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
