, the Word ST-IAT does not show any difference in its ability to predict intention based on whether individuals perceive low (r = 0.08, p = 0.19) versus high (r = 0.10, p = 0.16) social pressure to donate; correlations with other explicit measures likewise do not show a strong pattern to differentiate these groups. One potential reason for this, as demonstrated in our pilot study (Warfel, France, & France, 2012), is that the Image ST-IAT is a more valid implicit measure in this context. The next hypothesis allows further opportunity to explore potential differences with respect to levels of decisiveness and consideration. Hypothesis 3 Hypothesis three was disconfirmed, as neither decisiveness nor consideration moderated the ability of implicit measures to predict donation intention or behavior. Interestingly, however, decisiveness was shown to moderate the relationship between explicit attitude and intention. In the paragraphs below, possible reasons this hypothesis was disconfirmed will be given, beginning with the distinction between decisive and indecisive individuals, and later regarding the distinction between those high versus low in consideration. Although the voting behavior literature demonstrated that implicit measures predict voting choice of undecided voters better than explicit measures, being an undecided voter is different in some key respects relative to indecision about whether to donate blood. As an undecided voter, the uncertainty is not regarding whether you are

123 going to vote or not, but rather which of two candidates you prefer. Due to this 123 distinction, implicit measures in the voting literature can more directly tap associations with the criterion measure of interest: namely, the two candidates; however, while blood donation implicit measures are capturing affective attitude associations, they are not directly tapping the criterion measure of donation behavior. Many barriers outside the control of participants can impede donation behavior. Further, a large variety of factors can influence uncertainty about blood donation, including fears about the process (i.e. blood, needles), barriers in one s personal life (i.e. time, resources), or lack of knowledge about the risks and benefits of donation. On the other hand, indecision about voting choice is far less complicated in the sense that choice of candidate is purely a matter of preference; barriers outside of the participants control are nonexistent. Due to this distinction, it may be much harder to predict the behavior of indecisive nondonors with any single measure relative to undecided voters. Additionally, indecisive nondonors are likewise uncertain of their intention to donate blood, which may lower the accuracy of this measure, and thus predicting donation behavior among these individuals poses a unique challenge. Interestingly, however, decisiveness was shown to moderate the relationship between explicit attitude and intention, such that explicit attitude was associated with stronger intentions for decisive individuals compared to indecisive individuals. The fact that a strong, well-established predictor of intention is stronger for individuals who are more decisive about their decision is in line with what was expected; indeed self-report measures should be stronger for those who can more accurately report their beliefs and opinions about a given topic. For reasons stated above, however, implicit measures are

124 simply not strong enough in this context and for this population of individuals to 124 demonstrate stronger predictive power for those who are indecisive relative to those who are more decisive. With respect to lack of significant findings regarding the distinction between individuals who have spent more versus less time considering blood donation, the consumer behavior literature may also differ from the present study in key ways. The idea that implicit measures would be stronger predictors for individuals who have spent little time thinking about blood donation originally stemmed from the observation that implicit measures were better at predicting less common consumer behaviors (i.e. choosing eco-friendly products, condom use). While spending less time thinking about these consumer behaviors may be the reason they were more predictive, it is also possible that these particular implicit measures were simply more reliable or valid in some respect. Exploratory Hypothesis The exploratory hypothesis a test of predictive power of the implicit measures in the presence of six explicit predictors was not examined due to the fact that implicit measures were not shown to be predictive in the presence of explicit attitudes alone (hypothesis two). When considering the present results overall, it is clear that the implicit measures were weak predictors while the explicit measures were powerful predictors of nondonor intention and behavior; hence, implicit measures are unable to demonstrate incremental predictive variance beyond explicit measures in this context. In the sections to follow, additional factors affecting the present findings will be discussed, limitations will be addressed, and future directions will be elaborated upon.

125 Image versus Word ST-IAT 125 In line with our pilot study results (Warfel, France, & France, 2012), the Image ST-IAT performed better than the Word ST-IAT across nearly all validation procedures. For example, as shown in Table 9, only the Image ST-IAT significantly predicted donation intention. Although reasons for the superior performance of images are uncertain, evidence in the cognitive literature does suggest that images and words are processed and represented differently (Farah, Wilson, Drain, & Tanaka, 1998; Glaser & Glaser, 1989; Seymour, 1973). Generally, images are processed more quickly than words, and therefore may be more likely to tap hidden attitudes. More importantly, specific resulting differences depend on the particular stimuli used, as some stimuli are a better reflection of the target category than others (De Houwer, Tiege-Mocigemba, Spruyt, & Moors, 2009). In this study, all four blood donation images were clearly depicting individuals donating blood, whereas the four blood donation words (i.e., Red Cross, Vein, Blood, and Needle) may or may not have elicited similar mental representations. As a whole, the target words may not have been as clear a reflection of the blood donation target concept and as a result may not have evoked an equivalent affective reaction. The general distinction between images and words may also partially explain why the Social- Identity ST-IAT demonstrated weaker psychometrics; the next section provides additional explanation for the generally lower performance of the Social-Identity ST- IAT. Limitations Although the present study was well-designed, some inherent limitations exist with regard to implicit and behavioral measurement techniques. As a general rule,

126 126 reliability of implicit measures should be judged independent of self-report measures; while general guidelines recommend internal consistency alpha levels should be at least above 0.70 (Streiner & Norman, 2006), error variance introduced when a participant s eye blink, sneeze, or external distraction coincides with the appearance of a stimulus can create task-irrelevant variability, and this factor can easily reduce the reliability of reaction-time tasks (Lane, Banaji, & Nosek, 2007). The internal consistency reliability values of the Image and Word ST-IATs (Cronbach s α = 0.72 and 0.74, respectively) are comparable or slightly better than our pilot study (Cronbach s α = 0.64 and 0.68, respectively) and others who used a similar implicit measure (e.g. Karpinski & Steinman, 2006, Cronbach s α = ; Bluemke & Friese, 2008, r = ). The Social- Identity ST-IAT, on the other hand, had somewhat lower reliability (Cronbach s α = 0.58), which may help to explain its generally weaker findings. Generally, the inherent greater error variance of implicit measures is an important factor to consider when examining their internal consistency values. This may be a major reason implicit measures are at a disadvantage relative to explicit measures, as the latter generally demonstrate much higher internal consistency reliability values. Likewise, this elucidates why few studies have successfully demonstrated incremental validity of implicit measures over and above explicit ones. As previously mentioned, the main limitation of the present study is that both the sign-up and 30 day follow-up behavioral measures were dichotomous and had limited representation of those who attempted donation; additionally, it is important to note that both behavioral measures were self-report, and a direct measure of behavior would have been ideal. Regardless of how they are measured, dichotomy is an inherent aspect of

127 these measures, and future researchers using a behavioral measure of blood donation 127 should obtain a sample large enough ensure representation in all cells is adequate. Another way to increase representation of those who attempt donation is to include a longer follow-up period. If possible, multiple follow-up periods of up to six months in length would be ideal to ensure enough time for individuals to make a plan to donate. Finally, the sample was composed solely of college students who have never donated blood, which limits the generalizability of the results. Past research suggests donor behavior develops over the lifespan, and thus the predictability of implicit measures may vary as a function of age, donor identity, or life stage. Conclusions and Future Directions Although both the Image and Word ST-IATs in this study were shown to be reliable and valid instruments for use in this context, the overarching finding for the present hypotheses was null predictive and incremental effects. As a whole, the present study s results have shown implicit measurement may not be useful for predicting the behavior of individuals who have never donated blood and may have no interest in doing so. Interestingly, based on results of combined donor and nondonor samples (Warfel, France, & France, 2012), implicit measurement may be stronger for predicting the future behavior of donors than nondonors. Perhaps future studies should focus their efforts on utilizing implicit measures to improve prediction among lapsed donors or nondonors who express interest in blood donation. Further, due to the limitations of the present behavioral measures, future studies assessing behavior should aim to increase representation of those who do attempt to donate, possibly by lengthening the behavioral follow-up.

128 128 One potential way future researchers can further improve our understanding of underlying attitudes toward blood donation is to utilize physiological measurement techniques to coincide with implicit measurement. Individual differences in physiological arousal (e.g. autonomic nervous system arousal, brain evoked potentials) during various implicit measurement tasks would provide a more objective method of distinguishing reactions to the various target stimuli. These physiological techniques would provide information regarding participants affective responses to implicit measures, which may, in turn, prove to mediate the prediction of donation behavior. To conclude, the present study has shown that blood donation implicit measurement although reliable and valid in this context has limited predictive capability and does not add unique variance to existing self-report measures for a nondonor college student population. However, future use of implicit measurement strategies in this context could yield important practical benefits, because accurate measures of underlying attitudes toward blood donation could promote more efficient and effective development of advertisements, brochures, and other media designed to educate and attract potential donors.

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144 Retrovirus Epidemiology Donor Study (REDS): Rationale and methods. 144 Transfusion, 35(11),

145 Appendix A: Demographic and Brief History Questionnaire 145

146 Appendix B: Blood Donation Attitude 146

147 Appendix C: Self-Efficacy 147

148 148 Appendix D: Subjective Norm, Personal Moral Norm, and Descriptive Norm

149 149

150 Appendix E: Donation Anxiety and Anticipated Regret 150

151 Appendix F: Behavioral Intention 151

152 Appendix G: Blood Donation Decisiveness and Consideration 152

153 153 Appendix H: Blood Donation Sign-up Behavior and 30-day Follow-up of Behavior Blood Donation Sign-up There are several opportunities to give blood at local blood drives on or near Ohio University s campus. We have listed these dates and times below. Are you willing to sign up to give blood at a blood drive? YES NO If yes, please print your name in a timeslot below and we will contact you via to confirm the date, time, and location to provide further details. If neither of these dates work for you, please indicate that you would like us to you with additional blood drive dates and locations available for sign-up. 9:00 10:00 11:00 12:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 Blood Drive Date Blood Drive Location Blood Drive Date Blood Drive Location reminder 48 hours before sign-up time: Subject: Upcoming Blood Donation Sign-up Time Reminder Dear Research Participant, Recently, you participated in a research study and signed-up to donate blood at a local drive that is scheduled to take place from [start time] to [end time] at [location] on [date]. You indicated that you would like to attend the drive and donate at [sign-up time]. If you have any questions, feel free to contact me by replying to this . Best, Regina M. Warfel, M.S. Primary Researcher

154 30-day Follow-up Contact Subject: 30 Day Blood Donation Follow-up Question for the Study Exploring Unconscious Thoughts Re: 30 Day Blood Donation Follow-up Question for the Study Exploring Unconscious Thoughts Dear Research Participant, You participated in a research study 30 days ago, and agreed to answer the single followup question shown below: Within the last 30 days, did you attend a blood drive with the intention to donate blood? Please respond with YES or NO. Thank you, Clinical Psychophysiology Laboratory P.S. If you respond you will be entered into a drawing to win one of two $50 Amazon giftcards. If you do not respond within 3 days, we will contact you one more time either via or text message. Text message: A message from the OU Psychology Department: Within the last 30 days, did you attend a blood drive with the intention to donate blood? Please respond with YES or NO. Note: If you respond you will be entered into a drawing to win one of two $50 Amazon giftcards.

155 Appendix I: Informed Consent 155

156 156 Risks and Discomforts Benefits Confidentiality and Records

157 Contact Information 157 If you have any questions regarding your rights as a research participant, please contact Jo Ellen Sherow, Director of Research Compliance, Ohio University, (740)

158 Appendix J: Screening Criteria 158

159 159

160 Appendix K: Debriefing Form 160 Debriefing Form Thank you very much for participating in this study. Your participation is extremely valuable for our improved understanding of blood donation implicit attitudes and implicit social-identity measures. The purpose of this study is to examine whether these implicit measures predict blood donation intentions and behavior. Studies show that various selfreport measures are predictive of one s decision to donate blood in the future. We are examining whether measures of underlying attitudes or automatic blood donation associations may further improve our prediction of blood donation behavior. To address this question, we asked you to complete several self-report questionnaires as well as three implicit reaction time tasks regarding blood donation. You were also given the option to sign-up for a local blood drive within the next several weeks. In one month we will send you a single follow-up question via as well as text message. When you respond to this follow-up questionnaire, you will be entered in a lottery. It is crucial to the study that you respond to this follow-up question, if at all possible. If you have any questions, please ask them now or feel free to contact us (see informed consent form for contact information). Blood donation sign-up information: Date: Time: Location:

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