The Construct and Predictive Validity of Psychosocial Correlates of Television Viewing
Journal of Health Disparities Research and Practice
Volume 9
Issue 1 Spring
Article 4
© Center for Health Disparities Research, School of Public Health, University of Nevada, Las Vegas
2016
The Construct and Predictive Validity of Psychosocial Correlates
of Television Viewing
Raheem Paxton , University of North Texas Health Science Center, raheem.paxton@unthsc.edu
Pascal Jean-Pierre , Pascal.Jean-Pierre.1@nd.edu
Saehwan Park , University of North Texas Health Science Center at Fort Worth,
saehwan.simon.park@gmail.com
See next page for additional authors
Follow this and additional works at: https://digitalscholarship.unlv.edu/jhdrp
Part of the Community Health and Preventive Medicine Commons, Health Psychology Commons, Neoplasms
Commons, Other Medicine and Health Sciences Commons, Quantitative Psychology Commons, and the Women's
Health Commons
Recommended Citation
Paxton, Raheem; Jean-Pierre, Pascal; Park, Saehwan; Gao, Yong Dr.; Herrmann, Stephen; and Norman, G J.
(2016) "The Construct and Predictive Validity of Psychosocial Correlates of Television Viewing," Journal of
Health Disparities Research and Practice: Vol. 9: Iss. 1, Article 4.
Available at: https://digitalscholarship.unlv.edu/jhdrp/vol9/iss1/4
This Article is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV
with permission from the rights-holder(s). You are free to use this Article in any way that is permitted by the
copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from
the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/
or on the work itself.
This Article has been accepted for inclusion in Journal of Health Disparities Research and Practice by an
authorized administrator of Digital Scholarship@UNLV. For more information, please contact
digitalscholarship@unlv.edu.
The Construct and Predictive Validity of Psychosocial Correlates of Television
Viewing
Abstract
Background: Many studies have examined the consequences of prolonged television viewing, but few
studies have examined the psychological states that contribute to this behavior. In this study, we
evaluated the construct and predictive validity of psychosocial correlates of television viewing in a
population of African American (AA) breast cancer survivors (BCS).
Methods: AA BCS (N = 342, Mean age = 54 years) completed measures of decisional balance, selfefficacy, family support, and time spent watching television online. Exploratory structural equation
modeling (ESEM) was used to examine the construct and predictive validity as well as the differential
item functioning of the instruments among population subgroups.
Results: The construct validity of the measures was supported among subgroups. The scales were
measuring the construct similarly among the education and body size groups, but not among age groups.
Subsequent analysis indicated that pros (β = -0.19, P < 0.05), cons (β = 0.18, P < 0.05), and self-efficacy (β
= -0.16, P < 0.05) were significantly associated with time spent watching television.
Conclusions: Minor modifications may be needed to support the validity and reliability of the decisional
balance and self-efficacy subscales among older survivors. More studies are needed to modify these
measures to establish sufficient levels of construct and predictive validity in this population.
Keywords
African American; breast cancer; cancer survivorship; reliability; sedentary behavior; validity
Cover Page Footnote
Acknowledgement: We wish to thank the women of the Sisters Network Inc. for participating in our study
and staff members of the Sisters Network for facilitating all data collection activities. Funding: This
research was supported in part by National Cancer Institute grant 5K01CA158000 to RJP.
Authors
Raheem Paxton, Pascal Jean-Pierre, Saehwan Park, Yong Gao Dr., Stephen Herrmann, and G J. Norman
This article is available in Journal of Health Disparities Research and Practice: https://digitalscholarship.unlv.edu/
jhdrp/vol9/iss1/4
38 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Journal of Health Disparities Research and Practice
Volume 9, Issue 1, Spring 2016, pp. 46-59
© 2011 Center for Health Disparities Research
School of Community Health Sciences
University of Nevada, Las Vegas
The Construct and Predictive Validity of Instruments Measuring
the Psychosocial Correlates of Television Viewing
Raheem J. Paxton, University of North Texas Health Science Center
Pascal Jean-Pierre, University of Notre Dame
Sae-Hwan Park, University of North Texas Health Science Center
Yong Gao, Boise State University
Stephen Herrmann, Sanford Health
Gregory J. Norman, University of California – San Diego
ABSTRACT
Background: Many studies have examined the consequences of prolonged television viewing,
but few studies have examined the psychological states that contribute to this behavior. In this
study, we evaluated the construct and predictive validity of psychosocial correlates of television
viewing in a population of African American (AA) breast cancer survivors (BCS).
Methods: AA BCS (N = 342, Mean age = 54 years) completed measures of decisional balance,
self-efficacy, family support, and time spent watching television online. Exploratory structural
equation modeling (ESEM) was used to examine the construct and predictive validity as well as
the differential item functioning of the instruments among population subgroups.
Results: The construct validity of the measures was supported among subgroups. The scales were
measuring the construct similarly among the education and body size groups, but not among age
groups. Subsequent analysis indicated that pros (β = -0.19, P < 0.05), cons (β = 0.18, P < 0.05),
and self-efficacy (β = -0.16, P < 0.05) were significantly associated with time spent watching
television.
Conclusions: Minor modifications may be needed to support the validity and reliability of the
decisional balance and self-efficacy subscales among older survivors. More studies are needed to
modify these measures to establish sufficient levels of construct and predictive validity in this
population.
Keywords: African American, breast cancer, cancer survivorship, reliability, sedentary
behavior, television viewing, validity
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
39 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
INTRODUCTION
Sedentary behavior (i.e., watching TV, sitting, reclining, or lying down) has emerged as a
major risk factor for chronic disease.1 In particular, prolonged periods of sedentary behavior
have been associated with an increased risk for developing colorectal, endometrial, and ovarian
cancer.1 Out of all forms of sedentary behavior, television viewing has been associated with the
worst outcomes because it is often linked with increased caloric intake of calorie dense foods2
and is associated with a lower metabolic rate than other forms of sedentary behavior (e.g., riding
or driving in an automobile).3, 4 Aside from sleeping, television viewing occupies the most time
in domestic settings.5 Sedentary behaviors (e.g., television viewing) may have an even greater or
compounding impact in people who are struggling with incapacitating diseases and/or treatment
outcomes such as cancer.
Excessive television is a maladaptive lifestyle behavior. Among cancer survivors,
prolonged sitting was shown to be associated with diminished quality of life,6 weight gain,7
larger waist circumference,8 ischemic heart disease,9 and premature mortality.10 The negative
health impact of prolonged sitting along with excessive TV viewing time in survivors
underscores the urgent need for the development and testing of effective interventions to mitigate
this problem. This may be true especially for African American breast cancer survivors who
report excessive sedentary behavior and multiple comorbid conditions. According to a recent
study of African American (AA) breast cancer survivors, 43% reported excessive television
viewing (i.e., watched television for ≥ 2 hours/day) and approximately 70% reported at least one
comorbid condition in addition to cancer.11 Thus, there is a need for studies to examine the
factors that predispose AA BCS to prolonged periods of sedentary behavior overall, but more
specifically television viewing given its adverse consequences.
There is a need for systematic studies that assess the underlying psychological and
situational reasons why people engage in excessive television viewing. However, limited data
exist on the psychosocial correlates of television viewing. Previous studies assessing these
correlates have focused almost elusively on adolescents, with one study published in a healthy
adult population.12 Norman et al.13-15 examined the psychometric properties of several
psychosocial correlates (i.e., decisional balance, self-efficacy, social support, and behavior
change strategies) for sedentary behavior and found that these items were significantly associated
with time spent sitting. Van Dyck et al.12 applied these instruments to an adult population and
observed that similar results. An important caveat that has been overlooked in assessing the
predictive validity of instruments previously designed for another population is the assessment of
the psychometric properties. Establishing the construct and predictive validity of these
instruments is a necessity, especially in vulnerable populations with high rates of sedentary
behavior and television viewing. Assessing these measures in a population of African American
breast cancer survivors will not only address current gaps in the literature, but also provides new
data on a high risk, underrepresented, and vulnerable population.
The aims of the current paper was to assess the construct and predictive validity of
instruments that measure the constructs of decisional balance, self-efficacy, and social support
using a robust psychometric procedure called Exploratory Structural Equation Modeling.16-18.
Specifically, we will:
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
40 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
a) Determine the constructs validity of measures of decisional balance (i.e., pros & cons)
and self-efficacy for reducing television viewing and social support for sedentary
behavior reduction;
b) Determine the measurement equivalence/invariance (or differential item functioning) of
the instruments among age groups, body size groups, and educational levels to ensure
that the items are being measured similarly among subgroups; and
c) Determine whether the instruments are associated with time spent watching television.
METHODS
A total of 342 AA BCS from the Sisters Network, Inc. were surveyed to assess
psychosocial correlates of television viewing. The Sisters Network is the largest AA breast
cancer survivorship organization in the United States. The Sisters Network is a national
organization that contains 40 affiliate chapters in 19 geographically distinct states. BCS were
recruited for the present study between May of 2012 and July of 2012 via multiple email blasts
and posting of anonymous survey links on social media blog sites affiliated with the Sisters
Network. Detailed information related to our recruitment methods and response rates were
described elsewhere.19 Eligibility criteria included (a) being 18-80 years old at diagnosis, (b)
diagnosed with operable invasive breast cancer, (c) not currently undergoing treatment (with the
exception of hormone therapy), and (d) have no evidence of recurrent disease. Institutional
Review Board approval was obtained at the University of Texas MD Anderson Cancer Center
prior to data collection and it was assumed that by reading the consent form on the initial survey
web page and answering survey questions, women gave their consent to participate in the current
study. The protocol was later approved by the Institutional Review Board at the University of
North Texas Health Science Center following the transfer of the primary author. The consenting
procedure was approved by the Institutional Review Board at each institution.
Measures
Television-viewing Time. Time spent watching television or videos/movies were reported
by participants separately for weekdays and weekend days during the previous week. Total
television time was calculated as the sum of the time participants watched television on
weekdays and weekend days. This measure has been shown to have reasonable reliability and
validity for estimating television-viewing time in adults.20
Psychosocial variables. The items used in the current study were adapted from validated
questionnaires previous developed for adolescents.12, 14 The original items were adapted and
applied to a population of adults in a previous study.12 Van Dyck et al.12 adapted 4 items each
that represented pros (e.g., I think watching TV is boring), cons (e.g., I enjoy watching TV for
many hours at a time), and self-efficacy (e.g., confidence to turn off the TV even when there is a
program on that you enjoy) for reducing television time. Three family support items were
adapted from similar items that were initially developed for physical activity.15 Example of the
“my family encourages…,” my family discussed…,” my family helped me to think of ways…”
All items were rated on a 5-point Likert scale. Pro and Con items were rated from strongly
disagree (1) to strongly agree (5), self-efficacy items were rated from I’m sure I can’t (1) to I’m
sure I can (5), and family support items were rates from never (1) to very often (5).
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
41 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Body size groups. The study participants’ self-reported height and weight were used to
compute their BMI (weight in kilograms divided by height in meters squared: kg/m 2). Study
participants were categorized as obese if their BMI was ≥30 kg/m2 and non-obese ≤30 kg/m2.
This cut-off was chosen because ~80% of the population self-reported a BMI > 25 kg/m2.
Socio-demographic and Medical Data. All socio-demographic and medical data were
self-reported by participants. We collected data on the following variables: current age,
education, time since diagnosis, disease stage at diagnosis, and comorbid conditions. Comorbid
conditions (e.g., cardiovascular disease, blood sugar/diabetes, digestive disorders, arthritis, and
osteoporosis) were summed to represent an ordinal number.
Statistical Analysis
Descriptive statistics were computed for the sociodemographic and medical
characteristics. Construct validity of the relevant instruments were examined using Exploratory
Structural Equation Modeling (ESEM). The measurement equivalence/invariance (ME/I) of
these instruments were evaluated among age groups (i.e., 18-49, 50-59, 60+), weight status
groups (i.e., non-obese and obese), and educational levels (i.e., < college graduate and college
graduate). These specific sub populations were explored given the sample size for each group
and the large percentage of women with college educations and self-reporting a BMI ≥ 30 kg/m2.
Exploratory Structural Equation Modeling
Exploratory Structural Equation Modeling (ESEM) is the integration of exploratory
factor analysis (EFA) and structural equation modeling (SEM) in an effort to provide a flexible
measurement structure for item indicators.19 The ESEM has all of the benefits of traditional EFA
such as factor rotations, while enabling the inclusion of path coefficients (among covariates and
other factors), multi-group analysis, and test measurement equivalence/invariance (ME/I).19
ESEM also provides fit statistics and modification indices similar to those generated in
traditional SEM. We chose ESEM in lieu of traditional Confirmatory Factor Analysis (CFA) to
facilitate exploration of the true structure validity of these instruments. CFA prevents cross
loading of items, leading to over-estimated factor correlations and distorted relationships.19 In
contrast, ESEM provides flexibility when knowledge of the measurement structure is limited.
All models were examined with the Maximum Likelihood estimator that is robust to nonnormal distributions (i.e., ESTIMATOR = MLR) and a Geomin rotation algorithm. ESEM
models were calculated with full-information maximum (FIML) estimation in MPlus version 6.0
(Muthen & Muthen, 1998-2008). FIML uses an iterative process and simultaneous estimating
equations to account for the presence of missing data.21 FIML yields accurate fit indices and
parameter estimates with up to 25% simulated missing data.21 The extent of missing data in this
study ranged from 0% for sociodemographic characteristics to 24.6% for social support items,
which is under the recommended threshold.
Model Fit
Criteria for establishing fit of ESEM models are similar to that of traditional CFA and
SEM. All models are evaluated based on how well structural model resembles close, exact, and
absolute fit to the data. According to Hu and Bentler,22 the Comparative Fit Index (CFI) and the
Standardized Root Mean Square Residual (SRMR) are optimal for examining structural models
with smaller sample sizes. The CFI and SRMR reveal the models closely fitted the data when
values are ≥0.95 and ≤0.08, respectively. Hu and Bentler22 proposed that using cut off values ≥
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
42 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
0.96 for the CFI in combination with values of ≤ 0.10 for the SRMR resulted in lower type I and
II error rates. These fit statistics were chose over other criteria (i.e., χ2 and the Root Mean Square
Error Approximation) which are sensitive to sample time and inflate error rates.22
Multi-group Factorial Invariance
Assessment of measurement equivalence/invariance is a multistage approach.23 In the
first series of ESEMs we examined the fit of the measurement model for the overall population
and individually for each sub-group. We then tested models that sequentially imposed constraints
to model parameters to insure equality of the overall measurement structure, factor loadings, and
item intercepts among subgroups. Three sequential levels of invariance tests were assessed here.
In the first model, we tested the extent to which the same pattern of fixed and free model
parameters was equivalent among groups (i.e., configural invariance).23 In the second model, we
tested the extent to which the factor loadings for the items were measured equivalently among
groups (i.e., metric invariance).23 Finally, in the last model, we tested the extent to which the
item intercepts were measured equivalently among groups (i.e., scalar invariance).23 Once the
models were computed, we determined ME/I by evaluating the difference in Chi-square in
relation to change (Δ) in degrees of freedom of the model with fewer constraints. Change in CFI
of less than or equal to 0.01 suggests that the invariance of an instrument should not be
rejected.24 Therefore, if the Chi-Square difference test is significant, but the CFI change is less
than 0.01, there is some evidence for the equivalence/invariance of the model structure or
parameters among groups.24
Differential Item Functioning (DIF) also known as measurement bias was also examined
for factors that failed to pass test for ME/I.25 To assess DIF in this study, we used a multipleindicator multiple cause (MIMIC) model.26 MIMIC models can be used to identify subgroup
differences in a latent construct.26 These models are extensions of item-response theory modes
but can include simultaneous test of several characteristics.
Lastly, structural models were constructed to assess the relationship between
psychosocial constructs and time spent sitting and watching television per day. Structural models
were adjusted for the following covariates: body mass index, age, years out from diagnosis, and
disease stage of diagnosis. All statistical tests were two-sided and significance was determined at
p < 0.05.
RESULTS
Sample Characteristics
The study population of 342 surveyed AA BCS has the mean age of 53.5 years. Most
(45%) of the participants presented with stage II disease and were on average 7-years post
diagnosis. Approximately half (52%) of participants were college graduates, 48% reported a
BMI in the obese category, and 43% reported watching television equal to or greater than 2
hours per day. Sample characteristics are reported in Table 1.
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
43 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Structural validity and reliability
The measurement model for pros, cons, and self-efficacy for reducing time spent
watching television and family support for sedentary behavior reduction was a close fit to the
data (CFI ≥ 0.95, RMSEA ≤ 0.08, SRMR ≤ 0.08). Statistically significant correlations were
observed between pros and cons (r = -0.34, P < 0.01), self-efficacy and cons (r = -0.33, P <
0.01), and family support and cons (r = 0.13, P = 0.05). All factor loading, intercepts, and factor
variances were appropriate sign and magnitude. Several items (i.e., TV is boring, enjoy watching
TV, watching TV is relaxing, and confidence to limit TV during) cross-loaded on other factors
(See Table 2). The overall fit of the measurement model revealed a close fit to the data for each
population sub groups (See Table 3). Internal consistency reliability for pros, cons, self-efficacy,
and family support were 0.54, 0.80, 0.80, and 0.87, respectively (data not tabled).
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
44 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Table 2. Factor structure of psychosocial constructs
Item
I think watching TV is boring
Factor 1
Factor 2
Factor 3
Factor 4
0.35
-0.3
0.01
-0.05
0.7
0.08
-0.03
-0.05
I would Feel lazy and sluggish if I watched TV for
many hours
0.91
-0.08
0.03
0.05
Watching TV sometimes hurts my eyes and gives me
a headache
0.29
0.12
-0.08
0.08
I enjoy watching TV for many hours at a time
-0.29
0.8
-0.02
0.07
I find sitting and watching TV very relaxing
0.32
0.71
-0.02
0.04
Watching TV is one of my favorite forms of
entertainment
0.06
1.12
-0.01
-0.01
Watching TV is my way to escape from the world
-0.03
0.87
0.18
-0.06
Turn off the TV even when there is a program on I
enjoy
-0.03
0.05
0.73
0.05
Limit my TV time to one hour a day
0.22
-0.24
0.75
0.03
Leave the room where the TV is on even if others are
watching TV
0.02
-0.02
0.73
-0.05
Plan ahead of time what TV shows I will watch
during the week
-0.07
0.04
0.67
0.01
My family encouraged me to spend less time being
sedentary
-0.01
0.11
-0.01
0.93
My family discussed how sedentary habits can be
unhealthy
0.07
-0.1
0.08
0.94
-0.04
0.01
-0.03
1.07
Watching TV takes time away from doing more
important things
My family helped me to think of ways to reduce the
time I spend sedentary
Factor 1 = Pros, Factor 2 = Cons, Factor 3= Self-efficacy; Factor 4 = Family Support; Items representing
a particular subscale were reported in bold font.
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
45 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Test for ME/I
Age groups: The measurement model constraining the factor structure revealed a close fit
to the data (χ2 = 186.9, df = 153, p-value = 0.03, CFI = 0.98, SRMR = 0.04) among different age
groups. Subsequent nested models of the factor loading and factor means and intercepts yielded a
close fit to the data. However, the change (Δ) in CFI was ≥0.01 when constraints were imposed
on the factor loadings (See Table 4). No further tests for invariance were performed.
Obesity status: The measurement model constraining the factor structure revealed a close
fit to the data (χ2 = 136.8, df = 102, p-value < 0.01, CFI = 0.98, SRMR = 0.03) among body size
groups. Subsequent nested models of the factor loading and factor means and intercepts yielded a
close fit to the data and values estimating the Δ in CFI support evidence of ME/I for the
measurement model among body size groups (See Table 4).
Education: The measurement model constraining the factor structure revealed a close fit
to the data (χ2 = 131.4, df = 102, p-value = 0.03, CFI = 0.98, SRMR = 0.03) among education
levels. Subsequent nested models of the factor loading and factor means and intercepts yielded a
close fit to the data and estimates of the Δ in CFI were appropriate in magnitude suggest that the
measurement model is ME/I among educational levels (See Table 4).
Post hoc tests for differential item functioning.
The MIMIC model examining the relationship between age group and the measurement
model revealed a close fit to the data (χ2 = 72.9, df = 62, p-value = 0.16, CFI = 0.99, SRMR =
0.02). Statistically significant path coefficients were observed between age group and selfefficacy (β = -0.17, P < 0.05) and age group and pros (β = -0.27, P < 0.01), suggesting age group
differences in the measurement of these constructs (See Figure 1).
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
46 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Structural relationships
The hypothesized structural model examining the relationship between pros, cons, and
self-efficacy for reducing television time and family support for reducing sedentary behavior
with total hours of television for the week adjusted for covariates revealed a close fit to the data
(χ2 = 137.2, df = 106, p-value = 0.99, CFI = 0.98, SRMR = 0.03). Statistically significant path
coefficients were observed between self-efficacy (β = -0.16, P < 0.05), pros (β = -0.19, P < 0.05),
and cons (β = -0.18, P < 0.05) for reducing television time and total television time after adjusted
for covariates. Cons were significantly correlated with pros (r = -0.31, P < 0.01), self-efficacy
(r= -0.33, P < 0.01), and family support (r = 0.13, P = 0.05). No other correlations were
observed. The hypothesized structural model accounted for 14% of the total variance in time
spent watching television.
Power
A posthoc Monte Carlo simulation study was performed to determine the power
associated with the fit indices for the final structural model. A total of 500 replications were
selected for the procedure. The mean SRMR was 0.022 with a standard deviation of 0.002. All
individual factor loadings in the model had power estimates for the appropriate scale that
exceeded 0.95. The power estimates of the relationship between the study covariates and the
psychosocial factors were all below 0.80. Similarly, the power estimates of the relationship
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
47 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
between self-efficacy (0.98), pros (0.97), and cons (1.00) with time spent watching television
were appropriate in magnitude, whereas family support was not (0.10).
DISCUSSION
In this study, we found that the psychosocial constructs of pros, cons and self-efficacy for
reducing television viewing, and family support for sedentary behavior reduction adequately fit
the data. Test for ME/I indicated that the overall structure was equivalent/invariant among body
size groups and educational levels, but not age groups. MIMIC models suggested measurement
differences in the constructs of self-efficacy and pros for reducing television time. Test for
predictive validity indicated that constructs of self-efficacy, pros, and cons for reducing
television time were significantly associated with television viewing. Overall, these data provide
preliminary support for the construct validity of instruments for self-efficacy and decision
balance for television in a population of AA BCS, but further work is needed.
To our knowledge, this is the first study to assess the construct and predictive validity of
psychosocial measures of time spent watching television in a population of breast cancer
survivors. Van Dyke et al.12 was the only study that we know of to assess the instruments in an
adult population. Overall, our analyses indicated that the measurement model adequately fit the
data and it provided preliminary support for further testing of these constructs in other
populations of cancer survivors. We identified items that may be problematic and may not
represent simple structure. In particular, items of ‘television viewing is boring,’ ‘I enjoy
watching television,’ and ‘sitting and watching television is relaxing’ cross-loaded on other
factors. However, it should be noted that these items were correlated highly with time spent
watching television in a sample of healthy adults.12 The flexible structure of ESEM allows for
cross-loadings and enables us to leave the measurement models intact for further exploration.
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
48 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Test for ME/I provided evidence of model equivalency among different educational
levels and body sizes, but not age groups. Test for structural validity in the context of ME/I test
are essential to ensure that a questionnaire is measuring constructs consistently among
subgroups. Our data indicate that among AA breast cancer survivors, the decisional balance and
self-efficacy scales are not measuring the same constructs equally in each age group. Van Dyke
et al12 suggested that a lack of interaction terms among sociodemographic variables in the
context of psychosocial and home-environmental variables indicates similar conceptual meaning.
However, no prior tests for invariance were conducted in those populations. Generally, the
establishment of ME/I is necessary before any meaningful inferences between groups can be
drawn about the items of interest.27, 28 When such tests are not applied, there is a risk of making
subgroup comparisons on nonequivalent measures, which may lead to biased interpretation.
Test for DIF indicated that the questionnaire did not measure the constructs of pros and
self-efficacy for reducing television viewing equally between age groups, suggesting differential
items performance by age group. Subsequent analyses indicated the decisional balance items
cross-loaded for all groups; however, most of the items cross-loaded in our oldest survivors. The
magnitude of the cross loading resulted in a non-significant correlation between the decisional
balance subscales for women aged 60 and older. The correlations between the subscales for
younger women were statistically significant and negatively correlated. We are not sure why the
subscales were not significantly correlated. It could be that older women are torn between the
positive and negative attributes of television time. Being of an older age will likely mean that
these individuals are further out from diagnosis. As women move further away from the
teachable moment, sedentary behaviors may become habitual. It could be that the ability level
differed by age group or that these items were much more relevant to younger survivors than to
the older ones.29 Younger survivors may have demands that may be different from those
experienced by older survivors. Characteristics such as having children in the home, full-time
employment, and the lack of personal time may contribute to differences in the perception of
television viewing in this population. Our results suggest that more work is needed in the
development and adaptation of psychosocial instruments for reducing time spent watching
television in AA breast cancer survivors.
In our final test examining the relationship between psychosocial constructs for reducing
television time, all constructs were significantly associated with total TV time, with the
exception of family support. Family support may have not been associated with television time
because the item for written exclusively for sedentary behavior reduction and not reductions in
television time. Of interest was the magnitude of the beta coefficients for the psychosocial
correlates. Van Dyck et al.12 models accounted for a total of 39% of the variance in total
television time, but included also home-environmental items such as television size and number
of televisions in the home. Our models only accounted for 14% of the total variance in time spent
watching television. Despite the low percentage of variance accounted for, our data are
consistent with other studies in adolescent and adult populations suggesting that pros, cons, and
self-efficacy for reducing sedentary time are important correlates of behavior.13, 30
There were a number of strengths associated with this study including being the first
study among cancer survivors to examine psychosocial correlates of television viewing, a robust
statistical procedure to examine reliability and validity, a modest sample size, and a high-risk and
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
49 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
underrepresented population of cancer survivors. In addition, a posthoc power analysis was
conducted and indicated appropriate fit of the model to the data based on mean levels of the
SRMR and the fact that power for the factor loading and beta coefficients estimating the
relationship between the correlates and outcome all exceeded 0.80. Notwithstanding the strength,
there were notable limitations in the present data. First, these data were cross-sectional and
limited our ability to make causal inferences on television viewing. We are also surprised that the
internal consistency reliabilities were relatively low for some measures. Additionally, family
support was not related specifically to reductions in television viewing, but reductions in
sedentary behavior. Importantly, use of a web-based survey is associated with some degree of
selection bias as reported elsewhere.19 Further, our analysis pertains to a select group of AA BCS
and may not have external validity beyond this population.
CONCLUSION
Research on sedentary behaviors in cancer survivors is in its infancy, and few studies
have examined the correlates in these maladaptive behaviors for this population. The present
analysis provides preliminary evidence for validity and reliability for psychosocial measures for
reducing television time in AA breast cancer patients, albeit more work is needed to ensure that
the instruments function well in all age groups. Further adaptations are warranted and we
encourage future adaptations of the scales in various cancer survivor population.
ACKNOWLEDGEMENTS
We wish to thank the women of the Sisters Network Inc. for participating in our study
and staff members of the Sisters Network for facilitating all data collection activities. This
research was supported in part by National Cancer Institute grant 5K01CA158000 to RJP.
REFERENCES
Ainsworth BE, Haskell WL, Leon AS, et al. Compendium of physical activities: classification of
energy costs of human physical activities. Med Sci Sports Exerc. 1993;25(1):71-80.
American time use Survey - 2007 Results. U.S. Dept. of Labor 2007.
Asparouhov T, Muthen B. Exploratory Structural Equation Modeling. Struct Equ Modeling.
2009;16(3):397-438.
Blass EM, Anderson DR, Kirkorian HL, et al. On the road to obesity: Television viewing
increases intake of high-density foods. Physiol Behav. 2006;88(4-5):597-604.
Campbell PT, Patel AV, Newton CC, et al. Associations of recreational physical activity and
leisure time spent sitting with colorectal cancer survival. J Clin Oncol. 2013;31(7):876885.
Cheung GW RR. Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance.
Struct Equ Modeling. 2002;9:233-255.
Enders CK, Bandalos, D.L. The relative performance of full information maximum likelihood
estimation for missing data in structural equation models. Struct Equ Modeling.
2001;8:430-457.
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
50 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Hawkes AL, Lynch BM, Owen N, Aitken JF. Lifestyle factors associated concurrently and
prospectively with co-morbid cardiovascular disease in a population-based cohort of
colorectal cancer survivors. Eur J Cancer. 2011;47(2):267-276.
Hu L, Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis. Struct Equ
Modeling. 1999;6:1-55.
Lynch BM, Cerin E, Owen N, et al. Television viewing time of colorectal cancer survivors is
associated prospectively with quality of life. Cancer Causes Control. 2011;22(8):11111120.
Lynch BM, Dunstan DW, Vallance JK, Owen N. Don't take cancer sitting down: a new
survivorship research agenda. Cancer. 2013;119(11):1928-1935.
Lynch BM, Dunstan DW, Winkler E, et al. Objectively assessed physical activity, sedentary time
and waist circumference among prostate cancer survivors: findings from the National
Health and Nutrition Examination Survey (2003-2006). Eur J Cancer Care.
2011;20(4):514-519.
Marsh HW, Ludtke O, Muthen B, et al. A new look at the big five factor structure through
exploratory structural equation modeling. Psychol Assess. 2010;22(3):471-491.
Marsh HW, Muthen B, Asparouhov T, et al. Exploratory Structural Equation Modeling,
Integrating CFA and EFA: Application to Students' Evaluations of University Teaching.
Struct Equ Modeling. 2009;16(3):439-476.
Marsh HW, Nagengast B, Morin AJ. Measurement invariance of big-five factors over the life
span: ESEM tests of gender, age, plasticity, maturity, and la dolce vita effects. Dev
Psychol. 2013;49(6):1194-1218.
Marsh HW. Confirmatory factor analysis models of factorial invariance: A multifaceted
approach. Struct Equ Modeling. 1994;1:5-34.
Meredith W, Teresi JA. An essay on measurement and factorial invariance. Med Care.
2006;44(11 Suppl 3):S69-77.
Messick S. Standards of validity and the validity of standards in performance assessment. Educ
Meas. 1995;14:5-8.
Muthen LK, Muthen, B.O. Mplus User's Guide. Los Angeles, CA: Muthen & Muthen 19982009.
Norman GJ VA, Roesch SC, Sallis JF, Calfas KJ, Patrick K. Development of decisional balance
and self-efficacy measures for adolescent sedentary behaviors. Psychol Health.
2004;19:561-575.
Norman GJ, Sallis JF, Gaskins R. Comparability and reliability of paper- and computer-based
measures of psychosocial constructs for adolescent physical activity and sedentary
behaviors. Res Q Exerc Sport. 2005;76(3):315-323.
Norman GJ, Schmid BA, Sallis JF, et al. Psychosocial and environmental correlates of
adolescent sedentary behaviors. Pediatrics. 2005;116(4):908-916.
Paxton RJ, Nayak P, Taylor WC, et al. African-American breast cancer survivors' preferences for
various types of physical activity interventions: a Sisters Network Inc. web-based survey.
J Cancer Surviv. 2014;8(1):31-38.
Paxton RJ, Taylor WC, Chang S, et al. Lifestyle behaviors of African American breast cancer
survivors: a Sisters Network, Inc. study. PLoS One. 2013;8(4):e61854.
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/
51 The Construct and Predictive Validity of Instruments Measuring the Psychosocial Correlates
of Television Viewing
Paxton et al.
Salmon J, Owen N, Crawford D, et al. Physical activity and sedentary behavior: a populationbased study of barriers, enjoyment, and preference. Health Psychol. 2003;22(2):178-188.
Van Dyck D, Cardon G, Deforche B, et al. Socio-demographic, psychosocial and homeenvironmental attributes associated with adults' domestic screen time. BMC Public
Health. 2011;11:668.
Vandenberg RJ, Lance, C.E. A review and synthesis of the measurement invariance literature:
Suggestions, practices and recommendations for organizational research. Organ Res
Methods. 2000;3:4-70.
Wijndaele K, Lynch BM, Owen N, et al. Television viewing time and weight gain in colorectal
cancer survivors: a prospective population-based study. Cancer Causes Control.
2009;20(8):1355-1362.
Williams DM, Raynor, H. A., & Ciccolo, J. T. . A review of TV viewing and its association with
health outcomes in adults. Am J Lifestyle Med. 2008;2(3):250-259.
Zabinski MF, Norman GJ, Sallis JF, et al. Patterns of sedentary behavior among adolescents.
Health Psychol. 2007;26(1):113-120.
Journal of Health Disparities Research and Practice Volume 9, Issue 1, Spring 2016
http://digitalscholarship.unlv.edu/jhdrp/