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UNIT RESEARCH METHODS 26 Unit Research Methods How Do We Learn How to Promote Development ?
ELLEN SI ( INNER AND THE HUMAN DEVELOPMENT TEACHING LEARNING GROUP Learning Objectives How do we learn how to promote development ?
What are best practices , and why are they important ?
Why are all best practices ?
Explain why science is a powerful way of knowing and process of learning about ourselves and the world . Identify limitations and areas of improvement for science as a social enterprise . A primary goal of this course is to help you learn more about how to constructively development . Whether we are aware of it or not , all of us are shaping development every day through our decisions and actions . Sometimes this is when we are raising children or teaching preschoolers . In these situations , we know we are trying to help our children and students learn and grow . We think carefully about how we were raised or taught , about our professional training . We take these responsibilities seriously . We know what a difference people in our lives have made to our development . However , in cases where we are not explicitly charged with promoting development , we may not think as carefully about our effects on like our friends , romantic partners , or parents . We may not think about our own development , the ways we nurture or disparage ourselves . In this class , we are calling all the ways we development developmental practices . By this , we mean all the decisions and actions we take in our professional and personal lives that shape our own and others development . When we hear the word practices , we often think about professional practices . But we also include personal practices , like how we parent , make decisions about vocations , and nurture our friends . From this perspective , we are all developmental practitioners even though we may not feel like we really understand how best to do this job . Developmental science , along with other important sources of information , contribute to our understanding of best developmental practices . Science is a powerful process of learning , but it also has its limitations . Science uses multiple kinds of , ways of collecting information , and designs , each with its strengths and limitations and its hidden assumptions . Since research methods are central in producing valid and useful knowledge , we have to be thoughtful and critical about the processes and tools of science . Learning more about research methods in developmental science can also contribute to your learning more about important ways to promote healthy development , both your own and others . What is science and how does it with other ways of knowing ?
At its core , science is a way of knowing a set of practices for learning about the world . There are many other ways of knowing , including our intuition , emotions , and observations the beliefs and customs of our families and neighbors the opinions of friends and peers communications from political and religious authorities and messages from the media . If we bundle all these other sources of information together , they make up our personal From this history , we form opinions about the contents of development how people change and remain the same , what is normal , the causes of healthy and unhealthy development , and what we should do to be good parents , educators , and friends . How Do We Learn How to Promote Development ?
27 Our experiences are embedded in particular cultural and historical . These have many strengths , but they also have their own implicit biases . Our personal convictions , based on a lifetime of experiences in these societal and historical times , are naturally very compelling . We even have a name for the sets of gripping assumptions that underlie them Naive of human development . A second source of information about how to support development can be called professional experience . Many and professions shape development , like parenting , education , nursing , social work , coaching , and so on . And each comes with its own set of trainings , traditions , and practices . Some of these practices are drawn from research ( as we will discuss shortly ) others from personal experience of what has worked in the past , and others are simply the way things have always been Take , for example , the practices you see in our classroom Learning takes place in groups , with a leader called the teacher , and involves readings , assignments , and grades . Such practices are based on a society history of carrying out these tasks , and they are reinforced by educational and training programs . Professional experiences are also embedded in the institutions of our time and place , as seen in schools , health care systems , human services , and other workplaces . These organizations , and our education and training , provide us with skills and information . At the same time , they have their own implicit biases . And , just like personal experience , professional experience has its own baked in assumptions about humans and how they develop . What are the limitations of personal and professional experience ?
The limitations of personal and professional experience are easiest to see in the past , when for example , doctors used procedures , like , to treat patients that were not effective , or schools employed corporal punishment , or women were not allowed into certain professions . Our personal experiences can also be limiting . Often the ways we were raised seem right to us , even though all of us have absorbed implicit biases and none of us were raised by perfect caregivers . We often sense that our caregivers made mistakes , even as we ourselves repeating those same parenting practices . Professionally and personally , we are not always sure how to shake free of our past and do things a better way . Our personal and professional experiences are important sources of information about development . But the growing recognition of their limitations has led to the rise of what are called or practices . Seen most clearly in medicine , these practices come out of the study of alternative ways to care for patients and treat different conditions . Whole centers are dedicated to studying and compiling best medical practices . And doctors , as professionals , are expected to adhere to them . As knowledge about medicine progresses , these best practices are continually updated with new evidence . Where do best practices for promoting development come from ?
Best practices emerge at the intersection between the study of development and the knowledge of expert practitioners . Lessons about best developmental practices are gathered , not from individual studies that test explanations and interventions , but from whole lines of work that over time replicate from multiple perspectives . These are called bodies of evidence , and they converge on insights about the most effective ways to support development . For example , even though pediatricians in the warned mothers that picking babies up when they cry reinforces their crying and spoils them , a body of evidence on attachment revealed this advice to be wrong . Infants cry less and are more secure if caregivers respond consistently and sensitively to their needs . Thats why practices are called . In a complementary fashion , expert practitioners bring knowledge about what works . Experts emerge from everyday walks of life , like wise and skilled elders , teachers , caregivers , coaches , and social workers . As we are reinventing practices , we can on and bring forward their insights and lessons . For example , many cultural traditions highlight the precious nature of children and the elderly , as treasures to be cherished , and underscore the centrality of family , community , and cultural heritage in supporting their and development . These insights can be 28 How Do We Learn How to Promote Development ?
used to critique and reconsider our current practices , in which those who care for children and the elderly are often undervalued , underpaid , and not well supported by the larger community . The lessons gained from evidence and expert practitioners are called best practices , and they are transforming all the and professions that shape development . A whole area of research , called implementation science , studies methods to promote the adoption and integration of practices , interventions and policies into routine settings . Sometimes practitioners need support to help them adopt these practices because ways of doing things run counter to conventional practice , or require more effort to learn or more work to execute . Or they can be introduced in ways that alienate practitioners . However , if implementation scientists create respectful and collaborative partnerships with parents , professionals , and other stakeholders , they can together contribute to and learn about such best practices . In the long run , best practices help all practitioners ( including people not usually considered practitioners like caregivers , romantic partners , and friends ) become more and effective in their efforts to promote their own and others development . Where does the idea of culture into the search for best practices ?
One important lesson learned from implementation science is that best practices have to be culturally attuned to the people and places where they are adopted . Sometimes , good practices are narrowly and evidence from the study of white middle class participants . It does not make sense to treat these practices as a module that should be inserted everywhere . Many different cultural variations on a given practice are As a result , have been pressed to identify the essential ingredients in effective practices and study how those ingredients can be incorporated in very different ways by people from different cultural , ethnic , and socioeconomic backgrounds . That doesn mean , however , that every common practice from every culture is good for development . The complexity of incorporating culture into best practices can be seen in the example of nutrition and diet . On the one hand , science has the combination of essential nutrients that all humans need to support the development of healthy bodies and minds . On the other hand , there are as many variations of healthy diets as there are cultures around the world . Any diet that provides all the that humans need are examples of best practices . On the third hand , however , it is not true that any old foods caregivers give children represent a healthy diet . The study of nutrition provides a set of criteria we can use to scrutinize and critique like sugary soft drinks or fast for their nutritional value . From this perspective , we can identify dietary practices , even ones that are very common , that we can conclude are not good for development . Almost everyone agrees that practitioners should be using practices . At the same time , there is vigorous debate about what these best practices really are . The tension between science , practice , and culture is productive . In general , science is good at narrow , relatively , explanations and optimization strategies . Cultures offer critiques of these strategies as well as many variations on how they can be implemented . And practitioners create more ways to integrate strategies , along with many other practices , into their daily interactions with developing people . Wise practitioners are always pushing to ask questions that apply more directly to the problems they are trying to solve and the people they are trying to support . This kind of science is sometimes called applied science because it is and aims to study and help solve important social problems . Are all recommended practices based on good science ?
No . Many people claim backing for their ideas and advice , when it does not really exist . This is called or bad popular science . There are many ways to recognize bad popular science , as explained in this supplementary section . or Bad Popular Science How Do We Learn How to Promote Development ?
29 Science as a Powerful Way of Knowing Science is not perfect and it needs to be guided by strong ethical principles , but nevertheless it is a powerful way of knowing and an important source of knowledge . Science is based on the assumption that careful and systematic observation and thought are processes we can use to better understand ourselves and our world . The process of science describes a way of learning . knowledge is built by testing ideas using evidence gathered from the social and natural world . Initially , these ideas are tentative intuitions , but as they cycle through the process of science again and again , they are examined and tested in different ways , so we become increasingly about their validity . Through this same recursive process , the ideas themselves are , revised , and integrated into more powerful understandings . Over time , this process serves to construct complex knowledge that can be used for many purposes to solve everyday problems , address societal issues , develop tools and technology , and make informed decisions . Such understandings satisfy our curiosity and lead to new questions . The work of science has many strengths . First , it is a public enterprise . It takes place as part of a community that , questions , and evaluates everyone work . This international community is composed of peer experts , who are charged with thinking through the quality of the research , the validity of the , and interpretations of the evidence . Scientists are trained to be skeptical and when bad science is introduced , it is usually detected and called out by experts . When independent scientists from all over the world come to the same conclusion , this strengthens our in the quality of the evidence . Second , science is informed by deep reservoirs of accumulated knowledge , but it is also inherently open , challenged daily by new ideas and updated with new evidence . Scientists spend their adult lives developing expertise , learning everything there is to know about their areas of study . As part of research teams , they construct dense , detailed , and rich understandings of complex phenomena . They use their curiosity , creativity , and determination to produce new knowledge and insights . At the same time , scientists question everything . They remain skeptical . They look for and limitations in their own and others work and consider alternatives . When research is done ethically , scientists goal should not be to prove their own theories right . They should be committed to out what is really happening , even if that means proving their ideas wrong . The best science , which is not as common as it should be , is dedicated to this higher pursuit . Third , science and reinvents itself . Scientists create new tools and strategies that allow us to see more and learn more from what we are seeing . In research on human development , these innovative methods and technologies range from new devices to collect data daily , to new laboratory experiments and simulations , and new ways to learn about what babies are thinking . Some of the most exciting breakthroughs are produced by new tools for collecting and integrating information . This way of learning also leads to major shifts in understanding , called scientific revolutions or paradigm shifts , when accepted theories are stood on their heads and we must reconsider everything we thought we knew . Science is a challenging and fascinating process . The evidence it produces , messy and confusing at times , leads cumulatively to insights and understanding . It is an important way of knowing and this collective public process of observing and making sense of what we see and then , based on this new way of thinking , going back taking a second ( and third and fourth ) look . Over the last 100 years , much has been learned about humans and how we develop , but many more questions remain . We strongly encourage you to incorporate the knowledge that science has gleaned in your own everyday and practices . In fact , you might even consider a career in developmental science ! 30 How Do We Learn How to Promote Development ?
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What are the shortcomings of science as a way of knowing ?
Science is embedded in particular and so is subject to all the shortcomings of any social enterprise . Serious critiques focus on the assumptions underlying western science today , and the ways they contribute to exclusion and distortion in ongoing work . Science today is dominated by researchers from the United States , Canada , and northern Europe . The most prestigious journals are published in English , which is also considered the international language at conferences . Psychologists routinely study phenomena from a perspective ( Teo , 2003 ) but they assume that this position is the both normal and universal . They are often unaware of the perspectives of psychologists , and dismiss knowledge from researchers . Such research is called African psychology or Turkish psychology without labeling western science as psychology . Second , scientists often assume that science has a monopoly on ways of knowing . However , many disciplines outside of the social and natural sciences reveal important insights about the nature of humans and how they change and grow . These include the humanities and arts , like creative writing , memoirs , novels , science ) theatre arts ( plays , motion pictures ) music ( songs , drumming , and choral singing ) and dance . These are sometimes called illuminative tools , and can be useful in capturing and sharing insights about the human experience . In the same vein , many cultures have accumulated knowledge about a wide range of human activities , like teaching and rearing children , and supporting families . This knowledge is often more , systemic , and better attuned to humanistic values , like environmental stewardship and social justice . However , scientists often dismiss or exclude this knowledge from teaching and learning ( 2017 ) Systemic practices that exclude groups of people from science and universities narrow the range of talent , cultural expertise , and lived experience researchers can bring to bear on these important issues . A third critique focuses on assumptions commonly held by scientists from the dominant culture that distort the study of marginalized or minority groups . Scientists ( often unknowingly ) accept entrenched societal myths about marginalized groups . For example , until 1973 , homosexuality was as a mental illness in the American Psychological Association Diagnostic and Statistical Manual of Mental problems are also obvious in deeply problematic programs of research that explain achievement gaps and differential rates for children and youth from ethnic and minority backgrounds , by arguing for in children or cultural disadvantage in families ( 1997 ) These issues are explained as individual or family problems , instead of being studied as the accumulated results of systemic societal inequities . The resilience and strengths of families and communities in the face of these inequities ( 2005 ) are often either dismissed or relied upon as the sole avenue of intervention , rendering discriminatory institutions invisible . These biased diatribes have a long history in the community . Such disinformation , which is both harmful and dangerous , continues to appear in mainstream outlets today and is used to justify and protect racist policies and institutions . In condemning such research , it is important to not only on the individual prejudices that underlie these research programs , but also on the larger and academic systems that sponsor , publish , and amplify this work . An important source of insights and critique can be found in the work in Black or Studies , Indigenous Nation Studies , Latino Studies , and Women , Gender , and Studies . How Do We Learn How to Promote Development ?
I 31 By study from the dominant culture , scientists , and researchers in these surface and challenge default assumptions , and offer alternative critical accounts of development . Fourth , current theories and research methods have been critiqued from inside developmental science based on their underlying assumptions about humans and their development ( 2015 ) You have seen how theories fall into camps and the use of theories creates a kind of tunnel vision about our target phenomena . It turns out that research methods also have assumptions baked into them . For example , if we bring children into the lab to learn more about them , we are assuming that we can remove a person from their natural context and still understand their functioning . For this reason , we often refer to research , instead of research methods , to acknowledge that all methods bring along with them their own assumptions about ways of knowing or . Individual scientists , as well as the research community as a whole , must regularly and actively on and critique our methods , to understand the role they play in shaping our understanding of developmental processes . Epistemology is the branch of philosophy concerned with ways of knowing . are theories of knowledge what can be known , what as valid knowledge , how knowledge is gained , what kinds of methods or tools can be used for learning , who can be a knowledge builder , and so on . Each of human development not only has its own assumptions about the nature of people and their development , but also its own assumptions about epistemology , or ways of knowing . For example , mechanistic theories assume that people and are made up of parts , so they can be taken apart and studied separately . In contrast , contextual assume that interactions are the basis of all development , so they must be studied together if you take them apart to study them you destroy your target phenomenon . Developmental science will be stronger to the extent that scientists intentionally and openly discuss , criticize , improve the process of science itself . Scientists must open their collective minds to international researchers , multiple disciplines , and a wide range of cultures as important sources of knowledge . Universities , as institutions that practice science and train scientists , must also open their collective doors , by more actively welcoming , recruiting , nurturing , and learning from researchers from a broad range of backgrounds , especially those who have been historically marginalized and excluded . As science and universities are more successful in their inclusion efforts , researchers should also be ready to participate in cultural transformations within the enterprise . Global and hubs along with partnerships create platforms that support complementary ways of knowing , and can enrich and transform processes of developmental science . Take Home Messages about Science as a Way of Knowing and Learning about the World We would underscore four big ideas from this section . A primary reason science is a crucial way of knowing is that it complements other ways of knowing , like personal and professional experience , which together help us identify and test best practices for promoting our own and others development . By practices , we mean our decisions and actions , not only at work but also at home ( in our parenting , how we relate to and support ourselves , our family members and friends , contribute to our communities and political systems , and so on ) All ways of knowing , including science and experience , are historically and culturally embedded , and so best practices need to be continually scrutinized for and attuned culturally when they are collaboratively adapted to improve personal or professional practices . Cultures and current practices are rich sources of developmental knowledge . 32 How Do We Learn How to Promote Development ?
. Science is a powerful way of knowing because it is a process that relies on careful thought and observation of the social and natural world , is carried out by the community as a public enterprise , and is inherently open and continually critiquing itself . Science itself is a social enterprise and so has serious shortcomings . Critiques focus on the exclusion kinds of research ( and ways of knowing ( the harmful effects of researchers from dominant cultures analyses of the development and functioning of people from marginalized and oppressed groups , and the assumptions baked into conventional scientific methods . Science and universities will from ongoing openness , reflection , critique , reform , and transformation of its practices and institutions . Adapted from Skinner , 2019 ) Lifespan developmental systems theory , methodology , and the study of applied problems . An Advanced Textbook . New York , NY . References , 2015 ) Processes , relations and . In . Theory and Method . Volume of the Handbook psychology and developmental science ( ed . Wiley . 2017 ) Cracking the fortress Can we really psychology ?
South African Journal of Psychology , 47 ( Teo , 2003 ) Ethnocentrism as a form of intuition in psychology . Theory Psychology , 13 ( 1997 ) Contemporary thinking . The evolution of deficit thinking Educational thought and practice , 2005 ) Whose culture has capital ?
A critical race theory discussion of community cultural wealth . Race ethnicity and education , Video Attribution Developmental Science ?
by the Society for Research in Child Development is licensed All Rights Reserved and is embedded here according to YouTube terms of service . How Do We Learn How to Promote Development ?
33 Lifespan Developmental Research ELLEN SKINNER jULIA AND THE HUMAN DEVELOPMENT TEACHING LEARNING GROUP Learning ' Lifespan Explain the importance of complementary and converging operations . Recognize the steps in deductive . inductive , and collaborative . Be familiar with the many methods use to gather information , including observations and , psychological tests and assessments . laboratory tasks , psychophysiological assessments , archival data or artifacts . case studies . and . Identify the general strengths and limitations of different methods ( eg , reactivity . social desirability , accessibility , Because and practitioners use bodies of evidence to transform systems and change practices in the world , it is crucial that researchers produce the highest quality evidence possible , and evaluate and critique it thoughtfully . The tools that scientists use to generate such knowledge are called research methods or . Many textbooks describe the method , as if there were only one way of knowing . Just as lifespan development spans multiple disciplines , each with their own preferred and , we believe that there are multiple methods , or multiple perspectives , each one providing a complementary line of sight on a given target phenomenon . Social and developmental sciences have been critiqued for our reliance on a narrow range of , favoring methods that quantify observations ( via surveys , ratings , or numerical ) and control extraneous variables or confounds , either statistically or , for example , by bringing people into the lab . Sometimes social scientists seem to favor these more quantitative methods , and to discount that are more situated , and , sometimes called qualitative methods . 34 Lifespan Developmental Research
However , it has become clear that these are not antagonistic . Instead , they are complementary ways of knowing or lines of sight on target phenomena , each whole and important in its own right , but incomplete . We think about these multiple perspectives the same way that they are described in the parable of the six blind men and the elephant . In this story , each person makes contact with a different part of the elephant and comes to his own the one who encounters its legs explains that elephants are tree trunks , the ears reveal it to be a fan , the a wall , the tail a rope , the trunk a snake , and the tusk a spear . Each one understanding is correct , but unknown to all of them , each is also incomplete . They need the views from all of these perspectives , what we sometimes call lines of sight , to fully appreciate the elephant in its wholeness and complexity . In the same way , Figure 21 multiple , and disciplinary are needed to understand our developmental phenomena in their wholeness and complexity . We a lifespan developmental systems perspective especially useful in articulating this view ( Reese , 1977 , Elder , 2001 , 2006 , 2010 , 2015 Skinner , 2019 ) The best research and graduate training programs in human development teach their doctoral students about a wide range of and and see them all as parts of converging operations . What is meant by converging operations ?
This was an idea , brought to the attention of almost 50 years ago ( 1973 ) to help deal with the unsettling realization that every method ever devised to conduct studies has serious shortcomings . The main idea is that good science needs a wide variety of differing , so that the strengths of one can compensate for the limitations of others . From this perspective , a body of evidence is much stronger when from multiple complementary converge on the same conclusions . That is why we favor developmental science that incorporates from many disciplines , and continues to question and critique those as part of its practice . Deductive In discussions of methods , the procedure that is often highlighted is the deductive in which a scientist starts with a hypothesis and then conducts a series of observations to test whether the on the ground are consistent with this hypothesis . In this process , the scientist thinking ( the theory ) and follows this up with out how to look ( conduct the study or observation ) in ways that test the validity of this theory . This process unfolds in multiple recursive or circular steps , including . Formulate a question . Use initial observations to articulate a research question a . Review previous studies ( known as a literature review ) to determine what has been found to date Formulate a working theory of the target phenomenon and propose a hypothesis . Conduct a study . Select or create a method of gathering information relevant to the hypothesis a . Who ?
Determine the people to be included . What ?
Measurement . Determine the measures to be used to capture the phenomena of interest Lifespan Developmental Research I 35 Where ?
Setting . Determine the setting where the study will take place How ?
Design . Determine the study design . Interpret the results . In light of everything you know , examine what the likely mean a . Consider the limitations of the study . Draw conclusions , including rejecting the hypothesis and revising the original theory Suggest future studies . Publish . Make the available to others a . Share information with the community . Invite scrutiny of work by other experts Inductive A second set of procedures is more inductive . This process , often called grounded theory , starts with a general question and then constructs a theory of the phenomenon based , not on the community or researchers preconceived notions , but on the researchers actual observations of many experiences on the ground . As you can see , in the process , the scientist looking ( the observations and experiences in the target setting ) and uses this process to scaffold thinking ( theorizing or building a mental model of what has been observed ) This process also unfolds in multiple recursive or circular steps , including . Find a question . Begin with a broad area of interest and identify a research problem a . Review the literature to justify the importance of the problem . See how the problem into a larger set of issues Identify the in other work on the topic . Gather information . Through extended hand engagement , learn about the target a . Who ?
Gain entrance into a group and natural setting relevant to the problem of study . What ?
Ask , broad grand tour types of questions when interviewing and observing participants focus on participant perspectives How ?
Gather notes about the setting , the people , the structure , the activities or other areas of interest collect artifacts , pictures . Modify research questions as the study evolves and follow the emergent questions . Make sense of the information . In light of all the , reflect on what your observations likely mean a . Note own participation and biases . Note patterns or , uncover themes , categories , Focus on centrality of meaning of the participants Explore new areas deemed important by participants . Report . Put together a coherent narrative that incorporates the themes and connections uncovered a . Check back in with participants to get their perspectives on your interpretations 36 Lifespan Developmental Research
. Publish . Make the available to others a . Share information with the community . Invite scrutiny of work by other experts Collaborative A third set of is based on the assumption that knowledge , research , and effective social action can best be among researchers and community participants , incorporating the strengths and perspectives of all the stakeholders involved in a particular set of issues . This approach , often called participatory action research , holds that complex social issues can not be well understood or resolved by expert research , pointing to interventions from outside of the community which often have disappointing results or unintended side effects . In collaborative approaches , researchers and community partners build a genuine trusting relationship , and this cooperative partnership is the basis on which all decisions about the project are made from articulating a set of research questions , to identifying data collection strategies , analysis and interpretation of information , and dissemination and application of . The process is inherently . Researchers build a collaborative partnership with community members who are already living with , involved in , or working on the problem of interest . Hence , this work is situated within neighborhoods and community organizations or groups , and builds on their strengths and priorities . Instead of taking individuals out of communities and into lab settings for study or providing individualized therapy to broken individuals , the goal of this work is to help facilitate change within the community itself , making it a more supportive context for all its inhabitants . Participatory . As the collaboration develops , members discuss and learn more about each other so that together they can and frame a common agenda for research and action . These projects incorporate researchers expertise and goals , but they foreground the knowledge , concerns , and needs of community partners . For example , researchers interested in homeless youth could reach out to organizations and begin conversations exploring whether they would like to work together . These conversations would also soon involve the homeless youth themselves , consistent with the slogan popularized by the disability rights movement , Nothing about us without us ! Community knowledge is considered irreplaceable as it provides key insights about target issues . Action . All research activities are anchored and oriented by the larger goal of enhancing strategic action that leads to social change and community transformation as part of the research program . Community action make take the form of public education surrounding community issues ( eg , information campaigns , changing existing policies that harm groups of people ( harsh discipline policies at school ) creating new public spaces ( community gardens and farmers markets ) and so on . Research . The community action plan is informed by organizing existing information and collecting new information from key stakeholders relevant to the community issues under scrutiny . Methods to conduct these studies are planned together in ways that researchers believe will produce high quality information and that collaborators believe will be useful to them in making progress on their agenda . All partners are also involved in the scrutiny , visualization , discussion , and interpretation of data , and make joint decisions about how it should be disseminated and used going forward . These efforts feed into next steps in both research and action . Ongoing collaboration . Such partnerships typically last for many years . Researchers are thoughtful about how to bring them successfully to a close , making sure that an ongoing goal of the collaboration is to build capacity within the community partnership so members can sustain collective action after the research team leaves . Lifespan Developmental Research 37
A good way to become more familiar with these collaborative , inductive , and deductive research methods is to look at journal articles , which are written in sections that follow these steps in the process . In general , the structure follows abstract ( summary of the article ) introduction or literature review , methods explaining how the study was conducted , results of the study , discussion and interpretation of , and references . Methods of Gathering Information Methods is also a name given to many different procedures scientists use to make their observations or collect information . Since are interested in a wide range of human capacities , they want to know not only about people actions and thoughts , as expressed in words and deeds , but also about underlying processes , like abilities , emotions , desires , intentions , and motivations . Moreover , they want to go deeper , looking into biological and neurophysiological processes , and they want to consider many factors outside the person of study , looking at social relationships and interactions , as well as environmental materials , tasks , and , and societal . And , as lifespan researchers , they want to study these capacities at all ages , from the tiniest babies to the oldest grandmothers . No wonder developmental scientists need so many tools , and are inventing more all the time . Every time you come across a conclusion in a textbook or research article , for example , when you read that olds do not yet have a sense of self , you should stop and ask , How do you know that ?
That is a great question . And a great attitude . Over and over , we will want to scrutinize the evidence scientists are using to make their conclusions , considering carefully the extent to which the methods scientists use justify the conclusions they make . If a baby ca yet talk , how would we know whether they have a sense of self ?
And even when a child can talk , what is the connection between what they are telling us and what they are truly thinking ?
You can be sure that these kinds of questions stoke lively debates in circles . As with more generally , science is strengthened by the use of a variety of approaches to collecting information . The shortcomings of one can be compensated for by the strengths of others . If we that a new mother says that she is feeling stressed , and her best friend agrees , and we see elevated cortisol levels , and her survey results are higher than usual , and she becomes irritated when her makes a well , we think we have captured something meaningful here . We are always in favor of multiple sources of data , and we especially appreciate methods that get us thick , rich information , as close to lived experience in context as possible . Here are some examples of methods commonly used in developmental research today Observations Looking at People and their Actions Often considered the basic building blocks of developmental science , observational methods are those in which the researcher carefully watches participants , noting what they are doing , saying , and expressing , both verbally and . Researchers can observe participants doing just about anything , including working on tasks , playing with toys , reading the newspaper , or interacting with others . Observations are ideal for gathering information about people verbal and physical behavior , but it is less clear whether internal states , like emotions and intentions , can be unambiguously discerned through observation . Naturalistic observations take place when researchers conduct observations in the regular settings of everyday life . This method allows researchers to get very close to the phenomenon as it actually unfolds , but researchers worry that their participation may impact participants behaviors ( a problem called reactivity ) And , since researchers have little control over the environment , they realize that the different behaviors they observe may be due to differences in situational factors . Laboratory observations , in contrast , take place in a specialized setting created by the researcher , that is , the lab . 38 Lifespan Developmental Research
For example , researchers bring babies and their caregivers to the lab in a systematic procedure known as the strange situation , which you will learn about in the section on attachment . Observing in the lab allows researchers to set up a space and to have control over situational factors . However , researchers worry that the nature of the situation may have an impact on people behavior , and that the behaviors people show in the lab are not typical of the ones they show in regular of daily life ( a problem called ) Video or audio observations can be gathered using automatic recording devices that collect information even when a researcher is not present . For example , researchers ask caregivers to record family dinners or teachers to tape class sessions or place a small recording device on a young child chest that records every word the child says or hears . These records can then be watched or listened to by researchers . Such procedures reduce reactivity , but the resultant recordings are narrower in scope than what researchers could hear or see if they were present observing in the actual context . Local expert observers can provide researchers with information about the verbal and behavior of participants they have observed or interacted with many times . For example , caregivers and teachers can report on their children and students , and even children can provide their perspectives . Reports from others typically incorporate many more observations than a researcher could collect ( a teacher sees a child in class every week day ) so the information is more representative of the target typical behavior . However , researchers worry that information could be distorted , for example , because reporters are biased or are not trained to observe or categorize the behaviors they have witnessed . Participant observations , which are especially common in anthropology and sociology , take place when researchers gain entrance into a setting , not as an observer , but as a participant , with the aim of gaining a close and intimate familiarity with a given group of individuals or a particular community , and their behaviors , relationships , and practices . These observations are usually conducted over an extended period of time , sometimes months or years , which means that the observer can directly observe variations and changes in actions and interactions . Such observations provide rich and detailed information , but are limited to the setting . Listening to People and their Thoughts When researchers are studying people , one of the most common ways of gathering information about them is by asking them , via methods . These can range from informal interviews or requests for participants to write responses to prompts , all the way to surveys , when participants can only choose among options . data are ideal for learning about people inner thoughts or opinions , but researchers worry that participants may distort the truth to present themselves in a favorable light ( a problem called social desirability ) There is also debate about whether participants have access to some of their internal processes , like their genuine motivations . Surveys gather information using standardized questionnaires , which can be administered either verbally or in writing . Surveys capture an enormous range of psychological and social processes , and their items can be tested for their reliability and validity ( called psychometric properties ) but they typically yield only surface information . Researchers worry that participants may misinterpret questions and realize that the information so collected is restricted to exactly those questions and responses . Standardized , structured , or interviews involve researchers directly asking a series of predetermined questions . Because researchers are present , they can ask follow up questions and participants can ask for . This allows researchers to learn more from participants than they could from standardized questionnaires , but researchers worry that their presence could cause reactivity , such as when participants want to provide more socially desirable responses in a setting than on an anonymous survey . interviews typically use targeted questions or prompts to get the conversation flowing , and then follow the interview where ever it leads . This allows for more customized questioning and answers , as Lifespan Developmental Research 39
researchers probe responses for greater clarity and understanding . However , since each respondent participates in a different interview conversation , it can be to compare responses from person to person . Focus groups involve group interviews , in which a small number of people ( discuss a series of questions or prompts in guided or open discussion with a trained facilitator . In this format , focus group members listen and can react to each others comments and build discussion at the group level . Responses to prompts are used when researchers ask participants to write down their thoughts . These can range from relatively unstructured free writes to short answers to a series of questions . Daily diaries , often organized electronically , allow participants to respond to online questions or prompts many days in a row . Psychological Tests and Assessments Mental Capacities and Conditions Most of us are familiar with tests that measure , for example , IQ or other mental abilities , and with diagnostic assessments that classify people according to psychological conditions . When you read about the aging of intelligence , for example , some of those studies utilize measures of and intelligence . Tests to measure mental abilities have been created for people of all ages , although it is not always clear how the measures used at different ages are connected to each other . Laboratory Tasks Interactions that Elicit or Capture Psychological Processes Researchers create and invent all manner of tasks for participants to work on , either in the lab or in real life settings ( at home or school ) These tasks allow researchers to set up activities that can assess a range of psychological attributes for people of all ages , ranging from abilities to regulatory capacities ( using the Heads Shoulders Knees and Toes task ) behaviors , learned helplessness , theories of mind , social information processing , rejection sensitivity , and so on . Many YouTube videos show children and adolescents participating in these tasks , and it is instructive to try to out exactly what is captured in each one . If you would like to see an example , you can watch a video of The Shopping Cart Study ( not required , bonus information ) Psychophysiological Assessment Underlying Neurophysiological Functioning Researchers also use a range of methods to capture information about neurophysiological functioning across the lifespan , including technology that can measure heart rate , blood pressure , hormone levels , and many kinds of brain activity to help explain development . These assessments provide information about what is happening under the skin , and researchers can see how these biological processes are connected to behavioral development . Usually connections are neurophysiology contributes to the development of behavior , and behaviors shape physiological functioning and development . Archival Data or Artifacts Information from Business as Usual Researchers sometimes utilize information that has already been collected as a regular part of daily life . Such data include , for example , students grades and achievement tests scores , documents or other media , drawings , work products , or other materials that might provide information about participants developmental progress or causal factors contributing to their development . These kinds of data have the advantage of low reactivity and high authenticity , in that they were created or gathered in the normal course of events , but it is sometimes unclear exactly what they mean or what constructs they measure . Case Study All of the Above with Carefully Selected Participants or Settings One of the best ways to gather in depth information about a person or group of people ( a classroom , school , or neighborhood ) is through a research methodology called the case study . Researchers focus on only one or a small number of target units , usually carefully selected for characteristics ( an individual as wise , a 40 I Lifespan Developmental Research
homeless teenager , a very effective school , or a workplace with high turnover rates ) and amass everything they can about that person or place . Researchers typically conduct interviews with people , including friends or family of the target person , and collect archival data and artifacts , which they might discuss in depth with participants . For example , they might go through the person photo albums and discuss memories of their early life . Classical examples of case studies are the baby diaries , in which researchers like Jean and William Stern conducted extensive observations on their individual children and took detailed notes about every aspect of their behavior and development . They also tested some of their hypotheses about development , by giving their babies toys to play with or engaging them in interesting tasks . These case studies were conducted over years . When case studies also extend into the past of the individual ( for example , when researchers are interested in life review processes ) they can be called biographical methods . Sometimes researchers who focus on a group of individuals or a setting are particularly interested in the cultural context and its functioning . These studies can be called . Drawn originally from anthropology , ethnographic methods describe an approach in which researchers carefully study and document people and their cultural settings , usually through extensive , interviews , and engagement in the setting . In these studies , as in all investigations , researchers are the students and the people in the setting are the teachers . Researchers strive to create a , narrative account that privileges the perspectives of the people studied . Take Home Messages about Lifespan Developmental We would highlight four main themes from this section . Because and practitioners use bodies of evidence to transform systems and change practices in the world , it is crucial that researchers produce the highest quality evidence possible , and evaluate and critique it thoughtfully . Developmental science incorporates multiple from many disciplines , and these deductive , inductive , and collaborative make our conclusions stronger because they provide complementary lines of sight on our target phenomena . Science is also strengthened by the use of a variety of approaches ( or methods ) for collecting information , including observations , and other strategies , because together they provide a richer picture of our target developing phenomena . The advantages of using multiple and sources of information are highlighted by the idea of converging operations , which points out that this practice allows the shortcomings of one method to be compensated for by the strengths of others , and reminds us that bodies of evidence are stronger when from multiple complementary and sources of information converge on the same conclusions . Adapted from Skinner , 2019 ) Lifespan developmental systems theory , methodology , and the study of applied problems . An Advanced Textbook . New York , NY . Lifespan Developmental Research 41
References , 1973 ) The control of developmental process Why wait ?
In . Reese ( Lifespan developmental psychology Methodological issues ( New York Academic Press . Reese , 1977 ) developmental psychology Introduction to research methods . Oxford , England . Elder , 2001 ) Developmental science . New York Cambridge University Press . 2006 ) Developmental science , developmental systems , and contemporary theories of human development . In ( Vol . Ed . and Damon ( Handbook of child psychology Vol , Theoretical models of human development ( John Wiley Sons , 2010 ) development Concepts and issues . In ( Vol . Ed . The handbook of development Vol . Cognition , biology , and methods ( Wiley . 2015 ) Handbook of child psychology and developmental science ( Vol , Theory and method . John Wiley . Sons Skinner , 2019 ) Lifespan developmental systems , methodology , and the study of applied problems . An Advanced Textbook . New York , NY . Media Blind men and elephant 42 I Lifespan Developmental Research
Descriptive and Explanatory Designs ELLEN SKINNER AND THE HUMAN DEVELOPMENT TEACHING LEARNING GROUP Learning Descriptive and Explanatory Designs Compare and contrast the three goals of lifespan developmental science . the three descriptive designs , namely , longitudinal , and identify their fatal flaws , strengths . Explain how sequential designs deal with the fatal of the two basic designs , and how each design fits into a program of developmental research . the two explanatory designs , namely , and experimental , show how they can be used in laboratory or field settings , and identify their strengths and limitations . Describe explanatory designs that extend these two and compensate for some of their limitations . Goals of Lifespan Developmental Science Research methods are tools that serve ways of knowing , and their utility depends on the extent to which they can help researchers reach their goals . From a lifespan perspective , developmental science has three primary goals to describe , explain , and optimize human development ( Reese , 1977 see Table ) Because these goals are embedded within the larger created by the lifespan perspective , they target two kinds of development ( patterns of normative change and stability and ( patterns of differential change and stability . When researchers say they are interested in understanding normative stability and change , they mean typical or regular graded patterns of individual change and constancy . When researchers want to understand differential development , they mean the different pathways that people can follow over time , including differences in the amount , nature , and direction of change . Moreover , researchers understand that some development entails quantitative changes ( often called trajectories ) and others involve qualitative shifts , such as the reorganization of existing forms or the emergence of new forms . Description is the most basic task for all scientists . For developmental scientists , description involves depicting , portraying , or representing patterns of development in their target phenomena . This includes description of normative development , or typical quantitative and qualitative changes and , as well as identifying the variety of different quantitative and qualitative pathways the phenomena can take . Explanations refer to explicit accounts of the factors that cause , or produce the patterns of changes and stability that have been described . These are completely different from descriptions themselves . Descriptions answer questions like what ?
the nature of the target phenomena ) how ?
the ways in which phenomena can change or remain the same ) and when ?
the ways in which these patterns appear as a function of age or time ) whereas explanations focus on why ?
Descriptive and Explanatory Designs 43 The goal of optimization refers to research and intervention activities designed to out how to promote healthy development ( also referred to as or thriving ) and the development of resilience . This task goes beyond description and explanation in two ways . First , in order to optimize development , trajectories and pathways must be as that represent optimal development . These kinds of trajectories are often better than normative development , and so represent rare or even imaginary pathways , especially for groups living in highly risky environments . The search for optimal pathways the assumption that individuals hold much more potential and plasticity in their development than is typically expressed or observed . The second way that optimization goes beyond description and explanation is that even when explanatory theories and research have all the conditions needed to promote optimal development , researchers and must still discover the strategies and levers that can consistently bring about these developmental conditions . One way to understand the difference between explanation and optimization is that , if explanations focus on the antecedents of a developmental phenomenon , then optimization efforts focus on the antecedents of these antecedents . To learn more about these three goals , have a look at the expanded reading at this link . Describe Ex lain Goals of Lifespan Developmental Science . DESCRIBE development across the lifespan Delineate , depict , identify patterns of Normative stability and change How do people typically develop and remain the same ?
ii . Differential stability and change What are the variety of different pathways that development ( and stability ) can follow ?
Target questions i . What ?
the nature of the target phenomena . ii . How ?
the ways in which phenomena can change or remain the same . iii . When ?
the ways in which these patterns appear as a function of age or time . EXPLAIN development across the lifespan Causal factors that shape Normative stability and development Why do people develop along a typical path ?
ii . Differential stability and change What accounts for different patterns of development ?
Target questions i . Why ?
What are the underlying or overarching causes or factors that shape development ?
ii . Causality , factors , promote , undermine , foster , nurture , support . OPTIMIZE development across the lifespan 44 Descriptive and Explanatory Designs a . Identify optimal pathways of development ( thriving , resilience ) i . Including changes and stability ii . Typical and a variety of pathways iii . Can be rare or imaginary . Create or sustain conditions that will support optimal development i . Identify causal factors needed to support optimal development ii . Locate levers to shift systems so that they sustain these positive causal factor The term design refers to when , where , and how data are collected , so the term can be used in three different ways . Descriptive developmental designs refer to when data are collected . These included , longitudinal , sequential designs . Explanatory designs refer to how and where data are collected , so they include experimental naturalistic ( or correlational ) designs , and data collected in laboratory settings . As a result , designs combine all three of these features , for example , researchers conduct longitudinal experiments , lab experiments , and naturalistic longitudinal studies . In the next sections , these kinds of designs are explained in more detail . Kinds of Study Designs When , How , and Where Are Data Collected ?
When ?
Cross Data at one time point ( from multiple cohorts ) Descriptive Developmental Sectional Designs Longitudinal Data at multiple time points ( from one cohort ) Sequential Data at multiple time points ( from multiple cohorts ) Explanatory How ?
Experimental Study in researcher administers the potential causal variable Causal Designs Naturalistic Study in researcher observes and records data as it unfolds Data at a designated location set up for research Where ?
Laboratory Purposes Field Data in the natural settings of everyday life Describing Development , Longitudinal , and Sequential Designs Developmental designs are ways of collecting data ( information ) about people that allow the researcher to see how people differ or change with age . There are two simple developmental designs and longitudinal . Descriptive and Explanatory Designs 45
Why is it challenging to how people differ or change with age ?
It is challenging because peoples lives are always embedded in historical time . As soon as a person is born , he or she is inserted into a historical moment . And , as the person ages and changes , society is changing right along with them . For example , if you were born in 1990 , I know exactly how old you were when the Twin Towers fell , when the Great Recession hit , and when iTunes opened . From a research design perspective , we can say that peoples development is confounded with historical changes ( that is , with the historical events and general societal trends that occur during their lifetimes ) The problem with either of the simple developmental designs is that they do not allow clear inferences about ( whether differences between age groups are really due to age or to historical differences , or ( whether changes in people over time are really due to age changes or to historical changes . That is why lifespan researchers need to use designs that give them more information than simple or longitudinal studies do , such as sequential designs . Can you remind me about the design ?
A DESIGN collects information ( at one point in time ( one time of measurement ) on ( groups of people who are different ages . Design for a ( study ( conducted in 1960 , using ( six age groups TIME of MEASUREMENT 1960 30 40 50 60 70 80 The good news about studies is that we get information about a wide range of age differences in a short period of time ( one time of measurement ) and we get information about differences between groups of people of dif ages . From a developmental perspective , however , the bad news about studies is that we don get any information about the thing we are most interested in , that is , change . Cross sectional studies provide no information ( about how people change , about pathways ( or trajectories ) different people take or ( about how earlier events or experiences predict later functioning . What is the problem with designs ?
The problem is that you CAN NOT infer that differences between the age groups are the same as age changes . Why not ?
Because differences between age groups could be generational differences . What looks like age differences could really be cohort differences . Generational cohorts are groups of people who were born at the same time . They are sometimes 46 Descriptive and Explanatory Designs
just called generations and have been given labels , like the baby boomers , generation , the millennial generation , and so on . What are COHORT effects ?
Cohort effects are the lifelong effects of belonging to a generation . They reflect the idea that being born at a time your development . Each of us has a series of experiences based on growing up during a historical period . Cohort effects also reflect the idea that historical events and trends have a different impact on people depending on how old they are . The Great Recession may have a very different effect on your development if you were or 18 or 28 or 80 . Cohort effects are the idea that people developmental pathways may differ based on the cumulative effects of these differences in experiences . If you want to learn more about different generations , you could watch this video about Generations throughout History ( not required , bonus information ) What problem does that create for designs ?
The problem is that the people in the different age groups also belong to different cohorts . In studies age is always confounded with cohort . Differences between the groups COULD be age differences or they COULD be cohort differences . You can see this confounding in the study depicted below . The participants who are 30 also belong to the 1930 cohort the participants who are 40 also belong to the 1920 birth cohort , and so on . If we know the persons age and the time of measurement , we can out their birth cohort . COHORT TIMES of MEASUREMENT 1960 1930 30 1920 40 1910 50 1900 60 1890 70 1880 80 Developmental researchers want to know if people differ according to their ages . But the fatal with studies is that any differences between the groups in this design could be EITHER age differences OR generational ( cohort ) differences . Ages are completely confounded with cohorts ( years of birth or generations ) Any study could also be called an study . What is a LONGITUDINAL design ?
It a study that examines ( one group of people ( repeatedly over multiple time points . Here is a longitudinal ( LONG ) study design that includes six times of measurement Descriptive and Explanatory Designs 47 COHORT TIMES of MEASUREMENT 1960 1970 1980 1990 2000 2010 1950 10 20 30 40 50 60 LONG 1940 1930 1920 1910 1900 1890 1880 The good news about a study is that it provides information about how people CHANGE or DEVELOP , as well as information about different people pathways or trajectories . It also al experiences or events predict later outcomes . are these really age changes ?
ows researchers to see whether earlier Maybe , but they could also be historical changes . The fatal with longitudinal studies is that changes in people over time could be EITHER due to age changes OR historical changes between times of measurement . As people age , the historical time they inhabit changes right along with them . Age and time of measurement are completely confounded , 48 Descriptive and Explanatory Designs all lives are embedded in historical time . TIMES of MEASUREMENT COHORT 1960 1970 1980 1990 2000 2010 1950 10 20 30 40 50 60 LONG 1940 1930 1920 1910 1900 1890 1880
Can you see that age change is confounded with historical change in this longitudinal study ?
People who were 10 were also living in 1960 , when they were 20 , they were living in 1970 , and so on . So , when people were changing from age 10 to age 20 , the society they were living in also changed from 1960 to 1970 . Therefore , people changes COULD be due to age ( development ) OR they could be due to historical changes over the times during which data were collected , or both . If the simple developmental designs don work , what kinds of designs can developmental researchers use ?
SEQUENTIAL designs are one good answer . They allow researchers to look at BOTH age changes and historical changes or multiple cohorts . There are many kinds of sequential designs . The one that we will learn about is called a sequential design . It provides the most developmental information in the shortest amount of time . It allows researchers to look at differences between people in terms of cohorts , and also to examine historical changes . Simple Developmental Designs Advantages , Disadvantages , and Fatal Flaws Advantages Provides an overview of age differences Suggests windows where changes may be occurring Quick inexpensive to conduct Fatal Haw What looks like age differences could really be cohort or generational differences ( confound ) Disadvantages Pattern of age differences might not hold at other times of measurement ( No information about age changes or effects of early experiences Longitudinal Advantages Provides information about actual change with age Shows how people can develop differentially Reveals connections between early and later development Fatal Haw What looks like age changes could really be historical changes ( confound ) Disadvantages Pattern of age changes might not hold for other cohorts ( Have to deal with effects of repeated testing and drop out Measures are also aging , may be obsolete Expensive and time consuming to conduct What is a design ?
designs combine and longitudinal designs . A study starts with a study that the researcher then follows up longitudinally for multiple measurement points . Here is a design ( in green ) that has times of measurement , with ( shown in the bottom row in yellow ) and longitudinal sequences ( shown in the last column in blue ) Descriptive and Explanatory Designs 49
TIMES of MEASUREMENT COHORT 1960 1970 1980 1990 2000 2010 1950 10 20 30 1940 20 30 40 LONG 1930 30 40 SO 1920 40 50 60 1910 50 60 70 1900 60 70 SO 1890 70 80 90 1880 80 90 100 The good news about a study is that this allows the researcher to compare longitudinal sequences across the same ages for cohorts . So , for example , the researcher can compare what it like to go from age 20 to age 30 from 1960 to 1970 ( with what it like to go from age 20 to age 30 from 1970 to 1980 ( This way , the researcher can determine whether changes were due to age or differences . This design also allows you to see if people who are the same age but from different cohorts are different from each other . For example , whether the from the 1950 co re 1930 cohort . If the age groups do not di cohort or are different from the from the 1940 fer , you can zip the different longitudinal sequences together TIMES of MEASUREMENT COHORT 1960 1970 1980 1950 10 20 30 1940 20 30 40 LONG 1930 30 40 SO 1920 40 50 60 1910 50 60 70 1900 60 70 SO 1890 70 80 90 1880 80 90 100 In this way , you can get imaginary longitudinal information pretty quickly . That why the 50 Descriptive and Explanatory Designs
design is also called an accelerated longitudinal design or the most The bad news about sequential designs is that they are complex to conduct and to analyze . Do SEQUENTIAL DESIGNS get rid of all the problems with developmental designs ?
No , but they do allow you to look at them . It may be that age differences ARE generational differences or that age changes ARE shaped by historical changes . If you are a , thats the phenomenon you are actually interested in . However , you may need a historian to make sense of historical and cohort effects , and you may need to collect historical information as you go . SIMPLE DEVELOPMENTAL DESIGNS Have their problems , but la . Quick overview of possible age differences . Helpful in collecting initial information about a new area of study because it can show where the developmental action might be . May be okay if there arent big cohort differences ( eg , in age increments in school ) Can repeat the study again soon to see if it replicates . Longitudinal . Good for . Helpful if you want to see age changes . Helpful if you want to see different pathways or trajectories . Helpful if you want to see how early events shape later development . SEQUENTIAL DESIGNS Most complete information . 23 . Good for because it does provide longitudinal data . Can see if age changes replicate over cohorts . Because of zipping , they do not take your whole life . Allows you to locate transition points and beginnings . Then you can focus on where interesting changes are taking place . Descriptive and Explanatory Designs 51
Explaining Development Experimental and Naturalistic Designs Description is the task of depicting , portraying , or representing patterns of development in the target phenomena , including patterns of normative changes and , as well as the variety of different quantitative and qualitative pathways . In contrast , explanation refers to an account of the causes that together are to produce the patterns of changes and stability that we have described What sets of factors cause , or produce these different patterns of normative and differential change or stability over time ?
Explanation focuses on the weighty question of Why ?
Goals of Explanatory Studies in Lifespan Development Basically , explanatory studies are trying to answer two questions What are the causes of normative and differential patterns of development as they unfold in the actual of daily life ?
What are the mechanisms by which those causes exert their effects in shaping development ?
Of the many features of research designs , the ones most relevant to explanation refer to the where and how of collecting data , so next we consider studies that use experimental and naturalistic designs conducted in the lab field . In order to answer the causal questions of interest to lifespan developmental researchers , we want to create designs that allow us to make valid inferences about causes and effects as they unfold in the actual of daily life . This is the information we will need to take with us into our optimization efforts . Let consider the four possible combinations of designs and settings ( see Table below ) one at a time . Combinations of Settings and Designs . Settings Designs Laboratory Setting Field Setting Experimental design . Lab experiments . Field experiments Naturalistic design . Naturalistic lab studies . Naturalistic studies LABORATORY What is an experimental design ?
The features of an experimental design are twofold . The researcher ( decides exactly what the causal agent ( treatment ) will be , and ( determines who will get it ( treatment group ) and who will not ( control group ) So the challenges are also twofold ( to create a beautiful package of causal agents and ( to ensure that the treatment and control groups are identical on everything but the treatment . Creating a plausible treatment that contains the causal ingredient is always an art , but researchers have become increasingly sophisticated in ( isolating the putative cause by creating control groups who receive every single part of the experience of the treatment group except the causal agent and ( ensuring that control groups are identical to the treatment group on preexisting attributes , both 52 Descriptive and Explanatory Designs
known ( which can be equated via matching or controlling for measured attributes ) and unknown ( which can be equated via random assignment Campbell Stanley , 1963 , Cook , Campbell , 2002 ) In many design classes , you will learn that the best design ( and some will say the only design ) for demonstrating causality is the experiment . So , for some researchers , the experiment ( and not the time machine ) is considered the gold standard . And , in the olden days , it was often assumed that experiments only happen in the laboratory , so experimental designs and laboratory settings are often merged in students minds . So let take a minute to consider experiments in labs . Do labs provide advantages for detecting causality ?
Indeed they do . They can not be beat for settings in which the researcher has complete control over both key design features . Researchers can ensure that assignment to experimental or control conditions is completely random and they can guarantee that the hypothesized causal variable is administered exactly as prescribed . Labs also provide very clear lines of sight on our potential causes and potential effects . Do lifespan developmental researchers care about random assignment ?
Yes . Huge problems are created by the fact that in the of daily life people are not randomly assigned to causal are particular personal characteristics that belong to the people who get in the way of particular causal forces , or who participate in them directly . And so , if we are going to distinguish conditions that launched someone on a particular developmental trajectory from the causal factors that we are interested in discerning , we have to create groups that are the same on everything before we start our causal show . Randomized assignment is one strategy to accomplish this , as well as its more systematic options , such as block randomization ( randomly assigning different categories of people ) matching , propensity score matching , and so on . Why are we so excited about exact control of the causal factor ?
Well , that the magic of experimental designs . The researcher is like the fairy godmother who waves her wand and introduces the potentially new future for the treatment group . So the researcher knows that the treatment group got the potential causal factor , and how much of the factor , and so on . And then the researcher has approximately a control groups , who got shades of everything but the hypothesized active ingredient . These can be very creative , the control group with nothing , with only attention , with a visit to the lab but no causal factor , with a causal factor that looks like the actual causal factor but really isn . Remember placebos , which were added to drug trials so that the control group even gets the experience of taking a pill ?
NATURALISTIC LAB STUDIES Are there lab studies that do not involve experiments ?
Yes . They are called naturalistic lab studies because researchers bring participants into the lab setting , but do not administer a dose of a potential causal agent . What would be some uses for naturalistic lab studies ?
One important use is to measure constructs that you ca capture outside of the lab . There are some phenomena of great interest that are not visible without specialized instrumentation or procedures that can be administered only in the lab setting . All manner of neurophysiological constructs can only be measured in the lab using complex equipment , like , as well as the assessment of internal states and capacities , like executive function or delay of or implicit bias . Researchers can work with their participants in the lab in order to make these internal processes more Descriptive and Explanatory Designs I 53
visible . One great example is the talk aloud protocol , in which individuals ( even children ) are trained to narrate their mental processes as they work on a task or watch a demonstration . These protocols provide stream of consciousness information that would not be accessible in any other way . A second important reason researchers might turn to lab settings is to create conditions where they can trigger and then observe interactions that are relatively rare in settings . For example , research on learned helplessness often brings children into controlled settings where researchers can watch them work with solvable and then with unsolvable puzzles , mazes , and concept tasks , while monitoring their strategies , efforts , and actions over time . And , of course they always end with success experiences . Another example is the Strange Situation in which researchers trigger the attachment system in the lab setting , by sending in a stranger and asking the caregiver to leave , and then observing the child actions . These experiences are both rigorously standardized and intuitively compelling for participants . They can simulate transgressions ( eg , when a child hears a child in the next room knock over her block creation ) competitions ( eg , when two children are playing a videogame and one messes up ) exclusion ( when a child is left out of a ball tossing game ) and so on . Such standardization creates comparability among participants . It allows researchers to be sure that their differential responses are not due to differential provocations , but to their individual reactions to identical experiences . In all these cases , naturalistic observation may seem more desirable , but social processes can be impossible to tease apart as they unfold in daily . For example , children run into fewer tasks they can not solve than children and so it is harder to catch them in failure situations , and in schools teachers do not assign impossible tasks , and so observers could go for weeks without seeing their phenomena . And , by the way , after about years of age , kids are busy trying to hide their true reactions to negative events ( a phenomenon called masking ) which makes it harder for observers to actually detect undesired states ( like anxiety or boredom ) in the . In fact , precisely because people and their are so intertwined , we sometimes bring our participants into the lab to see what they can do without the scaffolds or interference of social partners . What are the main advantages of experimental and naturalistic lab studies ?
Their claims to fame are control and precision . Because we have exact control of the causal factor and exact control over who receives it , we can make unambiguous causal inferences . These may be especially appreciated when we are trying to untangle the directions of effects in reciprocal proximal processes or transactions . Moreover , we have precision in our measurement or observation of the causes and effects . We can see below the surface ( into neurophysiology or cognitive functioning ) We can even trigger phenomena that are rare or hard to see in the wild , and record their details using our video cameras or talk aloud protocols . What are the disadvantages of studies conducted in lab settings ?
Well , for lifespan developmental researchers , they have some pretty serious limitations . Let think about three big ones . First , labs and are more than settings to us . They are . And are not just geographic and architectural locations , in the sense that you can simply pick people up and set them down in new places . have tentacles that reach out and wind themselves around people , and people have roots that reach down into places . They are connected , mutually created , even , so that our most likely causal forces , our proximal processes , can not even be constituted when we look at only one without the other . When researchers split the child from his or her context , it destroys the phenomenon itself , like removing the heart from the body in order to see how it works . You cant . Once you remove it , it does work anymore . So we worry that lab studies can alter or distort our phenomena . So don conduct research in laboratory settings ?
Not at all . are just very wary about the idea of the setting and very aware of what is lost by leaving 54 Descriptive and Explanatory Designs the scene of the crime , that is , the of daily life . The field is an intrinsic and crucial part of the target we are trying to understand , and if we are going to bring our whole phenomenon into the lab , we have to know all the relevant elements of the context and effectively simulate them in the lab . Otherwise , this kind of distortion can be a threat to internal validity . What is the second limitation of laboratory studies ?
Second , we assume that all our causal factors are embedded in and shaped by them . Instead of thinking about the lab as a place where researchers can get more pristine information about their target phenomena ( the child and their behavior ) the lab has come to be regarded as one context with its own attributes ( novelty ) and set of social partners ( the experimenter ) that are exerting their own effects on the child . So if we are looking at the functioning of proximal processes in the lab , we can be sure that the lab context is shaping then , which means we cant be sure that they actually operate the same way in the of daily life . As a result , we always have to admit that any causal links we may have created in the lab have to be couched as can cause our target and not as does cause our target . We have to wait and see if these same processes are operating in the actual that form the natural for our participants . Otherwise , this kind of can be a threat to external validity . Third , the time span over which assume that causal effects accumulate can not be easily simulated in the lab . The causal processes of interest to unfold over months and years and decades , across multiple , so although we can use the lab to measure the effects of causal factors by bringing our participants back to the lab as many times as we want to , if we want to actually lookat the causal processes having their effects over months or years , it will be to achieve that in the lab setting . And are there corresponding disadvantages with experimental designs ?
Well , for some , they do have a fatal . As noted by many , the seemingly insurmountable problem with experimental designs is that it is not possible to randomly assign or manipulate the causal forces that are of biggest interest to . No one can randomly assign their participants to a particular age group ( I have a coin and you will be in the group Oh no , I wanted to be 10 ! or to a particular cohort or developmental history . In fact , most of the causal factors that are of interest to us ca ethically be manipulated at happy parent family versus the unhappily married parents , the delinquent peer group versus friends , school failure versus success , peer rejection versus popularity , high stress reactivity , dangerous neighborhoods , or height . Before you ask , we will just add that this same issue applies to all areas of psychology . Many applied problems can not be , dangerous job conditions , psychopathology , serious medical diagnosis , intimate partner violence , and so on . So there are serious limitations to how much experimental designs can help applied researchers study the conditions and causes that matter most to them . FIELD EXPERIMENTS Wait ! What about field experiments designed to promote development ?
Yes , indeed . And these experiments can even be conducted as randomized controlled trials ( Cook , 2009 ) And , yes , we can ethically study any old target we please as long as we are trying to promote unfavorable developmental trajectories , to maintain resilient ones , and in general to prevent adverse and promote healthy development . What are randomized controlled trials ?
Descriptive and Explanatory Designs 55 As you may know , this methodology was borrowed from clinical trials of medical treatments , and it is cool in many ways . It has time in its design , which is always welcome news to . compare ( at least ) two groups who should be equivalent to each other ( based on random assignment ) one of which has received the treatment and the other a placebo , so that researchers can examine the effects of the treatment over and above the effects of knowing that one is being treated . Then after a amount of time for the treatment to do its work , changes in the treatment and control group can be compared over however many time points the design includes . In recent decades , in settings have become de for settling causal claims about practices , programs , and policies . experiments provide a standard of proof that has made them essential to both the community and to applied decision makers and stakeholders ( see the What Works Clearinghouse ) Do these kinds of studies have certain advantages ?
Indeed , they do . Some researchers see experiments as the best of both worlds . On the one hand , studies get to keep all the compelling features of experimental designs that are so helpful in making unambiguous causal inferences . But on the other hand , we have escaped from the lab setting and returned to the of everyday life , so they generally evince higher authenticity and external validity . Often treatments are even administered by natural social partners in everyday . Parents , teachers , bosses , coaches , or mentors receive systematic training and then bring their newly acquired attributes back into the settings of homes , classrooms , and workplaces , where researchers can determine whether they subsequently change the proximal processes and the development that takes place there . Are there disadvantages to experiments ?
Many researchers consider to be the gold standard , but they have three important limitations for . First , the very thing we like about out of the lab and into the make the administration of the causal factor somewhat messy . We lose some of our beloved control and precision . Especially if researchers decide that the treatment ( often an intervention program ) will be administered through intermediaries ( like teachers or caregivers or social workers ) it can be a giant headache . A whole area of study , called implementation research , focuses on implementation how to make sure that participants actually make contact with the active ingredients of the potential causal factor . Moreover , in the real world , participants can drop out of the treatment ( eg , the teacher or parent training ) any time they want , but they do not drop out of the treatment group in our design . To maintain the equivalence between treatment and control groups accomplished by our initial random assignment , participants can not voluntarily switch we have suddenly introduced bias . So the treatment group is often labeled the intent to treat group , meaning that participants were initially assigned to this group , even if they end up receiving no treatment whatsoever . Its like doctors who send the treatment pills home with their patients and then hope for the best , but never get to count the pills that are left in the bottle at the end of the trial . If patients do not improve , they ca really say whether the drug did work or whether the patients just didn take their pills . Unsettling from a causal inference perspective . A second limitation , and one that experiments share with lab experiments , is that they can not tell us what caused these unhealthy pathways of development in the place , any more than studying aspirin can tell us what causes headaches or how to prevent them . So additional work will always be needed to in the causal puzzle of the factors that contribute to and maintain development or lead to psychopathology . Such studies would be essential to prevention efforts . Third , in both lab and ) have inherent limitations in providing causal explanations . At the end of the day , the only thing that this design can tell you is yes or That is , the only information it yields is whether the two treatment and are different . You can add many features , for example , many indicators of disease or health , you can measure dosage and its effects , over several time periods , and so on . However , would say that , after all this work , the only thing we have in our hands is a causal description but not the thing that we most want , that 56 Descriptive and Explanatory Designs
is , a causal explanation . For the drug companies , everything they want to know about causal explanations is contained in the drug itself . To the extent that they care about how the drug works , its mechanisms of effects have already been studied ( and of course , we take many drugs that are effective , but whose mechanisms of effects are unknown ) But as , our interventions contain hundreds of potential active ingredients . And so we want to poke our heads under the hood and look all around , watching the cogs engage and the wheels turn . We want to watch the tennis game or the dance , and see who is hitting the ball the hardest and how the players adapt to each others style over time and who is playing the music . In other words , we are on the trail of causal explanation and so we ca really be with yes or We will forever be asking Why ?
or Why not ?
and especially How did that work ?
So we will always be supplementing experimental and lab studies , and even studies , with studies using designs that can provide us with more complex accounts of the multiple causes of differential developmental trajectories and transformations . Take Home Messages about Different Explanatory Designs and Settings All designs , including experimental lab and designs , have advantages and disadvantages ( see Table ) Your job is to conduct each kind of study using the very best methods available ( that the looking part of a scientist role ) and then to keep straight on what you can and can not see ( that the thinking part of a scientist role ) That way , you will learn what there really is to be learned using experimental designs , and then start longing for what can be seen using alternative designs that provide other lines of sight . Descriptive and Explanatory Designs 57
Advantages and Disadvantages of Different Settings and Designs Laboratory Experiment Advantages Control and precision Unambiguous causal inference . Precise control of hypothesized causal factor . Precise measure of hypothesized effect . Treatment and control group are the same on known and unknown attributes ( potential ) Disadvantages May change phenomena . Limited to can cause versus does cause causal conclusions . May or may not work in actual . Many potential causal factors can not be manipulated . Naturalistic Laboratory Study Advantages Precision Measure constructs that are below the surface ( neurophysiology , capacities , knowledge ) Can observe proximal processes more closely . Can observe proximal processes without typical scaffolding or interference of social partners . Can trigger phenomenon that are rare or masked in the . Disadvantages Distortion Splitting of person from context may have destroyed causal factors . Hard to locate active ingredient of causal packages . and novelty of context , instrument , or trigger distorts causal phenomena . Field Experiment Advantages Control and Actual context Potential for causal inference . Potential to see how causes operate in . Potential to see effects in sita . Disadvantages Messiness Hard to precisely control the implementation of the potential causal factor . 58 Descriptive and Explanatory Designs
Especially if delivery agents are also naturalistic ( caregivers , teachers , social workers ) Limited to can cause versus does cause causal conclusions . Most potential causal factors can not be manipulated . Limited account of causal process . Naturalistic Advantages Authenticity Field Study Whole phenomenon is intact . Can discover causes that were not expected . Disadvantages Murkiness Hard to specify active ingredient of causal packages . Impossible to control all selection effects . Limited to may cause versus does cause causal conclusions . NATURALISTIC FIELD STUDIES What we appreciate about naturalistic studies is obvious from the limitations we encountered with lab and experimental designs In naturalistic studies we can examine the effects of potential causes we cant possibly administer ( maltreatment , ability tracking , peer rejection ) Moreover , we can watch these processes operate in their authentic , and we can follow them for months and years . But aren experiments the only way to show causality ?
Yes , experiments can provide important evidence of causal processes . But lets consider the kinds of causal evidence that can be provided by naturalistic studies . Are we talking about studies ?
Because we know for a fact that correlation does not prove Right , it is correct that correlation by itself does not prove causation . But let take a minute to understand why this is true , and then to see whether there are some things that researchers can do to improve the designs of their studies so that naturalistic studies , using more than correlations , can provide evidence about causes . Because , may not prone causation , but causal processes do generate correlations Since causes produce effects , effects with their causes . In fact , this covariation is a condition of causality ( see box ) As a result , correlations ( or covariation or contingencies , however you want to label them ) may be the smoke that leads us to our causal . The problem is that many things besides causation lead to correlations , and so we have to work hard to decipher the causal evidence among all the other kinds of covariation information we are examining . Descriptive and Explanatory Designs 59
John Stuart Mill ( 1843 ) on causality To establish causality , three basic conditions must be met The presumed effects ( must with their presumed cause ( The presumed cause ( must precede their effects in time and All other plausible alternative explanations for the effect must be excluded . Okay , can you break down the reasons that correlations do not prove causation ?
Yes , let start with a consideration of a typical correlation between two variables , and let pick variables that tap constructs we think could be causally connected , say , teacher involvement and student engagement ( see below ) Lets say that in this research we get a robust correlation between good measures of both variables . Why cant we conclude that teacher involvement student engagement ?
There are two main reasons . First , as also shown in the below , the connection between these two variables could be due to a reciprocal causal effect , in which student engagement teacher involvement . This direction of effects is conceptually plausible , since more engaged students could attract more positive teacher attention whereas more disaffected students could lead teachers to withdraw or treat students more harshly . Of course , the correlation could be due to both ( teachers on students ) and feedback ( students on teachers ) effects . and Causation The second possibility is that there is no causal effect at all between these two variables ( forward or backward ) Instead , they are both actually produced by a completely different cause ( the ominously named third variable ) and they only because they are both effects from the same cause . In our example , also shown in the , we selected students gender as our third variable because gender is a plausible cause of both variables . In Could he causal effect general , girls are more engaged and teachers show more involvement with them , whereas boys generally tend to Could be how by be less engaged and teachers show less warmth toward be ' them In this scenario as in all other scenarios involving . concurrent correlations , there are a very large number of Figure third variables ( alternative causes of both ) that could be in could be achievement ( engaged students perform better in school and teachers attend more to performing students ) or social class or student sense of well as a large number of third variables that we ca immediately imagine . So in naturalistic designs , we use the term third variable as shorthand for all the alternative causal explanations that could underlie the connections between our hypothesized antecedent and its possible consequence . Is there anything we can do to help solve these problems ?
Yes , including time in the design of our naturalistic studies helps us out quite a bit . What do you mean by adding time to a design ?
When we say we are adding time to a design , we mean that we are adding occasions or times of measurement or repeated measures to a design . Like in a longitudinal study ?
60 Descriptive and Explanatory Designs Yes , but maybe the most general description is time series because the design includes a series of different times of measurement . What are the advantages of adding time ?
Lets say that we add just one more time of measurement , so we have two waves in our study . The advantage is that now we have a way to check the condition of causality , namely , that causes precede their effects . So we can check out a correlation . Continuing with our example , with time in the design , we can look at whether the potential cause at Time predicts the potential outcome at Time . This is depicted in the below . So we are excited to be able to use the word predict correctly to describe our correlation . However , this is still just a bivariate correlation , so it does not allow a causal still has all the problems with those dreaded third variables or alternative causal explanations . But we can use our two time points to start looking at how our target outcome is changing from Time to Time and to see whether those changes can be predicted from where each person was on the potential cause at Time . Causation , and Time So what we like about this kind design ?
TIME , TIME , We get to look at developmental trajectories as our outcomes ( and if we add more time points , they will look more like trajectories ) we are looking directly at individual differences in trajectories , and we are looking at of individual differences in those trajectories . So , in our example , we can ask , Does teacher involvement at the beginning of the school year predict changes in students engagement from the beginning to the end of the school year ?
And if the empirical answer is yes ( the antecedent is a predictor of change from Time to Time ) we can say things like Figure 23 Students whose teachers were warmer and more involved with them at the beginning of the school year , also showed increases in their engagement over the school year whereas students whose teachers were less involved with them at the beginning of the school year , showed corresponding declines in their engagement as the year This is a descriptive statement , but it is consistent with a causal hypothesis . Any other advantages ?
Yes . We can also , using the same design , look at the reciprocal predictions , in that we can take our antecedent variable and examine how it changes from Time to Time , and see whether the variable we had been thinking of as a consequence ( which we now consider as a possible antecedent ) predicts these the above . In our example , we would be asking Do students initial levels of engagement at the beginning of the year predict changes in how much involvement their teachers provide them over the year ?
And , if the empirical answer is yes , we can say things like Students who were more engaged in fall experienced increasing involvement from their teachers as the year progressed , whereas students who were initially higher in disaffection experienced declines in their teachers involvement from fall to One of the most important things about a design with two points of measurement ( remember , we just added one more point ) is that it allows researchers to begin to pull apart the different directions of effects . A concurrent correlation contains information about both directions of effects , which can not logically be untangled , but the two analyses that we just ran can get the job looks at the feed forward prediction of teacher involvement on changes in subsequent student engagement , whereas the second looks at the feedback Descriptive and Explanatory Designs 61
prediction of student engagement on changes in subsequent teacher involvement . So the answers to the questions posed by these two sets of analyses could be could get two yes or two no or one of each . And if we get two yes , we have the possibility of a feedback loop , which feels like we are getting some hints about potential dynamics in the system . What about all those pesky third variables , those alternative explanations ?
Well , we have good news and bad news about them . What is the good news ?
The good news is that we have reduced them some . If you start thinking about the third variables in the concurrent correlation in our illustration , that is , all the factors that are positively correlated with both teacher involvement and student engagement , an enormous number come to mind ( achievement , SES , supportive caregivers , IQ , a sense of relatedness , and so on ) And here is the kicker , these are only the ones we can imagine , there are also unknown . However , when we include in our design and analyses change over time , we are using people as their own controls . This means that out of our potential consequence at Time , we are taking each participant starting value of the consequence at Time , which has in it by everything ( known and unknown ) that led up to the consequence at Time ( achievement , SES , supportive caregivers , IQ , a sense of relatedness , and so on ) as well as all the unknowns that created or predicted the consequence at Time . So , for example , if we think that achievement is a possible alternative causal explanation for the correlation between teacher involvement at Time and student engagement at Time ( meaning that high performing students are more engaged and teachers pay more attention to them ) when we control for student engagement Time , we take out all of the achievement that was responsible for engagement up to that point , so we have controlled for that as a potential confounder . By controlling for the same variable at an earlier point in time , we have scraped off all the known and unknown of engagement up until Time that could be a potential confounder , or a plausible difference , or an alternative causal chain . So then what is the bad news ?
The bad news is that the notorious third variables are not completely eliminated . Since we are still looking at a kind of the correlation between teacher involvement at Time and changes in student engagement from Time to Time we are still on the hunt for possible alternative causes of both . Remember , before we were looking for things that were correlated with both teacher involvement and student engagement , but now , with this design , we can narrow our candidates for third variables down to those that are correlated with both teacher involvement and changes in student engagement . What should we be thinking about in adding time to our study design ?
Lets start with some basic questions that we almost never know the answers are the right windows and the right time gaps between measurement points ?
We ran into this problem , called time and timing ( Schwartz , 2009 ) in the chapter on descriptive longitudinal designs , when we had to pick the developmental window ( time ) over which we thought our target change was likely to occur , and then decide on the spacing between measurement points ( timing ) needed to capture its hypothesized rate and pattern of change . In descriptive designs , we ask these questions about the developmental outcomes , but in explanatory designs , we also ask them about the causal process During what developmental window ( time ) are our target causal processes likely to be active ?
and What spacing ( timing ) should we use to capture the rate at which this causal process is likely to generate its effects ( both and feedback ) For example , if we are thinking about teacher involvement and student engagement , it seems like the beginning of a new school year would be a good moment for them to be calibrating to each other ( time ) but how long 62 Descriptive and Explanatory Designs
would that take ( timing ) week , a month , six weeks ?
Who knows ?
One rule of thumb is to use more measurement points than you think you will need , so you can look over different time gaps for your possible process . Correlation Causation and What do you mean more ?
Weren we excited to be Change over Time adding Just one more tune of measurement ?
Well , two is qualitatively better than one , so that good . TIME TIME , TIME TIME , But more really is merrier . Additional times of measurement allow researchers to look at these of change across additional time gaps , for example , from a predictor at Time to changes from Time to Time ( as depicted in the ) Multiple time points also open up a new with trajectories of mean level change as the outcomes of our Figure causal processes . A step in this direction has been referred to as a launch model because it tries to examine whether an individuals initial levels on an antecedent can predict the individuals trajectory on the target consequence . The term launch is used because such a model assumes that the initial levels of the potential causal variable may act like a catapult or rocket launcher to create the direction and angle of change in the object that is hurled , that is , the target outcome . Can we even use these designs to look at how changes in our antecedents predict changes in our outcomes ?
Yes , these have been called models ) but you cant really use the word predict to describe these connections . They are actually like correlations between growth curves , and then we land back in our concurrent correlations we still do know who is preceding whom . So it would probably be better to look at the connections between growth curves , for example , the connections between a growth curve from Time to Time to predict an outcome from Time to Time . Then you could also look at reciprocal effects by switching around your antecedents and consequences . Just like with the designs that incorporate only two measurement points , these two analyses can provide different estimates of the connections between the different portions of the growth curves . What is happening with all our third variables in these analyses ?
We can control for them , but it may be more interesting to look directly at their effects , by organizing our data into a niche study , where we look at the connections we are interested in for of boys or girls separately or for students high or low in achievement . If the connection between teacher involvement and student engagement is due to gender , it will disappear when we look at boys and girls separately . As you know , looking directly at the developmental patterns of different groups is more consistent with a lifespan developmental systems perspective , which holds that development is differentially shaped by the characteristics of the people and their . Descriptive and Explanatory Designs 63
Transition to Middle School Fit What is responsible for the dramatic losses observed in students motivation , engagement , academic performance , and across the transition to middle school ?
Researchers were interested in the factors that could explain these regular and declines in functioning . One group of researchers begin by examining the characteristics of schools that shifted from the organization of elementary schools to middle schools , things like larger student bodies , harsher discipline , and school days chopped up into shorter periods taught by different teachers . Because school transitions take place across age ranges , a competing explanation was neurophysiological idea that declines were the result of the tolls of puberty and adolescence , which would have taken place with or without a school transition . Clever researchers used study designs that allowed them to separate the age changes of adolescence from the environmental transition across middle school . Researchers compared students from school districts that were organized in three different ways ( schools in which buildings included kindergarten through eighth grade ( elementary and middle schools in which districts students from all elementary schools ( into larger middle schools ( and ( elementary and junior high schools in which districts students from all elementary schools ( into larger junior high schools ( The results of these kinds of studies were ( Eccles , 1989 ) Adolescence was not the risk factor for declines in drops were apparent at whatever age the school transition took place ( grade for districts with middle schools or grade for those with junior high schools ) and , most important , such drops were not seen ( or were greatly reduced ) across the same ages in districts that did not require school transitions ( schools ) The best account of these issues seems to be provided by ( Eccles et , 1993 ) in which the changes students typically experience over the transition to middle or junior high school include features ( more distant and less caring relationships , more competitive and learning goals , more impersonal discipline , and fewer choices about academic work ) that turn out to be a very bad match for the changing needs of adolescents ( for stronger adult relationships outside the family , more intrinsic motivation , and greater autonomy in learning ) These design ideas for naturalistic studies seem much less systematic than the strategies we learned about for experimental studies . Yes , it can sometimes feel like the wild west out here on the frontier where we are trying to extract valid inferences from naturalistic studies . But keep in mind that you are not alone . Researchers from many other disciplines , like epidemiology , sociology , and economics ( 2010 , 2008 ) are also developing useful methods for tracking down causal processes , because they can no more create controlled in which they crash stock markets or start epidemics or shift social can developmental scientists . As part of this search , they are uncovering an important set of tools for causal inferences in the careful application of sound statistical methods ( eg , et , 2014 see box ) Take Home Messages for Naturalistic Field Studies We would highlight four . The is a resounding Yes ! to the question of whether naturalistic field studies can contribute to rich causal accounts of development . These are studies that contain everything you want to know about your target phenomenon , so it is worthwhile to out how to decipher that information in ways that yield valid causal accounts . The second take home is a resounding Whoa ! because the ways that causes likely operate in the complex dynamic system of which our target phenomenon is a part can be truly . So we need to pause and get our glasses on our noses before we start sifting through designs and strategies . Third , our most trusty tool is time times of measurement in longitudinal and time series designs , the time windows over which we choose to hover , and the timing ( spacing ) of our measurements so they map onto the pace of our causal processes . 64 ( Descriptive and Explanatory Designs
. Our is that the search for design ideas for wresting causal information out of naturalistic field studies will take you into some beautiful and uncharted territory . When you encounter a complex problem ( like historical or cohort effects ) your immediate reaction may How do I avoid this problem ?
or How do I solve this problem ?
But instead of resorting to coping , we would encourage you to react with curiosity . In other words , the best advice that lifespan developmental researchers can give themselves is always Do ignore . Do evade . Turn around and look . These are not methodological problems you have encountered . They are messages from your target developmental phenomena . And , if you have ears to hear , you can learn a great deal by walking directly towards them . Take Home Messages for the Goals of Developmental Science and Descriptive and Explanatory Designs Take home messages are also contained in the summary tables for the three goals of developmental science , and the tables describing ( longitudinal , and sequential designs , and for ( experimental and correlational designs in the laboratory and the . For the designs , we would like you to be able to each one , identify the advantages and disadvantages of each , and discuss when it would make sense to use each of them in a program of research . Be sure to revisit the concept of converging it helps pull together the idea that the strengths of each design can help compensate for the weaknesses of other designs ! Adapted from Skinner , 2019 ) Lifespan developmental systems theory , methodology , and the study of applied problems . An Advanced Textbook . New York , NY . Supplemental Materials This chapter discusses the use of qualitative methods in psychology and the ways in which qualitative inquiry has participated in a radical tradition . Kidder . Fine ( 1997 ) Qualitative inquiry in psychology A radical tradition . In Fox and ( Critical psychology An introduction ( Thousand Oaks CA Sage . This article discusses the approach and method of Participatory Action Research and implications for promoting healthy development . 2017 ) participatory action research Overview and potential for enhancing adolescent development . Child Development Perspectives 11 ( doi The following article examines systematic racial inequality within the context of psychological research . Roberts Goldie ( 2020 ) Racial inequality in psychological research Trends of the past and recommendations for the future . Perspectives on Science 1745691620927709 . Descriptive and Explanatory Designs 65
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