Personal Financial Literacy: A National Needs Assessment

Personal Financial Literacy: A National Needs Assessment

Research Report - 2002

Prepared for

By

John J. Usera, Ph.D.Institute for Educational Leadership & Evaluation

Chiesman Foundation For Democracy, Inc.1641 Deadwood Avenue

Rapid City, SD 57702

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Table of Contents

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Theoretical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Financial Literacy Quotient (FLQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . 11General Beliefs (GBS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Practice & Skills (PSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Specific Knowledge (SKS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Researcher Background - John Usera, Ph.D. . . . . . . . . . . . . 35

© 2002 Institute for Educational Leadership & Evaluation, All Rights Reserved

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Personal Financial Literacy

Acknowledgments

The following evaluation report was supported by Consumer Credit CounselingService of the Black Hills and the American Center for Credit Education in RapidCity, South Dakota. The success of this report required the efforts of a group ofindividuals dedicated to helping people become better managers of their personalfinances.

Participating Consumer Credit Counseling Service Agencies:

CCCS of Alaska Anchorage, Alaska

CCCS of the North Coast Arcata, California

CCCS of Central Oregon Bend, Oregon

CCCS of the Tri-State Evansville, Indiana

Family Support of Fairchild Fairchild AFB, Washington

CCCS of Greater Wyoming Gillette, Wyoming

CCCS of Montana Great Falls, Montana

CCCS of Southern Nevada Las Vegas, Nevada

CCCS of the Mid Ohio Valley, Inc. Parkersburg, West Virginia

CCCS of Racine Racine, Wisconsin

CCCS of the Black Hills Rapid City, South Dakota

CCCS of the Sacramento Valley Sacramento, California

CCCS of Mid-Willamette Valley, Inc. Salem, Oregon

CCCS of San Diego & Imperial Co. San Diego, California

CCCS of Sioux Land Sioux City, Iowa

CCCS Family Services Wilmington, North Carolina

The Village Fargo, North Dakota

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Executive Summary

This study was initiated in order to determine the profile of an individualexperiencing financial problems seeking assistance from a credit counselor orpersonal financial manager. In creating this profile, it was necessary to determinesome basic demographic background, learn what the person knew about personalfinances, and determine the level of actual practice.

There were 5,981 self-selected respondents from 17 credit counseling sitesacross the United States that completed the Financial Literacy Questionnaire. Therespondents were 60.9% female with an average age of 36.3 years. The majority ofthe respondents were White (70.6%) with 10.5% Black, 2.8% American Indian, 9.9%Hispanic, and 2.2% Asian. The majority of the respondents completed a high schooleducation (58.2%), while 19.3% had completed four years of college or more. Thirty-nine percent of the respondents were married, 36.6% were single, and 16.7%were divorced.

The survey instrument asked 15 questions to obtain a Financial LiteracyQuotient (FLQ). This FLQ was divided into three subscores: General Beliefs (GBS),Practice & Skills (PSS), and Specific Knowledge (SKS). The scores are a means tomeasure the level of understanding and application of personal financialmanagement. Each score and subscore had a range from 0 to 100. Some of theresults of the scoring were:

• The mean FLQ score for all the respondents was 70.3 with a rangefrom 25 to 100. Half of the respondents scored below 71.7.

• The mean FLQ score for the female respondents was 69.7 and for themale respondents it was 71.3.

• The mean GBS for all the respondents was 80.9 with a range from 25to 100. Half of the respondents scored below 78.6.

• The mean PSS for all the respondents was 64.7 with a range from 25to 100. Half of the respondents scored below 65.

• The mean SKS for all the respondents was 62.6 with a range from 25to 100. Half of the respondents scored below 60.

Some of the major findings based upon the scoring and further analysis ofthe data include:

• The youngest age group (15 to 25) had a 3.9% lower average FLQscore than the highest scoring age groups (26 to 45) and the oldestage group (66 plus) had a 6.2% lower average FLQ score than thehighest scoring age groups.

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• The White ethnic group mean FLQ score was statistically significantlyhigher than the other ethnic groups. This group’s mean score was5.9% above the other ethnic groups.

• Married and divorced respondents scored statistically significantlyhigher on the Financial Literacy Questionnaire than other maritalstatus categories. The married and divorced groups’ mean scoreswere 7.6% above the other marital status groups.

• Individuals with a higher education level scored statisticallysignificantly higher on the Financial Literacy Questionnaire than othereducational levels. The respondents with a master’s degree or higherhad a mean score 10.3% above the other educational levels.

The General Beliefs Subscore (GBS) asked questions about “commonsense” financial practices. “Do late payments impact seeking a home loan?” “Should you keep enough in your savings account to cover three months ofexpenses?” Some of the major findings based on GBS were:

• The youngest age group (15 to 25) scored statistically significantlylower than the other age groups. The youngest age group’s meansubscore was 3.1% below other age groups.

• The White and Black ethnic groups’ mean score was statisticallysignificantly higher than the other ethnic groups. Both groups’ meansubscores were 6.2% above the other ethnic groups.

• Married respondents scored higher on the GBS than the other maritalstatus groups. The married group had a mean score 14.4% abovethe other marital status groups. Widows had the lowest subscores.

• Respondents with a higher education level scored the highest on theGBS while elementary school completers scored the lowest. There wasa separation of 9.8% in the average score between the two groups.

The Practice & Skills Subscore (PSS) asked questions about the actualpractice of personal finance. Questions in the area asked about the practice ofpaying bills, budgeting, reading credit reports, and setting financial goals. Some ofthe major findings based on PSS were:

• The American Indian ethnic group scored higher than the other groupsin the PSS. The American Indian group scored 4.4% higher than theother ethnic groups.

• There was no significant difference among the marital and age groupsfor the PSS.

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• Respondents with a higher education level scored 8.4% higher on thePSS than the elementary school completers who had the lowest score.

The Specific Knowledge Subscore (SKS) measures the level of knowledgeabout specific financial management facts. Questions in this area ask aboutbankruptcy, interest rates, car payments, and credit reports. Some of the majorfindings based on SKS were:

• The age group, 36 to 45, scored the highest on the SKS, while theyoungest age group, 15 to 25, scored the lowest by 12.1%.

• The married respondents scored the highest on the SKS while widowsscored the lowest by 10.6%.

• The White ethnic group scored the highest on the SKS while the Asianethnic group scored the lowest by 7.6%.

This research study found strong evidence to support the need for personalfinancial literacy education and counseling. The average person seeking assistancein managing credit or other related personal finance areas has some basic personalfinancial literacy. The mean scores for the Financial Literacy Quotient and threesubscores obtained in this study support this profile. The reason why a person seeksassistance is because knowledge and practice were not matched.

Individuals experiencing money problems have difficulty with creditmanagement, budgeting, and knowledge of credit guidelines. This study shows thathighly educated respondents had an excellent knowledge about credit, but had adeficiency in the skills of budgeting and credit management. There were manypeople who had budgeting skills, but lacked knowledge about credit management. Insome cases individuals needed a course on personal financial management, whileothers need some guidance counseling on how to apply what they know aboutpersonal finances.

This study supports the importance of promoting and encouraging individualsto learn how to be good personal finance managers. Regardless of age, education,marital status, or ethnicity, education and counseling were two interventions neededby the majority of the individuals seeking assistance for financial problems. Oneintervention alone may not be sufficient for seeking assistance or improving anindividual’s personal financial management skills and knowledge.

1Kratzer, C., Brunson, B., Garman, E., Kim, J., and Joo, S. (1998). Financial Education In The Workplace Results of A Research Study. Journal of Compensation & Benefits, 14(3), pp 24-27.

Malach, A. (2001). Home Ownership Education and Counseling: Issues inResearch and Definition. Philadelphia: Federal Reserve Bank.

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Overview

This study was conducted to develop a profile on the level of understanding ofpersonal financial management by individuals seeking financial counseling andrelated services. This research supports the need for personal financial educationand identify some of the areas effective educational programs must address. TheFinancial Education Needs Assessment was commissioned to match need witheducational and counseling strategies.

The survey technique was employed because it provides a quick and accuratemanner in which to get information about the individuals’ knowledge of personalfinance in the United States. The questionnaire was designed collaboratively by thestaff of the American Center for Credit Education, personal financial managementcounselors and instructors, and the Institute for Educational Leadership &Evaluation. The expectations and attributes of this questionnaire included questionsabout the respondent’s demographics, personal beliefs about personal finance, andknowledge about credit and budgeting.

The results of this study were presented to the American Center for CreditEducation staff and to credit counselors. The sharing of the results from this studywill provide evidence and support for a personal financial literacy and educationalprogram such as Credit When Credit Is Due. Also, it will serve as a baseline studyto compare future needs assessment studies. Some studies have been completed toshow the benefits of workplace financial education, but no national study has beendone to substantiate a broad base need for financial education.1

Methodology

A self-selected sample of 5,981 potential consumer and finance counselingclients were surveyed at 17 office sites throughout the United States. As part of theintake and information process, before the client met with financial managementcounselor, the client was asked to complete a 15-item questionnaire about personalfinancial management. The instrument was designed to be completed within tenminutes with a minimal amount of explanation and instruction. All questionnaireswere completed anonymously and with no reference to a specific person or potentialclient. The questionnaires were sent directly to the Rapid City office, coded andentered into a database.

The collected data was analyzed using the Minitab Statistical System. The

2Messick, S. (1989). Validity. In J.H. McMillan & S. Schumacher, Researchin education (p. 235). New York: Longman Publishers.

3Cronbach, L.J. (1971). Test Validation. In R.L. Thorndike (Ed.), Educationalmeasurement (2nd edition). Washington, D.C.: American Council ofEducation.

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analyses included the construction of frequency distribution tables and testing forindependence and association of categorical data between two or more variables atthe " = 0.05 level. Statistical tests included chi-squared tests for goodness of fit andindependence of responses to a set of demographic data.

The procedures were designed to obtain high statistical power based onsample size, significance (alpha level), and observed effect. The statistical power forthis study was computed to be 0.85 at an alpha level of 0.05.

In order to assure that proper interpretation and use of the informationgathered through this survey was achieved, calculations were performed for validityand reliability coefficients. Validity refers to the appropriateness, meaningfulness,and usefulness of the specific inferences made from the survey results. “It is anintegrated evaluative judgement of the degree to which empirical evidence andtheoretical rationales support the adequacy and appropriateness of the inferencesand actions based on the assessment techniques employed.”2 Reliability refers to theconsistency of measurement, the extent to which the results are similar over differentforms of the same instrument or occasions of data collecting. Reliability coefficientfor this questionnaire was computed to be 0.79 using the Kuder-Richardson formula(KR21). The coefficient of validity was calculated to be 0.81 using Cronbachmethods.3

Theoretical Framework

The Financial Literacy Quotient (FLQ) is a score that reflects the level ofcompetency regarding personal financial management by an individual completingthe Financial Literacy Questionnaire. A perfect score on the Financial LiteracyQuestionnaire would be 100 with an acceptable FLQ score being 80 or higher. TheFLQ is divided into three subscores: General Beliefs, Practice & Skills, and SpecificKnowledge.

The maximum score for General Beliefs is 100 with an acceptable subscorebeing 80 or higher. The General Beliefs subscore focuses on items that reflectgeneral knowledge about personal finances that could be obtained from experienceor course work.

The maximum score for Practice & Skills is 100 with an acceptable subscorebeing 80 or higher. The Practice & Skills subscore reflects an individuals’ actualpractice in managing finances or having good management practices in place.

The maximum score for Specific Knowledge is 100 with an acceptable

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subscore being 80 or higher. The Specific Knowledge subscore reflects informationabout personal finances that requires knowledge obtained from providers,educational sources, or direct experience.

The analysis of this data provides the financial counselor, financial educator,and the consumer with information about what is needed in order to be a goodpersonal financial manager. The respondents in this study had taken the first step toseek assistance about some area of personal finances. The questionnaire was usedto determine the level of personal financial literacy the potential client may or may notpossess. This information is helpful in determining the type and level of resources acredit counseling or education center may need to have in place if it is to meetadequately the needs of the potential clients.

The questionnaire results must be evaluated in the context of the potentialclients and what skills or knowledge they already possess as they seek additionalcounseling assistance. The FLQ is designed to help the credit counseling centersdetermine the needs that exist and to ascertain what services might be needed inorder to serve clients. It has been substantiated that credit counseling services,personal financial literacy education, and debt reduction programs are beneficial. Buta profile of the potential client has not been developed to date. This questionnaire isa step in that direction. The results of this study should give the providers valuableinformation to make decisions about meeting the financial management needs of alltheir clients.

Demographics

The respondents were located in 14 states with the largest number ofrespondents in the 26-35 year old age category (n = 1,798; 30.1%). The averageage for the sample was approximately 36.3 years. The gender composition of therespondents was 60.9% female and 39.1% male.

Table I

Age Distribution of Respondents

AgeMale Female

Total Percentn p n p

15 to 25 484 20.9% 771 21.3% 1,255 21.1%

26 to 35 690 29.8% 1,096 30.3% 1,786 30.1%

36 to 45 594 25.6% 858 23.7% 1,452 24.5%

46 to 55 379 16.3% 574 15.9% 953 16.1%

56 to 65 120 5.2% 214 5.9% 334 5.6%

66 & Older 52 2.2% 103 2.8% 155 2.6%

Total 2,319 100% 3,616 100% 5,935 100%

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Table II shows the educational level of the respondents. Participants wereasked to report the highest level of completed education. The majority of therespondents (n = 3,404; p = 58.3%) reported having a high school education orequivalent (GED). Over 19% (n = 1,134; p = 19.4%) of the respondentscompleted a four-year or post-graduate degree program.

Table II

Educational Level of Respondents

CompletedMale Female


Elementary School 50 2.2% 64 1.8% 114 2.0%

High School 1,358 59.6% 2,046 57.4% 3,404 58.3%

Associate Degree 434 19.0% 756 21.2% 1,190 20.4%

Bachelor Degree 349 15.3% 571 16.0% 920 15.7%

Masters Degree orHigher

88 3.9% 126 3.5% 214 3.7%

Total 2,279 100% 3,563 100% 5,842 100%

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Table III shows the ethnicity of the respondents. Caucasians (White)comprised 70.6% (n =4,138) of the respondents followed by Blacks at 10.5% (n= 615) and Latinos at 9.9% (n = 581). There were 2.0% (n = 119) who did notidentify an ethnic group and 2.2% (n = 130) classified themselves as “Other”.

Table III

Ethnicity Distribution of Respondents

Ethnicity

Male FemaleTotal Percent

n p n p

White 1611 70.6% 2,527 70.6% 4,138 70.6%

Black 230 10.1% 385 10.8% 615 10.5%

American Indian 60 2.6% 103 2.9% 163 2.8%

Hispanic/Latino 240 10.5% 341 9.5% 581 9.9%

Asian 99 4.3% 136 3.8% 235 4.0%

Other 41 1.8% 89 2.5% 130 2.2%

Total 2,281 100% 3,581 100% 5,862 100%

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Table IV shows the marital status for respondents. Married respondentshad the highest rate at 38.9% (n = 2,304) followed by the single respondents at36.6% (n = 2,166). A higher percentage of males were married, while thereported percentage of married females were equal to the percentage of singlefemales.

Table IV

Marital Status Distribution of Respondents

StatusMale Female


Single 882 38.1% 1,284 35.6% 2,166 36.6%

Married 1,039 44.9% 1,265 35.1% 2,304 38.9%

Separated 97 4.2% 223 6.2% 320 5.4%

Divorced 277 12.0% 716 19.9% 993 16.8%

Widow 18 0.8% 111 3.1% 129 2.2%

No Report 2 0.1% 3 0.1% 5 0.1%

Total 2,315 100% 3,602 100% 5,917 100%



Findings

Financial Literacy Quotient

The Financial Literacy Quotient (FLQ) is a score that reflects the correctresponse to a set of 15 questions about personal finance management. Aperfect FLQ score is 100. The FLQ documents the level of knowledge, belief,and practice about finances. A low FLQ (less than 80) will show a deficiency inone or more of the three areas assessed in the questionnaire. This assessmentcan be used to establish individual need for financial education, assistance ordirect intervention through a counselor .

Figure V shows the distribution of the scores obtained from therespondents to the Financial Literacy Questionnaire. The mean score was 70.3with a standard deviation of 12.6. The median score (50th percentile) is 71.7with the first quartile at 63.3 and the third quartile at 80.0. The range of scoresfor the FLQ was 25.0 to 100.

There was a significant difference found among the mean scores of thesix age groups (F = 11.96, p < 0.0001). The youngest age group (15 to 25) andthe oldest age group (66 +) were statistically lower than the other four middle-age groups. The mean score for the lowest age group was 66.8 while thehighest age groups’ (26 to 35 and 36 to 45) mean score was 71.2.



There was a significant difference found among the mean scores of thesix ethnic categories (F = 16.71, p < 0.0001). The White group had a mean FLQscore of 71.25 while the Hispanic group had a mean score of 67.05. The Asiangroup also had a low mean score of 67.06.

There was a significant difference found among the mean scores for thesix marital categories ( F= 8.86, p < 0.0001). The widows’ mean score was thelowest on the FLQ at 66.0 while the divorced and married groups had the highestFLQ mean scores at 71.42 and 71.37 respectively.

There was a significant difference found among the mean scores for thefive education levels (F = 24.90, p < 0.0001). The post-graduate degree grouphad the highest FLQ mean score at 74.29 while the group with only elementaryschool education had a mean score of 66.61.

General Beliefs

The Financial Literacy Quotient is divided into three subscores: GeneralBeliefs, Practice & Skills, and Specific Knowledge. The General BeliefsSubscore (GBS) is based on seven (7) items from the Financial LiteracyQuestionnaire. These items asked about “common sense” financial practices. For example: “Do late payments impact seeking a home loan?”; “Should youkeep enough in your savings account to cover 3 months of expenses?”; and “Ifyou do not have enough money to pay your bills, should you call your lendersright away and tell them?” A low GBS (less than 80) would indicate a need foreducation about basic financial practices. This area of the FLQ focuses ongeneral knowledge about personal financial management that most individualswould have acquired during their life time.



Figure VI shows the distribution of the subscores obtained from therespondents to the Financial Literacy Questionnaire. The mean GBS was 80.9with a standard deviation of 14.7. The median score (50th percentile) is 78.6with the first quartile at 67.9 and the third quartile at 89.3. The range of scoresfor the GBS was 25.0 to 100.

There was a significant difference found among the mean subscores ofthe six age groups (F = 6.86, p < 0.0001). The youngest age group (15 to 25)and the oldest age group (66 +) were statistically lower than the other four middleage groups. The mean score for the lowest age group was 77.6 while the twohighest age groups’, 26 to 35 and 46 to 55, mean subscores was 81.70 and81.57 respectively.

There was a significant difference found among the mean subscores ofthe six ethnic categories (F = 17.36, p < 0.0001). The White group had a meanGBS of 81.98 while the Black group had a mean subscore of 80.17. Theremaining ethnic groups had mean subscores that range from 76.93 to 79.87.

There was a significant difference found among the mean scores for thesix marital categories ( F= 5.37, p < 0.0001). The widows’ mean GBS was thelowest at 70.0 while the married group had the highest GBS mean at 81.75.



There was a significant difference found among the mean scores for thefive education levels (F = 18.07, p < 0.0001). The post-graduate degree grouphad the highest GBS mean at 85.00 while the group with elementary schooleducation had a mean score of 76.69.

Table V

Late Payments

AgeTrue False Not Sure

n p n p n p

15 to 25 150 2.6% 914 15.6% 185 3.2%

26 to 35 312 5.3% 1321 22.6% 86 1.5%

36 to 45 322 5.5% 1017 17.4% 110 1.9%

46 to 55 200 3.4% 664 11.3% 85 1.5%

56 to 65 77 1.3% 224 3.8% 36 0.6%

66 to 75 38 0.6% 94 1.6% 20 0.3%

Total 1,099 18.8% 4,234 72.3% 522 8.9%

Additional analysis of the items used to determine the General BeliefSubscore indicates some useful trends to assist in counseling and education ofclients. One of the items focused on late payments. Table V shows whether therespondents believe late payments on bills are a problem (true), or no problem(false), when seeking a home loan. A chi-square analysis indicated that anassociation existed between the age of the respondent and the response given(P2 = 98.9, df = 10, p < 0.0001). Over 72.3% of the respondents stated that latepayments would effect getting a home loan. About 10% of the respondents werenot too sure how their record regarding bill payments would effect getting a homeloan.

An association was found between the ethnicity of the respondent and

the type of response given (P2 = 27.61, df = 10, p < 0.033). See Figure VII. Theresponse rate for this item was also dependent upon the respondents’ completed

educational level (P2 = 19.3, df = 8, p < 0.013). The more education a personhad the higher the rate of appropriate response. The range of the selected“false” response from elementary school to post secondary school was 67.0% to71.5%.



Another item that effected the GBS was the amount of savings requiredto cover household expenses. Table VI shows the number and percentage ofrespondents who believed that you should keep enough in a savings account tocover three months of expenses. A chi-square analysis indicated that anassociation existed between the age of the respondent and the response given

(P2 = 31.93, df = 10, p < 0.001). Over 83.7% of the respondents agreed thattheir savings account should contain enough money to cover three months ofexpenses. About 6.2% of the respondents did not agree with the statement.

Table VI

Savings Account Amount


n p n p n p

15 to 25 1004 16.9% 90 1.5% 159 2.7%

26 to 35 1530 25.7% 93 1.6% 170 2.9%

36 to 45 1214 20.4% 103 1.7% 132 2.2%

46 to 55 822 13.8% 55 0.9% 79 1.3%

56 to 65 282 4.7% 14 0.2% 43 0.7%

66 to 75 121 2.0% 13 0.2% 21 0.4%

Total 4,973 83.7% 368 6.2% 604 10.2%



An association was also found between the ethnicity of the respondent and

the type of response given (P2 = 28.9, df = 10, p < 0.016). The response rate for

this item was dependent upon a person’s educational level (P2 = 55.5, df = 8, p <0.001).

Table VII shows the number and percentage of respondents who believedthat no more than 25% of a persons income should be used on installment debt.

The response was dependent upon the individual’s age (P2 = 180.2, df = 10, p <0.0001). Over 47% of the respondents agreed that no more than 25% of a personsincome should be dedicated to the installment debt. About 44% of the respondentswere not sure about the relationship between the amount of a person’s income andthe amount of installment debt.

Table VII

Installment Debt


n p n p n p

15 to 25 432 7.3% 113 1.9% 702 11.9%

26 to 35 793 13.4% 133 2.2% 857 14.5%

36 to 45 757 12.8% 136 2.3% 551 9.3%

46 to 55 532 9.0% 73 1.2% 346 5.9%

56 to 65 196 3.3% 24 0.4% 119 2.0%

66 to 75 97 1.6% 14 0.2% 40 0.7%

Total 2,807 47.5% 493 8.3% 2,615 44.2%

An association was found between the gender of the respondent and the

type of response given (P2 = 44.9, df = 2, p < 0.0001). The percentage of malerespondents that said the statement was true was approximately 51.0% as opposedto the female response rate of 45.9%. The response rate for this item was

dependent upon a person’s educational level (P2 = 87.5, df = 8, p < 0.001). Thehigher the educational level, the more likely the appropriate response. Over 43% ofthe high school graduates stated the statement was true while 66.8% of the postgraduates stated the statement was true.

The response rate for this statement is dependent upon the ethnicity of the

respondent (P2 = 39.3, df = 10, p < 0.008). The highest rate of correct response wasgiven by 49.4% of the White group, 46.7% of the Black group, and 44.4% of theAsian group.

Table VIII and Figure VIII show the number and percentage of respondentswho believed that if you do not have enough money to pay your bills, you should callyour lender right away and tell them. The response was found dependent upon the



ethnicity of the respondent (P2 = 46.5, df = 10, p < 0.001). Eighty-six percent(85.6%) of the White respondents agreed with the statement. The Black andAmerican Indian respondents agreed that you should call your lender if you are notable to pay your bills at a rate of 83.3% and 87.2% respectively.

Table VIII

Not Enough Money To Pay Bills

EthnicityTrue False Not Sure

n p n p n p

White 3,539 60.4% 162 2.8% 436 7.4%

Black 513 8.8% 34 0.6% 69 1.2%

American Indian 143 2.4% 4 0.1% 17 0.3%

Hispanic/Latino 451 7.7% 47 0.8% 81 1.4%

Asian 176 3.0% 18 0.3% 39 0.7%

Other 111 1.9% 6 0.1% 13 0.2%

Total 4,933 84.2% 271 4.6% 655 11.2%



Table IX and Figure IX show an association between the gender of therespondent and the type of response (P2 = 26.2 df = 2, p < 0.002). Eighty-eightpercent (88.2%) of the males and 84.1% of the females agreed with the statementthat two people can purchase the same item and still pay vastly different amounts.

Table IX

Purchase of Goods

GenderTrue False Not Sure

n p n p n p

Male 2,038 34.5% 98 1.7% 175 3.0%

Female 3,023 51.2% 155 2.6% 417 7.1%

Total 5,061 85.7% 253 4.3% 592 10.0%

Table X shows the number and percentage of respondents who believed thatwhen you use a credit card you are borrowing money from a bank. The response

was found dependent upon the ethnicity of the respondent (P2 = 30.9, df = 10, p <0.001). Sixty-nine percent (69.2%) of the White respondents, 76.3% of the Blackrespondents, 73.0% of the American Indian respondents, 75.4% of the Latinorespondents and 72.9% of the Asian respondents agreed with the true statement.

An association was found between the age of the respondent and the type ofresponse (P2 = 23.9, df = 10, p < 0.039). The older age categories had a highercorrect response rate (72.7%) while the 46 to 55 and the 56 to 65 categories had alower correct response rate of 67.6% and 68.5% respectively. The rate of response



was also dependent upon the gender of the respondent (P2 = 24.5, df = 2, p <0.001). Seventy-four percent (74.2%) of the males and 68.8% of the femalesbelieved that using a credit card was the same as borrowing money from a bank.

Table X

Credit Card Same As Bank Loan


n p n p n p

White 2,837 49.2% 683 11.8% 578 10.0%

Black 453 7.9% 74 1.3% 67 1.2%

AmericanIndian

119 2.1% 27 0.5% 17 0.3%

Hispanic orLatino

426 7.4% 74 1.3% 66 1.1%

Asian 164 2.8% 34 0.6% 27 0.5%

Other 97 1.7% 13 0.2% 15 0.3%

Total 4,096 71.0% 905 15.7% 770 13.3%



Table XI and Figure XI show the majority of the respondents believed that anindividual’s credit history had an effect on the ability to get a job or rent anapartment. There was an association found between the age of the respondent and

the type of response (P2 =40.5, df =10, p < 0.001). The 26 to 35 age category hadthe highest correct response rate at 91.5%while the older age category had thelowest correct response rate at 77.4%.

An association was found between the ethnicity of the respondent and type of

response (P2 = 49.5, df = 10, p < 0.001). Ninety-one percent (90.8%) of the Whiterespondents, 87.4% of the Black respondents, 86.2% of the American Indianrespondents, 83.1% of the Latino respondents, and 84.0% of the Asian respondentsagreed that a person’s credit history has an effect on one’s ability to get a job or rentan apartment.

Table XI

Personal Effect of Credit History

(Your credit history has no effect on your ability to get a job or rent an apartment.)


n p n p n p

15 to 25 62 1.1% 1042 18.8% 69 1.2%

26 to 35 91 1.6% 1542 27.8% 53 1.0%

36 to 45 76 1.4% 1214 21.9% 65 1.2%

46 to 55 64 1.2% 768 13.8% 49 0.9%

56 to 65 21 0.4% 279 5.0% 14 0.3%

66 to 75 16 0.3% 106 1.9% 15 0.3%

Total 330 6.0% 4,951 89.3% 265 4.8%

The rate of response to this statement was also found be dependent upon the

level of education completed by the respondent (P2 = 24.7, df = 8, p < 0.001). Overninety-two percent (92.5%) of the respondents who completed post graduate studiesagreed with the connection between credit history and employment while 80.8% ofthe respondents with an elementary school education agreed with the statement.



Practice & Skills

The Practice & Skills Subscore (PSS) measures the level of financialmanagement actually used by the respondent. This subscore is based on five (5)items specifically connected to a particular skill or practice. Questions were askedabout the practice of paying bills, budgeting, reading credit reports, and settingfinancial goals. A score of 80 reflects average skill and practice levels. Therespondents had a mean PSS of 64.7 (s = 15.4) and a median subscore of 65.0. Therange of subscore was from 25 to 100.

There was no significant difference found among the mean Practice & SkillsSubscores (PSS) of the six age groups (F = 1.03, p < 0.396). The range of thescores were from the youngest age group (15 to 25) at 63.96 to the oldest age group(66 +) at 65.35.

There was a significant difference found among the mean PSS of the sixethnic categories (F = 2.95, p < 0.012). The American Indian group had the highestmean PSS at 65.91, while the Hispanic group had the lowest mean subscore at63.01. The remaining ethnic groups had mean subscores that ranged from 63.45 to65.64.

There was no significant difference found among the mean PSS for the sixmarital categories (F = 1.46, p < 0.188). The single respondent mean PSS score



was the lowest at 64.01 while the divorced group had the highest PSS mean score at65.55.

There was a significant difference found among the mean subscores for thefive education levels (F = 9.98, p < 0.0001). The post-graduate degree group had thehighest PSS mean score at 67.62, while the group with elementary school educationhad a mean score of 61.95.

Analysis of the Practice & Skills questionnaire items show some discriminatingfactors about the level of skills individual potential clients have when seeking financialmanagement assistance. For example Table XII shows that a high percentage of therespondents indicated that they pay their bills on time. The next highest rate wasgiven to the statement that they occasionally pay bills on time. There was anassociation between gender and the statement selected by males and females (P2 =21.8, df = 3, p < 0.001). There was very little difference noted in the level ofstatement selection by both genders when asked, “What statement best describesyour current bill paying practices?”



Table XII

Bill Paying Practices

StatementMale Female

n % n %

I pay my bills on time. 918 43.7% 1,418 43.0%

I occasionally pay my bills on time. 803 38.2% 1,268 38.4%

I hardly ever pay my bills on time. 278 13.2% 521 15.8%

I do not pay the bills in my household. 104 4.9% 93 2.8%

Total 2,103 100% 3,300 100%

The respondent’s age was associated with the selection of one of thestatements (P2 = 164.5, df = 15, p < 0.0001). The younger age categories, ages 15to 25, were less likely to pay their bills on time (38.2%), while the older agecategories, ages 66 and older, indicated they paid their bills on time (81.34%). Therange for the intermediate age categories (26 to 65) was 40.0% to 58.8%.

Figure XIII shows that paying of bills on time was dependent upon thecompleted educational level (P2 = 42.7, df = 15, p < 0.004). It was noted that themore education an individual had the higher the probability of the respondent to payhis or her bills on time. The post-graduate respondent rate for paying bills on time



was 54.8% as compared to the high school graduate rate at 40.6%. The elementaryschool completers had a response rate of 52.0% for paying bills on time.

Table XIII shows there was no association between gender and therespondents’ selection of a statement that best describes their knowledge aboutcredit reporting (P2 = 7.04, df = 3, p < 0.071). Approximately an equal percentage ofmales and females have not seen their credit report (28% versus 27%). An equalpercentage of males and females have seen their credit report and it was good (23%versus 22%).

There was an association between the age of the respondent and theselection of the statement that best describes what the respondent knew about his orher credit report (P2 = 344.1, df = 15, p < 0.0001). The older the person the higherthe probability of having a good credit report. Fourteen percent (13.9%) of theyounger age category (15 to 25) had seen their credit report with a good rating. Thirty-eight percent (38.5%) of the older age category (66 to 75) had seen their creditreport with a good rating. In the age range between 26 and 45, 40.2% to 44.3% ofthe respondents reported seeing their credit report with a bad rating. In the agerange between 56 and 75, 13.9% to 31.8% of the respondents reported seeing theircredit report with a bad rating. In all of the categories, over one-fourth of therespondents had not seen their credit report.

Table XIII

Knowledge About Credit Report


n % n %

I have never seen a copy of my credit report. 588 28.0% 877 26.9%

I have seen my credit report but I did notunderstand it.

273 13.0% 454 13.9%

I have seen my credit report and my creditrecord was poor.

751 35.8% 1,230 37.7%

I have seen my credit report and my creditrecord was good.

486 23.2% 702 21.5%

Total 2,098 100% 3,263 100%

An association was noted between the ethnicity of the respondent and theselection of a statement (P2 = 138.2, df = 15, p < 0.001). The Asian respondent(25.9%), the White respondents (23.5%) and the Latino respondents (23.1%)reported seeing a good credit report. The Black respondents (15.1%) and theAmerican Indian respondents (16.4%) reported a low probability of having a goodcredit report.



Having seen a credit report with a good or bad rating was dependent upon thecompleted educational level (P2 = 85.7, df = 12, p < 0.003). It was noted that themore education an individual had the higher the probability of the respondent havinga good credit report (24.9% to 32.3%). Sixteen percent (16.0%) of the elementaryschool and 19.1% of the high school graduates reported seeing a good credit report. In all of the educational level categories the rate of bad credit reports exceeded thegood credit reports by a range of 4.2% to 23%.

The respondents were asked to select the statement that best described theirfinancial goals (Table XIV). There was an association found between the gender ofthe respondent and the selection of one of the four statements (P2 = 21.0, df = 3, p <0.0001). The majority of the respondents (55.6%) stated that they had somefinancial goals but were not sure how to make them come true. A higher percentageof the male respondents (9.1%) had a positive feeling about their financial goals asopposed to the female respondents (6.5%).

The age of the respondent was associated with the selection of the statementthat best describes their situation regarding financial goals (P2 = 89.8, df = 15, p <0.0001). The older the respondent the less likely that he or she had any financialgoals. In the age category 66 to 75, 22.3% of the respondents reported not havingany real financial goals, while in the age category 26 to 35, 6.1% of the respondentsreported not having any financial goals. Between the ages of 36 and 65, 8.24% to12.6% of the respondents did not have any financial goals.

An association was noted between the ethnicity of the respondent and theselection of a statement regarding financial goals (P2 = 35.5, df = 15, p < 0.003). The



Asian respondents (9.1%) and the American Indian respondents (10.8%) had thehighest probability of not having financial goals. The Black respondents (6.3%), theLatino respondents (7.8%) and the White respondents (8.6%) had a lower probabilityof not having financial goals.

Table XIV

Financial Goals


n % n %

I do not have any real financial goals. 186 8.9% 261 8.0%

I have some financial goals but I do notknow how to make them come true.

1,094 52.2% 1,890 57.7%

I have some financial goals and I havecreated a plan to make them come true.

625 29.8% 910 27.8%

I have some financial goals and they arecoming true.

190 9.1% 214 6.5%

Total 2,095 100% 3,275 100%

Table XV

Monthly Budget


n % n %

I do not have enough money to have abudget.

474 23.0% 819 25.3%

I would like to have a monthly budget,but do not know how to make one.

568 27.5% 1,037 32.0%

I have a budget for the monthly bills butnot for everyday expenses.

650 31.5% 944 29.2%

I have a monthly budget and I use it toplan for all of my monthly expenses.

372 18.0% 437 13.5%

Total 2,064 100% 3,237 100%

Having a financial goal was dependent upon the level of education completed (P2 = 50.5, df = 12, p < 0.016). The higher the level of completed education, thehigher the probability of a respondent having successful financial goals. Ten percent(10.3%) of the respondents having successful financial goals had completed an



elementary school education, while 9.3% of the respondents with successful financialgoals had completed post graduate studies.

The respondents were asked to select the statement that best describes theirmonthly spending plan or budget (Table XV). There was an association foundbetween the gender of the respondent and the selection of one of the fourstatements (P2 = 31.8, df = 5, p < 0.001). Forty-one percent (41.0%) of the males didnot know how to make a budget or did not think it necessary to make one. Thirty-ninepercent (38.8%) of the female respondents did not know or did not think it necessaryto have a budget.

An association was noted between the ethnicity of the respondent and the

selection of a statement regarding budget design (P2 = 86.6, df = 15, p < 0.001). The Asian respondents (24.5%) and the Hispanic respondents (17.7%) had thehighest probability of having a successful budget. The American Indian respondents(13.5%), the black respondents (13.8%) and the White respondents (14.5%) had alower probability of having a successful budget.

The age of the respondent was associated with the making of a budget (P2 =75.8, df = 15, p < 0.001). The largest percentage of respondents who reportedhaving no budget or did not know how to make a budget were the 26 to 35 year oldsat 55.6% and the 36 to 45 years olds at 56.2%. The smallest percentage ofrespondents who did not have a budget were the oldest group, 66 +, at 50.0%.



Having a successful budget was dependent upon the level of educationcompleted (P2 = 82.1, df = 12, p < 0.001). The higher the level of completededucation (20.4% for post-graduates and 18.6% for the college graduates) thehigher the probability of a respondent having a successful budget. The rate ofsuccessful budget planning and use was lower for those individuals with lesseducation. Secondary school graduates had a rate of 13.9%. Elementary educationcompleters had a rate at 15.2% and associate degree respondents had a rate at15.6%.

Specific Knowledge

The Specific Knowledge Subscore (SKS) measures the respondents’ level ofknowledge about specific financial management facts. This subscore is based onfive (5) items specifically connected to a particular skill or practice. Questions wereasked about bankruptcy, interest rates, car payments, and credit reports. A score of80 reflects average knowledge about personal finances. The respondents had amean SKS of 62.6 (s = 18.1) and a median subscore of 60.0. The range ofsubscores was from 30 to 100.



There was a significant difference found among the mean Specific Knowledge Subscores of the six age groups (F = 22.6, p < 0.0001). The lowest subscores werefound in the youngest age group (15 to 25) at 58.68 and in the oldest age group (66+) with a subscore at 56.90. The highest subscore was found in the age group, 36 to45, at 64.73.

There was a significant difference found among the mean SKS of the sixethnic categories (F = 14.6, p < 0.0001). The White group had the highest meanSKS at 63.83 while the Asian group had the lowest mean subscore at 57.15. Theremaining ethnic groups had mean subscores that range from 58.96 to 61.73.

There was a significant difference found among the mean SKS for the sixmarital categories ( F= 12.0, p < 0.0001). The widow mean SKS was the lowest at57.56 while the married group had the highest mean SKS at 64.38.

There was a significant difference found among the mean SKS for the fiveeducation levels (F = 17.59, p < 0.0001). The post-graduate degree group had thehighest SK mean subscore at 66.74 while the group with elementary schooleducation had a mean score of 58.90.

Table XVI and Figure XVII show the number and percentage of respondentswho believed that bankruptcy will only stay on your credit record for seven years. The

response was found dependent upon the age of the respondent (P2 = 69.4, df = 10, p< 0.001). This distribution of responses was approximately equal for each responsepossibility. Thirty-three percent (32.8%) of respondents gave the correct response offalse.

Table XVI

Bankruptcy On Your Credit Record


n p n p n p

15 to 25 520 8.6% 335 5.6% 393 6.5%

26 to 35 645 10.7% 630 10.4% 518 8.6%

36 to 45 491 8.1% 540 9.0% 419 7.0%

46 to 55 308 5.1% 353 5.9% 292 4.8%

56 to 65 136 2.3% 94 1.6% 107 1.8%

66 to 75 67 1.1% 27 0.4% 154 2.6%

Total 2,167 35.9% 1,979 32.8% 1,883 31.2%



There was an association found between the response rate and the person’sethnicity (P2 = 43.9, df = 10, p < 0.001). Thirty-four percent (34.3%) of the Whiterespondents, 35.2% of the Black respondents, and 30.7% of the Latino respondentsagreed that the statement was false.

Table XVII

Lowest Interest Rate Is The Best Loan


n p n p n p

White 639 10.9% 2,644 45.2% 855 14.6%

Black 187 3.2% 263 4.5% 161 2.8%

AmericanIndian

43 0.7% 71 1.2% 51 0.9%

HispanicLatino

174 3.0% 247 4.2% 158 2.7%

Asian 105 1.8% 87 1.5% 40 0.7%

Other 36 0.6% 58 1.0% 36 0.6%

Total 1,184 20.2% 3,370 57.6% 1,301 22.2%

Table XVII shows the number and percentage of respondents who believedthat the lowest interest rate on a loan always means the best loan. The response

was found dependent upon the ethnicity of the respondent (P2 = 194.9, df = 10, p <0.001). Sixty-three percent (63.9%) of the White respondents, 43.0% of the Black



respondents, 43.3% of the American Indian respondents, 37.5% of the Asianrespondents, and 42.7% of the Latino respondents agreed that the statement is false.

An association was found between the educational level of the respondentand the type of response given (P2 =107.6, df = 8, p < 0.001). The more educationcompleted the higher the percentage of correct responses. Respondents with anelementary school education gave the correct response at a rate of 47.8%, whilerespondents with a post graduate education gave the correct response at a rate of75.1%.

The response rate was also dependent upon the age of the respondent (P2 =41.4, df = 10, p < 0.001). The younger age categories of 15 to 55 years had a highercorrect response rate (57.4% to 60.3%), while the older age categories of 56 to 75years had a lower correct response rate (44.4% to 47.6%).

An association was found between the gender of the respondent and the typeof response (P2 = 29.8, df = 2, p < 0.001). Sixty-one percent (61.4%) of the malesand 55.0% of the females disagreed that the lowest interest rate always means thebest loan.

One-third of the respondents (32.8%) were not sure about the ability tosurrender their vehicle to get out of a loan if they could not afford the car payments. Table XVIII shows that 58.3% of the males and 48.5% of the females indicated thatthis was not a true statement. The response rate was dependent upon the gender ofthe respondent (P2 =76.4, df = 2, p < 0.001).

The response rate for this statement was dependent upon the age of the

respondent (P2 = 99.7, df = 10, p < 0.004). The older the respondent the morelikely they disagreed with the ability to surrender a vehicle to get out of a loan. The youngest age category response rate was 41.6% while the older agecategory had a response rate of 55.6%.

Table XVIII

Surrender of Vehicle

GenderTrue False Not Sure

n p n p n p

Male 337 6.1% 1,255 22.8% 560 10.2%

Female 482 8.8% 1,626 29.5% 1,245 22.6%

Total 819 14.9% 2,881 52.3% 1,805 32.8%

The respondents were asked to determine which of the statements in TableXIX describes your right to check your credit report for accuracy. There was anassociation found between the gender of the respondent and the selection of one ofthe four statements (P2 =20.3, df = 2, p < 0.001). The majority of the respondents



(72.6%) accepted the statement that could get their credit report upon request if theywere turned down for credit based on the report.

Table XIX

Checking Credit Report Accuracy


n % n %

If you are turned down for credit based on acredit report, the records can be checked forfree.

1,459 69.6% 2,418 74.5%

Your credit report can be checked at any timefor free.

564 26.9% 755 23.3%

You can not see your credit report. 28 1.3% 32 1.0%

All credit records are the property of the U.S.government and your lenders and the FBI arethe only ones who can look at them.

46 2.2% 39 1.2%

Total 2,097 100% 3,244 100%



The respondent’s age was associated with the selection of one of thestatements (P2 = 78.4, df = 15, p < 0.001). A high percentage (77.2%) of the respondents, ages 26 to 35, agreed that they could get their credit report free if theywere turned down for credit, while the youngest, ages 15 to 25, and oldest, ages 66to 75, categories favored the other statements (66.3% and 63.3% respectively).

Conclusion

This research study found strong evidence to support the need for personalfinancial literacy education and counseling. The average person seeking assistancein managing credit or other related personal finance areas has some basic personalfinancial literacy. The mean scores for the Financial Literacy Quotient and threesubscores obtained in this study support this profile. The reason why a person seeks assistance is because knowledge and practice were not matched.

Individuals experiencing money problems have difficulty with creditmanagement, budgeting, and knowledge of credit guidelines. This study shows thathighly educated respondents had an excellent knowledge about credit, but had adeficiency in the skills of budgeting and credit management. There were manypeople who had budgeting skills, but lacked knowledge about credit management. Thus in some cases individuals needed a course on personal financial management,while others needed some guidance counseling on how to apply what they knewabout personal finances.

This study supports the importance of promoting and encouraging individualsto learn how to be good personal finance managers. Regardless of age, education,marital status, or ethnicity, education and counseling were two interventions neededby the majority of the individuals seeking assistance for financial problems. Oneintervention alone may not be sufficient for seeking assistance or improving anindividuals personal financial management skills and knowledge.



Appendix A

Acknowledgments

The following persons are being acknowledged for their special contributions to theCredit When Credit is Due (CWCID) program and to the research and evaluationprocess that contributed to this study.

Paul Strassels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Author & Inspiration for CWCID

Consumer Credit Counseling Service of the Black Hills Board of Directors:

Harold Jansen Dennis Schroedter Claudia Vucurevich

Nyla Knigge Todd Schweiger Dave Mortimer

Angie Einerwold Patricia Link Dave Semerad

Nancy Hook Katherine Miller Dennis Whetzal

Lisa Anderson

Consumer Credit Counseling Service of the Black Hills Staff:

Bonnie Spain Pat Allen Theresa Booher

Debra Culp Barb Garcia Elaine Grauel

Barbara Hamilton Cara Johnson Kristen Levander

Bonnie Muzzy Conrad Otterness Julie Stone

Nathan Thompson



Appendix B

Researcher’s Background

Dr. John Usera served as the principal investigator for the Personal FinancialLiteracy: A National Needs Assessment Study. Currently he is the President/CEO ofthe Chiesman Foundation For Democracy. He was appointed to this position in July1997. His duties as Chief Executive Officer include the oversight of the Foundation’sprojects, initiation of new and innovative civic education programs, editor of all itspublications, and Director of the Institute for Educational Leadership & Evaluation,which promotes educational leadership and performs independent programevaluations of educational and social programs that contribute to the betterment ofdemocratic institutions and organizations.

His previous position was Dean of the College of Education and Associate Professorof Science & Mathematics Education at Black Hills State University. Before beingappointed Dean in 1993, he held the position of Vice President for Academic Affairsat Lamar University in Orange, Texas and the rank of Associate Professor ofChemistry. He held the position of Dean of Instructional Services for LabetteCommunity College which included oversight of institutional research, library,instructional and administrative technology, and public relations.

Dr. Usera has been involved in education on all levels, for thirty years, fromelementary school teacher to middle and high school teacher; from communitycollege chemistry and mathematics instructor to a university instructor in the areas ofstatistics, research design, educational research, and chemistry. He holds adoctorate (Ph.D.) in chemistry from Kansas State University, a masters degree (M.A.)in analytical chemistry and a masters degree (M.S.) in mathematics from theUniversity of South Dakota. His undergraduate degrees (B.S. and B.S.Ed.) are inchemistry, mathematics and secondary education from Black Hills State University.

As President of the Chiesman Foundation he serves on the Kids Voting South DakotaBoard, Badlands Head Start & Early Head Start Board, American Indian and RuralNursing Board, Black Hills State University Chiesman Endowment for PromotingDemocracy Committee, Rapid City Diocese Synod 2002 Committee, and the CatholicSocial Services Board.

Some examples of recent programs evaluated by the Institute for EducationalLeadership & Evaluation under the leadership and authorship of Dr. Usera include:

� Girl Neighborhood Power (1999, 2000, 2001, and 2002). Youth & FamilyServices, Rapid City.

� Health Connections (2000, 2001, and 2002). Girls Incorporated of Rapid City.

� Youth Development Comparative Study of Pre and Early Adolescent Females(2000). Adolescent Family Life, U.S. Department of Health & Human



Services.

� School-To-Work Opportunities (1999, 2000, and 2001). Beadle & SpinkEnterprise Community and Cornerstones Career Learning Center Partnership,Huron.

� Character Counts! (1999). Rapid City Area School District.

� Boys Health Program Evaluation: A Comparison Study (2001). South DakotaDepartment of Health.

� Boys Health Program Evaluation: A Causal Pathway Analysis (2002). SouthDakota Department of Health.

� The New Spectrum Learning Program: Teacher Learning Series (2000 &2001). Workforce LA, Los Angeles School District.

� Evaluation of Some Actions Taken To Reduce Drug Use & Related CriminalActivity Among Youth In Lawrence County (2000). Community OrientedPolicing Services, U.S. Department of Justice.

� Trilateral Relationship of Media, Government & Citizens (2001). KetteringFoundation, Ohio.

� American Indian Spirituality & Practice of Religion In Western South Dakota:Phase I (2001 & 2002). Diocese of Rapid City.

� Rapid City Weed & Seed Evaluation Report (2001 and 2002). ExecutiveOffice of Weed & Seed, Washington, D.C.

� Credit When Credit Is Due: A National Program Evaluation (2001). AmericanCenter for Credit Education. Rapid City, South Dakota.

Dr. Usera has been involved in program evaluation and research for over 16 years. During this time he has evaluated programs funded by the U.S. Department ofHealth, U.S. Department of Education, U.S. Department of Labor, U.S. Department ofJustice, Texas Higher Education Coordinating Board, Kansas Department ofEducation, South Dakota Department of Health & Human Services, South DakotaDepartment of Education & Cultural Affairs, South Dakota Board of Regents, andnumerous private foundations. Other programs evaluated include university andcollege professional preparation programs from allied health programs (nursing,radiologic technical, respiratory care technicians, etc.) to teacher education programs.

Documents

Personal Financial Literacy: A National Needs Assessment