Some examples of descriptive questions include:. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior.
Evaluation research is designed to assess the effectiveness of policies or programs. For instance, research might be designed to study the effectiveness of safety programs implemented in schools for installing car seats or fitting bicycle helmets. Do children who have been exposed to the safety programs wear their helmets? Do parents use car seats properly? If not, why not? We have just learned about some of the various models and objectives of research in lifespan development.
All types of research methods have unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data.
Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected. Observational studies , also called naturalistic observation, involve watching and recording the actions of participants.
This may take place in the natural setting, such as observing children at play in a park, or behind a one-way glass while children are at play in a laboratory playroom. The researcher may follow a checklist and record the frequency and duration of events perhaps how many conflicts occur among 2-year-olds or may observe and record as much as possible about an event as a participant such as attending an Alcoholics Anonymous meeting and recording the slogans on the walls, the structure of the meeting, the expressions commonly used, etc.
The researcher may be a participant or a non-participant. What would be the strengths of being a participant? What would be the weaknesses? In general, observational studies have the strength of allowing the researcher to see how people behave rather than relying on self-report. One weakness of self-report studies is that what people do and what they say they do are often very different. A major weakness of observational studies is that they do not allow the researcher to explain causal relationships.
Yet, observational studies are useful and widely used when studying children. It is important to remember that most people tend to change their behavior when they know they are being watched known as the Hawthorne effect and children may not survey well. Case studies involve exploring a single case or situation in great detail. Information may be gathered with the use of observation, interviews, testing, or other methods to uncover as much as possible about a person or situation.
Case studies are helpful when investigating unusual situations such as brain trauma or children reared in isolation. And they are often used by clinicians who conduct case studies as part of their normal practice when gathering information about a client or patient coming in for treatment.
Case studies can be used to explore areas about which little is known and can provide rich detail about situations or conditions. However, the findings from case studies cannot be generalized or applied to larger populations; this is because cases are not randomly selected and no control group is used for comparison.
Oliver Sacks as a good example of the case study approach. Figure 2. A survey is a common tool for collecting research data. Surveys are familiar to most people because they are so widely used. Surveys enhance accessibility to subjects because they can be conducted in person, over the phone, through the mail, or online.
A survey involves asking a standard set of questions to a group of subjects. Surveys typically yield surface information on a wide variety of factors, but may not allow for an in-depth understanding of human behavior. Of course, surveys can be designed in a number of ways. They may include forced-choice questions and semi-structured questions in which the researcher allows the respondent to describe or give details about certain events.
So a lot of time and effort should be placed on the construction of survey items. One of the benefits of having forced-choice items is that each response is coded so that the results can be quickly entered and analyzed using statistical software.
The analysis takes much longer when respondents give lengthy responses that must be analyzed in a different way. Surveys are useful in examining stated values, attitudes, opinions, and reporting on practices. However, they are based on self-report, or what people say they do rather than on observation, and this can limit accuracy.
Validity refers to accuracy and reliability refers to consistency in responses to tests and other measures; great care is taken to ensure the validity and reliability of surveys.
In this video, Harvard psychologist Dan Gilbert explains survey research that was conducted to explore the way our preferences change over time. Content analysis involves looking at media such as old texts, pictures, commercials, lyrics or other materials to explore patterns or themes in culture.
Passages in text or television programs can be randomly selected for analysis as well. Again, one advantage of analyzing work such as this is that the researcher does not have to go through the time and expense of finding respondents, but the researcher cannot know how accurately the media reflects the actions and sentiments of the population.
Secondary content analysis, or archival research, involves analyzing information that has already been collected or examining documents or media to uncover attitudes, practices or preferences. There are a number of data sets available to those who wish to conduct this type of research. The researcher conducting secondary analysis does not have to recruit subjects but does need to know the quality of the information collected in the original study.
And unfortunately, the researcher is limited to the questions asked and data collected originally. Census Data is available and widely used to look at trends and changes taking place in the United States visit the United States Census website and check it out. There are also a number of other agencies that collect data on family life, sexuality, and on many other areas of interest in human development go to the NORC at the University of Chicago website or the Henry J Kaiser Family Foundation website and see what you find.
When scientists passively observe and measure phenomena it is called correlational research. Here, researchers do not intervene and change behavior, as they do in experiments. In correlational research, the goal is to identify patterns of relationships, but not cause and effect.
Importantly, with correlational research, you can examine only two variables at a time, no more and no less. You could use a correlational design—which is exactly what Professor Elizabeth Dunn at the University of British Columbia did when she conducted research on spending and happiness.
She asked people how much of their income they spent on others or donated to charity, and later she asked them how happy they were. Do you think these two variables were related? Yes, they were! The more money people reported spending on others, the happier they were. To find out how well two variables correlate, you can plot the relationship between the two scores on what is known as a scatterplot.
In the scatterplot, each dot represents a data point. Importantly, each dot provides us with two pieces of information—in this case, information about how good the person rated the past month x-axis and how happy the person felt in the past month y-axis. Which variable is plotted on which axis does not matter.
Figure 3. Each dot represents an individual. The association between two variables can be summarized statistically using the correlation coefficient abbreviated as r. A correlation coefficient provides information about the direction and strength of the association between two variables.
For the example above, the direction of the association is positive. This means that people who perceived the past month as being good reported feeling more happy, whereas people who perceived the month as being bad reported feeling less happy. With a positive correlation , the two variables go up or down together. In a scatterplot, the dots form a pattern that extends from the bottom left to the upper right just as they do in Figure 1.
The r value for a positive correlation is indicated by a positive number although, the positive sign is usually omitted. Here, the r value is. A negative correlation is one in which the two variables move in opposite directions. That is, as one variable goes up, the other goes down. Figure 2 shows the association between the average height of males in a country y-axis and the pathogen prevalence or commonness of disease; x-axis of that country. In this scatterplot, each dot represents a country.
Notice how the dots extend from the top left to the bottom right. What does this mean in real-world terms? It means that people are shorter in parts of the world where there is more disease. The r value for a negative correlation is indicated by a negative number—that is, it has a minus — sign in front of it. Here, it is —. Figure 4. Each dot represents a country Chiao, The strength of a correlation has to do with how well the two variables align. At this point you may be thinking to yourself, I know a very generous person who gave away lots of money to other people but is miserable!
Or maybe you know of a very stingy person who is happy as can be. Yes, there might be exceptions. If an association has many exceptions, it is considered a weak correlation. If an association has few or no exceptions, it is considered a strong correlation. A strong correlation is one in which the two variables always, or almost always, go together. In the example of happiness and how good the month has been, the association is strong. The stronger a correlation is, the tighter the dots in the scatterplot will be arranged along a sloped line.
The r value of a strong correlation will have a high absolute value a perfect correlation has an absolute value of the whole number one, or 1.
In other words, you disregard whether there is a negative sign in front of the r value, and just consider the size of the numerical value itself.
If the absolute value is large, it is a strong correlation. A weak correlation is one in which the two variables correspond some of the time, but not most of the time.
Figure 3 shows the relation between valuing happiness and grade point average GPA. People who valued happiness more tended to earn slightly lower grades, but there were lots of exceptions to this.
The r value for a weak correlation will have a low absolute value. If two variables are so weakly related as to be unrelated, we say they are uncorrelated, and the r value will be zero or very close to zero. In the previous example, is the correlation between height and pathogen prevalence strong? Compared to Figure 3, the dots in Figure 2 are tighter and less dispersed. The absolute value of —. Therefore, it is a strong negative correlation.
Figure 5. If generosity and happiness are positively correlated, should we conclude that being generous causes happiness? Similarly, if height and pathogen prevalence are negatively correlated, should we conclude that disease causes shortness? For example, in the first case, it may be that happiness causes generosity, or that generosity causes happiness. Or, a third variable might cause both happiness and generosity, creating the illusion of a direct link between the two.
For example, wealth could be the third variable that causes both greater happiness and greater generosity. This is why correlation does not mean causation—an often repeated phrase among psychologists. In this video, University of Pennsylvania psychologist and bestselling author, Angela Duckworth describes the correlational research that informed her understanding of grit.
Experiments are designed to test hypotheses or specific statements about the relationship between variables in a controlled setting in efforts to explain how certain factors or events produce outcomes.
A variable is anything that changes in value. Concepts are operationalized or transformed into variables in research which means that the researcher must specify exactly what is going to be measured in the study.
For example, if we are interested in studying marital satisfaction, we have to specify what marital satisfaction really means or what we are going to use as an indicator of marital satisfaction.
What is something measurable that would indicate some level of marital satisfaction? Would it be the amount of time couples spend together each day? Nurture refers to the impact of the environment, which involves the process of learning through experiences.
Stability implies personality traits present during infancy endure throughout the lifespan. In contrast, change theorists argue that personalities are modified by interactions with family, experiences at school, and acculturation. This capacity for change is called plasticity. For example, Rutter discovered than somber babies living in understaffed orphanages often become cheerful and affectionate when placed in socially stimulating adoptive homes.
Developmental psychology as a discipline did not exist until after the industrial revolution when the need for an educated workforce led to the social construction of childhood as a distinct stage in a person's life. The notion of childhood originates in the Western world and this is why the early research derives from this location. Initially, developmental psychologists were interested in studying the mind of the child so that education and learning could be more effective.
Developmental changes during adulthood is an even more recent area of study. This is mainly due to advances in medical science, enabling people to live to old age. Charles Darwin is credited with conducting the first systematic study of developmental psychology. In he published a short paper detailing the development of innate forms of communication based on scientific observations of his infant son, Doddy.
However, the emergence of developmental psychology as a specific discipline can be traced back to when Wilhelm Preyer a German physiologist published a book entitled The Mind of the Child. In the book, Preyer describes the development of his own daughter from birth to two and a half years. Importantly, Preyer used rigorous scientific procedures throughout studying the many abilities of his daughter.
In Preyer's publication was translated into English, by which time developmental psychology as a discipline was fully established with a further 47 empirical studies from Europe, North America and Britain also published to facilitate the dissemination of knowledge in the field. During the s three key figures have dominated the field with their extensive theories of human development, namely Jean Piaget , Lev Vygotsky and John Bowlby Indeed, much of the current research continues to be influenced by these three theorists.
McLeod, S. Developmental psychology. Simply Psychology. Children experience rapid physical changes through infancy and early childhood. In a longitudinal study, a researcher observes many individuals born at or around the same time and observes them as they age.
In a cross-sectional study, a researcher observes differences between individuals of different ages at the same time. This generally requires fewer resources than the longitudinal method, and because the individuals come from different cohorts, shared historical events are not as unique. However, this method may not be the most effective way to study differences between participants, as these differences may result not from their different ages but from their exposure to different historical events.
Cross-sequential designs combine both longitudinal and cross-sectional design methodologies. A researcher observes members of different birth cohorts at the same time, and then tracks all participants over time, charting changes in the groups. While much more resource-intensive, this method results in a clearer distinction between changes that can be attributed to individual or historical environment and changes that are truly universal.
Microgenetic design studies the same cohort over a short period of time. In contrast to longitudinal and cross-sectional designs, which provide broad outlines of the process of change, microgenetic designs provide an in-depth analysis of children's behavior while it is changing.
Boundless Psychology. Human Development. Introduction to Human Development. Concept Version Learning Objective Assess the various scientific research methods for investigating human development. Key Points To study changes in individuals over time, developmental psychologists use systematic observation; self-reports, clinical interviews, or structured observation; case studies ; and ethnography or participant observation.
Three common research methods are the experimental method which investigates cause and effect , correlational method which explores relationships between variables , and the case study approach which provides in-depth information about a particular case.
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