Name: tracking the same people over time
Uploaded: 2020-02-12T11:07:39.000Z
Duration: 2 min 20 s
Channel: JoVE

K12 Standards

Virginia  Standards of Learning

HIgh School

Biology

BIO.1

BIO.1.b

Longitudinal Research

Sometimes the goal of a psychological study may be to understand how people&rsquo;s attitudes and behaviors change over time, or to determine what factors may predict future abilities.

These objectives can be accomplished using a longitudinal design&mdash;a research study where data are repeatedly collected from the same group of individuals for a period of time, whether it&rsquo;s as short as a few weeks or months or as long as several decades.

For example, if a researcher wants to know whether college students&rsquo; exercise routines change over the course of their first semester of college, she can use a longitudinal approach and ask students to repeatedly report their workout regiments. She may find that as students get more caught up in their studies, they go to the gym less often.

In addition, the same researcher may keep track of a group of people for twenty years, because she wants to explore how their exercise routines shift across their 20s, 30s and 40s. This approach allows her to best measure changes, within individuals, over time.

In this case, she may discover that those who enjoyed running outdoors in their 20s maintain blood pressure levels, display low amounts of stress, and are more lik

longitudinal research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again at age 40.

Let&#39;s consider another example. In recent years there has been significant growth in the popular support of same-sex marriage. Many studies on this topic break down survey participants into different age groups. In general, younger people are more supportive of same-sex marriage than are those who are older (Jones, 2013). Does this mean that as we age we become less open to the idea of same-sex marriage, or does this mean that older individuals have different perspectives because of the social climates in which they grew up? Longitudinal research is a powerful approach because the same individuals are involved in the research project over time, which means that the researchers need to be less concerned with differences among cohorts affecting the results of their study.

Often longitudinal studies are employed when researching various diseases in an effort to understand particular risk factors. Such studies often involve tens of thousands of individuals who are followed for several decades. Given the enormous number of people involved in these studies, researchers can feel confident that their findings can be generalized to the larger population. The Cancer Prevention Study-3 (CPS-3) is one of a series of longitudinal studies sponsored by the American Cancer Society aimed at determining predictive risk factors associated with cancer. When participants enter the study, they complete a survey about their lives and family histories, providing information on factors that might cause or prevent the development of cancer. Then every few years the participants receive additional surveys to complete. In the end, hundreds of thousands of participants will be tracked over 20 years to determine which of them develop cancer and which do not.

Clearly, this type of research is important and potentially very informative. For instance, earlier longitudinal studies sponsored by the American Cancer Society provided some of the first scientific demonstrations of the now well-established links between increased rates of cancer and smoking (American Cancer Society, n.d.).

As with any research strategy, longitudinal research is not without limitations. For one, these studies require an incredible time investment by the researcher and research participants. Given that some longitudinal studies take years, if not decades, to complete, the results will not be known for a considerable period of time. In addition to the time demands, these studies also require a substantial financial investment. Many researchers are unable to commit the resources necessary to see a longitudinal project through to the end.

Research participants must also be willing to continue their participation for an extended period of time, and this can be problematic. People move, get married and take new names, get ill, and eventually die. Even without significant life changes, some people may simply choose to discontinue their participation in the project. As a result, the attrition rates, or reduction in the number of research participants due to dropouts, in longitudinal studies are quite high and increases over the course of a project. For this reason, researchers using this approach typically recruit many participants fully expecting that a substantial number will drop out before the end. As the study progresses, they continually check whether the sample still represents the larger population, and make adjustments as necessary.
This text is adapted from <a href="https://cnx.org/contents/Sr8Ev5Og@10.24:iMyFZJzg@11/2-2-Approaches-to-Research" target="_blank">OpenStax, Psychology. OpenStax CNX.</a>

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals ...

Education

JoVE Core

Social Psychology

Foundations of Social Psychology

Research Methods

KEY TERMS AND CONCEPTS

tracking the same people over time

Longitudinal research tracks the same group of people over time. Researchers use this design when they want to study change in human development or across the lifespan. Data are collected from the same participants repeatedly over an extended period.

A simple example is a survey on dietary habits. The same people might answer at age 20, again at age 30, and again at age 40. This repeated testing helps researchers see how responses change over the years.

Longitudinal research can also help answer questions about social attitudes. One study might compare support for same-sex marriage across age groups, where younger people are often more supportive than older adults. A cross-sectional result like that cannot show whether people change as they age or whether different generations grew up in different social climates.

This design is powerful because the same individuals stay in the study over time. That means researchers do not need to worry as much about cohort differences, or differences between groups of people born in different time periods. Longitudinal studies are also often used in disease research to look for risk factors. Some of these studies follow tens of thousands of people for decades, which can make the results more useful for the larger population.

The Cancer Prevention Study-3, or CPS-3, is one example. Participants answer questions about their lives and family histories when they join the study. They then receive new surveys every few years so researchers can see which participants develop cancer and which do not. Earlier American Cancer Society longitudinal studies also helped show the link between smoking and higher cancer rates.

Longitudinal research has limits. It takes a great deal of time, so results may not be known for years or even decades. It also takes a lot of money, and many researchers cannot support a project for that long. Participants may move, marry, change names, get ill, or die, and some may simply stop taking part. This loss of participants is called attrition, and it usually grows as the study continues. For that reason, researchers often recruit many people at the start and keep checking whether the sample still represents the larger population.

Tracking the Same People Over Time

the scientific method

Research Questions and Falsifiable Hypotheses

Research questions and falsifiable hypotheses help scientists separate facts from opinions. Facts are observable realities. Opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In science, facts are established with evidence from empirical research, which means research based on observation and experience. Scientific knowledge grows through the scientific method, a process that connects ideas with real-world evidence. Researchers test theories and...

case studies

Studying Rare Cases in Psychology

Case studies help psychologists study rare people or rare situations in detail. This research method is used in observational research when scientists focus on one person or just a few individuals. Because the group is so small, researchers can collect very rich information and gain a deep understanding of the case. A well-known example is the New York Times story about Krista and Tatiana Hogan, Canadian twin girls who are conjoined at the head. Scientists have evidence that the twins may be...

naturalistic observations

Watching Behavior in Real Settings

Naturalistic observation is a research method for watching behavior in its real setting. Researchers try to record what people or animals do without changing the situation. The goal is to see behavior as naturally as possible. People often act differently when they know they are being watched. That is why observers must be inconspicuous, or hard to notice. In a classroom hand-washing example, a student observer might stand at a sink and pretend to put in contact lenses while secretly recording...

surveys

Survey Data in Psychology

Survey data in psychology comes from sets of questions answered by research participants. Surveys can be given on paper, online, or by speaking with people directly. They are usually quick to complete, which makes them useful when researchers want information from many people. A survey helps researchers study a sample, which is a smaller group chosen from a population, the larger group they want to understand. Because surveys can reach larger and more diverse samples, the results may reflect...

archival research

Using Records to Study Behavior

Archival research uses existing records and data sets to study behavior and answer research questions. Researchers do not collect new data from participants in this method. Instead, they look back at records that already exist to find patterns and relationships. A researcher might, for example, examine the academic records of students who enrolled in college over the past ten years. Those records could show how long students took to finish their degrees, along with course loads, grades, and...

Longitudinal research tracks the same group of people over time. Researchers use this design when they want to study change in human development or across the lifespan. Data are collected from the same participants repeatedly over an extended period. A simple example is a survey on dietary habits. The same people might answer at age 20, again at age 30, and again at age 40. This repeated testing helps researchers see how responses change over the years. Longitudinal research can also help...

cross-sectional research

Studying Age Groups at One Point in Time

Cross-sectional research compares different groups of people at the same time. Researchers use it when they want to study how a trait or behavior varies across age groups or other segments of the population. For example, a researcher interested in dietary habits could compare 20-year-old, 30-year-old, and 40-year-old individuals in one study. This approach does not require waiting 20 years to track how one group changes from decade to decade. It is a shorter-term way to collect data. <br...

group design

Experimental and Control Groups

Experimental and control groups are the two parts of a basic experiment. The groups should be the same except for one difference: the experimental manipulation. The experimental group gets the treatment or variable being tested, and the control group does not. Because that is the only planned difference, researchers can link any change between the groups to the manipulation rather than to chance. A clear example is a study on violent television and children’s behavior. In that setup, the...

factorial design

Factorial Analysis in ANOVA

Factorial analysis is a research design that uses ANOVA, or analysis of variance, to test how more than one independent variable affects a dependent variable. The independent variables are also called factors. A worker productivity study can show how salary and skill level may each influence the result. In this example, salary can be grouped into three levels: low, moderate, and high. Skill level can be grouped into two levels: entry level and experienced. The study would be called a two-way...

the placebo effect

How Expectations Shape Placebo Results

The placebo effect shows how expectations and beliefs can shape what people experience. In some situations, simply expecting something to happen can make it feel real. This effect is often discussed when scientists test a new medicine. A common example is a study on depression treatment. A pharmaceutical company may compare two groups. The experimental group receives the medication, and the control group does not. To keep participants from knowing which treatment they got, researchers may give...

blind procedures

Single-Blind and Double-Blind Studies

Single-blind and double-blind studies help researchers reduce bias in an experiment. Bias can happen when expectations affect how a study is carried out or how results are read. In this kind of research, blinding keeps those expectations from shaping the outcome. In a single-blind study, the participants do not know whether they are in the experimental group or the control group. The researcher who designed the study still knows who is in each group. This setup helps limit the effect of...

ethics in research

Research Oversight for Human and Animal Studies

Research oversight protects human dignity and safety in modern science. Good research must be ethical, and researchers must show that their studies meet that standard. This idea is especially important when studies involve people or animals. Studies with human participants follow strict rules to prevent harm. Research institutions that receive federal support for human studies must have access to an institutional review board, or IRB. An IRB is a committee made up of administrators,...

correlations

Reading Correlation Coefficients

Correlation describes a relationship between two or more variables. It does not automatically mean that one variable causes the other. When variables are correlated, one changes as the other changes, and that relationship can be measured with a correlation coefficient. A correlation coefficient is a number from -1 to +1. It is usually written as r. The number shows the strength of the relationship. Values closer to 1, whether positive or negative, show a stronger relationship and make changes...

cause and effect

Why Correlation Can Mislead

Correlation can be misleading when two variables move together for a different reason. A confounding variable, or hidden factor, may be causing the pattern instead of one variable directly affecting the other. The ice cream and crime example shows this clearly. When temperatures rise, more people go outside, buy ice cream, and may also have more chances for conflict. A correlation does not prove causation. Even when a relationship looks strong, we should not assume that one variable causes the...

reliability and validity

Testing Measurement Quality in Research

Reliable and valid measurements are essential in data collection and research. Reliability means a tool or test gives the same result in a consistent way. In psychology, that means the instruments used to collect data should produce reproducible results. Consistency alone is not enough. A kitchen scale can show the same reading every time, but it can still be wrong if it is not calibrated correctly. In that case, the scale is reliable because it is consistent, but it does not measure the...

regression toward the mean

Regression Toward the Mean in Data

Regression toward the mean is a statistical pattern in which very high or very low values move closer to the average when they are measured again. A blood pressure reading taken at one moment is one example. The change is usually caused by random error and chance, not by a real underlying shift. This idea matters because it can affect many kinds of research. If scientists do not account for regression toward the mean, they may think a treatment worked when the difference was really just a...

measures of central tendency

Mean, Median, and Mode in Data

Mean, median, and mode are the main ways to describe the center of a data set. The center is also a way to describe location. The mean and the median are the two most widely used measures of center. The mean is the arithmetic mean, which most people call the average. In everyday use, average and mean are often used to mean the same thing. The mode is another measure of center. It is the value that appears most often in a data set. If two values occur the same number of times, the data set is...

variation: normal distribution, range, and standard deviation

variation-normal-distribution-range-and-standard-deviation

Measuring Data Spread with Range and Variance

Variation describes how far scores are from each other in a data set. In psychology, researchers organize measurements of traits, features, or characteristics, called variables, in different ways. Qualitative data, such as ethnicity, can be counted to show the proportion and variety of groups in a sample or population. Quantitative data allow for more calculations, including measures of central tendency and variability. The mean, mode, and median are central tendency measures. They identify a...

statistical significance

Testing Whether Group Differences Matter

Statistical analysis helps determine whether differences between an experimental group and a control group are meaningful. After data are collected, researchers check whether the results are likely to be real or just due to chance. If a difference is caused by chance, it is not considered meaningful. In psychology, a group difference is usually called significant when the chance of it happening by accident is 5 percent or less. Another way to say this is that if the experiment were repeated...

key terms and concepts

Learn how longitudinal research follows the same participants over time, why it is useful for studying change, and what limits long-term studies.

Tracking the Same People Over Time | JoVE K12

JoVE Core Social Psychology offers a comprehensive learning experience on the principles of social behavior through concise, animated video lessons. The series explores key topics such as self-concept, social cognition, group dynamics, stereotypes, and relationships. It provides a structured foundation for understanding human behavior in social situations. Students gain insights into how individuals think, feel, and behave in social contexts while examining the psychological processes that influence interactions and judgments. By integrating theory with real-world applications, these lessons offer a clear and engaging approach to mastering essential concepts in social psychology.

social psychology

BIO.1.f

Chemistry

CH.1

CH.1.b

CH.1.f

Texas Essential Knowledge and Skills

High School

Earth Systems Science

112.49.c.4.

112.49.c.4.B.

CHEM.4

CHEM.4.B

CHEM.4.C

Georgia Standards of Excellence

9-12

Epidemiology

SEPI3.

SEPI3.b

The Scientific Method

The scientific method is a detailed, empirical problem-solving process used by biologists and other scientists. This iterative approach involves ...

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in ...

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the ...

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A ...

The student will demonstrate an understanding of scientific and engineering practices by asking questions and defining problems
• ask questions that arise from careful observation of phenomena and/or
organisms, from examining models and theories, and/or to seek additional
information
• determine which questions can be investigated within the scope of the
school laboratory or field to determine relationships between independent
and dependent variables
• generate hypotheses based on research and scientific principles
• make hypotheses that specify what happens to a dependent variable when
an independent variable is manipulated

BIO.1.a

Case Studies

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological ...

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, ...

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be ...

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer ...

Cross-Sectional Research

In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people&#39;s dietary ...

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except ...

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable ...

The Placebo Effect

The placebo effect occurs when people&#39;s expectations or beliefs influence or determine their experience in a given situation. In other words, simply ...

Blind Procedures

Ideally, the people who observe and record the children&rsquo;s behavior are unaware of who was assigned to the experimental or control group, in order to ...

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not ...

The student will demonstrate an understanding of scientific and engineering practices by planning and carrying out investigations
 • individually and collaboratively plan and conduct observational and experimental investigations 
 • plan and conduct investigations or test design solutions in a safe and ethical manner including considerations of environmental, social, and personal effects
• determine appropriate sample size and techniques 
 • select and use appropriate tools and technology to collect, record, analyze, and evaluate data

Introduction to Statistics

The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is ...

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. ...

How Data are Classified: Numerical Data

Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data ...

Ogive Graph

An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, ...

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data ...

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. ...

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a ...

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct ...

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show ...

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can ...

Pareto Chart

A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the ...

Pie Chart

A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each ...

What is Central Tendency?

Descriptive statistics describe or summarize relevant characteristics of a sample and aid in the analysis of data of interest. When analyzing large ...

Arithmetic Mean

The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the ...

Mean From a Frequency Distribution

Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the ...

What is a Mode?

The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one ...

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected ...

Median

Besides mean, the median is a widely used measure of central tendency. Typically, median is defined as the central or middle value of a data set, measured ...

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control ...

Midrange

A somewhat easy to compute quantitative estimate of a data set&#8217;s central tendency is its midrange, which is defined as the mean of the minimum and ...

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and ...

What is Variation?

Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of ...

Range

The range is one of the measures of variation. It can be defined as the difference between a dataset&#39;s highest and lowest values. For example, in the ...

Standard Deviation

The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The ...

Standard Error of the Mean

The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is ...

Calculating Standard Deviation

The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. ...

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in ...

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more ...

The student will demonstrate an understanding of scientific and engineering practices by interpreting, analyzing, and evaluating data 
 • construct and interpret data tables showing independent and dependent variables, repeated trials, and means
 • construct, analyze, and interpret graphical displays of data
 • use data in building and revising models, supporting an explanation for phenomena, or testing solutions to problems
 • analyze data using tools, technologies, and/or models to make valid and reliable scientific claims or determine an optimal design solution

BIO.1.c

What is a Hypothesis?

A hypothesis can be a simple sentence or statement about a property or any phenomenon observed or predicted for a population. It is usually a claim about ...

Null and Alternative Hypotheses

The actual hypothesis testing begins by considering two hypotheses. They are termed&#160; the null hypothesis and the alternative hypothesis. These ...

critical-region-critical-values-and-significance-level

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample ...

P-value

P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value. &#160;P-value is the probability that, if the null ...

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed ...

The student will demonstrate an understanding of scientific and engineering practices by  constructing and critiquing conclusions and explanations
• make quantitative and/or qualitative claims regarding the relationship between dependent and independent variables 
• construct and revise explanations based on valid and reliable evidence obtained from a variety of sources including students' own investigations, models, theories, simulations, and peer review 
• apply scientific ideas, principles, and/or evidence to provide an explanation of phenomena and design solutions 
• compare and evaluate competing arguments or design solutions in light of currently accepted explanations and new scientific evidence
• construct arguments or counterarguments based on data and evidence differentiate between a scientific hypothesis and theory

BIO.1.d

Tracking the Same People Over Time

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