ABSTRACT

The purpose of this presentation is to assess the psychometric properties of a self-report questionnaire measuring family early literacy practices (FELP). Rash analysis was conducted to evaluate item response patterns across diverse subgroups. Preliminary findings indicate the FELP is a reliable and valid measure for assessing family early literacy practices. Session attendees will learn about the validation of a self-report questionnaire. The benefits of analyzing item response patterns among diverse populations will be discussed.

INTRODUCTION

-Whitehurst and colleagues have found positive correlations and longitudinal stability between early literacy skills and later reading skills, which supports the need for early intervention (1999).

-Researchers have determined that indicators of a rich home environment include parent and guardians’ responsiveness to

their children’s early literacy abilities, involvement with their children in language and literacy-based activities and routines, and the provision of literacy materials (Hart & Risley, 1995; Roberts, Jurgens, & Burchinal, 2005).

-As children generally spend a large portion of time with their families, family involvement in literacy activities is strongly related to child achievement (Evans, 1998).

-Recent research has investigated the relationship between parental beliefs and behaviors regarding literacy and outcome

of their children (Meagher, Arnold, Doctoroff & Baker, 2008; Weigel, Martin, & Bennet, 2006).

-It has been hypothesized that these behaviors and expectations manifest as literacy promoting behaviors which benefit child learning (Goodnow & Collins, 1990; Roskos & Christie, 2000).

-The present study examines the psychometric properties of the family early literacy practices (FELP) questionnaire in a large and diverse sample of preschool age children from Jacksonville, Florida. The aim of the study is to answer the following questions:

1 ∙ Does the original factor structure of the FELP remain consistent for a large and diverse sample of

preschool-age children? 2 ∙ Do item response patterns differ among diverse subgroups (e.g. native versus non-native English speakers)?

DISCUSSION 1 ∙ Results indicate the FELP has good reliability and validity for parents of preschool age children. 2 ∙ Exploratory factor analysis of the FELP yielded support for a five-factor model, which aligned with the theoretical construct of the FELP. 3 ∙ Internal consistency of the FELP was satisfactory. However the reliability coefficient of the modeling and monitoring factor of the FELP was below acceptable limits (Cronbach’s alpha =

.67). This is not surprising given the fact that this FELP factor only consists of four items. 4 ∙ This study also provides support for the construct validity of the FELP for parents of preschool age children. Rasch analysis revealed that the threshold for four of the five factors

(exception was Modeling and Monitoring) was in the appropriate order. This means the thresholds increased in a positive direction as the category label increased. 5 ∙ The people mean measure for Expectations was the highest at .69, which is more than two-thirds of a standard deviation above the mean of the items. This demonstrates that

people typically endorsed many positive items for the Expectation factor, which ask parent’s about their beliefs about their children’s future education. The high mean measures for this scale suggest that most people positively endorsed those items. This finding is not surprising given that you might expect parents to believe positively about their children’s academic future.

6 ∙ Rasch analysis supported the reliability findings from the factor analysis. 7 ∙ Rasch analysis demonstrates that response patterns varied by primary language spoken in the home for all 5 factors.

Future Research: ◦ Include preschool age children who may not be attending a formal preschool setting in an effort to include a more diverse sample in future studies. ◦ Evaluate DIF between diverse subgroups. ◦ Conduct validity and reliability assessments of the FELP in Spanish with Spanish speaking families to investigate whether this measure translates to a Spanish speaking population. ◦ Compare the family measures to child outcome data with a diverse population.

References: Evans, M. (1998). Parental involvement in beginning reading: Preliminary report from a three year longitudinal study. Canadian Journal of School Psychology, 14(1), 11-20. Goodnow, J. J. & Collins, A. W. (1990). Development according to parents: The nature, sources, and consequences of parents' ideas. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc. Hart, B., & Risley, T. R. (1980). In vivo language intervention: Unanticipated general effects. Journal of Applied Behavior Analysis,13(3), 407-432. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149 –74. Meagher, S. M., Arnold, D. H., Doctoroff, G. L., & Baker, C. N. (2008). The relationship between maternal beliefs and behavior during shared reading. Early Education and Development, Special Issue: Parent-Child

Interaction and Early Literacy Development, 19(1), 138-160. Roberts, J., Jurgens, J., & Burchinal, M. (2005). The role of home literacy practices in preschool children's language and emergent literacy skills. Journal of Speech, Language, and Hearing Research, 48(2), 345-

359. Roskos, K. A. & Christie, J. F. (2000). Play and literacy in early childhood: Research from multiple perspectives. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers. Weigel, D. J., Martin, S. S., & Bennett, K. K., (2006). Contributions of the Home Literacy Environment to Preschool-Aged Children's Emerging Literacy and Language Skills. Early Child Development and Care, 176(3-

4), 357-378. Whitehurst, G. J., Zevenbergen, A. A., Crone, D. A., Schultz, M. D., Velting, O. N., & Fischel, J. E. (1999). Outcomes of an emergent literacy intervention from Head Start through second grade. Journal of

Educational Psychology, 91(2), 261-272.

METHOD Participants

• 2079 parents of preschool age children (48.6% female and 48.8% male) from a variety of educational preschool settings in Jacksonville, Florida. • Education: 43.7% Bachelors or higher, 49.7% HS or Some College, 5% No HS Diploma. • Income: 27.2% over $75K, 28.5% $40-74K, 37.4% under $40K. • Ethnicity: 53.1% White, 21.4% Black, 24.1% Other. • Language: 86.7% English, 11.2% Other. • Parents completed the FELP questionnaire. • Multiple imputation enabled all participant

information to remain. Instrumentation • The FELP is a research-developed questionnaire (Likert Scale).

• There are 5 factors: Skill Building, Interactive Reading/ Enjoyment, Modeling and Monitoring, Expectations, and Child Skill. • Parental Behaviors: Skill Building, Interactive Reading/ Enjoyment, and Modeling and Monitoring. • Parental Beliefs: Expectations and Child Skill.

• Exploratory Factor Analysis used to obtain factors (majority Caucasian middle-class families) revealed the FELP had 5 robust factors with adequate alpha coefficients ranging from .77 to .87.

Measuring Family Early Literacy Practices Across Diverse Preschool Families

Results

Data Entered Data Analyzed

Data Collected

Data Analysis -Exploratory factor analysis

• Examine the factor structure of the FELP.

-Reliability • Internal consistency

coefficients (Cronbach’s alphas) and item-total correlations of the total scale and subscales of the FELP.

-Rasch Analysis

• Examine the construct of the FELP.

• The Rating Scale Model (RSM), used to test the unidimensionality of each hypothesized factor (Andrich, 1978; Masters, 1982).

• DIF was used to evaluate the item response pattern among diverse subgroups. An acceptable range for the t value is between -2 to 2 (Bond & Fox, 2007).

FELP Questionnaire

Skill Building (n=10)

.92

.94 Expectations (n=7)

Child Skills (n=6)

Interactive Reading / Enjoyment (n=8)

.86

4

FELP Factor Structure Factor Loadings Factor Correlation Matrix

Dr. Laura Bailet, Dr. Suzanne Murphy, and Dr. Cynthia Zettler-Greeley

Nemours BrightStart! Dyslexia Initiative

Jacksonville, FL

Kandia Lewis and Dr. Marika Ginsburg-Block

University of Delaware Newark, DE

FELP Factor Structure

F1-SB F2-IR F3-MM F4- Ex F5-CS

Item Examples I teach my child letter sounds.

I try to make reading a fun time for my child.

I make sure my child sees me reading.

I am confident that my child will receive a good education.

Compared to other children his/her age, my child is just as good or better at reading new words.

I teach my child to read words.

I let my child select the books when we read together.

I read to myself. I am confident that my child will complete high school.

Compared to other children his/her age, my child is just as good or better at identifying the sounds of letters.

FELP

Parental Beliefs (13 items)

Parental Practices (32 items)

Child Skill (6 items) α= .882

Expectations (7 items) α= .935

Skill Building (17 items) α= .918

Interactive Reading/Enjoyment (11 items) α= .922

Modeling & Monitoring (4 items) α= .676

Reliability Statistics Means, Std. deviation, Cronbach’s

Alpha, Item Correlation FELP M(SD) α Item-total r

F1 - Skill Building

(17 items)

56.01 (13.57) .918 .208-.747

F2- Interactive Reading/Enjoyment

(11 items)

40.63 (9.49) .922 .325-.777

F3- Expectations

(7 items)

33.17 (3.21) .935 .504-.857

F4 - Child Skill

(6 items)

21.85 (4.94) .882 .377-.772

F5 - Modeling & Monitoring

(4 items)

12.34 (3.47) .676 .217-.532

Rasch Analysis Summary Statistics

Item Response Pattern

Person Factor Separation Reliability Real RMSE Model RMSE Mean Measure

Skill Building 2.20 .87 .22 .20 .07

Interactive Reading/ Enjoyment

1.78 .84 .26 .24 .19

Modeling & Monitoring .47 .49 .31 .29 -.17

Expectations .63 .82 .38 .34 .69

Child Skill 2.20 .87 .22 .20 .07

Item Factor Separation Reliability Real RMSE Model RMSE Mean Measure

Skill Building 4.06 .94 .48 .48 .37

Interactive Reading/ Enjoyment

4.16 .95 .54 .54 .46

Modeling & Monitoring 3.74 .93 .64 .64 .62

Expectations 4.68 .96 .59 .59 .59

Child Skill 4.06 .94 .48 .48 .37

-6

-4

-2

0

2

4

6

8

FE

LP

Q3

3

FE

LP

Q3

4

FE

LP

Q3

5

FE

LP

Q3

6

FE

LP

Q3

7

FE

LP

Q3

8

t-v

alu

e

Item

English

Other

Child Skill - Language

-6

-5

-4

-3

-2

-1

0

1

2

3

FE

LP

Q3

9

FE

LP

Q4

0

FE

LP

Q4

1

FE

LP

Q4

2

FE

LP

Q4

3

FE

LP

Q4

4

FE

LP

Q4

5

t-va

lue

Item

English

Other

Expectations - Language

-6

-4

-2

0

2

4

6

8

10

FE

LP

Q2

FE

LP

Q6

FE

LP

Q9

FE

LP

Q1

0

FE

LP

Q1

1

FE

LP

Q1

3

FE

LP

Q1

4

FE

LP

Q1

5

FE

LP

Q2

2

FE

LP

Q2

7

FE

LP

Q3

0

t-va

lue

Item

English

Other

Interactive Reading/ Enjoyment - Language

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

FE

LP

Q1

FE

LP

Q1

6

FE

LP

Q1

7

FE

LP

Q2

5

t-va

lue

Item

English

Other

Modeling and Monitoring - Language

-4

-3

-2

-1

0

1

2

3

4

5

6

FE

LP

Q3

FE

LP

Q4

FE

LP

Q5

FE

LP

Q7

FE

LP

Q8

FE

LP

Q1

2

FE

LP

Q1

8

FE

LP

Q1

9

FE

LP

Q2

0

FE

LP

Q2

1

FE

LP

Q2

3

FE

LP

Q2

4

FE

LP

Q2

6

FE

LP

Q2

8

FE

LP

Q2

9

FE

LP

Q3

1

FE

LP

Q3

2

t-va

lue

Item

English

Other

Skill Building - Language

RESULTS

Structure Matrixa

Factor

1-SB 2-IR 3-Ex 4-CS 5-MM

FELPQ1 .352

FELPQ2 .676

FELPQ3 .445

FELPQ4 .633

FELPQ5 .694

FELPQ6 .753

FELPQ7 .667

FELPQ8 .414

FELPQ9 .618

FELPQ10 .656

FELPQ11 .856

FELPQ12 .590

FELPQ13 .752

FELPQ14 .847

FELPQ15 .849

FELPQ16 .774

FELPQ17 .569

FELPQ18 .688

FELPQ19 .738

FELPQ20 .776

FELPQ21 .670

FELPQ22 .507

FELPQ23 .653

FELPQ24 .677

FELPQ25 .642

FELPQ26 .560

FELPQ27 .649

FELPQ28 .596

FELPQ29 .564

FELPQ30 .676

FELPQ31 .601

FELPQ32 .620

FELPQ33 .604

FELPQ34 .776

FELPQ35 .834

FELPQ36 .824

FELPQ37 .649

FELPQ38 .763

FELPQ39 .758

FELPQ40 .805

FELPQ41 .874

FELPQ42 .885

FELPQ43 .826

FELPQ44 .776

FELPQ45 .813

Extraction Method: Principal Axis Factoring.

Rotation Method: Oblimin with Kaiser Normalization.

a. Imputation Number = 1

Factor Correlation Matrixa

Factor 1-SB 2-IR 3-Ex 4-CS 5-MM 1-SB 1.000 .570 .138 .336 .340

2-IR .570 1.000 .169 .247 .488

3-Ex .138 .169 1.000 .295 .102

4-CS .336 .247 .295 1.000 .177

5-MM .340 .488 .102 .177 1.000

Extraction Method: Principal Axis Factoring.

Rotation Method: Oblimin with Kaiser Normalization.

a. Imputation Number = 1