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A Primer of Methods in Biobehavioral Research: Improving a Study’s Design, Analysis, and Write Up

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The Maze Book

Part of the book series: Neuromethods ((NM,volume 94))

Abstract

This chapter describes several best practices for collecting, storing, analyzing, and writing up data generated from biobehavioral research. The goal is to provide a foundation for researchers who wish to consider more advanced topics of experimental design and statistical inference in their work. The principles considered here are general and apply to basic biobehavioral research using a variety of different model systems and animals. Common issues and pitfalls are discussed, and considerations draw on both traditional principles and newer ideas.

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Notes

  1. 1.

    This chapter often refers to control and treatment groups for ease of presentation. Please recognize that these statements also apply to two, or in some cases more, different treatment groups, none of which may serve as a control group in a specific experiment.

  2. 2.

    In cases in which the researcher can make a strong argument that the variable could not theoretically be affected by the manipulation (e.g., gender, age, but not neuronal physiological properties), the variable can be assessed after the manipulation.

  3. 3.

    Spreadsheets are convenient and normally adequate for this task. Note that they typically have fewer built-in error checks on the data, permit fewer statistical calculations, and have fewer graphing capabilities. This limitation can make the data checking process more error prone. Error checking features become increasingly important as the size of the dataset increases.

  4. 4.

    In Greek mythology, Procrustes was a host who invited his guests to rest in a special bed whose length exactly matched that of whomever would lie down in it. When the guest laid down in the bed, Procrustes would stretch the guest on a rack or chop off his legs so that he would precisely fit the bed. Chopping up perfectly good interval- or ratio-level variables is another Procrustian practice that is good neither for humans nor data.

  5. 5.

    When two independent groups (unrelated subjects) are being compared, either ANOVA or a t-test may be used. In this case, the F for the ANOVA and t for the t-test produce equivalent results, F = t 2, so the p-values of the two tests will match exactly. With more than two groups, only ANOVA can be performed.

  6. 6.

    As discussed briefly later, when the dependent variable is the count of the number of behaviors performed by the subject, alternative analyses (e.g., Poisson regression) may be preferred. As a rule of thumb for count variables, if the means of each condition are greater than 10, then traditional analysis of variance is regarded as appropriate.

  7. 7.

    The average effect can be interpretable even when there is an interaction in a special case, when the direction of the treatment effects is consistent for all levels of the moderator variable. For example, consider the present significant treatment effect modified by a significant treatment × hemisphere interaction. If follow-up tests showed that the mean of the high-dose condition was greater than the mean of low-dose condition, which, in turn, was greater than the mean of the vehicle (control) condition within both the left and the right hemispheres, the average effect of treatment provides important information. The interaction changes the magnitude, but not the direction of the effects.

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Author information

Authors and Affiliations

  1. Banner Sun Health Research Institute, Sun City, AZ, 85351, USA

    Joshua S. Talboom

  2. Behavioral Neuroscience Program, Department of Psychology, Arizona State University, Tempe, AZ, 85287, USA

    Heather A. Bimonte-Nelson Ph.D.

  3. Arizona Alzheimer’s Consortium, Phoenix, AZ, 85006, USA

    Joshua S. Talboom & Heather A. Bimonte-Nelson Ph.D.

  4. Department of Psychology, Arizona State University, Tempe, AZ, 85287, USA

    Stephen G. West

Authors
  1. Joshua S. Talboom
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  2. Stephen G. West
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  3. Heather A. Bimonte-Nelson Ph.D.
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Correspondence to Heather A. Bimonte-Nelson Ph.D. .

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Editors and Affiliations

  1. Arizona State University, Tempe, Arizona, USA

    Heather A. Bimonte-Nelson

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Talboom, J.S., West, S.G., Bimonte-Nelson, H.A. (2015). A Primer of Methods in Biobehavioral Research: Improving a Study’s Design, Analysis, and Write Up. In: Bimonte-Nelson, H. (eds) The Maze Book. Neuromethods, vol 94. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2159-1_12

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  • DOI: https://doi.org/10.1007/978-1-4939-2159-1_12

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