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1.11: Developmental Research Designs

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Learning Outcomes

Compare advantages and disadvantages of developmental research designs (cross-sectional, longitudinal, and sequential)

Now you know about some tools used to conduct research about human development. Remember, research methods are tools that are used to collect information. But it is easy to confuse research methods and research design. Research design is the strategy or blueprint for deciding how to collect and analyze information. Research design dictates which methods are used and how. Developmental research designs are techniques used particularly in lifespan development research. When we are trying to describe development and change, the research designs become especially important because we are interested in what changes and what stays the same with age. These techniques try to examine how age, cohort, gender, and social class impact development.

Cross-sectional designs

The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time. Let’s suppose that researchers are interested in the relationship between intelligence and aging. They might have a hypothesis (an educated guess, based on theory or observations) that intelligence declines as people get older. The researchers might choose to give a certain intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time; they are taking a “cross-section” of people at one point in time. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Would that be a valid (accurate) interpretation of the results?

Text stating that the year of study is 2010 and an experiment looks at cohort A with 20 year olds, cohort B of 50 year olds and cohort C with 80 year olds

No, that would not be a valid conclusion because the researchers did not follow individuals as they aged from 20 to 50 to 80 years old. One of the primary limitations of cross-sectional research is that the results yield information about age differences not necessarily changes with age or over time. That is, although the study described above can show that in 2010, the 80-year-olds scored lower on the intelligence test than the 50-year-olds, and the 50-year-olds scored lower on the intelligence test than the 20-year-olds, the data used to come up with this conclusion were collected from different individuals (or groups of individuals). It could be, for instance, that when these 20-year-olds get older (50 and eventually 80), they will still score just as high on the intelligence test as they did at age 20. In a similar way, maybe the 80-year-olds would have scored relatively low on the intelligence test even at ages 50 and 20; the researchers don’t know for certain because they did not follow the same individuals as they got older.

It is also possible that the differences found between the age groups are not due to age, per se, but due to cohort effects. The 80-year-olds in this 2010 research grew up during a particular time and experienced certain events as a group. They were born in 1930 and are part of the Traditional or Silent Generation. The 50-year-olds were born in 1960 and are members of the Baby Boomer cohort. The 20-year-olds were born in 1990 and are part of the Millennial or Gen Y Generation. What kinds of things did each of these cohorts experience that the others did not experience or at least not in the same ways?

You may have come up with many differences between these cohorts’ experiences, such as living through certain wars, political and social movements, economic conditions, advances in technology, changes in health and nutrition standards, etc. There may be particular cohort differences that could especially influence their performance on intelligence tests, such as education level and use of computers. That is, many of those born in 1930 probably did not complete high school; those born in 1960 may have high school degrees, on average, but the majority did not attain college degrees; the young adults are probably current college students. And this is not even considering additional factors such as gender, race, or socioeconomic status. The young adults are used to taking tests on computers, but the members of the other two cohorts did not grow up with computers and may not be as comfortable if the intelligence test is administered on computers. These factors could have been a factor in the research results.

Another disadvantage of cross-sectional research is that it is limited to one time of measurement. Data are collected at one point in time and it’s possible that something could have happened in that year in history that affected all of the participants, although possibly each cohort may have been affected differently. Just think about the mindsets of participants in research that was conducted in the United States right after the terrorist attacks on September 11, 2001.

Longitudinal research designs

Middle-aged woman holding a picture of her younger self.

Longitudinal research involves beginning with a group of people who may be of the same age and background (cohort) and measuring them repeatedly over a long period of time. One of the benefits of this type of research is that people can be followed through time and be compared with themselves when they were younger; therefore changes with age over time are measured. What would be the advantages and disadvantages of longitudinal research? Problems with this type of research include being expensive, taking a long time, and subjects dropping out over time. Think about the film, 63 Up , part of the Up Series mentioned earlier, which is an example of following individuals over time. In the videos, filmed every seven years, you see how people change physically, emotionally, and socially through time; and some remain the same in certain ways, too. But many of the participants really disliked being part of the project and repeatedly threatened to quit; one disappeared for several years; another died before her 63rd year. Would you want to be interviewed every seven years? Would you want to have it made public for all to watch?

Longitudinal research designs are used to examine behavior in the same individuals over time. For instance, with our example of studying intelligence and aging, a researcher might conduct a longitudinal study to examine whether 20-year-olds become less intelligent with age over time. To this end, a researcher might give an intelligence test to individuals when they are 20 years old, again when they are 50 years old, and then again when they are 80 years old. This study is longitudinal in nature because the researcher plans to study the same individuals as they age. Based on these data, the pattern of intelligence and age might look different than from the cross-sectional research; it might be found that participants’ intelligence scores are higher at age 50 than at age 20 and then remain stable or decline a little by age 80. How can that be when cross-sectional research revealed declines in intelligence with age?

The same person, "Person A" is 20 years old in 2010, 50 years old in 2040, and 80 in 2070.

Since longitudinal research happens over a period of time (which could be short term, as in months, but is often longer, as in years), there is a risk of attrition. Attrition occurs when participants fail to complete all portions of a study. Participants may move, change their phone numbers, die, or simply become disinterested in participating over time. Researchers should account for the possibility of attrition by enrolling a larger sample into their study initially, as some participants will likely drop out over time. There is also something known as selective attrition— this means that certain groups of individuals may tend to drop out. It is often the least healthy, least educated, and lower socioeconomic participants who tend to drop out over time. That means that the remaining participants may no longer be representative of the whole population, as they are, in general, healthier, better educated, and have more money. This could be a factor in why our hypothetical research found a more optimistic picture of intelligence and aging as the years went by. What can researchers do about selective attrition? At each time of testing, they could randomly recruit more participants from the same cohort as the original members, to replace those who have dropped out.

The results from longitudinal studies may also be impacted by repeated assessments. Consider how well you would do on a math test if you were given the exact same exam every day for a week. Your performance would likely improve over time, not necessarily because you developed better math abilities, but because you were continuously practicing the same math problems. This phenomenon is known as a practice effect. Practice effects occur when participants become better at a task over time because they have done it again and again (not due to natural psychological development). So our participants may have become familiar with the intelligence test each time (and with the computerized testing administration).

Another limitation of longitudinal research is that the data are limited to only one cohort. As an example, think about how comfortable the participants in the 2010 cohort of 20-year-olds are with computers. Since only one cohort is being studied, there is no way to know if findings would be different from other cohorts. In addition, changes that are found as individuals age over time could be due to age or to time of measurement effects. That is, the participants are tested at different periods in history, so the variables of age and time of measurement could be confounded (mixed up). For example, what if there is a major shift in workplace training and education between 2020 and 2040 and many of the participants experience a lot more formal education in adulthood, which positively impacts their intelligence scores in 2040? Researchers wouldn’t know if the intelligence scores increased due to growing older or due to a more educated workforce over time between measurements.

Sequential research designs

Sequential research designs include elements of both longitudinal and cross-sectional research designs. Similar to longitudinal designs, sequential research features participants who are followed over time; similar to cross-sectional designs, sequential research includes participants of different ages. This research design is also distinct from those that have been discussed previously in that individuals of different ages are enrolled into a study at various points in time to examine age-related changes, development within the same individuals as they age, and to account for the possibility of cohort and/or time of measurement effects. In 1965, K. Warner Schaie [1] (a leading theorist and researcher on intelligence and aging), described particular sequential designs: cross-sequential, cohort sequential, and time-sequential. The differences between them depended on which variables were focused on for analyses of the data (data could be viewed in terms of multiple cross-sectional designs or multiple longitudinal designs or multiple cohort designs). Ideally, by comparing results from the different types of analyses, the effects of age, cohort, and time in history could be separated out.

Consider, once again, our example of intelligence and aging. In a study with a sequential design, a researcher might recruit three separate groups of participants (Groups A, B, and C). Group A would be recruited when they are 20 years old in 2010 and would be tested again when they are 50 and 80 years old in 2040 and 2070, respectively (similar in design to the longitudinal study described previously). Group B would be recruited when they are 20 years old in 2040 and would be tested again when they are 50 years old in 2070. Group C would be recruited when they are 20 years old in 2070 and so on.

Shows cohorts A, B, and C. Cohort A tests age 20 in 2010, age 50 in 2040, and age 80 in 2070. Cohort B begins in 2040 and tests new 20 year-olds so they can be compared with the 50 year olds from cohort A. Cohort C tests 20 year olds in 2070, who are compared with 20 year olds from cohorts B and A, but also with the original groups of 20-year olds who are now age 80 (cohort A) and age 50 (cohort B).

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons—changes and/or stability with age over time can be measured and compared with differences between age and cohort groups. This research design also allows for the examination of cohort and time of measurement effects. For example, the researcher could examine the intelligence scores of 20-year-olds in different times in history and different cohorts (follow the yellow diagonal lines in figure 3). This might be examined by researchers who are interested in sociocultural and historical changes (because we know that lifespan development is multidisciplinary). One way of looking at the usefulness of the various developmental research designs was described by Schaie and Baltes (1975) [2] : cross-sectional and longitudinal designs might reveal change patterns while sequential designs might identify developmental origins for the observed change patterns.

Since they include elements of longitudinal and cross-sectional designs, sequential research has many of the same strengths and limitations as these other approaches. For example, sequential work may require less time and effort than longitudinal research (if data are collected more frequently than over the 30-year spans in our example) but more time and effort than cross-sectional research. Although practice effects may be an issue if participants are asked to complete the same tasks or assessments over time, attrition may be less problematic than what is commonly experienced in longitudinal research since participants may not have to remain involved in the study for such a long period of time.

When considering the best research design to use in their research, scientists think about their main research question and the best way to come up with an answer. A table of advantages and disadvantages for each of the described research designs is provided here to help you as you consider what sorts of studies would be best conducted using each of these different approaches.

https://assessments.lumenlearning.co...essments/16509

[glossary-page] [glossary-term]attrition:[/glossary-term] [glossary-definition]occurs when participants fail to complete all portions of a study[/glossary-definition]

[glossary-term]cross-sectional research:[/glossary-term] [glossary-definition]used to examine behavior in participants of different ages who are tested at the same point in time; may confound age and cohort differences[/glossary-definition]

[glossary-term]longitudinal research:[/glossary-term] [glossary-definition]studying a group of people who may be of the same age and background (cohort), and measuring them repeatedly over a long period of time; may confound age and time of measurement effects[/glossary-definition]

[glossary-term]research design:[/glossary-term] [glossary-definition]the strategy or blueprint for deciding how to collect and analyze information; dictates which methods are used and how[/glossary-definition]

[glossary-term]selective attrition:[/glossary-term] [glossary-definition]certain groups of individuals may tend to drop out more frequently resulting in the remaining participants longer being representative of the whole population[/glossary-definition]

[glossary-term]sequential research design:[/glossary-term] [glossary-definition]combines aspects of cross-sectional and longitudinal designs, but also adding new cohorts at different times of measurement; allows for analyses to consider effects of age, cohort, time of measurement, and socio-historical change[/glossary-definition] [/glossary-page]

Schaie, K.W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64(2), 92-107. ↵
Schaie, K.W. & Baltes, B.P. (1975). On sequential strategies in developmental research: Description or Explanation. Human Development, 18: 384-390. ↵

Contributors and Attributions

Modification, adaptation, and original content. Authored by : Margaret Clark-Plaskie for Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Research Methods in Developmental Psychology. Authored by : Angela Lukowski and Helen Milojevich. Provided by : University of Calfornia, Irvine. Located at : https://nobaproject.com/modules/research-methods-in-developmental-psychology?r=LDcyNTg0 . Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Woman holding own photograph. Provided by : Pxhere. Located at : https://pxhere.com/en/photo/221167 . License : CC0: No Rights Reserved

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Developmental Psychology Research Methods

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Jose Luis Pelaez Inc/Getty Images

Cross-Sectional Research Methods

Longitudinal research methods, correlational research methods, experimental research methods.

There are many different developmental psychology research methods, including cross-sectional, longitudinal, correlational, and experimental. Each has its own specific advantages and disadvantages. The one that a scientist chooses depends largely on the aim of the study and the nature of the phenomenon being studied.

Research design provides a standardized framework to test a hypothesis and evaluate whether the hypothesis is correct, incorrect, or inconclusive. Even if the hypothesis is untrue, the research can often provide insights that may prove valuable or move research in an entirely new direction.

At a Glance

In order to study developmental psychology, researchers utilize a number of different research methods. Some involve looking at different cross-sections of a population, while others look at how participants change over time. In other cases, researchers look at how whether certain variables appear to have a relationship with one another. In order to determine if there is a cause-and-effect relationship, however, psychologists much conduct experimental research.

Learn more about each of these different types of developmental psychology research methods, including when they are used and what they can reveal about human development.

Cross-sectional research involves looking at different groups of people with specific characteristics.

For example, a researcher might evaluate a group of young adults and compare the corresponding data from a group of older adults.

The benefit of this type of research is that it can be done relatively quickly; the research data is gathered at the same point in time. The disadvantage is that the research aims to make a direct association between a cause and an effect. This is not always so easy. In some cases, there may be confounding factors that contribute to the effect.

To this end, a cross-sectional study can suggest the odds of an effect occurring both in terms of the absolute risk (the odds of something happening over a period of time) and the relative risk (the odds of something happening in one group compared to another).

Longitudinal research involves studying the same group of individuals over an extended period of time.

Data is collected at the outset of the study and gathered repeatedly through the course of study. In some cases, longitudinal studies can last for several decades or be open-ended. One such example is the Terman Study of the Gifted , which began in the 1920s and followed 1528 children for over 80 years.

The benefit of this longitudinal research is that it allows researchers to look at changes over time. By contrast, one of the obvious disadvantages is cost. Because of the expense of a long-term study, they tend to be confined to a smaller group of subjects or a narrower field of observation.

Challenges of Longitudinal Research

While revealing, longitudinal studies present a few challenges that make them more difficult to use when studying developmental psychology and other topics.

Longitudinal studies are difficult to apply to a larger population.
Another problem is that the participants can often drop out mid-study, shrinking the sample size and relative conclusions.
Moreover, if certain outside forces change during the course of the study (including economics, politics, and science), they can influence the outcomes in a way that significantly skews the results.

For example, in Lewis Terman's longitudinal study, the correlation between IQ and achievement was blunted by such confounding forces as the Great Depression and World War II (which limited educational attainment) and gender politics of the 1940s and 1950s (which limited a woman's professional prospects).

Correlational research aims to determine if one variable has a measurable association with another.

In this type of non-experimental study, researchers look at relationships between the two variables but do not introduce the variables themselves. Instead, they gather and evaluate the available data and offer a statistical conclusion.

For example, the researchers may look at whether academic success in elementary school leads to better-paying jobs in the future. While the researchers can collect and evaluate the data, they do not manipulate any of the variables in question.

A correlational study can be appropriate and helpful if you cannot manipulate a variable because it is impossible, impractical, or unethical.

For example, imagine that a researcher wants to determine if living in a noisy environment makes people less efficient in the workplace. It would be impractical and unreasonable to artificially inflate the noise level in a working environment. Instead, researchers might collect data and then look for correlations between the variables of interest.

Limitations of Correlational Research

Correlational research has its limitations. While it can identify an association, it does not necessarily suggest a cause for the effect. Just because two variables have a relationship does not mean that changes in one will affect a change in the other.

Unlike correlational research, experimentation involves both the manipulation and measurement of variables . This model of research is the most scientifically conclusive and commonly used in medicine, chemistry, psychology, biology, and sociology.

Experimental research uses manipulation to understand cause and effect in a sampling of subjects. The sample is comprised of two groups: an experimental group in whom the variable (such as a drug or treatment) is introduced and a control group in whom the variable is not introduced.

Deciding the sample groups can be done in a number of ways:

Population sampling, in which the subjects represent a specific population
Random selection , in which subjects are chosen randomly to see if the effects of the variable are consistently achieved

Challenges in Experimental Resarch

While the statistical value of an experimental study is robust, it may be affected by confirmation bias . This is when the investigator's desire to publish or achieve an unambiguous result can skew the interpretations, leading to a false-positive conclusion.

One way to avoid this is to conduct a double-blind study in which neither the participants nor researchers are aware of which group is the control. A double-blind randomized controlled trial (RCT) is considered the gold standard of research.

What This Means For You

There are many different types of research methods that scientists use to study developmental psychology and other areas. Knowing more about how each of these methods works can give you a better understanding of what the findings of psychological research might mean for you.

Capili B. Cross-sectional studies . Am J Nurs . 2021;121(10):59-62. doi:10.1097/01.NAJ.0000794280.73744.fe

Kesmodel US. Cross-sectional studies - what are they good for? . Acta Obstet Gynecol Scand . 2018;97(4):388–393. doi:10.1111/aogs.13331

Noordzij M, van Diepen M, Caskey FC, Jager KJ. Relative risk versus absolute risk: One cannot be interpreted without the other . Nephrology Dialysis Transplantation. 2017;32(S2):ii13-ii18. doi:10.1093/ndt/gfw465

Kell HJ, Wai J. Terman Study of the Gifted . In: Frey B, ed. The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation . Vol. 4. Thousand Oaks, CA: SAGE Publications, Inc.; 2018. doi:10.4135/9781506326139.n691

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research . Nurse Res . 2016;23(6):20–25. doi:10.7748/nr.2016.e1382

Misra S. Randomized double blind placebo control studies, the "Gold Standard" in intervention based studies . Indian J Sex Transm Dis AIDS . 2012;33(2):131-4. doi:10.4103/2589-0557.102130

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Using Science to Inform Educational Practices

Developmental Research Designs

Sometimes, especially in developmental research, the researcher is interested in examining changes over time and will need to consider a research design that will capture these changes. Remember, research methods are tools that are used to collect information, while r esearch design is the strategy or blueprint for deciding how to collect and analyze information. Research design dictates which methods are used and how. There are three types of developmental research designs: cross-sectional, longitudinal, and sequential.

Video 2.9.1. Developmental Research Design summarizes the benefits of challenges of the three developmental design models.

Cross-Sectional Designs

The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time. Let’s suppose that researchers are interested in the relationship between intelligence and aging. They might have a hypothesis that intelligence declines as people get older. The researchers might choose to give a particular intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time; they are taking a “cross-section” of people at one point in time. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Would that be a valid (accurate) interpretation of the results?

Figure 2.9.1. Example of cross-sectional research design

No, that would not be a valid conclusion because the researchers did not follow individuals as they aged from 20 to 50 to 80 years old. One of the primary limitations of cross-sectional research is that the results yield information about age differences not necessarily changes over time. That is, although the study described above can show that the 80-year-olds scored lower on the intelligence test than the 50-year-olds, and the 50-year-olds scored lower than the 20-year-olds, the data used for this conclusion were collected from different individuals (or groups). It could be, for instance, that when these 20-year-olds get older, they will still score just as high on the intelligence test as they did at age 20. Similarly, maybe the 80-year-olds would have scored relatively low on the intelligence test when they were young; the researchers don’t know for certain because they did not follow the same individuals as they got older.

With each cohort being members of a different generation, it is also possible that the differences found between the groups are not due to age, per se, but due to cohort effects. Differences between these cohorts’ IQ results could be due to differences in life experiences specific to their generation, such as differences in education, economic conditions, advances in technology, or changes in health and nutrition standards, and not due to age-related changes.

Another disadvantage of cross-sectional research is that it is limited to one time of measurement. Data are collected at one point in time, and it’s possible that something could have happened in that year in history that affected all of the participants, although possibly each cohort may have been affected differently.

Longitudinal Research Designs

Figure 2.9.2. Example of a longitudinal research design

Since longitudinal research happens over a period of time (which could be short-term, as in months, but is often longer, as in years), there is a risk of attrition. Attrition occurs when participants fail to complete all portions of a study. Participants may move, change their phone numbers, die, or simply become disinterested in participating over time. Researchers should account for the possibility of attrition by enrolling a larger sample into their study initially, as some participants will likely drop out over time. There is also something known as selective attrition— this means that certain groups of individuals may tend to drop out. It is often the least healthy, least educated, and lower socioeconomic participants who tend to drop out over time. That means that the remaining participants may no longer be representative of the whole population, as they are, in general, healthier, better educated, and have more money. This could be a factor in why our hypothetical research found a more optimistic picture of intelligence and aging as the years went by. What can researchers do about selective attrition? At each time of testing, they could randomly recruit more participants from the same cohort as the original members to replace those who have dropped out.

Another limitation of longitudinal research is that the data are limited to only one cohort. As an example, think about how comfortable the participants in the 2010 cohort of 20-year-olds are with computers. Since only one cohort is being studied, there is no way to know if findings would be different from other cohorts. In addition, changes that are found as individuals age over time could be due to age or to time of measurement effects. That is, the participants are tested at different periods in history, so the variables of age and time of measurement could be confounded (mixed up). For example, what if there is a major shift in workplace training and education between 2020 and 2040, and many of the participants experience a lot more formal education in adulthood, which positively impacts their intelligence scores in 2040? Researchers wouldn’t know if the intelligence scores increased due to growing older or due to a more educated workforce over time between measurements.

Sequential Research Designs

Figure 2.9.3. Example of sequential research design

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons—changes and/or stability with age over time can be measured and compared with differences between age and cohort groups. This research design also allows for the examination of cohort and time of measurement effects. For example, the researcher could examine the intelligence scores of 20-year-olds at different times in history and different cohorts (follow the yellow diagonal lines in figure 2.9.3). This might be examined by researchers who are interested in sociocultural and historical changes (because we know that lifespan development is multidisciplinary). One way of looking at the usefulness of the various developmental research designs was described by Schaie and Baltes (1975): cross-sectional and longitudinal designs might reveal change patterns while sequential designs might identify developmental origins for the observed change patterns.

Comparing Developmental Research Designs

Table 2.9.1. Advantages and disadvantages of different research designs

Candela Citations

Developmental Research Design. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/developmental-research-designs/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Developmental Research Designs. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/adolescent/chapter/developmental-research-design/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Developmental Research Methods

Scott A. Miller - University of Florida, USA
Description

The Fifth Edition of the classic Developmental Research Methods presents an overview of methods to prepare students to carry out, report on, and evaluate research on human development across the lifespan. The book explores every step in the research process, from the initial concept to the final written product, covering conceptual issues of experimental design, as well as the procedural skills necessary to translate design into research. Incorporating new topics, pedagogy, and references, this edition conveys an appreciation of the issues that must be addressed, the decisions that must be made, and the obstacles that must be overcome at every phase in a research project, capturing both the excitement and the challenge of doing quality research on topics that matter.

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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NEW TO THIS EDITION:

New topics expose students to current research issues in human development. Topics include: emotional development, bullying, early forms of moral understanding, the “replication crisis” in psychology, the role of gestures in cognitive development, the study of false belief in infancy, the “teenage brain” and its implications for adolescent behavior, the study of the “oldest old,” and the population of centenarians.
Key Terms lists now appear at the end of each chapter to help students master the vocabulary of research methods.
New boxes, exercises, glossary items, and tables and figures bring the book completely up to date.
Approximately 400 new references reflect recent scholarship in the field

KEY FEATURES:

Flexible chapters provide instructors the ability to cover material in any order they prefer.
A student-friendly design and engaging approach provides extended coverage of especially interesting and important contemporary topics through chapter boxes, tables, figures, and photos.
Built-in study tools , including exercises, chapter-ending summaries, key terms lists, a glossary, citation of further sources, and relevant websites, help students master key content.

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For instructors, select a purchasing option.

This title is also available on SAGE Knowledge , the ultimate social sciences online library. If your library doesn’t have access, ask your librarian to start a trial .

Psychological Research

Developmental Research Designs

Video 2.9.1. Developmental Research Design summarizes the benefits of challenges of the three developmental design models.

Cross-Sectional Designs

The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time. Let’s suppose that researchers are interested in the relationship between intelligence and aging. They might have a hypothesis that intelligence declines as people get older. The researchers might choose to give a particular intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time; they are taking a “cross-section” of people at one point in time. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Would that be a valid (accurate) interpretation of the results?

Figure 2.9.1. Example of cross-sectional research design

No, that would not be a valid conclusion because the researchers did not follow individuals as they aged from 20 to 50 to 80 years old. One of the primary limitations of cross-sectional research is that the results yield information about age differences not necessarily changes over time. That is, although the study described above can show that the 80-year-olds scored lower on the intelligence test than the 50-year-olds, and the 50-year-olds scored lower than the 20-year-olds, the data used for this conclusion were collected from different individuals (or groups). It could be, for instance, that when these 20-year-olds get older, they will still score just as high on the intelligence test as they did at age 20. Similarly, maybe the 80-year-olds would have scored relatively low on the intelligence test when they were young; the researchers don’t know for certain because they did not follow the same individuals as they got older.

Longitudinal Research Designs

Figure 2.9.2. Example of a longitudinal research design

Another limitation of longitudinal research is that the data are limited to only one cohort. As an example, think about how comfortable the participants in the 2010 cohort of 20-year-olds are with computers. Since only one cohort is being studied, there is no way to know if findings would be different from other cohorts. In addition, changes that are found as individuals age over time could be due to age or to time of measurement effects. That is, the participants are tested at different periods in history, so the variables of age and time of measurement could be confounded (mixed up). For example, what if there is a major shift in workplace training and education between 2020 and 2040, and many of the participants experience a lot more formal education in adulthood, which positively impacts their intelligence scores in 2040? Researchers wouldn’t know if the intelligence scores increased due to growing older or due to a more educated workforce over time between measurements.

Sequential Research Designs

Figure 2.9.3. Example of sequential research design

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons—changes and/or stability with age over time can be measured and compared with differences between age and cohort groups. This research design also allows for the examination of cohort and time of measurement effects. For example, the researcher could examine the intelligence scores of 20-year-olds at different times in history and different cohorts (follow the yellow diagonal lines in figure 3). This might be examined by researchers who are interested in sociocultural and historical changes (because we know that lifespan development is multidisciplinary). One way of looking at the usefulness of the various developmental research designs was described by Schaie and Baltes (1975): cross-sectional and longitudinal designs might reveal change patterns while sequential designs might identify developmental origins for the observed change patterns.

Comparing Developmental Research Designs

Table 2.9.1. Advantages and disadvantages of different research designs

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Developmental Research Designs

Margaret Clark-Plaskie; Lumen Learning; Angela Lukowski; Helen Milojevich; and Diana Lang

Compare advantages and disadvantages of developmental research designs (cross-sectional, longitudinal, and sequential)
Describe challenges associated with conducting research in lifespan development

Cross-sectional designs

The majority of developmental studies use cross-sectional designs because they are less time-consuming and less expensive than other developmental designs. Cross-sectional research designs are used to examine behavior in participants of different ages who are tested at the same point in time (Figure 1). Let’s suppose that researchers are interested in the relationship between intelligence and aging. They might have a hypothesis (an educated guess, based on theory or observations) that intelligence declines as people get older. The researchers might choose to give a certain intelligence test to individuals who are 20 years old, individuals who are 50 years old, and individuals who are 80 years old at the same time and compare the data from each age group. This research is cross-sectional in design because the researchers plan to examine the intelligence scores of individuals of different ages within the same study at the same time; they are taking a “cross-section” of people at one point in time. Let’s say that the comparisons find that the 80-year-old adults score lower on the intelligence test than the 50-year-old adults, and the 50-year-old adults score lower on the intelligence test than the 20-year-old adults. Based on these data, the researchers might conclude that individuals become less intelligent as they get older. Would that be a valid (accurate) interpretation of the results?

Longitudinal research designs

Longitudinal research involves beginning with a group of people who may be of the same age and background (cohort) and measuring them repeatedly over a long period of time (Figure 2 & 3). One of the benefits of this type of research is that people can be followed through time and be compared with themselves when they were younger; therefore changes with age over time are measured. What would be the advantages and disadvantages of longitudinal research? Problems with this type of research include being expensive, taking a long time, and subjects dropping out over time. Think about the film, 63 Up , part of the Up Series mentioned earlier, which is an example of following individuals over time. In the videos, filmed every seven years, you see how people change physically, emotionally, and socially through time; and some remain the same in certain ways, too. But many of the participants really disliked being part of the project and repeatedly threatened to quit; one disappeared for several years; another died before her 63rd year. Would you want to be interviewed every seven years? Would you want to have it made public for all to watch?

Sequential research designs

Sequential research designs include elements of both longitudinal and cross-sectional research designs. Similar to longitudinal designs, sequential research features participants who are followed over time; similar to cross-sectional designs, sequential research includes participants of different ages. This research design is also distinct from those that have been discussed previously in that individuals of different ages are enrolled into a study at various points in time to examine age-related changes, development within the same individuals as they age, and to account for the possibility of cohort and/or time of measurement effects. In 1965, Schaie [2] (a leading theorist and researcher on intelligence and aging), described particular sequential designs: cross-sequential, cohort sequential, and time-sequential. The differences between them depended on which variables were focused on for analyses of the data (data could be viewed in terms of multiple cross-sectional designs or multiple longitudinal designs or multiple cohort designs). Ideally, by comparing results from the different types of analyses, the effects of age, cohort, and time in history could be separated out.

Studies with sequential designs are powerful because they allow for both longitudinal and cross-sectional comparisons—changes and/or stability with age over time can be measured and compared with differences between age and cohort groups. This research design also allows for the examination of cohort and time of measurement effects. For example, the researcher could examine the intelligence scores of 20-year-olds in different times in history and different cohorts (follow the yellow diagonal lines in figure 3). This might be examined by researchers who are interested in sociocultural and historical changes (because we know that lifespan development is multidisciplinary). One way of looking at the usefulness of the various developmental research designs was described by Schaie and Baltes [3] : cross-sectional and longitudinal designs might reveal change patterns while sequential designs might identify developmental origins for the observed change patterns.

Challenges Associated with Conducting Developmental Research

The previous sections describe research tools to assess development across the lifespan, as well as the ways that research designs can be used to track age-related changes and development over time. Before you begin conducting developmental research, however, you must also be aware that testing individuals of certain ages (such as infants and children) or making comparisons across ages (such as children compared to teens) comes with its own unique set of challenges. In the final section of this module, let’s look at some of the main issues that are encountered when conducting developmental research, namely ethical concerns, recruitment issues, and participant attrition.

Ethical Concerns

As a student of the social sciences, you may already know that Institutional Review Boards (IRBs) must review and approve all research projects that are conducted at universities, hospitals, and other institutions (each broad discipline or field, such as psychology or social work, often has its own code of ethics that must also be followed, regardless of institutional affiliation). An IRB is typically a panel of experts who read and evaluate proposals for research. IRB members want to ensure that the proposed research will be carried out ethically and that the potential benefits of the research outweigh the risks and potential harm (psychological as well as physical harm) for participants.

What you may not know though, is that the IRB considers some groups of participants to be more vulnerable or at-risk than others. Whereas university students are generally not viewed as vulnerable or at-risk, infants and young children commonly fall into this category. What makes infants and young children more vulnerable during research than young adults? One reason infants and young children are perceived as being at increased risk is due to their limited cognitive capabilities, which makes them unable to state their willingness to participate in research or tell researchers when they would like to drop out of a study. For these reasons, infants and young children require special accommodations as they participate in the research process. Similar issues and accommodations would apply to adults who are deemed to be of limited cognitive capabilities.

When thinking about special accommodations in developmental research, consider the informed consent process. If you have ever participated in scientific research, you may know through your own experience that adults commonly sign an informed consent statement (a contract stating that they agree to participate in research) after learning about a study. As part of this process, participants are informed of the procedures to be used in the research, along with any expected risks or benefits. Infants and young children cannot verbally indicate their willingness to participate, much less understand the balance of potential risks and benefits. As such, researchers are oftentimes required to obtain written informed consent from the parent or legal guardian of the child participant, an adult who is almost always present as the study is conducted. In fact, children are not asked to indicate whether they would like to be involved in a study at all (a process known as assent) until they are approximately seven years old. Because infants and young children cannot easily indicate if they would like to discontinue their participation in a study, researchers must be sensitive to changes in the state of the participant (determining whether a child is too tired or upset to continue) as well as to parent desires (in some cases, parents might want to discontinue their involvement in the research). As in adult studies, researchers must always strive to protect the rights and well-being of the minor participants and their parents when conducting developmental research.

This video from the US Department of Health and Human Services provides an overview of the Institutional Review Board process.

You can view the transcript for “How IRBs Protect Human Research Participants” here (opens in new window) .

Recruitment

An additional challenge in developmental science is participant recruitment. Recruiting university students to participate in adult studies is typically easy. Many colleges and universities offer extra credit for participation in research and have locations such as bulletin boards and school newspapers where research can be advertised. Unfortunately, young children cannot be recruited by making announcements in Introduction to Psychology courses, by posting ads on campuses, or through online platforms such as Amazon Mechanical Turk. Given these limitations, how do researchers go about finding infants and young children to be in their studies?

The answer to this question varies along multiple dimensions. Researchers must consider the number of participants they need and the financial resources available to them, among other things. Location may also be an important consideration. Researchers who need large numbers of infants and children may attempt to recruit them by obtaining infant birth records from the state, county, or province in which they reside. Some areas make this information publicly available for free, whereas birth records must be purchased in other areas (and in some locations birth records may be entirely unavailable as a recruitment tool). If birth records are available, researchers can use the obtained information to call families by phone or mail them letters describing possible research opportunities. All is not lost if this recruitment strategy is unavailable, however. Researchers can choose to pay a recruitment agency to contact and recruit families for them. Although these methods tend to be quick and effective, they can also be quite expensive. More economical recruitment options include posting advertisements and fliers in locations frequented by families, such as mommy-and-me classes, local malls, and preschools or daycare centers. Researchers can also utilize online social media outlets like Facebook, which allows users to post recruitment advertisements for a small fee. Of course, each of these different recruitment techniques requires IRB approval. And if children are recruited and/or tested in school settings, permission would need to be obtained ahead of time from teachers, schools, and school districts (as well as informed consent from parents or guardians).

And what about the recruitment of adults? While it is easy to recruit young college students to participate in research, some would argue that it is too easy and that college students are samples of convenience. They are not randomly selected from the wider population, and they may not represent all young adults in our society (this was particularly true in the past with certain cohorts, as college students tended to be mainly white males of high socioeconomic status). In fact, in the early research on aging, this type of convenience sample was compared with another type of convenience sample—young college students tended to be compared with residents of nursing homes! Fortunately, it didn’t take long for researchers to realize that older adults in nursing homes are not representative of the older population; they tend to be the oldest and sickest (physically and/or psychologically). Those initial studies probably painted an overly negative view of aging, as young adults in college were being compared to older adults who were not healthy, had not been in school nor taken tests in many decades, and probably did not graduate high school, let alone college. As we can see, recruitment and random sampling can be significant issues in research with adults, as well as infants and children. For instance, how and where would you recruit middle-aged adults to participate in your research?

A tired looking mother closes her eyes and rubs her forehead as her baby cries.

Another important consideration when conducting research with infants and young children is attrition . Although attrition is quite common in longitudinal research in particular (see the previous section on longitudinal designs for an example of high attrition rates and selective attrition in lifespan developmental research), it is also problematic in developmental science more generally, as studies with infants and young children tend to have higher attrition rates than studies with adults. For example, high attrition rates in ERP (event-related potential, which is a technique to understand brain function) studies oftentimes result from the demands of the task: infants are required to sit still and have a tight, wet cap placed on their heads before watching still photographs on a computer screen in a dark, quiet room (Figure 5).

In other cases, attrition may be due to motivation (or a lack thereof). Whereas adults may be motivated to participate in research in order to receive money or extra course credit, infants and young children are not as easily enticed. In addition, infants and young children are more likely to tire easily, become fussy, and lose interest in the study procedures than are adults. For these reasons, research studies should be designed to be as short as possible – it is likely better to break up a large study into multiple short sessions rather than cram all of the tasks into one long visit to the lab. Researchers should also allow time for breaks in their study protocols so that infants can rest or have snacks as needed. Happy, comfortable participants provide the best data.

Conclusions

Lifespan development is a fascinating field of study – but care must be taken to ensure that researchers use appropriate methods to examine human behavior, use the correct experimental design to answer their questions, and be aware of the special challenges that are part-and-parcel of developmental research. After reading this module, you should have a solid understanding of these various issues and be ready to think more critically about research questions that interest you. For example, what types of questions do you have about lifespan development? What types of research would you like to conduct? Many interesting questions remain to be examined by future generations of developmental scientists – maybe you will make one of the next big discoveries!

attrition : occurs when participants fail to complete all portions of a study
cross-sectional research : used to examine behavior in participants of different ages who are tested at the same point in time; may confound age and cohort differences
i nformed consent : a process of informing a research participant what to expect during a study, any risks involved, and the implications of the research, and then obtaining the person’s agreement to participate
Institutional Review Boards (IRBs) : a panel of experts who review research proposals for any research to be conducted in association with the institution (for example, a university)
longitudinal research : studying a group of people who may be of the same age and background (cohort), and measuring them repeatedly over a long period of time; may confound age and time of measurement effects
research design : the strategy or blueprint for deciding how to collect and analyze information; dictates which methods are used and how
selective attrition : certain groups of individuals may tend to drop out more frequently resulting in the remaining participants no longer being representative of the whole population
sequential research design : combines aspects of cross-sectional and longitudinal designs, but also adding new cohorts at different times of measurement; allows for analyses to consider effects of age, cohort, time of measurement, and socio-historical change
This chapter was adapted from Lumen Learning's Lifespan Development , created by Margaret Clark-Plaskie for Lumen Learning and adapted from Research Methods in Developmental Psychology by Angela Lukowski and Helen Milojevich for Noba Psychology, available under a Creative Commons NonCommercial Sharealike Attribution license . ↵
Schaie, K. W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64 (2), 92-107. ↵
Schaie, K.W. & Baltes, B.P. (1975). On sequential strategies in developmental research: Description or Explanation. Human Development, 18, 384-390. ↵

Developmental Research Designs Copyright © 2022 by Margaret Clark-Plaskie; Lumen Learning; Angela Lukowski; Helen Milojevich; and Diana Lang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

6 Developmental Research Designs

These designs examine what changes and what stays the same in a human life. Chronological age , cohort membership , and time of measurement are the basic elements of research designs looking at development. The frustrating thing about doing this kind of research is that you only can vary two of these three elements at a time. The two that you choose will determine the third element. Therefore no single study can definitively tell you about how human beings develop. However, combining results of multiple studies and using more complex designs, such as cross-sequential designs, can help us get closer to the truth.

Cross-sectional research involves beginning with a sample that represents a cross-section of the population. Respondents who vary in age, gender, ethnicity, and social class might be asked to complete a survey about television program preferences or attitudes toward the use of the Internet. The attitudes of males and females could then be compared as could attitudes based on age. In cross-sectional research, respondents are measured only once. This method is much less expensive than longitudinal research but does not allow the researcher to distinguish between the impact of age and the cohort effect. Different attitudes about the Internet, for example, might not be altered by a person’s biological age as much as their life experiences as members of a cohort.

Longitudinal research involves beginning with a group of people who may be of the same age and background, and measuring them repeatedly over a long period of time. One of the benefits of this type of research is that people can be followed through time and be compared with them when they were younger. A problem with this type of research is that it is very expensive and subjects may drop out over time. (The film 49 Up is a example of following individuals over time. You see how people change physically, emotionally, and socially through time.) What would be the drawbacks of being in a longitudinal study? What about 49 Up? Would you want to be filmed every 7 years? What would be the advantages and disadvantages? Can you imagine why some would continue and others drop out of the project?

Cross-sequential research involves combining aspects of the previous two techniques; beginning with a cross-sectional sample and measuring them through time. This is the perfect model for looking at age, gender, social class, and ethnicity. But here the drawbacks of high costs and attrition are here as well.

the amount of time elapsed since an individual’s birth, typically expressed in terms of months and years.

a group of individuals who share a similar characteristic or experience. The term usually refers to an age (or birth) cohort, that is, a group of individuals who are born in the same year and thus of similar age.

the moment in time when the participants' responses are recorded.

a research design in which individuals, typically of different ages or developmental levels, are compared at a single point in time. An example is a study that involves a direct comparison of 50-year-olds with 80-year-olds. Given its snapshot nature, however, it is difficult to determine causal relationships using a cross-sectional design. Moreover, a cross-sectional study is not suitable for measuring changes over time, for which a longitudinal design is required.

the study of a variable or group of variables in the same cases or participants over a period of time, sometimes several years. An example of a longitudinal design is a multiyear comparative study of the same children in an urban and a suburban school to record their cognitive development in depth. A longitudinal study that evaluates a group of randomly chosen individuals is referred to as a panel study, whereas a longitudinal study that evaluates a group of individuals possessing some common characteristic (usually age) is referred to as a cohort study.

a study in which two or more groups of individuals of different ages are directly compared over a period of time. It is thus a combination of a cross-sectional design and a longitudinal design. For example, an investigator using a cross-sequential design to evaluate children’s mathematical skills might measure a group of 5-year-olds and a group of 10-year-olds at the beginning of the research and then subsequently reassess the same children every 6 months for the next 5 years.

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Research and Development

Research and development are the processes businesses follow to develop and introduce new products and improve existing services.

Home > Research Glossary > Research and Development

What is research and development?

Research and development (R&D) refers to the activities that businesses engage in to:

Develop and introduce new products and services or
Innovate and improve on existing products and services

R&D can be an invaluable tool for building and enhancing your business. It involves conducting a thorough investigation into your industry, your competitors, and your customers and uncovering the data and insights that are most important to your company.

Armed with this information, you can be more strategic and better informed for meeting the needs of both your customers and your organization. So, finding this type of accurate, complete information in a timely fashion is essential to making smarter, more impactful business decisions.

Why are data and insights important for your R&D strategy

Data and the insights you draw from that data are critical to any R&D strategy. They help you:

Paint a more complete picture of the industry landscape, of a competitor, or of a particular individual
Identify existing or emerging trends
Unlock new business opportunities
Build successful strategies so you can confidently make the right decisions for your organization
Mitigate market disruptions and be risk resilient

What’s more, data provides crucial insights into the factors influencing your business not just today, but also well into the future. Equipped with this knowledge, you’re in a much stronger position to create long-term value for your customers, markets, and relationships.

What kind of data do you need for your R&D strategy

No one can dispute that the Internet is an amazing tool. With it, we have at our fingertips immediate access to seemingly immeasurable amounts of free data – facts, statistics, and insights. But the Internet also comes with its limitations and hazards, especially when it comes to important research. Some challenges of using only the open web to conduct research include:

Questionable sources
Outdated information
Fake news or misinformation
Inconvenient paywalls or other research dead ends
Information gaps or, conversely, content overload

In today's information-on-demand age, traditional search engines and general online research just won’t suffice for a robust R&D strategy. You need a smarter, more efficient approach, one that:

Avoids these internet obstacles
Takes your research beyond the one-dimensional and draws from wide-ranging, first-rate sources
Indexes and filters the research, so you’re getting only the data that’s most important to your organization
Turns that data into actionable insights that strengthen your decision-making and help you achieve your business objectives

So, it’s not about just any data – it’s about the right data. Trusted, well-vetted, and comprehensive information is critical for robust R&D. That means knowing where to get such valuable data and having the research tools in place to deliver it are key to developing a successful R&D strategy.

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Vaccines, Blood & Biologics
Science & Research (Biologics)

New Approaches to the Development and Evaluation of Vaccines Against Hepatitis Viruses

Marian Major, PhD

Office of Vaccines Research and Review Division of Viral Products Laboratory of Hepatitis Viruses

[email protected]

Dr. Major is a virologist who currently holds the position of Chief, Laboratory of Hepatitis Viruses, Division of Viral Products, CBER/FDA. She obtained a Ph.D. from the University of Warwick, U.K. Dr. Major has been studying vaccine approaches for the prevention of HCV infection since 1994. She was one of the first people to show that natural infection from HCV can elicit protective T cell responses, opening the door for further vaccine development. She has written several reviews on the progress of HCV vaccines; completed a pivotal meta-analysis of HCV vaccine studies in chimpanzees; and showed, using an in silico model, that a vaccine against HCV would reduce transmission among people who inject drugs without necessarily achieving sterilizing immunity. The lab’s research findings have been published in international, peer-reviewed journals, and Dr. Major has co-authored several book chapters and review articles on HCV, including co-authoring publications with two of the 2020 recipients of the Nobel Prize in Physiology and Medicine, Dr. Charles Rice and Dr. Harvey Alter. As a result of her research, Dr. Major is a co-inventor on four patents: one on HCV peptide vaccines (2004), the second on HCV neutralization (2013), the third on an HCV neutralizing antibody (2016), and a fourth on HCV neutralizing epitopes, antibodies and methods (2017).

General Overview

Despite the development of direct-acting antivirals, vaccines against hepatitis C virus (HCV) are still needed. Our research program focuses on learning more about the way hepatitis C virus (HCV) causes disease in people and how the immune system responds to this virus or to vaccines designed to prevent HCV infection.

This research addresses a serious public health threat: an estimated 3.2 million Americans have chronic HCV infection, and studies suggest that over 150 million people worldwide are infected (about 3.3% of the world's population). Approximately 85% of people infected with HCV develop persistent infections that can eventually cause severe liver problems, such as cirrhosis and liver cancer (hepatocellular carcinoma, or HCC). HCV infection is considered one of the major risk factors for liver cancer in the U.S., Europe, and Japan. Furthermore, HCC is one of the few cancers that is increasing in frequency and rate of mortality in the US: studies show that about 50% of HCC cases arise from chronic HCV infection.

Despite these statistics, no vaccine has been developed to prevent HCV infection. Therapy for HCV has improved over the past ten years, with the advent of direct-acting antiviral drugs. Yet for most of the world, daily treatment with these drugs is not realistic, because they are expensive and may have undesirable side effects. In addition, little evidence indicates that patients cured of a chronic HCV infection have protective immune responses that would prevent HCV reinfection. A vaccine against HCV would therefore contribute enormously to reducing the incidence of disease.

In response to this need, our laboratory is developing tools to help us study immune responses to HCV infection and to HCV vaccines. These studies include using a small-animal model for HCV infection; identifying the types of immune responses that demonstrate that a vaccine may be successful; and the development of neutralization tests for the virus (i.e., tests that show whether effective antibodies have been induced against the virus). We are also working with collaborators to develop mathematical models for HCV transmission, so that intervention strategies can be tested in silico before testing in at-risk populations. These studies will be critical to our ability to guide manufacturers of HCV vaccines and to the ability of FDA to evaluate the safety and efficacy of these products.

Scientific Overview

Our laboratory is focused on developing scientific tools to better understand and characterize immune responses to hepatitis C virus (HCV).

Studies include the identification of immunologic correlates of protection, development of neutralization tests for the virus, and assessment of effective methods for induction of protective immune responses. Many of these studies have used reagents and data previously obtained from chimpanzees, the only animal model for HCV infection and immunity.

Previously, HCV could not be propagated in a robust cell culture system, which made classical in vitro neutralization tests impossible. In 2005 other investigators showed that RNA from a genotype 2a strain of HCV replicates in Huh 7.5 cells (a human hepatoma cell line) and produces infectious virus (HCVcc). However, several HCV genotypes have great variability in the envelope region, which is targeted by neutralizing antibodies. We therefore generated genotype 1a/2a, 1b/2a and 3a/2a chimeric viruses carrying the respective structural proteins (core, E1, E2, p7) in the replicating backbone of a 2a virus. We showed that these chimeric viruses replicate in cell culture as efficiently as the 2a genotype and have used them to assess cross-neutralizing antibody in large sets of samples.

We are developing biomarkers to predict clearance of virus in vaccinees. We have performed qualitative comparisons of the recall T-cell responses in recovered or re-challenged chimpanzees with the recall responses in vaccinated chimpanzees. The results demonstrated specific differences in memory T-cells associated with HCV clearance. To replace the chimpanzee model, we have now transitioned to using immunocompetent mice to compare T-cell responses induced by different vaccine vectors. Even though these mice cannot be challenged with HCV, qualitative and quantitative differences in T-cells induced following vaccination can be studied.

Important Links

ORCID ID: 0000-0003-0874-359X
Innovation and Regulatory Science - Research Summary: FDA model suggests that a hepatitis C vaccine could reduce transmission of the virus among injecting drug users

Publications

Vaccine 2023 Dec 23 [Epub ahead of print] Vaccine-associated respiratory pathology correlates with viral clearance and protective immunity after immunization with self-amplifying RNA expressing the spike (S) protein of SARS-CoV-2 in mouse models. Kachko A, Selvaraj P, Liu S, Kim J, Rotstein D, Stauft CB, Chabot S, Rajasagi N, Zhao Y, Wang T, Major M
Int J Mol Sci 2023 Feb 14;24(4):3774 Distinct conformations of SARS-CoV-2 Omicron spike protein and its interaction with ACE2 and antibody. Lee M, Major M, Hong H
Vaccine 2023 Jan 23;41(4):955-64 Assessing the impact of hepatitis B immune globulin (HBIG) on responses to hepatitis B vaccine during co-administration. Zubkova I, Zhao Y, Cui Q, Kachko A, Gimie Y, Chabot S, Murphy T, Schillie S, Major M
J Virol 2022 Sep;96(18):e0116621 Enhanced in vitro and in vivo potency of a T cell epitope in the Ebola virus glycoprotein following amino acid replacement at HLA-A*02:01 binding positions. Chabot S, Gimie Y, Obeid K, Kim J, Meseda CA, Konduru K, Kaplan G, Sheng Fowler L, Weir JP, Peden K, Major ME
PLoS One 2022 Mar 10;17(3):e0264983 Modeling hepatitis C micro-elimination among people who inject drugs with direct-acting antivirals in metropolitan Chicago. Tatara E, Gutfraind A, Collier NT, Echevarria D, Cotler SJ, Major ME, Ozik J, Dahari H, Boodram B
Gastroenterology 2022 Feb;162(2):562-74 An antigenically diverse, representative panel of envelope glycoproteins for HCV vaccine development. Salas JH, Urbanowicz RA, Guest JD, Frumento N, Figueroa A, Clark KE, Keck Z, Cowton VM, Cole SJ, Patel AH, Fuerst TR, Drummer HE, Major M, Tarr AW, Ball JK, Law M, Pierce BG, Foung SKH, Bailey JR
Proc Winter Simul Conf 2019 Dec;2019:1008-1019 Multi-objective model exploration of hepatitis C elimination in an agent-based model of people who inject drugs. Tatara E, Collier NT, Ozik J, Gutfraind A, Cotler SJ, Dahari H, Major M, Boodram B
Vaccine 2019 May 1;37(19):2608-16 Modeling indicates efficient vaccine-based interventions for the elimination of hepatitis C virus among persons who inject drugs in metropolitan Chicago. Echevarria D, Gutfraind A, Boodram B, Layden J, Ozik J, Page K, Cotler SJ, Major M, Dahari H
Methods Mol Biol 2019;1911:421-32 Detection of antibodies to HCV E1E2 by lectin-capture ELISA. Major M, Law M
Sci Transl Med 2018 Jul 11;10(449):eaao4496 Modeling of patient virus titers suggests that availability of a vaccine could reduce hepatitis C virus transmission among injecting drug users. Major M, Gutfraind A, Shekhtman L, Cui Q, Kachko A, Cotler SJ, Hajarizadeh B, Sacks-Davis R, Page K, Boodram B, Dahari H
J Virol 2018 Mar;92(6):e01742-17 Determinants in the IgV domain of human HAVCR1 (TIM-1) are required to enhance hepatitis C virus entry. Kachko A, Costafreda MI, Zubkova I, Jacques J, Takeda K, Wells F, Kaplan G, Major ME
Antiviral Res 2017 Aug;144:281-5 Modeling HCV cure after an ultra-short duration of therapy with direct acting agents. Goyal A, Lurie Y, Meissner EG, Major M, Sansone N, Uprichard SL, Cotler SJ, Dahari H
PLoS One 2017 Jul 21;12(7):e0181578 Qualitative differences in cellular immunogenicity elicited by hepatitis C virus T-Cell vaccines employing prime-boost regimens. Tan WG, Zubkova I, Kachko A, Wells F, Adler H, Sutter G, Major ME
Gut 2016 Jan;65(1):4-5 Hepatitis C: new clues to better vaccines? Major ME
Hepatology 2015 Dec;62(6):1670-82 Antibodies to an interfering epitope in hepatitis C Virus E2 can mask vaccine-induced neutralizing activity. Kachko A, Frey SE, Sirota L, Ray R, Wells F, Zubkova I, Zhang P, Major ME
PLoS One 2015 Sep 30;10(9):e0137993 Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections. Gutfraind A, Boodram B, Prachand N, Hailegiorgis A, Dahari H, Major ME
PLoS One 2015 Aug 21;10(8):e0135901 Mathematical modeling of hepatitis C prevalence reduction with antiviral treatment scale-up in persons who inject drugs in metropolitan Chicago. Echevarria D, Gutfraind A, Boodram B, Major M, Del Valle S, Cotler SJ, Dahari H
EBioMedicine 2015 Jun 30;2(8):857-65 Reverse engineering of vaccine antigens using high throughput sequencing-enhanced mRNA display. Guo N, Duan H, Kachko A, Krause BW, Major ME, Krause PR

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May 2, 2024

Purdue University chosen by Indiana Office of Energy Development for small modular nuclear reactor study

A Purdue University-led team was selected by the Indiana Office of Energy Development as the successful respondent to a request for proposals for a study to research small modular nuclear reactor technology and the potential impacts should the technology be deployed in Indiana. (Purdue University photo/Kelsey Lefever)

WEST LAFAYETTE, Ind. — Purdue University has been selected by the Indiana Office of Energy Development (IOED) to research small modular nuclear reactor (SMR) technology and analyze the potential impacts should the technology be deployed in Indiana. This partnership comes following Purdue’s selection as the successful respondent to IOED’s Request for Proposals for an Indiana-focused SMR study.

“The energy transition is ongoing and will be for decades to come. In Indiana, we’ve added a lot of tools to our toolbox to help better manage the energy transition, but the conversations must continue,” said Ryan Hadley, executive director, Indiana Office of Energy Development. “This SMR study is reflective of a proactive spirit to learn more about Indiana’s possible energy future. We are eager to learn more from this opportunity.”

The SMR study aligns with the IOED’s mission to provide comprehensive energy planning and policy development for Indiana that is affordable, stable, reliable and inclusive of a diverse and balanced generation mix.

“As a state and national leader in nuclear engineering education and research, along with our proven track record in innovation and energy generation, we are uniquely positioned to work with the Office of Energy Development on this exciting endeavor,” said Dr. Seungjin Kim, the Capt. James F. McCarthy, Jr. and Cheryl E. McCarthy Head of the School of Nuclear Engineering at Purdue University, who is leading the study as the principal investigator (PI). “We carefully assembled a project team that consists of experts from Purdue University as well as Argonne National Laboratory, Energy Systems Network and Ivy Tech Community College.”

The assembled team, in coordination with the IOED, will perform an extensive study on topics related to SMR technology:

Current status of SMR technology
State and local economic impact
Workforce development and employment
Safety, environmental impact and nuclear waste, siting considerations
Community engagement needs and best practices

Interest in SMR technology in Indiana has grown in recent years. The Indiana General Assembly has passed legislation related to SMR technology development ( IC 8-1-8.5-12.1 ), and Purdue and Duke Energy published an interim report related to their ongoing study investigating the feasibility of using advanced nuclear energy to meet the West Lafayette campus community’s long-term energy needs. Through collaboration with some of the leading experts in advanced nuclear technology in both the public and private sector, Purdue and Duke Energy identified a number of findings and recommended next steps in the interim report, which are succinctly summarized in a two-page fact sheet .

“Purdue began seriously researching the practical application of small modular nuclear technology with Duke Energy in 2022 when we kicked off our joint feasibility study,” said Ryan Gallagher, associate vice president of Purdue Facilities Operations and Environmental Health and Safety and a co-PI for the project. “Since then, we have continued to expand our knowledge on topics including technology development, approval and implementation timelines, siting considerations and other factors that position us well to help the state evaluate the potential benefits SMRs could bring to Indiana.”

Work on the SMR study began earlier this year and will be completed by Oct. 31, 2024.

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Module 1: Lifespan Development

Developmental research designs, learning outcomes.

Compare advantages and disadvantages of developmental research designs (cross-sectional, longitudinal, and sequential)

Cross-sectional designs

Figure 1 . Example of cross-sectional research design

Longitudinal research designs

Figure 2 . Longitudinal research studies the same person or group of people over an extended period of time.

Longitudinal research involves beginning with a group of people who may be of the same age and background (cohort) and measuring them repeatedly over a long period of time. One of the benefits of this type of research is that people can be followed through time and be compared with themselves when they were younger; therefore changes with age over time are measured. What would be the advantages and disadvantages of longitudinal research? Problems with this type of research include being expensive, taking a long time, and participants dropping out over time. Think about the film, 63 Up , part of the Up Series mentioned earlier, which is an example of following individuals over time. In the videos, filmed every seven years, you see how people change physically, emotionally, and socially through time; and some remain the same in certain ways, too. But many of the participants really disliked being part of the project and repeatedly threatened to quit; one disappeared for several years; another died before her 63rd year. Would you want to be interviewed every seven years? Would you want to have it made public for all to watch?

Figure 3 . Example of a longitudinal research design

Sequential research designs

Figure 4. Example of sequential research design

Schaie, K.W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64(2), 92-107. ↵
Schaie, K.W. & Baltes, B.P. (1975). On sequential strategies in developmental research: Description or Explanation. Human Development, 18: 384-390. ↵
Modification, adaptation, and original content. Authored by : Margaret Clark-Plaskie for Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Research Methods in Developmental Psychology. Authored by : Angela Lukowski and Helen Milojevich. Provided by : University of Calfornia, Irvine. Located at : https://nobaproject.com/modules/research-methods-in-developmental-psychology?r=LDcyNTg0 . Project : The Noba Project. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
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Developmental Research

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First Online: 01 January 2014
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Kees Klaassen 2 &
Koos Kortland 2

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Introduction

Developmental research is a particular way of addressing the basic questions of why and how to teach what to whom. It involves a cyclical process of small-scale in-depth development and evaluation, at a content-specific level, of exemplary teaching-learning sequences. It aims to produce an empirically supported justification of the inner workings of such a sequence, which is claimed to be an important contribution to the expertise of teachers, curriculum developers, and educational researchers.

The Inner Workings of a Teaching-Learning Sequence

Two related elements are involved in the intended justification of a teaching-learning sequence about some topic. First, a detailed description of the desired (by the researcher) development in what students believe, intend to achieve, are pleased about, and so on, in relation to the topical contents. Second, a detailed explanation of why students’ beliefs, intentions, emotions, etc., can be expected to develop as described, given...

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Klaassen, K., Kortland, K. (2013). Developmental Research. In: Gunstone, R. (eds) Encyclopedia of Science Education. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6165-0_155-1

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Science and Technology Directorate
S&T Releases Market Survey Report for Non-Detonable Training Aids for Explosive Detection Canines

News Release: DHS S&T Releases Market Survey Report for Non-Detonable Training Aids for Explosive Detection Canines

For immediate release s&t public affairs , 202-286-9047.

WASHINGTON - The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) has released a new market survey report to help emergency responders identify non-detonable training aids for explosive detection canines. Non-detonable training aids emulate the scent of explosives, allowing canines to learn the specific odor of different types of explosives while eliminating the inherent risks of using traditional, live explosives. They are carefully designed and maintained to create a controlled and safe environment for training, with a focus on safety, effectiveness, and consistency in preparing canines for their crucial roles in security and public safety.

A canine near the rear of a car. Non-Detonable Training Aids for Explosives Detection Canines Market Survey Report. February 2024. S&T and NUSTL logos.

S&T’s National Urban Security Laboratory (NUSTL)—in conjunction with Johns Hopkins Applied Physics Laboratory—administered the Non-Detonable Trainings Aids for Explosive Detection Canines Market Survey Report , which provides information on 12 non-detonable training aid products ranging in price from $15 to $550. This report is based on information gathered from manufacturer and vendor materials, open-source research, industry publications, and a government-issued request for information. The report is part of NUSTL’s System Assessment and Validation for Emergency Responders (SAVER) program, which assists emergency responders in making procurement decisions.

“The Detection Canine Program at S&T plays a critical role in advancing the safety and effectiveness of explosive detection canines in the field,” said Guy Hartsough, S&T Detection Canine Program, program manager. “NUSTL’s comprehensive report provides valuable resources in an ever-evolving landscape of threats, underscoring our dedication to enhancing the capabilities of our nation's security responders.”

“Explosive detection canines are critical to protecting the public as well as the first responders they assist. Rigorous training is required to prepare these dogs for the field” said NUSTL Director Alice Hong. “NUSTL’s latest market survey report equips explosive detection canine handlers with crucial insights into non-detonable training aids.”

Visit the SAVER website for market research and comparative assessments of commercially available products. Results are published to assist responders in making informed technology deployment and purchasing decisions for their agency’s specific needs. SAVER documents with limited distribution are available to members of the SAVER Community by contacting [email protected] .

For more information about NUSTL and its mission as the only national laboratory dedicated to serving the nation’s first responders, visit www.dhs.gov/science-and-technology/national-urban-security-technology-laboratory

Science and Technology
Detection Canine
Explosive Detection Canine

COMMENTS

Developmental Research
Developmental research is a way of studying the inner workings of teaching-learning sequences and their effects on students' development. It involves a cyclical process of in-depth development, evaluation, and justification of exemplary sequences, based on empirical evidence and value-laden choices.
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Learn about the different types of research methods used to study human development, such as cross-sectional, longitudinal, correlational, and experimental. Each method has its advantages and disadvantages, and each aims to test a hypothesis and evaluate the cause-and-effect relationship of a phenomenon.
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Handbook of developmental research methods.
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A chapter that discusses the role of research in educational design and development activities, focusing on the rationale, principles, and methods of development research. It also explores some of the problems and dilemmas, and challenges for further action and reflection in this field.
Developmental Research Designs
Research design is the strategy or blueprint for deciding how to collect and analyze information. Research design dictates which methods are used and how. Developmental research designs are techniques used particularly in lifespan development research. When we are trying to describe development and change, the research designs become especially ...
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Purdue University chosen by Indiana Office of Energy Development for
Purdue University has been selected by the Indiana Office of Energy Development (IOED) to research small modular nuclear reactor (SMR) technology and analyze the potential impacts should the technology be deployed in Indiana. This partnership comes following Purdue's selection as the successful respondent to IOED's Request for Proposals for an Indiana-focused SMR study.
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Developmental Research
Developmental research is a particular way of addressing the basic questions of why and how to teach what to whom. It involves a cyclical process of small-scale in-depth development and evaluation, at a content-specific level, of exemplary teaching-learning sequences. It aims to produce an empirically supported justification of the inner ...
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S&T's National Urban Security Laboratory (NUSTL)—in conjunction with Johns Hopkins Applied Physics Laboratory—administered the Non-Detonable Trainings Aids for Explosive Detection Canines Market Survey Report, which provides information on 12 non-detonable training aid products ranging in price from $15 to $550.This report is based on information gathered from manufacturer and vendor ...
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1.11: Developmental Research Designs

Cross-sectional designs

Longitudinal research designs

Sequential research designs

Contributors and Attributions

Developmental Psychology Research Methods

Cross-Sectional Research Methods

At a Glance

Challenges of Longitudinal Research

Limitations of Correlational Research

Challenges in Experimental Resarch

What This Means For You

Developmental Research Designs

Cross-Sectional Designs

Longitudinal Research Designs

Sequential Research Designs

Comparing Developmental Research Designs

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Developmental Research Methods

Sample Materials & Chapters

Developmental Research Designs

Cross-Sectional Designs

Longitudinal Research Designs

Sequential Research Designs

Comparing Developmental Research Designs

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Developmental Research Designs

Cross-sectional designs

Longitudinal research designs

Sequential research designs

Challenges Associated with Conducting Developmental Research

Ethical Concerns

Recruitment

Conclusions

6 Developmental Research Designs

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