Experimental Aging Research
ISSN: 0361-073X (Print) 1096-4657 (Online) Journal homepage: http://www.tandfonline.com/loi/uear20
Quantitative Topics in Research on Aging J. J. Mcardle & Stanley A. Cohen To cite this article: J. J. Mcardle & Stanley A. Cohen (1991) Quantitative Topics in Research on Aging, Experimental Aging Research, 17:1, 3-5, DOI: 10.1080/03610739108253880 To link to this article: http://dx.doi.org/10.1080/03610739108253880
Published online: 27 Sep 2007.
Submit your article to this journal
Article views: 14
View related articles
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=uear20 Download by: [Deakin University Library]
Date: 06 November 2015, At: 02:14
3
Experimental Aging Research, Volume 17, Number 1, 1991, ISSN 0734-0664 “1991 Beech Hill Enterprises Inc.
Quantitative Topics in Research on Aging J. J. MCARDLE Downloaded by [Deakin University Library] at 02:14 06 November 2015
University of Virginia
STANLEY A. COHEN West Virginia University
The origins of the present series of articles on quantitative topics in aging research are presented. Specific articles are described, the purposes and goals of the quantitative series are set forth, and the relationship of this body of knowledge to the scientific status of the area of aging research is discussed.
T
he Spring 1985 issue of Experimental Aging Research contained a back cover page in large and bold typeface stating, “CAUSAL MODELS: Are We Regressing?” This announcement continued: “Experimental Aging Research extends an invitation for papers on causal modeling and LISREL. Causal Modeling approaches to the understanding of relationships among multiple independent and dependent variables have become popular in recent years. The computer program LISREL (Linear Structural Relationships) has become a popular tool for causal modeling. However, there have been a number of published papers suggesting that traditional regression approaches accomplish the same objectives as LISREL and that, with LISREL, a higher price must be paid with regard to violation of assumptions required by the analysis. Moreover, there are those that suggest that the emphasis on causal modeling has been overdone. On the other hand, some researchers and reviewers are advocating the approach as the ‘ultimate’ technology rather than simply an alternative methodological approach.” In May 1986 the 10th Annual Meeting of Life-Span Developmental Psychology researchers was held at West Virginia University. At this time, one of us (McArdle) used the provocative Experimental Aging Research call for papers as an overhead to introduce some atypical structural equation models: These models were described as “structural but not causal” models. The other one of us (Cohen) had originally drafted the announcement for Experimental Aging Research, and all he could say upon seeing this overhead was, “Thank goodness, somebody noticed!” You see, the response to the particular call for papers was underwhelming at best. This was surprising because for many years now researchers in aging have found the need to use LISREL. Also, at about the same time, this kind of organized request for papers on the use
of linear structural equation modeling had been published in other areas of research. We both agreed that Experimental Aging Research was an appropriate forum for such ideas, so we pursued this problem again together. We were mainly interested in putting together a series of papers which would serve two major purposes: (1) demonstrate “quantitative excellence,”and (2) “communicate with the individuals in the field.” We did not restrict ourselves to LISREL models, or even to concepts of causality and causal modeling. Instead, we tried to represent a broad message about the utility of quantitative thinking in the area of research on aging. To obtain relevant papers we made up a list of what we thought were the very best researchers in this area. In doing so we asked the advice of several well-known people whose research interests included the topics in which we were interested, and then we decided to make final decisions using a subjective process. As a consequence, many of the best individuals were overlooked, and we as editors are solely responsible for these oversights. Our plan went into effect in December 1987 when we sent invitation letters to a select few people who we knew were actively engaged in some form of quantitative analyses of data on aging. All written material was submitted to a peer review procedure during the next several years. The 1991 issues of Experimental Aging Research include this series of papers on “Quantitative Issues in Research on Aging.” Speci3c Quantitative Articles The specific articles within these issues cover a wide and diverse set of quantitative topics on the collection and analysis of data in aging. The assignment to the specific issues is not entirely arbitrary, however. Papers which present a broad variety of approaches to quantitative data
Correspondence regarding this article should be sent to J.J. McArdle, Ph.D., Department of Psychology, University of Virginia, Charlottesville, VA 22901. U.S.A.
Downloaded by [Deakin University Library] at 02:14 06 November 2015
4
analysis in aging research and those which are more technically oriented presentations are included in the series. The main goals of the series are described in the opening remarks by the Experimental Aging Research editor, Merrill F. Elias. These ideas lay a practical foundation for the quantitative analyses and discussions to follow. Two papers present models and techniques which are especially valuable for the analysis of binary variables collected in longitudinal samplings. The presentation by Linda Collins is called, “The Measurement of Dynamic Constructs in Longitudinal Aging Research: Quantifying Adult Development .” In this paper Collins presents an overview of some new methods for the organization and quantification of theoretically defined constructs about human development. One key idea here is that the measurement of a dynamic construct must start from a clearly defined substantive theory about human development, and she deals with longitudinal data using probabilistic Markov models. Collin’s paper is presented in this issue. The paper by John Willett and Judith Singer is titled “Applications of Survival Analysis to Aging Research.” This paper provides an introductory view of the use of biometric survival analysis, or event history analysis, to problems in research on aging. Two papers use the LISREL methodology to deal with age-based phenomena. Kenneth Jones, Marilyn Albert, Frank Duffy, Mary Hyde, Margaret Naeser, and Carolyn Aldwyn present “Modeling Age Using Cognitive, Psychosocial and Physiological Variables.” This paper describes a methodology for modeling age changes in small samples and with many variables. The example, based on a model linking different substantive domains, is informative and presented in some detail. The presentation by Jack McArdle and Carol Prescott, “Age-Based Construct Validation Using Structural Equation Modeling,” shows how the wellknown concepts of construct validation can be matched with the basic ideas of latent variable path analysis. The main example uses a large scale analysis of WAIS-R data using a variety of age-sensitive models estimated with LISREL. Two other papers deal with basic measurement of constructs. In the first paper, Roger Milsap and Bill Meredith discuss “Component Analysis in Multivariate Aging Research.” This is a clear presentation on the estimation of measurement factors which are invariant over longitudinal and cross-sectional sequences. The substantive analyses presented decompose longitudinal WAIS data from the Berkeley Growth Studies using multiple component models. The next paper, by John Nesselroade and Connie Jones, is titled “Multi-Modal Selection Effects in the Study of Adult Development: A Perspective on Multivariate, Replicated, Single-Subject, Repeated Measures Designs.” This paper expands upon the basic assumptions of data collection and makes the interesting claim that research on aging needs representative samples of persons (cases), variables (traces), and occasions (places). The effects of non-random selection are formalized, and the implications for a more extensive combination of data sampling is explored. The Nesselroade and Jones paper is found in this issue.
The paper by Gary Donaldson and John Horn is titled “Age, Cohort, and Time Developmental Muddles: Easy in Practice, Hard in Theory.” This paper provides a contemporary examination of the well-known problems of making inferences about developmental phenomena from analysis-of-variance designs. This paper also rather clearly points out difficult problems that emerge from the merger of theory and data in experimental aging research. Other papers in the Quantitative Methods series are slightly more technical in nature. In this issue we present the analysis of longitudinal growth curve models in a paper by Jack McArdle, Fumiaki Hamagami, Merrill Elias, and Mike Robbins entitled “Structural Modeling of Mixed Longitudinal and Cross-Sectional Data.” This paper describes the use of a structural equation modeling approach to deal with issues of (1) group differences in regression parameters, (2) differences in longitudinal and cross-sectional results, (3) differences due to longitudinal attrition, and (4)mixtures of these problems. Other topics of a technical nature will be presented in future issues. These include quantitative issues in configural frequency analysis (Alexander von Eye and John Nesselroade), multitrait-multimethod models (Keith Widaman), cluster analysis and fuzzy set models (Kenneth Manton and Max Woodbury), advanced event history models (William Gardner, Marion Meyer, and Robert Ketterlinus), psychometric measurement models (John Horn and Jack McArdle), and latent growth structural equation models for use with incomplete longitudinal and cross-sectional data (Jack McArdle and Fumiaki Hamagami). All topics are dealt with in some detail and relevant examples are provided.
Quantitative Research in Aging In our view quantitative techniques are especially critical to research on aging. In a broad sense, the advancement of quantitative techniques within virtually any body of knowledge seems to parallel the scientific status of the area. This interest and enthusiasm is seen in an increased emphasis on course work in graduate and undergraduate statistics and methodology. This interest is also seen in government support from, among others, the National Institute on Aging and the National Science Foundation. There are some unique features associated with the methodological problems of research on aging, and there are unique solutions associated with the quantification of research on aging. The factor invariance issue, for example, comes up in many areas of scientific work, but there have been some clear breakthroughs here (as shown in the Quantitative Methods series presentations by Milsap and Meredith, Horn and McArdle, and Nesselroade and Jones). Quantitative studies are now widely used to handle specific problems in research on aging, and to bridge the gap between aging and other areas of science. Some of this quantitative work is entirely new and comes directly from novel computer technology. Some have said that computer use in the social sciences has made as much of an impact as the telescope in astronomy
Downloaded by [Deakin University Library] at 02:14 06 November 2015
QUANTITATIVE TOPICS
or the microscope in biology. The broad impact of the computer technology is already clear: Computers can do many things - organize information, type papers, sort data, dial phone numbers (even at random). Computers also allow experimental statistics without a simple closed form solution, and several papers here promote this kind of computational research (e.g., Gardner et al., McArdle and Prescott, Manton and Woodbury). These techniques parallel other areas of science, especially computational physics and biology. We are well aware that no matter how fast the computer can calculate statistical information, and reduce the data into multistage components printed as wonderfully complex hydroelectric path diagrams, the relevant variables and the samplings of individuals are paramount to our understanding of the substance of the research. Indeed, the failings of previous quantitative methodologies have led us to be at least as cautious and critical as our opposite numbers in the biological and physical sciences. It is hard to say whether or not the current computer revolution will yield new substantive findings in aging; this is a reasonable goal, but not yet a realization. So we hope that this series of quantitative papers, taken together, will convey a sence of constructive directions
5
in the quantitative analysis of data in the field of aging. What stands out as important is the model-based assumptions underlying the analytic schemes. Any adequate technique for the analysis of data must state its particular way of describing both the manifest and latent processes in the developmental phenomena. You, the reader and consumer of these articles, should note the appropriateness of the techniques to your own research and data sets.
Acknowledgements We have enjoyed a lot of assistance in pulling together this series. We wish to thank many reviewers and for their suggestions about topics. This list includes all of the authors as well as Mark Aber, Ed Anderson, Steve Boker, David Epstein, Eric Turkheimer, David Featherman, John Loehlin, and Jan-Bernd Lohmoeller. The contributors were also very helpful, sticking to schedules, using express mailings, and so on. Still, this series of quantitative papers would not have been possible without the assistance, encouragement, flexibility, and support of the Experimental Aging Research editor, Merrill (Pete) Elias. Many thanks to everyone involved.
ISSN: 0361-073X (Print) 1096-4657 (Online) Journal homepage: http://www.tandfonline.com/loi/uear20
Quantitative Topics in Research on Aging J. J. Mcardle & Stanley A. Cohen To cite this article: J. J. Mcardle & Stanley A. Cohen (1991) Quantitative Topics in Research on Aging, Experimental Aging Research, 17:1, 3-5, DOI: 10.1080/03610739108253880 To link to this article: http://dx.doi.org/10.1080/03610739108253880
Published online: 27 Sep 2007.
Submit your article to this journal
Article views: 14
View related articles
Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=uear20 Download by: [Deakin University Library]
Date: 06 November 2015, At: 02:14
3
Experimental Aging Research, Volume 17, Number 1, 1991, ISSN 0734-0664 “1991 Beech Hill Enterprises Inc.
Quantitative Topics in Research on Aging J. J. MCARDLE Downloaded by [Deakin University Library] at 02:14 06 November 2015
University of Virginia
STANLEY A. COHEN West Virginia University
The origins of the present series of articles on quantitative topics in aging research are presented. Specific articles are described, the purposes and goals of the quantitative series are set forth, and the relationship of this body of knowledge to the scientific status of the area of aging research is discussed.
T
he Spring 1985 issue of Experimental Aging Research contained a back cover page in large and bold typeface stating, “CAUSAL MODELS: Are We Regressing?” This announcement continued: “Experimental Aging Research extends an invitation for papers on causal modeling and LISREL. Causal Modeling approaches to the understanding of relationships among multiple independent and dependent variables have become popular in recent years. The computer program LISREL (Linear Structural Relationships) has become a popular tool for causal modeling. However, there have been a number of published papers suggesting that traditional regression approaches accomplish the same objectives as LISREL and that, with LISREL, a higher price must be paid with regard to violation of assumptions required by the analysis. Moreover, there are those that suggest that the emphasis on causal modeling has been overdone. On the other hand, some researchers and reviewers are advocating the approach as the ‘ultimate’ technology rather than simply an alternative methodological approach.” In May 1986 the 10th Annual Meeting of Life-Span Developmental Psychology researchers was held at West Virginia University. At this time, one of us (McArdle) used the provocative Experimental Aging Research call for papers as an overhead to introduce some atypical structural equation models: These models were described as “structural but not causal” models. The other one of us (Cohen) had originally drafted the announcement for Experimental Aging Research, and all he could say upon seeing this overhead was, “Thank goodness, somebody noticed!” You see, the response to the particular call for papers was underwhelming at best. This was surprising because for many years now researchers in aging have found the need to use LISREL. Also, at about the same time, this kind of organized request for papers on the use
of linear structural equation modeling had been published in other areas of research. We both agreed that Experimental Aging Research was an appropriate forum for such ideas, so we pursued this problem again together. We were mainly interested in putting together a series of papers which would serve two major purposes: (1) demonstrate “quantitative excellence,”and (2) “communicate with the individuals in the field.” We did not restrict ourselves to LISREL models, or even to concepts of causality and causal modeling. Instead, we tried to represent a broad message about the utility of quantitative thinking in the area of research on aging. To obtain relevant papers we made up a list of what we thought were the very best researchers in this area. In doing so we asked the advice of several well-known people whose research interests included the topics in which we were interested, and then we decided to make final decisions using a subjective process. As a consequence, many of the best individuals were overlooked, and we as editors are solely responsible for these oversights. Our plan went into effect in December 1987 when we sent invitation letters to a select few people who we knew were actively engaged in some form of quantitative analyses of data on aging. All written material was submitted to a peer review procedure during the next several years. The 1991 issues of Experimental Aging Research include this series of papers on “Quantitative Issues in Research on Aging.” Speci3c Quantitative Articles The specific articles within these issues cover a wide and diverse set of quantitative topics on the collection and analysis of data in aging. The assignment to the specific issues is not entirely arbitrary, however. Papers which present a broad variety of approaches to quantitative data
Correspondence regarding this article should be sent to J.J. McArdle, Ph.D., Department of Psychology, University of Virginia, Charlottesville, VA 22901. U.S.A.
Downloaded by [Deakin University Library] at 02:14 06 November 2015
4
analysis in aging research and those which are more technically oriented presentations are included in the series. The main goals of the series are described in the opening remarks by the Experimental Aging Research editor, Merrill F. Elias. These ideas lay a practical foundation for the quantitative analyses and discussions to follow. Two papers present models and techniques which are especially valuable for the analysis of binary variables collected in longitudinal samplings. The presentation by Linda Collins is called, “The Measurement of Dynamic Constructs in Longitudinal Aging Research: Quantifying Adult Development .” In this paper Collins presents an overview of some new methods for the organization and quantification of theoretically defined constructs about human development. One key idea here is that the measurement of a dynamic construct must start from a clearly defined substantive theory about human development, and she deals with longitudinal data using probabilistic Markov models. Collin’s paper is presented in this issue. The paper by John Willett and Judith Singer is titled “Applications of Survival Analysis to Aging Research.” This paper provides an introductory view of the use of biometric survival analysis, or event history analysis, to problems in research on aging. Two papers use the LISREL methodology to deal with age-based phenomena. Kenneth Jones, Marilyn Albert, Frank Duffy, Mary Hyde, Margaret Naeser, and Carolyn Aldwyn present “Modeling Age Using Cognitive, Psychosocial and Physiological Variables.” This paper describes a methodology for modeling age changes in small samples and with many variables. The example, based on a model linking different substantive domains, is informative and presented in some detail. The presentation by Jack McArdle and Carol Prescott, “Age-Based Construct Validation Using Structural Equation Modeling,” shows how the wellknown concepts of construct validation can be matched with the basic ideas of latent variable path analysis. The main example uses a large scale analysis of WAIS-R data using a variety of age-sensitive models estimated with LISREL. Two other papers deal with basic measurement of constructs. In the first paper, Roger Milsap and Bill Meredith discuss “Component Analysis in Multivariate Aging Research.” This is a clear presentation on the estimation of measurement factors which are invariant over longitudinal and cross-sectional sequences. The substantive analyses presented decompose longitudinal WAIS data from the Berkeley Growth Studies using multiple component models. The next paper, by John Nesselroade and Connie Jones, is titled “Multi-Modal Selection Effects in the Study of Adult Development: A Perspective on Multivariate, Replicated, Single-Subject, Repeated Measures Designs.” This paper expands upon the basic assumptions of data collection and makes the interesting claim that research on aging needs representative samples of persons (cases), variables (traces), and occasions (places). The effects of non-random selection are formalized, and the implications for a more extensive combination of data sampling is explored. The Nesselroade and Jones paper is found in this issue.
The paper by Gary Donaldson and John Horn is titled “Age, Cohort, and Time Developmental Muddles: Easy in Practice, Hard in Theory.” This paper provides a contemporary examination of the well-known problems of making inferences about developmental phenomena from analysis-of-variance designs. This paper also rather clearly points out difficult problems that emerge from the merger of theory and data in experimental aging research. Other papers in the Quantitative Methods series are slightly more technical in nature. In this issue we present the analysis of longitudinal growth curve models in a paper by Jack McArdle, Fumiaki Hamagami, Merrill Elias, and Mike Robbins entitled “Structural Modeling of Mixed Longitudinal and Cross-Sectional Data.” This paper describes the use of a structural equation modeling approach to deal with issues of (1) group differences in regression parameters, (2) differences in longitudinal and cross-sectional results, (3) differences due to longitudinal attrition, and (4)mixtures of these problems. Other topics of a technical nature will be presented in future issues. These include quantitative issues in configural frequency analysis (Alexander von Eye and John Nesselroade), multitrait-multimethod models (Keith Widaman), cluster analysis and fuzzy set models (Kenneth Manton and Max Woodbury), advanced event history models (William Gardner, Marion Meyer, and Robert Ketterlinus), psychometric measurement models (John Horn and Jack McArdle), and latent growth structural equation models for use with incomplete longitudinal and cross-sectional data (Jack McArdle and Fumiaki Hamagami). All topics are dealt with in some detail and relevant examples are provided.
Quantitative Research in Aging In our view quantitative techniques are especially critical to research on aging. In a broad sense, the advancement of quantitative techniques within virtually any body of knowledge seems to parallel the scientific status of the area. This interest and enthusiasm is seen in an increased emphasis on course work in graduate and undergraduate statistics and methodology. This interest is also seen in government support from, among others, the National Institute on Aging and the National Science Foundation. There are some unique features associated with the methodological problems of research on aging, and there are unique solutions associated with the quantification of research on aging. The factor invariance issue, for example, comes up in many areas of scientific work, but there have been some clear breakthroughs here (as shown in the Quantitative Methods series presentations by Milsap and Meredith, Horn and McArdle, and Nesselroade and Jones). Quantitative studies are now widely used to handle specific problems in research on aging, and to bridge the gap between aging and other areas of science. Some of this quantitative work is entirely new and comes directly from novel computer technology. Some have said that computer use in the social sciences has made as much of an impact as the telescope in astronomy
Downloaded by [Deakin University Library] at 02:14 06 November 2015
QUANTITATIVE TOPICS
or the microscope in biology. The broad impact of the computer technology is already clear: Computers can do many things - organize information, type papers, sort data, dial phone numbers (even at random). Computers also allow experimental statistics without a simple closed form solution, and several papers here promote this kind of computational research (e.g., Gardner et al., McArdle and Prescott, Manton and Woodbury). These techniques parallel other areas of science, especially computational physics and biology. We are well aware that no matter how fast the computer can calculate statistical information, and reduce the data into multistage components printed as wonderfully complex hydroelectric path diagrams, the relevant variables and the samplings of individuals are paramount to our understanding of the substance of the research. Indeed, the failings of previous quantitative methodologies have led us to be at least as cautious and critical as our opposite numbers in the biological and physical sciences. It is hard to say whether or not the current computer revolution will yield new substantive findings in aging; this is a reasonable goal, but not yet a realization. So we hope that this series of quantitative papers, taken together, will convey a sence of constructive directions
5
in the quantitative analysis of data in the field of aging. What stands out as important is the model-based assumptions underlying the analytic schemes. Any adequate technique for the analysis of data must state its particular way of describing both the manifest and latent processes in the developmental phenomena. You, the reader and consumer of these articles, should note the appropriateness of the techniques to your own research and data sets.
Acknowledgements We have enjoyed a lot of assistance in pulling together this series. We wish to thank many reviewers and for their suggestions about topics. This list includes all of the authors as well as Mark Aber, Ed Anderson, Steve Boker, David Epstein, Eric Turkheimer, David Featherman, John Loehlin, and Jan-Bernd Lohmoeller. The contributors were also very helpful, sticking to schedules, using express mailings, and so on. Still, this series of quantitative papers would not have been possible without the assistance, encouragement, flexibility, and support of the Experimental Aging Research editor, Merrill (Pete) Elias. Many thanks to everyone involved.
