Best Practices in Quantitative Methods for Developmentalists (Paperback)

The role of quantitative methods in testing developmental

hypotheses is widely recognized, yet even very experienced

quantitative researchers often lack the knowledge required

for good decision-making on methodology. The end result is

a disconnect between research and practice in methods. The

purpose of this monograph is to fill a gap in the literature by

offering a series of overviews on common data-analytic

issues of particular interest to researchers in child development.

Our hope is that this monograph will make already

developed methods accessible to developmentalists so they

can understand and use them in their research. We start at

the beginning with chapters on data management and

measurement, two neglected topics in methods training

despite the fact that every investigation should begin with

proper consideration of each. We follow with two important

topics for developmental research, missing data and growth

modeling. Missing data can plague developmental work

because participants sometimes miss one or more assessment

points. Growth modeling methods offer researchers a

true means to assess change over time as compared with

cruder methods like difference scores and residualized

change scores. Then comes a discussion of mediation and

moderation, two tools that can be used to elucidate developmental

processes. Because so much developmental

science is non-experimental, we include a chapter on selection

bias that compares five modeling strategies. Proper attention

to data management, measurement, missing data, growth

modeling (whenever possible), mediation and moderation,

and potential selection bias is guaranteed to result in greater

precision in inference-making. Even when researchers make

good decisions about methods, it is critical for them to use

good judgment about the practical importance of findings,

so we conclude with this important discussion. We view this

monograph as a first step to getting quantitative researchers

started and we believe this referenc will help researchers

make better-informed decisions about methodology.