Current and upcoming readings.
The hotlinks are to electronic copies of papers in the ISU e-library. These are free if you access them from on campus. A few are free from off campus. Please do not pay for access to any of these! Instead, print it out the next time you're on campus.

Note: This page now contains only the more recent readings. The 'All readings' page has the complete list.

• Week of Apr 6
  • Repeated Measures Analysis: Littell et al., Chapter 8, especially section 8.4 on use of proc mixed. Skim or omit the mathematical details (e.g. 3rd paragraph on p 282).
  • Repeated Measures analysis using summary statistics: Senn, Stevens, and Chaturvedi, 2000, Tutorial in Biostatistics. Repeated measures in clinical trials: simple strategies for analysis using summary measures. Statistics in Medicine 19:861-877. read details on pp 861-863, 866(bottom) - 867(top), skim rest
  • Repeated Measures analysis by modeling correlations:
    At least one of:
    1) Animal Science: Littell et all 1998. Statistical Analysis of Repeated Measures Data Using SAS Procedures. J. An. Sci. 76:1216-1231. Skip material on PROC GLM and the technical appendix, unless interested. Note: the example code in this paper omits the KR option in SAS, because the option wasn't available in 1998.
    2) Agronomy: Piepho, Buchse and Richter, 2004. A mixed modelling approach for randomized experiments with repeated measures Read details on pp 230-middle of 233, skim discussion (pp 243-end). Look at rest only if really interested in lots of details.
    3) Vet Med: Grohn et al, 1999. Analysis of correlated continuous repeated observations: modelling the effect of ketosis on milk yield in dairy cows.
    4) Nutrition: Marshall et al. 1998. Improving power with repeated measures: diet and serum lipids.
    Note: Not as thorough a description of repeated measures modeling as in the other papers.

• Week of Apr 15: Analysis of repeated experiments (e.g. multilocation studies):
  • Littell et al. section 11.8, but omit 11.8.4 unless really interested in location index regression.
    Review 4.3.2 on difference between fixed and random blocks (i.e. expts), paying close attention to narrow and broad sense inference.
  • McIntosh, 1983. Analysis of combined experiments. Agronomy Journal
    Presents F ratios for various tests for fixed environments and for random environments. Assumes you know whether env. is fixed or random.

    McIntosh includes all possible interactions. I disagree and favor pooling after thinking carefully. Lecture will explain why.

• Apr 24, 27
  • Incorporating baseline information: Roberts and Torgerson, 1999, Understanding clinical trials: Baseline imbalance in randomized clinical trials. British Medical Journal 319:185-186.
  • ANCOVA or change scores: Cochran 1957 Analysis of covariance: its nature and uses. Biometrics 13:261-281.
    This is now the JSTOR "stable link". To get the pdf file, you need to click the pdf button (on the right, near the top). Read pp 261-264, bottom of 269 and top of 270 (first part of section 4), middle of 277 to middle of 278 (section 7). For the rest, appreciate the value of computers.
  • SAS for ANCOVA: Littell et al, Chapter 7, read sections 7.1-7.3, skim 7.4 and 7.5, omit 7.6

• Week of Apr 27, Practical advice:
  • Dyke, G. 1997. How to avoid bad statistics. Field Crops Research 51:165-187.
    argues for clear thinking about the experiment and its goal(s), appropriate statistical methods, and clear presentation of the results.
  • Riley, J. 2001. Presentation of statistical analysis. Experimental Agriculture. 37:115-123.
    These are the recommendations of a statistical reviewer for a major British Agronomy journal.