Which is the best developmental science research design?

There are various methods of research, each with its 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 by which to test a hypothesis and evaluate whether the hypothesis was 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.

There are a number of different ways to conduct research. Here are the most common. 

Cross-Sectional Research

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

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 either a smaller group of subjects or a narrower field of observation.

While revealing, 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.

We saw this with the Terman study wherein 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

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 is useful if you are unable to manipulate a variable because it is either impossible, impractical, or unethical. While you might submit, for instance, that living in a noisy environment makes you less efficient in the workplace, it would be impractical and unreasonable to inject that variable artificially.

Correlational research clearly has its limitations. While it can be used to 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.

Experimentation

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
• Randomization, in which subjects are chosen randomly to see if the effects of the variable are consistently achieved

While the statistical value of an experimental study is robust, it's one major shortcoming may be 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.

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