Measuring Financial Anxiety

Gilla K. ShapiroLondon School of Economics and Political Science

Brendan J. BurchellUniversity of Cambridge

There is a scarcity of information concerning the emotional aspects of financialmanagement. Two studies were conducted to evaluate the measurement of consciousand intuitive emotional anxiety toward one’s personal finances. Along with a self-reported financial anxiety questionnaire, a modified Emotional Stroop Test (EST) andDot-Probe Paradigm (DPP) were separately utilized to evaluate financial anxiety. Inboth studies, the self-reported financial anxiety questionnaire correlated significantlywith the implicit measures. Furthermore, the DPP was predominantly characterized byavoidance of financial information. Financial anxiety was shown to be a separateconstruct from depression and general anxiety. These findings indicate that those whoreport having financial anxiety also display reaction latencies in the processing offinancial information. Accordingly, financial behavior could be more comprehensivelyevaluated and policy could be better determined by incorporating financial anxiety intoeconomic models of financial illiteracy, mismanagement, and debt.

Keywords: financial anxiety, dot-probe paradigm, emotional Stroop Test, student debt, financialpolicy

Supplemental materials: http://dx.doi.org/10.1037/a0027647.supp

The ability to make informed and effectivedecisions regarding the management of moneyis important for individual success (Jorgensen,2007). The requirement for financial compe-tence is particularly essential in the current fi-nancial climate. Instead, individuals displaysurprisingly poor financial literacy and a wide-spread lack of knowledge on fundamental eco-nomic concepts (Jump$tart Coalition, 2004;NCEE, 2005; Warwick & Mansfield, 2000),patterns of overspending (Roberts & Jones,2001), and careless financial behavior (Henry,Weber, & Yarbrough, 2001). These problemsare interlinked and financial illiteracy has been

associated with both financial mismanagement(Hastings & Tejeda-Ashton, 2008) and debt(Lusardi & Tufano, 2009). The combined resultis that individuals find themselves in substantialdebt that can leave them seriously indebted foryears, situated in a “debt cycle” (Beal & Del-phachitra, 2003; Wang & Xiao, 2009).

Debt is not only problematic in itself, but isrelated to negative psychological repercussionsincluding a decreased sense of ability to manageone’s money, lower self-esteem, decreasedsense of financial wellbeing, lower productivity,and higher levels of overall stress (Garman,Leech, & Grable, 1996; Joo & Grable, 2000;Lange & Byrd, 1998). This is exemplified byRoberts, Golding, Towell, and Weinreb’s(1999) finding that British students who haveconsidered leaving university for financial rea-sons were more likely to report poorer mentalhealth and social functioning. Furthermore, in anationally representative household sample,Jenkins et al. (2008) found that 23.8% of peoplewith a mental disorder (i.e., neurotic disorder,psychotic disorder, alcohol dependence, or drugdependence) were in debt compared to 8.1% ofpeople without these disorders, and the moredebts people had the more likely they were tohave a mental disorder. Future research would

Gilla K. Shapiro, MPA Programme, Institute of PublicAffairs, London School of Economics and Political Science,London, England; Brendan J. Burchell, Department of So-ciology, University of Cambridge, Cambridge, England.

This work was conducted while the first author was astudent at the Department of Psychology, University ofCambridge. We are grateful for the critical comments andinput given by Dr. Luke Clark.

Correspondence concerning this article should be ad-dressed to Gilla K. Shapiro, MPA Programme, Institute ofPublic Affairs, London School of Economics and PoliticalScience, Houghton Street, London, England, WC2A 2AE.E-mail: gilla.shapiro@cantab.net

Journal of Neuroscience, Psychology, and Economics © 2012 American Psychological Association2012, Vol. ●●, No. ●, 000–000 1937-321X/12/$12.00 DOI: 10.1037/a0027647

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be beneficial in determining the causal path-ways of debt and mental illness.

The combination of financial illiteracy andmismanagement denotes that a substantial num-ber of individuals face the foreseeable risk ofconsiderable debt and associated psychologicaldifficulties (Warwick & Manfield, 2000). Re-searchers and policymakers have generally as-sumed that a lack of knowledge is responsiblefor financial illiteracy and mismanagement(though a causal link has not been proven to theauthors’ knowledge). Contrary to this view,there is beginning to be awareness of the im-portant emotional component to effective deci-sion making and financial competence(Burchell, 2003; Hanoch, 2002). While initiallyit became recognized through neurobiologicalstudies that an absence of emotions leads tosuboptimal decisions (Damasio, 1994), psycho-logical research has confirmed that strong emo-tional responses are also associated with poordecisions (particularly those of a financial na-ture) (Ackert, Church, & Deaves, 2003). AsRolls (1999) explains, positive feelings improveindividuals’ ability to organize and assimilateinformation, problem-solve, negotiate, andmake efficient decisions. Burchell’s (2002)combination of in-depth interviews followed bya telephone survey of 1,000 adults suggestedthat avoidant reactions were specific to personalfinances, and did not necessarily spill over intoother domains such as financial responsibilitiesin the workplace. Furthermore, Burchell (2002)found that out of three psychometrically soundsubscales measuring “knowledge,” “impor-tance” and “emotion” toward finances, the emo-tional scale was the best predictor of financialbehavior.

Based on this emerging research, it seemsplausible that a strong negative emotional re-sponse of financial anxiety is associated withfinancial illiteracy and mismanagement. Finan-cial anxiety has been defined as a psychosocialsyndrome whereby individuals have an uneasyand unhealthy attitude toward engaging with,and administering their personal finances in aneffective way (Burchell, 2003); this study gaverise to the term financial phobia being widelydiscussed in the popular media. However, fi-nancial anxiety, despite having important reper-cussions for understanding financial behavior,remains largely understated and uninvestigated.The purpose of the two studies described below

was to add to the relatively meager literature byinvestigating financial anxiety through develop-ing a self-report questionnaire (FAS) and adapt-ing intuitive tools (the Emotional Stroop Testand the Dot-Probe Paradigm) to measure thisphenomenon.

Methodological Note

The measurement of individuals’ financialanxiety toward dealing with their personal fi-nances remains largely uninvestigated and notools (to the authors’ knowledge) currentlymeasure this phenomenon. However, tools de-veloped in recent years and applied to the mea-surement of other phobias might be usefullyemployed to measure financial anxiety.

The construct of financial anxiety developedfrom investigations of financial attitudes (Lim& Sng, 2006). It is therefore not surprising thatthe few self-report measures that test for finan-cial anxiety are subscales developed to measuremore general, “multidimentional” money atti-tudes. The two foremost money-attitudes mea-sures are Yamauchi and Templer’s (1982)Money Attitude Scale (MAS), and Furnham’s(1984) Money Beliefs and Behavior Scale(MBBS). There is encouraging overlap betweenthese scales (Roberts & Jones, 2001), but whilethe longer (60-item) MBBS has been criticizedfor low reliability, the (29-item) MAS has in-stead shown stable factor structure and loadingsin multinational and ethnically diverse samples(Baker & Hagedorn, 2008; Medina, Saegert, &Gresham, 1996). However, neither of these anx-iety subscales would be applicable to studyingfinancial anxiety because questions on thesesubscales were consumer specific (e.g., “I ambothered when I have to pass up a sale”) andinappropriate for measuring general financialanxiety among all ages (e.g., “I save now toprepare for my old age”). Only Burchell (2003)has created a self-report (5-item) scale measur-ing general emotional orientation of individualstoward their personal finances.

That emotional responses have been associ-ated with psychophysiological signals (Dama-sio, 1994) indicates that financial anxiety can bemeasured by subconscious measures as well asconscious, subjective reports. Researchers havestudied emotion’s interference with attentionthrough utilizing different variants of the Strooptask or the Dot-Probe Paradigm. In order to

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measure financial anxiety, the two studies pre-sented in this paper developed a self-reportedFinancial Anxiety Scale (FAS) and adapted twowell-established implicit instruments (i.e., Emo-tional Stroop Test and Dot-Probe Paradigm),which have not hitherto been applied to mea-suring financial anxiety. These tools were de-veloped to measure financial anxiety and inves-tigate how reported financial anxiety is relatedto the involuntary processing bias of financialanxiety.

As Miu, Miclea, and Houser (2008) explain,the theoretical and methodological changes instudying emotions have stemmed from multi-disciplinary efforts bridging psychology, phys-iology and neuroscience. These studies extendthis works in its methodology of measuringfinancial anxiety.

Study 1

The first study utilized the Emotional StroopTest (EST), which is commonly used to dem-onstrate the existence of attentional biases in awide range of psychological disorders (Wil-liams, Macleod, & Mathews, 1996). In the EST,emotionally relevant (“threat”) words have beenfound to capture the attention of the individualand cause an attentional bias toward threateninginformation (Amir et al., 1996; Williams et al.,1996).

Though a popular methodology to test a widerange of anxieties, the EST has not beenadapted to measure financial anxiety. This studyadapted the EST to measure a subliminal bias tofinancial words and developed a financial anx-iety scale (FAS) to measure self-reported finan-cial anxiety. This study seeks to evaluatewhether a reported financial anxiety is related toa subliminal bias in processing financiallyloaded words.

Hypothesis. It was hypothesized that therewould be a negative relationship betweenthe difference in RTs of the financial andneutral words on the FEST with the self-reported responses on the Financial Anxi-ety Scale (FAS).1

Method

Participants. The participants in this studywere 38 unpaid volunteers, 22 males and 16

females, 19–22 years of age [M � 20.17, SD �.78], who were full-time undergraduate studentsresponding to fliers and emails. Similar to Ro-fey, Corcoran, and Tran (2004), this study ex-cluded those participants who were colorblindas this would have been problematic on theEmotional Stroop Test, as well as those who arenot fluent in English.

Instruments and measures.Financial Emotional Stroop Test (FEST).

The Financial Emotional Stroop Test (FEST)used eight words that were presented twice infour different colors (as in the original Stroop):red, green, blue, and yellow (see Table 1). Thefour different colors must be read aloud, whileignoring the word that is colored. For example,the word overdraft colored in blue must be readas blue, and the presentation of the word over-draft should be ignored.

There are three groups of words in the FEST:(1) a “classic condition” whereby classic incon-gruent color words were presented, (2) a “finan-cial condition” with financially loaded words,and (3) a “control condition” with neutral wordsthat correspond to the financial condition (seeTable 1). The classic words were based uponthe original Stroop Test (Stroop, 1935), whereasthe financial group of words and their neutralcounterparts were designed for this experiment.

Using a computer ST opposed to a card STwas disregarded based on Kindt, Bierman, andBrosschot’s (1996) study that showed “thehighest test–retest correlation for the standardStroop effect was found on the card format”(p.659). As specified in the German adaptationof the Stroop, participants in this study wereinstructed to read the colors of the words col-umn-wise as quickly and accurately as possible(Baumler et al., 1985 as reported by Stetter,Ackermann, Bizer, Straube, & Mann, 1995).Word length and syllables in the financial andneutral-control group of words were matched sothat word length and subvocalized reading wascontrolled (see Table 1).

Financial Anxiety Scale (FAS). The(FAS) measures an anxious disposition towardcognitive engagement with one’s personal fi-

1 A greater difference in RTs of the financial and neutralwords on the FEST indicates greater financial anxiety, whilea greater score of the FAS indicates less financial anxiety.

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nances (see Table 2).2 Some of the questionswere borrowed from Burchell (2003) and Da-vies and Lea (1995), while the authors formu-lated other questions. Lower numbers indicate ahigher level of financial anxiety on this 4-pointLikert scale, which ranged from 1 very true to 4completely untrue.

Procedure. The FAS was administeredfollowing the FEST. The neutral and financialwords in the FEST were counterbalanced andno learning effect was found.3

3.2. Results

The data was analyzed using SPSS ver-sion 16.0 for MacOS X (SPSS Inc., 1989–2007). On average, the student population re-ported having a mean score of 2.9 on the FAS(SD � 0.64).4

In comparing the response times on theFEST, the highest latency was recorded for theclassic condition [M � 11.24 s, SD � 1.83]. Onaverage the financial condition had a slightlyslower response time [M � 8.48 s, SD � 1.45]compared to the control condition [M � 8.30 s,SD � 1.25]. A paired sample t test was con-ducted to see if the mean of the financial con-dition is significantly greater than the mean ofthe neutral condition. The two-tailed t test re-vealed that these conditions means are not sig-nificantly different, t(37) � �1.115, ns.

The differential of the neutral response time andthe financial response time was calculated for eachparticipant, which was then correlated with eachparticipant’s FAS score. A one-tailed Pearson’sproduct-moment correlation between the interfaceresponse time and FAS confirmed a negative cor-

relation (r � �.308), which was significant ( p �.05) and had a medium effect size.

Discussion

The results indicate that those who reportedhaving higher levels of financial anxiety (on theFAS) also displayed a reaction latency on finan-cially loaded words. This significant correlationindicates that a reported financial anxiety isrelated to a subliminal bias in processing finan-cially loaded words.

While De Ruiter and Brosschot (1994) haveargued that vigilance and avoidance processesmay be detectable on the EST, other researchershave suggested that it is difficult to interpretwhether the EST displays vigilance or avoid-ance to threat words because the unattendedstimuli are spatially separate (as the financialand neutral words were tested and scored sep-arately) (Fox, 1993; MacLeod, Mathews, &Tata, 1986).

2 An exploratory factor analysis found that three of theoriginal variables included in the questionnaire were notassociated with the same factor. This was confirmed byinspecting the correlation matrixes. These three questionswere therefore excluded from the FAS (Table 2). The ex-clusion of these items increased the Cronbach’s alpha (� �.809 to � � .828).

3 Independent samples t-tests were run to see if the meansof the neutral and financial conditions were significantlydifferent between the two order-effect groups. For both thefinancial and neutral condition, it was found that the differ-ence was not significant [tFinancial (36) � .085, ns; tNeutral

(36) � �.413, ns].4 An average score of 1 indicates greatest possible anxiety,

while a score of 4 indicates least possible anxiety on the FAS.

Table 1Groups of Words Used in FEST

Classic condition Financial condition Control condition

Orange Overdraft NavigatesGreen Credit ApronBrown Bank ShoePurple Loan ChairTurquoise Cost FrogYellow Account MantleRed Debt PortBlue Finances Cinemas

Total number of syllables 12 14 14Total number of letters 44 46 44

Note. A color version of this table is available online as Supplementary Material.

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Study 2

This study dealt with two potential criticismsof Study 1. First, both low mood and a generalanxiety were not controlled in Study 1. Assymptoms of depression and anxiety are preva-lent among the student population (Ricciardi,2008), it is possible that the devised question-naire was vicariously being contaminated bythese variables rather than financial anxiety.This study aimed to verify if a financial anxietymeasure is distinct from more general symp-toms of anxiety or depression.

Second, the previous study is a modified ver-sion of the EST. Despite wide use, the EST hasbeen criticized as inconsistent, unable to di-rectly compare threat and neutral words (Fox,1993), and difficult to interpret (Morgan, Rees,& Curran, 2008). In response to the problemswith the EST, MacLeod et al. (1986) developedthe Dot-Probe Paradigm (DPP), which im-proves upon the EST as “it eliminates the pos-

sibility of response bias interpretations. . . .[and] it allows a test of the prediction that thepresence of a threatening term can both facili-tate and impair dot detection, in the same indi-vidual, depending on the threat word’s positionrelative to the dot” (MacLeod et al., 1986, p.18;Tata, Leibowitz, Prunty, Cameron, & Pickering,1996). Accordingly, the design of the DPP en-ables researchers to determine whether themechanism underlying financial anxiety is oneof vigilance or avoidance. The DPP has beenused on clinical and nonclinical samples(Ehrman et al., 2002; Pflugshaupt et al., 2005;Townshend & Duka, 2001). However, onlyMorgan et al. (2008) have conducted the DPPwith money-related words, but the focus ofMorgan et al.’s study was the attentional bias ofketamine users (and money words were used asa nondrug incentive control). The DPP has notbeen used to test financial anxiety. Study 2 wastherefore designed to retest and extend Study 1.

Table 2Financial Anxiety Scale

#

Factor loading on component1 before items a & d were

deselectedFactor loading on

component 1 Question

a° .438 —I find monitoring my bank or credit card accounts very

boring

b .736 .706I prefer not to think about the state of my personal

finances

c .737 .740Thinking about my personal finances can make me feel

guilty

d°• .373 —

There’s little point in saving money and being carefulwith it, because you could lose it all through no faultof your own

e• .486 .526I am worried about the debt I will have when I complete

my university education

f .643 .687Thinking about my personal finances can make me feel

anxious

g .642 .625I get myself into situations where I do not know where

I’m going to get the money to “bail” myself out

h .693 .709Discussing my finances can make my heart race or make

me feel stressed

i• .658 .652I do not make a big enough effort to understand my

finances

j .572 .567I do not think I am doing as well as I could

academically because I worry about moneyk� .784 .782 I find opening my bank statements unpleasant

l� .603 .623I would rather someone else who I trusted kept my

finances organized

Note. � New items incorporated into Study 2. • items that were excluded from Study 1’s analysis based on an exploratoryfactor analysis and their Chronbach’s alpha; however, these were retested because Study 1 had a small sample size (N �38). ° items that were excluded from Study 2’s analysis based on an exploratory factor analysis and their Chronbach’s �.

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Hypothesis 1. It was hypothesized that fi-nancial anxiety is a measurable phenome-non that is distinct from depression and ageneralized anxiety.

Hypothesis 2. Following the results fromStudy 1, it was hypothesized that a mod-erate relationship between self-reported fi-nancial anxiety (on the FAS) and implicitmeasured anxiety (on the DPP) would bereplicated.

Hypothesis 3. It was hypothesized thatavoidance would be the mechanism in-volved in financial anxiety (measured onthe DPP) rather than attentional capture.

Method

Participant recruitment and retention.An a priori power calculation through G�Powerwas conducted to investigate how many partic-ipants were necessary for this study (Faul &Erdfelder, 1992). It was possible to conduct thisanalysis because parameter estimates could bebased on the results from Study 1. Using theeffect size (r � .308) and Alpha (0.05) fromStudy 1, and recognizing that Cohen (1988)recommends a power of 0.8 (Field, 2005), 61participants were found to be necessary.

To allow for dropouts and erroneous data, 79unpaid volunteers were recruited by respondingto fliers and emails. These were 40 males and 39females between the ages of 18–22 [M � 20.05,SD � 1.19]. Recruitment requirements werefluency in English and that participants werefull-time undergraduate students.

One participant was initially excluded be-cause of a problem with their inputted data. Afurther four participants were excluded becausethey either had too many errors (less than 119correct out of 136 trials), a mean reaction time(RT) of less than 200 ms (indicating that thesubject consistently initiated a response beforethe onset of the target), or a mean RT of greaterthan 1,000 ms (indicating anomalous processingsuch as inattention to task) (Townshend &Duka, 2001). In total, 6.3% of participants wereexcluded (n � 5). There were no significantdifferences in the excluded participants’ demo-graphic characteristics.

Instruments and measures.Questionnaires. Three self-reported ques-

tionnaires were administered. The FAS (used in

Study 1) measured 10 questions on a 4-pointLikert Scale, which were equally weighted in acumulative score (see Table 2) and then aver-aged so to represent a score out of four. TheSpielberger’s State–Trait Anxiety Inventory(STAI) and Center for Epidemiologic StudiesDepression Scale (CES-D), both scored on a4-point Likert Scale, were respectively used toevaluate whether higher anxiety or low moodcan account for financial anxiety. The STAImeasures feelings such as tension, nervousness,and confusion (VanderZee, Sanderman, &Heyink, 1996), while the CES-D addressesitems such as enjoyment of life, hopefulness,and feelings of loneliness during the past week(Costello & Devins, 1989; Radloff, 1977; Zich,Atkison, & Greenfield, 1990).

Dot-Probe Paradigm. The computerizedDot-Probe Paradigm was modified and pro-grammed according to the specifications ofMacLeod et al. (1986). The program was run ona Microsoft Windows XP PC and stimuli werepresented on a 17-in. monitor.

This task had 136 trials. There were eightbaseline-neutral practice words at the beginningof the task, and 32 experimental word pairswere then presented and repeated four times ina computer-randomized order for each individ-ual. The 32 experimental words were a part ofthree trial conditions: (a) eight positive moneywords (e.g., jackpot) with their matched con-trols (e.g., “jasmine”), (b) eight negative moneywords (e.g., debt) with their matched controls(e.g., hunt), and (c) 16 neutral money words(e.g., bank) with their matched controls (e.g.,camp) (see Table 3). Each capitalized word wasideographically matched for number of letters,number of syllables, Kueera–Francis writtenfrequency, concreteness rating and for its com-mon part of speech (Wilson, 1987) and pre-sented at random. Furthermore, control wordsthat would have a special meaning for students(e.g., term) or that had a positive or negativeconnotation (e.g., laughter, evicted), were ex-cluded. The variation between the word groupswas explored and the means and standard devi-ations were not found to differ beyond thebounds of variability (see Table 4).

On each trial, the participant was first pre-sented with a fixation point at the center of thecomputer screen followed by a pair of wordspresented for 500 milliseconds. The experimen-tal words were presented at random. Once the

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words simultaneously disappeared, a “dotprobe” appears and replaces one of the twowords. The difference in RTs when a dot probereplaces the threat or neutral word either indi-cates an avoidance or attentional capture (“vig-ilance”) to the threat words (MacLeod et al.,1986; Mogg, Bradley, Bono, & Painter, 1997).The dot probe remained on the screen until theparticipant responded or for a total of 4 secondsif there was no response. Response latencieswere recorded to the nearest millisecondthrough Visual Basic.

Procedure. After the participant read theinstructions for the DPP, the participants com-pleted a practice trial, which was followed bythe probe detection task. The implicit DPP taskwas conducted before the questionnaires wereadministered so that the reporting on one’s fi-nances would not prime participants’ DPP re-sult. Participants then completed the question-naires. On average, the study was completedin 25 minutes.

Results: Data Reduction and Analysis

The data was analyzed using SPSS ver-sion 16.0 for MacOS X (SPSS Inc., 1989–2007).

Factor analysis on the FAS. An explor-atory factor analysis was done to investigatewhether the different variables in the FASloaded onto the same underlying factor.5 Thisanalysis suggested that two of the original itemsincluded in the questionnaire were not associ-ated with the same factor and slightly decreasedthe reliability of the scale (see Table 2). Thiswas confirmed by observing the correlation ma-trices. These two questions were therefore ex-

cluded from the FAS. The exclusion of theseitems increased the Cronbach’s alpha negligibly(� � .850 to � � .855),6 but more importantly,reduced the components in the analysis (fromthree to one). This also made theoretical senseas the loading on question a seemed to be drivenby boredom while d seemed to be driven bydistrust, and both seemed weakly related to thefactor component measuring financial anxiety.

An exploratory factor analysis of the remain-ing 10 items showed the scale was driven by asingle component, making it a unidimentionalscale.7 A single financial anxiety score wascomputed into a new variable.

Investigation the shared variation of FASwith depression and state anxiety. Thepopulation had a mean score of 2.91 on the

5 This test was “exploratory” because it was noted that afactor analysis is most reliable when there are at least 10–15participants per question or a sample of 300 or more, whilethe sample in this study (79 participants) did not satisfyeither condition. However, a Kaiser-Meyer-Olkin measureof sampling adequacy (KMO; a statistic that indicates theproportion of variance in one’s variables that might becaused by underlying factors) was found to be between .8and .9, indicating that the patterns of correlations are rea-sonably compact and that the factor analysis would likelyyield reliable results (Field, 2005). Also, a highly significantfinding on the Bartlett’s Test of Sphericity indicated that thevariables in this factor analysis are related and thereforesuitable for structure detection.

6 The excluded variables were the only two whose dele-tion would increase Cronbach’s �.

7 Though only one component has been reported (with aneigen value of 4.26), the eigen value for the second factorwas just above 1 (1.22). As noted above, a factor analysiswith just 79 cases will produce unstable eigen values, butthis is taken as evidence that there is not a strong caseagainst the unidimentionality of this solution.

Table 3Words Used in DPP, by Condition and Their Respective Control Words

Positive words Negative words Neutral words

Positivewords

Controlwords

Negativewords

Controlwords

Neutralwords

Controlwords

Neutralwords

Controlwords

Bonus Comet Credit Handle Bank Camp Financial ImmediateJackpot Jasmine Debt Hunt Account Measure Salary AgencyEarnings Bathroom Unemployed Changeable Finance Suspect Cash TapeScholarship Measurement Loan Fill Money Woman Dollar PocketBursary Caribou Bill Club Cost Turn Cheap StiffGrant Shift Bankrupt Converse Paid Hear Expensive CollectedBingo Flour Mortgage Friction Economic Military Insurance SelectionRich Fair Rent Jump Spending Describe Invoice Channel

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FAS (SD � 0.65), of 15.24 on the CES-D(SD � 8.84), and of 37.08 on the STAI(SD � 9.97).

The CES-D and STAI were significantly cor-related, r � .721, p (two-tailed) �.001. Finan-cial anxiety (FAS) was significantly correlatedwith low mood (CES-D), r � .461, and anxiety(STAI), r � .388 (all ps (two-tailed)�.001). Itis important to note that first-order partial cor-relations were conducted to address the overlapin the variation of the scales and to determinethe size of the unique portion of variance (con-trolling for the third scale).8 The correlationbetween the STAI and the FAS was not signif-icantly related when CES-D was controlled, r �.091, ns. When controlling for anxiety (STAI),there was a positive relationship between theFAS and CES-D, r � .283, p (two-tailed) �.05.Further, the partial correlation for the CES-Dand STAI (controlling for FAS) was r � .663, p(two-tailed) �.001.

A linear regression model was conducted tosee how much CES-D and STAI predicted fi-nancial anxiety (see Table 5). The assumptionof no multicollinearity was tested and wasfound to not have been violated (Tolerance �.480). This model shows that STAI and CES-Dtogether can account for 19.8% of the variance;

however, only CES-D significantly predictsFAS. This further suggests that the correlationbetween FAS and CES-D is real, but the corre-lation between FAS and STAI is spurious.

Analysis of the DPP. A repeated-mea-sures ANOVA was conducted to investigatewhether there was a significant difference byword group, threat word position, and probeposition. Mauchly’s test indicated that the as-sumption of sphericity had been violated for theinteraction effect between probe location andthreat word location, �2(2) � 12.41, p � .01.The degrees of freedom were corrected usingGreenhouse-Geisser estimates of sphericity(ε � .86). There was a significant interactioneffect between probe location and threat loca-tion, F(1, 72) � 4.075, p � .05, eta � .23. Thisindicates that RT toward the threat word dif-fered depending on the subsequent probe place-ment. Furthermore, there was no main effect ofthreat location, probe location or group. Therewas also no significant interaction effect be-tween probe location � group, threat location �group, or threat location � probe location �group.

Avoidance or vigilance? For each wordgroup on the DPP (positive, negative, neutral, aswell as a combined group), the difference be-tween the incongruent (threat word placed ontop while the probe was placed on bottom, orvisa versa) and congruent (threat and probe bothplaced on the top or on the bottom) RTs werecalculated. This “direction” calculation com-pares the RT of a participant on conditionswhen the dot probe replaces the threat word

8 This was necessary because STAI and CES-D have avery large shared variation, R2 � .52.

Table 4Summary of Linguistic Property Variation (by Condition)

Number of lettersNumber ofsyllables Written frequency Concreteness rating

N Mean SD N Mean SD N Mean SD N Mean SD

Positive words 8 6.50 2.27 8 2.00 .76 5 35.60 27.43 1 377.00 .Positive control words 8 6.50 2.27 8 2.00 .76 6 30.00 27.42 1 413.00 .Negative words 8 6.00 2.29 8 1.63 .74 8 39.25 46.73 5 435.00 57.34Negative control words 8 6.00 2.39 8 1.63 .74 8 39.00 46.63 5 432.00 50.99Neutral words 16 6.38 1.89 16 2.06 .93 15 98.60 83.42 11 458.91 104.23Neutral control words 16 6.38 1.89 16 2.06 .93 16 87.81 75.00 12 457.58 98.49Total words 64 6.31 2.04 64 1.94 .83 58 66.69 68.38 35 447.54 85.70

Table 5Regression Model

Model B SE B �

Constant 1.248 .807CES-D .323 .125 .377�

STAI .141 .177 .116

R2 (adj) � .198.p � .001. � p � .05.

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compared to when the dot probe replaces thecontrol word. A positive score indicates avoid-ance whereas a negative score indicates vigi-lance.

All scales showed an average of a positivescore, indicating that for most participants theoverall mechanism was avoidance of financialwords.

Relationship between FAS and the DPP.The negative financial word group in the DPPcalculations significantly correlated with theFAS scale, � � .218, p (one-tailed) �.05. Thisindicates that those who reported having higherlevels of financial anxiety (on the FAS) displaya greater RT delay on negative threat words.

Discussion

The analysis of depression and general anxi-ety indicated that though interrelated, the FASmeasure is a distinct construct from depression(CES-D) and general anxiety (STAI). It is coun-terintuitive that partial correlations indicated re-latedness of the FAS with the CES-D, but notthe STAI. This suggests that low mood andanxiety interacts with financial anxiety in dif-ferent ways and while mood may relate directly,anxiety seems to relate indirectly through mood.Nevertheless, the finding that FAS is distinctfrom depression and general anxiety indicatesthat FAS is a useful tool in evaluating self-reported financial anxiety in future studies.

The validity of the FAS is further enhancedby the significant relationship that was foundbetween the FAS and the involuntary, nondirec-tional DPP (on negative words). This findingconstructively extends the first study that inves-tigated the relationship between self-reportedfinancial anxiety and the financially adaptedEST. It is interesting that similar to other anx-ieties, a financial anxiety is associated with bi-ased attention.

The relationship between the FAS and DPPwas only observed for negative words (i.e., notpositive or neutral financial word groups),which is understandable as negative financialwords are most likely to provoke an anxiousavoidant response. However, though the ESTused mainly neutral words (which were presum-ably less sensitive to picking up anxiety com-pared to solely negative words), the EST stillresulted in a slightly stronger effect (r � .301)than the negative words in the DPP (r � .218).

The slight difference in effect size between thestudies may be nonsignificant given the smallsample size in Study 1. However, this closereplication gives credibility to the measure-ments used in these studies’ convergent ap-proach.

This finding does not suggest that anxiety isthe only reason for financial avoidance. Indeed,the correlation between a self-reported financialanxiety and avoidance to financial stimuli onlyhad a medium effect size (r2 � .09). This indi-cates that while financial anxiety is important,there are other factors at work (e.g., a lack ofinterest in finances), which may need to beconsidered in understanding financial avoid-ance.

The DPP, a more sophisticated measure thanthe EST (for reasons described above), alsoprovided insight into the mechanism of finan-cial anxiety. On all the word groups (positive,negative and neutral), avoidance (rather thanvigilance) was the most common strategy (evenon positive financial words, e.g., bonus). Thissuggests that financial anxiety behaves like aphobia, whereby individuals involuntarily at-tempt to reduce their anxious mood state byminimizing their encounter with fear-relevantstimuli (Thorpe & Salkovkis, 1999). From afunctional perspective, similar to other anxietieswhereby avoidance is the predominant mecha-nism, most individuals who are financially anx-ious seemingly use avoidance as a defensemechanism (Miu et al., 2008).

Future Research

This study developed novel instruments formeasuring financial anxiety. In so doing, thisstudy has answered but also raised questions.Examining financial anxiety in a larger and di-verse population would be useful in further in-vestigating the demographics of financial anxi-ety. As these studies were conducted in the U.K.in 2008–9, further studies would determine howgeneralizable these findings are across differentcountries, as well as determine the effect thatthe global economic recession may have had onfinancial anxiety. Specifically, the inclusion ofage, income (or parental income), size and num-ber of debts, and monthly budgets would bebeneficial to better ascertain how these variablesrelate to financial anxiety.

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Moreover, the results above are correlationsand do not imply causation. Accordingly, a lon-gitudinal approach would be suitable to testwhether there is a causal link between financialanxiety and debt by investigating whether fi-nancial anxiety performance on the DPP couldpredict subsequent debt (See Marissen et al.,2006, who found performance on DPP to pre-dict relapse in opiate-dependent individuals).

Though the correlations between the FASand the FEST and between the FAS and theDPP indicate that the FAS contains a constructvalidity, further psychometrics of the FAS mustbe conducted on a larger sample size before theFAS is widely accepted and utilized as a mea-sure of self-reported financial anxiety.

Furthermore, similar to Amin, Constable, andCanli (2004), in future neuroeconomic studiesthat investigate financial anxiety it would beuseful to investigate brain activation (particu-larly of the amygdala), galvanic skin response,as well as a wider range of peripheral variables(e.g., cardiovascular activity, facial electro-myography, salivary cortisol) in conjuncture tothe DPP and FEST when possible (Dunn, Dal-gleigh, & Lawrence, 2006). This would be in-formative in understanding how financial anxi-ety is neurologically similar to other phobiasand how temporal and spatial aspects of atten-tional mechanisms are registered. Examiningphysiological responses of the general popula-tion toward their personal finances would use-fully extend the work of Lo and Repin (2001),who studied the physiological characteristics ofprofessional securities traders when they wereengaging in live trading. Furthermore, neurobi-ological studies have strongly indicated thatthere is a biological basis to trait anxiety, andsome of the genetic bases of trait anxiety havebeen identified (Miu et al., 2008). It is likelythat this line of research will greatly advanceour knowledge of the relationship between stateand trait anxiety, as well as the relationshipsbetween specific and nonspecific anxiety disor-ders.

As Miu et al. (2008) explain, trait anxiety hasbeen evaluated by examining attention (i.e.,whether participant detect emotionally threaten-ing stimuli vs. neutral stimuli), interpretation(i.e., whether participants interpret stimuli aspotentially threatening), and memory (i.e.,whether participants favor the recall of informa-tion that is related to the potential threat). This

study has examined financial anxiety in relationto attention; however, future studies can extendupon this work by evaluating how financialanxiety relates to interpretation and memory offinancial stimuli.

The evidence presented here suggests thatfinancial anxiety behaves like a phobia (as mea-sured by an individual’s implicit reactions tofinancial stimuli); this suggests that treatmentsthat have proven to be successful in the im-provement of other phobias, such as CognitiveBehavioral Therapy, might be an effective wayof helping individuals with high levels of finan-cial anxiety. Further research would be neces-sary to evaluate what may improve one’s finan-cial anxiety.

Lastly, the integration of financial anxiety,attitudes, behaviors, and decisions into a modelof student debt would be useful in explaininghow these factors are interrelated and which areespecially problematic for student finances. Theparticular role of depression should be incorpo-rated into future work. This would require ameta-analysis and model development (Robert& Jones, 2001).

Concluding Remarks

Financial anxiety can become devastating forthe individual as well as an obstacle to the goalof governments in creating financially self-sufficient citizens (Burchell, 2003). Personal fi-nancial products continues to grow in numberand complexity; for instance, Internet loanshave recently become a big business, providingfast loans with typical annual interest rates inexcess of 2,000%. It is in the interest of gov-ernments to take financial anxieties seriouslyand help citizens cope better with their personalfinances. For example, it is important to makefinancial knowledge more accessible and finan-cial products more consumer-friendly, whichmay reduce individuals’ financial anxiety.

In both studies presented above, the self-reported financial anxiety questionnaire corre-lated significantly with the implicit measures.These findings indicate that those who reporthaving financial anxiety also display reactionlatencies in the processing of financial informa-tion. The development of these reported andsubliminal tools that measure financial anxietycan be usefully employed in future research tobetter understand financial anxiety. Accord-

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ingly, financial behavior could be more compre-hensively evaluated and policy could be betterdetermined by incorporating financial anxietyinto economic models of financial illiteracy,mismanagement, and debt.

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