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Increasing the Robustness of Meta-analysis Through Life History and Middle-Range Models: an Example from the Northeast Pacific

Journal of Archaeological Method and Theory volume 26, pages 581–618 (2019)Cite this article

Abstract

While most comparative and meta-analysis in archaeology would like to assume that taphonomic factors act randomly and do not bias the results of studies with many data points, archaeological records may suffer from systematic biases in preservation, sampling, recovery methods, analytic methods, reporting practices, or yet other factors. Using a life history and middle-range perspective, we outline an approach for assessing possible systematic biases and for explicitly evaluating factors that affect assemblages included in comparative analysis at any scale. We demonstrate the usefulness of the life history concept as a framework for holistically evaluating bias with a zooarchaeological case study from the northeast Pacific. Our comparative analysis of regional fishbone records shows that unusually high abundances of sablefish (Anoplopoma fimbria)—a nutritious and highly valued, yet rarely reported, species—at one large Native American village on the coast of Washington State, USA, cannot be explained by post-depositional destruction, screen size effects, sample size effects, or differences in fishbone identification methods. Though this study focuses on zooarchaeology, the framework we present has potential value for any large-scale meta-analysis that seeks to identify cultural and environmental patterns in “noisy” archaeological data.

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Notes

  1. We use “bias” to refer to any factor or process that affects a sample’s condition with respect to particular target measures or research questions. All samples are biased to some extent. There is no “mythical unbiased assemblage.” In meta-analysis, our task is to assess how biases affect the comparability of assemblages when addressing a particular research goal. For example, all archaeological assemblages that have been sieved are biased against specimens that are smaller than the minimum screen size used, but they can still be suitable for comparisons of small-bodied animal remains if suitably fine mesh was used in each case. Meanwhile, comparisons of other sample types may not be affected by which screen size was used, but they are still biased by other aspects of archaeological recovery practices.

  2. We hasten to add that materials in archaeological contexts may continue to play important symbolic roles in behavioral systems, even if they are only physically subject to natural forces (see Gifford-Gonzalez 2014; Lightfoot et al. 2011; McNiven 2012). Furthermore, humans may also cause post-depositional disturbances or reclaim deposited animal remains for new uses, temporarily reintroducing them to a systemic context.

  3. We follow Wolverton et al. (2016a) in assessing both statistical significance (the likelihood that a result was produced by chance) and practical significance, also called effect size (an assessment of whether a result has meaningful implications). As an example of the difference between these two concepts, consider two populations of fish with normally distributed fork lengths. If a statistical comparison shows that there is a difference between the mean fork lengths in each population at the α = 0.05 level of confidence, we would say the difference is statistically significant. But if the difference between the mean fork lengths is very small (say, 1 mm) relative to the pooled standard deviation of the two populations (± 10 mm), then we would say there is no practically significant difference (Cohen’s d = 0.1). Results can also be statistically insignificant, but highly practically significant.

  4. Animal remains small enough to fall through 3.2 mm mesh were cataloged and are available for particular research questions, such as that related to sablefish scarcity. However, since we do not know the finest mesh size that was used, or if it was used consistently during excavation, patterns in fish representation in these fine mesh samples are difficult to interpret.

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Acknowledgements

We are grateful to the Lower Elwha Klallam Tribe for their on-going support of the Čḯxwicən project, and we thank the Puyallup Tribe of Indians and the West Point Tribal Oversight Committee for supporting our reanalysis of Burton Acres and West Point fish remains. Most of the funding for Čḯxwicən analysis came from the National Science Foundation [grant numbers 1219468, 1353610, and 1663789]. Laura Phillips (Burke Museum, University of Washington) facilitated the loan of materials from Čḯxwicən, and the reanalysis of materials from Burton Acres, English Camp, and West Point. Kevin Cannell (Bonneville Power Administration) facilitated the loan of materials from Decatur Island. Washington State Department of Transportation helped subsidize loan costs for the Čḯxwicən collections. Dennis Lewarch and Lynn Larson (formerly of LAAS, Ltd.) directed site excavations at Čḯxwicən. Čḯxwicən geo-zooarchaeological project Co-PIs (Kristine Bovy, Sarah Campbell, Michael Etnier, Sarah Sterling) provided fundamental assistance with project development. We are extremely grateful for the contributions of the Pacific Northwest ichthyoarchaeologists whose work we review. Ross Smith (University of Oregon), Bob Kopperl (Willamette Cultural Resources Associates, Ltd.), and Iain McKechnie and Rebecca Wigen (both University of Victoria) loaned comparative fish skeletons and/or assisted with identifications. Former PSU students Tony Hofkamp, Kathryn Mohlenhoff, Emily Rocha, Shoshanna Rosenberg, and Laura Syvertson assisted in the lab. Siri Linz, Jess Milhausen, and Roger Crowley provided assistance at the Burke Museum. Kristina Dick and Laura Syvertson assisted with database management. Todd Rosenstiel (Portland State University) provided support for manuscript preparation. Finally, we thank Ken Ames and Shelby Anderson for serving on Nims’ thesis committee, and for discussions which led directly to our viewing metadata analysis as a middle-range research problem.

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  1. School of Social Sciences, University of Auckland, 11 Symonds Street, Auckland, 1010, New Zealand

    Reno Nims

  2. Te Pūnaha Matatini, A New Zealand Centre of Research Excellence, Auckland, New Zealand

    Reno Nims

  3. Department of Anthropology, Portland State University, 1721 SW Broadway, Portland, OR, 97201, USA

    Virginia L. Butler

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  1. Reno Nims
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Nims, R., Butler, V.L. Increasing the Robustness of Meta-analysis Through Life History and Middle-Range Models: an Example from the Northeast Pacific. J Archaeol Method Theory 26, 581–618 (2019). https://doi.org/10.1007/s10816-018-9383-1

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Keywords

  • Data quality
  • Data reuse
  • Life history models
  • Meta-analysis
  • Middle-range theory
  • Taphonomy