Medium Matters: A Decade of Media Consumption Predicts Positive and Negative Dimensions of Self-Perceptions of Aging

Abstract Objective Media consumption over time is suggested to be a significant contributor to how people develop their self-perceptions of aging (SPA); however, this association has only been investigated with cross-sectional methodologies. The current study used growth curve modeling to examine the influence of 10 years of television, newspaper, radio, and book consumption on positive and negative dimensions of SPA. Method Growth curve modeling on four waves of data from the German Aging Survey (N =2,969), a population-based representative survey of adults aged 40 to 95, was used to examine the longitudinal associations between media consumption and SPA trajectories. Results Across ten years, more television intake (B= -0.58, 95% CI [-0.94, -0.21]) was associated with lower perceptions of continuous growth. Inversely, greater book (B= 0.10, 95% CI [0.06, 0.13]) and radio (B= 0.52, 95% CI [0.29, 0.74]) consumption was significantly linked to higher perceptions of continuous growth. In parallel, more television (B= 0.88, 95% CI [0.52, 1.25]) and newspaper consumption (B= 0.46, 95% CI [0.04, 0.88]) was associated with higher perceptions of physical decline, while greater radio (B= -0.40, 95% CI [-0.64, -0.16]) and book (B= -0.05, 95% CI [-0.09, -0.00]) consumption was associated with lower perceptions of physical decline. Discussion This study provides longitudinal evidence that the type of media consumed over time is linked to people’s SPA. Not all types of media intake are negative as radio and book consumption was associated with better SPA across time. Age group differences were investigated and are discussed in the supplemental materials.

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The Use of Growth Curve Modeling to Analyze Change Over Time

Journal of Visual Impairment & Blindness ◽

10.1177/0145482x211000983 ◽

2021 ◽

Vol 115 (2) ◽

pp. 168-169

Author(s):

Robert Wall Emerson

Keyword(s):

Growth Curve ◽

Growth Curve Modeling ◽

Change Over Time ◽

Curve Modeling ◽

Over Time

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Ignoring individual differences in times of assessment in growth curve modeling

International Journal of Behavioral Development ◽

10.1177/0165025415577684 ◽

2015 ◽

Vol 40 (1) ◽

pp. 76-86 ◽

Cited By ~ 7

Author(s):

Patrick Coulombe ◽

James P. Selig ◽

Harold D. Delaney

Keyword(s):

Individual Differences ◽

Growth Curve ◽

Type I Error ◽

Growth Curve Modeling ◽

Type I ◽

Residual Variance ◽

Change Over Time ◽

Time Points ◽

Curve Modeling ◽

Over Time

Researchers often collect longitudinal data to model change over time in a phenomenon of interest. Inevitably, there will be some variation across individuals in specific time intervals between assessments. In this simulation study of growth curve modeling, we investigate how ignoring individual differences in time points when modeling change over time relates to convergence and admissibility of solutions, bias in estimates of parameters, efficiency, power to detect change over time, and Type I error rate. We manipulated magnitude of the individual differences in assessment times, distribution of assessment times, magnitude of change over time, number of time points, and sample size. In contrast to the correct analysis, ignoring individual differences in time points frequently led to inadmissible solutions, especially with few time points and small samples, regardless of the specific magnitude of individual differences that were ignored. Mean intercept and slope were generally estimated without bias. Ignoring individual differences in time points sometimes yielded overestimated intercept and slope variances and underestimated intercept–slope covariance and residual variance. Parameter efficiency as well as power and Type I error rates for the linear slope were unaffected by the type of analysis.

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Using Language Sampling to Measure Language Growth

Perspectives on Language Learning and Education ◽

10.1044/lle17.1.24 ◽

2010 ◽

Vol 17 (1) ◽

pp. 24-31 ◽

Cited By ~ 5

Author(s):

Raúl Rojas ◽

Aquiles Iglesias

Keyword(s):

English Language Learners ◽

Language Learners ◽

Growth Curve ◽

English Language ◽

Growth Curve Modeling ◽

Typical Development ◽

Language Sampling ◽

Curve Modeling ◽

Language Growth ◽

Over Time

Abstract This article illustrates how speech-language pathologists (SLPs) can use language sampling and growth curve modeling (GCM) to examine the language growth rates of English Language Learners. GCM data on language samples provides SLPs with powerful, new tools to evaluate actual progress over time instead of relying on single, static measurement endpoints to determine typical development.

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The application of growth curve modeling for the analysis of diachronic corpora

Language Dynamics and Change ◽

10.1163/22105832-00701001 ◽

2017 ◽

Vol 7 (1) ◽

pp. 102-125 ◽

Cited By ~ 3

Author(s):

Andrea Nini ◽

Carlo Corradini ◽

Diansheng Guo ◽

Jack Grieve

Keyword(s):

Growth Curve ◽

Corpus Linguistics ◽

Language Change ◽

Growth Curve Modeling ◽

Curve Modeling ◽

S Curve ◽

Over Time

This paper introduces growth curve modeling for the analysis of language change in corpus linguistics. In addition to describing growth curve modeling, which is a regression-based method for studying the dynamics of a set of variables measured over time, we demonstrate the technique through an analysis of the relative frequencies of words that are increasing or decreasing over time in a multi-billion word diachronic corpus of Twitter. This analysis finds that increasing words tend to follow a trajectory similar to the s-curve of language change, whereas decreasing words tend to follow a decelerated trajectory, thereby showing how growth curve modeling can be used to uncover and describe underlying patterns of language change in diachronic corpora.

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“Create in Me a Clean Heart”: The Role of Spirituality in Desistance From Substance Use

Journal of Drug Issues ◽

10.1177/0022042618823006 ◽

2019 ◽

Vol 49 (2) ◽

pp. 203-227 ◽

Cited By ~ 1

Author(s):

Beverly Reece Crank ◽

Brent Teasdale

Keyword(s):

Substance Use ◽

Growth Curve ◽

Growth Curve Modeling ◽

Multilevel Analyses ◽

Modeling Techniques ◽

Curve Modeling ◽

Pathways To Desistance ◽

The Impact ◽

Over Time

Although the impact of religion on behavior is robust and well-examined in many areas, the role spirituality plays in changes in drug use over time has received relatively little attention. Using a life-course theoretical framework, this relationship is examined through growth curve modeling techniques. Specifically, multilevel analyses are estimated testing within-person relationships between substance use desistance and spirituality. The Pathways to Desistance longitudinal data are analyzed and leading criminological predictors are included, to determine if spirituality has a unique impact on substance use net of these criminological factors, and if these impacts vary across gender. Results from these analyses suggest that the impact of spirituality on desistance varies by gender, with spirituality significantly increasing the odds of desistance from marijuana use for females, but not males.

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Social Contact with Family and Non-Family Members Differentially Affects Physical Activity: A Parallel Latent Growth Curve Modeling Approach

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052313 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2313

Author(s):

Yuta Nemoto ◽

Ryota Sakurai ◽

Hiroko Matsunaga ◽

Yoh Murayama ◽

Masami Hasebe ◽

...

Keyword(s):

Physical Activity ◽

Growth Curve ◽

Social Contact ◽

Growth Curve Modeling ◽

Latent Growth Curve Modeling ◽

Latent Growth Curve ◽

Latent Growth ◽

Cross Sectional ◽

Face To Face ◽

Curve Modeling

Background: Social contact leads to an increased likelihood of engaging in physical activity (PA). However, the influence of social contact on PA would be different depending on the social contact source. This study aimed to identify the association of changes in social contact with family and non-family members with the change in PA using a parallel latent growth curve modeling. Methods: Participants were randomly selected from among residents in the study area age ≥ 20 years (n = 7000). We conducted mail surveys in 2014, 2016, and 2019. The 1365 participants completed all surveys. PA was assessed with validated single-item physical activity measure. Social contact was assessed by summing frequencies of face-to-face and non-face-to-face contacts with family/relatives not living with the participant and friends/neighbors. Parallel latent growth curve modeling was used to assess the cross-sectional, prospective, and parallel associations of social contact with PA change. Results: There was a positive cross-sectional association between contact with friends/neighbors and PA, whereas prospective and parallel associations between contact with family/relatives and PA. Conclusion: Contacting friends/neighbors did not predict the change in PA, and a high frequency of contact with family/relatives at baseline and increasing contact with family/relatives was associated with increased PA over 5-year.

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