Additional Terms and Concepts

As emphasized throughout this text, the study of development is a science. Social scientists spend years in graduate school, studying methods and statistics. Chapter 1 touches on some of these matters (observation and experiments; correlation and statistical significance; independent and dependent variables; experimental and control groups; cross-sectional, longitudinal, and cross-sequential research), but there is much more. A few additional aspects of research are presented here, to help you evaluate research wherever you find it.

Who Participates?

population The entire group of individuals who are of particular concern in a scientific study, such as all the children of the world or all newborns who weigh less than 3 pounds.

The entire group of people about whom a scientist wants to learn is called the population. Generally, a research population is quite large—not usually the world’s entire population of almost 8 billion, but perhaps all the 4 million babies born in the United States last year, or all the 25 million Japanese currently over age 65.

participants The people who are studied in a research project.

sample A group of individuals drawn from a specified population. A sample might be the low-birthweight babies born in four particular hospitals that are representative of all hospitals.

representative sample A group of research participants who reflect the relevant characteristics of the larger population whose attributes are under study.

The particular individuals who are studied in a specific research project are called the participants. They are used as a sample of the larger group. Ideally, a large number of people are used as a representative sample, that is, a sample who reflect the entire population. Every peer-reviewed published study reports details on the sample.

Selection of the sample is crucial. Volunteers, or people with telephones, or people treated with some particular condition, are not a random sample, in which everyone in that population is equally likely to be selected. To avoid selection bias, some studies are prospective, beginning with an entire cluster (for instance, every baby born on a particular day) and then tracing the development of some particular characteristic.

For example, prospective studies find the antecedents of heart disease, or child abuse, or high school dropout rates—all of which are much harder to find if the study is retrospective, beginning with those who had heart attacks, experienced abuse, or left school. Thus, although retrospective research finds that most high school dropouts say they disliked school, prospective research finds that some who like school still decide to drop out and then later say they hated school, while others dislike school but stay to graduate. Prospective research discovers how many students are in these last two categories; retrospective research on people who have already dropped out does not.

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Research Design

blind The condition of data gatherers (and sometimes participants as well) who are deliberately kept ignorant of the purpose of the research so that they cannot unintentionally bias the results.

Every researcher begins not only by formulating a hypothesis but also by learning what other scientists have discovered about the topic in question and what methods might be useful and ethical in designing research. Often they include measures to guard against inadvertently finding only the results they expect. For example, the people who actually gather the data may not know the purpose of the research. Scientists say that these data gatherers are blind to the hypothesized outcome. Participants are sometimes “blind” as well, because otherwise they might, for instance, respond the way they think they should.

operational definition A description of the specific, observable behavior that will constitute the variable that is to be studied, so that any reader will know whether that behavior occurred or not. Operational definitions may be arbitrary (e.g., an IQ score at or above 130 is operationally defined as “gifted”), but they must be precise.

Another crucial aspect of research design is to define exactly what is to be studied. Researchers establish an operational definition of whatever phenomenon they will be examining, defining each variable by describing specific, observable behavior. This is essential in quantitative research (see Chapter 1), but it is also useful in qualitative research. For example, if a researcher wants to know when babies begin to walk, does walking include steps taken while holding on? Is one unsteady step enough? Some parents say yes, but the usual operational definition of walking is “takes at least three steps without holding on.” This operational definition allows comparisons worldwide, making it possible to discover, for example, that well-fed African babies tend to walk earlier than well-fed European babies.

Operational definitions are difficult but essential when personality traits are studied. How should aggression or sharing or shyness be defined? Lack of an operational definition leads to contradictory results. For instance, some say that infant day care makes children more aggressive, but others say it makes them less passive. Similarly, as explained in the Epilogue, the operational definition of death is the subject of heated disputes. For any scientist, operational definitions are crucial.

Reporting Results

meta-analysis A technique of combining results of many studies to come to an overall conclusion. Meta-analysis is powerful, in that small samples can be added together to lead to significant conclusions, although variations from study to study sometimes make combining them impossible.

You already know that results should be reported in sufficient detail so that another scientist can analyze the conclusions and replicate the research. Various methods, populations, and research designs may produce divergent conclusions. For that reason, handbooks, some journals, and some articles are called reviews: They summarize past research. Often, when studies are similar in operational definitions and methods, the review is a meta-analysis, combining the findings of many studies to present an overall conclusion.

Table 1.4 describes some statistical measures. One of them is statistical significance, which indicates whether or not a particular result could have occurred by chance.

effect size A way to indicate, statistically, how much of an impact the independent variable had on the dependent variable.

Another statistic that is often crucial is effect size, a way of measuring how much impact one variable has on another. Effect size ranges from 0 (no effect) to 1 (total transformation, never found in actual studies). Effect size may be particularly important when the sample size is large, because a large sample often leads to highly “significant” results (unlikely to have occurred by chance) that have only a tiny effect on the variable of interest.

Hundreds of statistical measures are used by developmentalists. Often the same data can be presented in many ways: Some scientists examine statistical analysis intently before they accept conclusions as valid. A specific example involved methods to improve students’ writing ability between grades 4 and 12. A meta-analysis found that many methods of writing instruction have a significant impact, but effect size is much larger for some methods (teaching strategies and summarizing) than for others (prewriting exercises and studying models). For teachers, this statistic is crucial, for they want to know what has a big effect, not merely what is better than chance (significant).

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Numerous articles published in the past decade are meta-analyses that combine similar studies to search for general trends. Often effect sizes are also reported, which is especially helpful for meta-analysis since standard calculations almost always find some significance if the number of participants is in the thousands. Here are three recent examples, to help you grasp the use and implications of meta-analyses and effect size.

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