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Strategies for the Collection and Use of Quantitative Data

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How can we obtain and use quantitative data most efficiently, given inevitable ... 2 treatments: fenced exclosure & control. 1 cluster of plants = 1 plot ... – PowerPoint PPT presentation

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Title: Strategies for the Collection and Use of Quantitative Data


1
Strategies for the Collection and Use of
Quantitative Data
Norma Fowler Section of Integrative
Biology University of Texas at Austin
2
Why are quantitative data not used as widely and
effectively as they might be?limited
funding......so...How can we obtain and use
quantitative data most efficiently, given
inevitable funding constraints?
3
  • Some reasons why quantitative data are not used,
    or are used incorrectly or inefficiently (other
    than funding limitations)
  • data not collected
  • data collected but not analyzed
  • inefficient sampling and experimental designs
  • statistically invalid sampling and experimental
    designs

4
  • Some reasons why quantitative data are not used,
    or are used incorrectly or inefficiently (other
    than funding limitations)
  • data not collected
  • data collected but not analyzed
  • inefficient sampling and experimental designs
  • statistically invalid sampling and experimental
    designs

5
  • data not collected the lack of long-term
    monitoring
  • long-term monitoring
  • reveals whether a management practice is working
  • provides baseline data for future studies and
    future management
  • is often relatively inexpensive
  • but....
  • it is not as glamorous as new initiatives
  • the need often outlasts the duration of project
    funding, the employment of a staff member, etc.

6
  • Some reasons why quantitative data are not used,
    or are used incorrectly or inefficiently (other
    than funding limitations)
  • data not collected
  • data collected but not analyzed
  • inefficient sampling and experimental designs
  • statistically invalid sampling and experimental
    designs

7
data not analyzed
  • because bookkeeping and number-crunching arent
    as much fun as field work?
  • because the funding has run out?

image from http//www.sostitle.com/
8
  • Some reasons why quantitative data are not used,
    or are used incorrectly or inefficiently (other
    than funding limitations)
  • data not collected
  • data collected but not analyzed
  • inefficient sampling and experimental designs
  • statistically invalid sampling and experimental
    designs

9
  • the three most common types of important design
    problems seem to be
  • replication
  • no replication
  • no statistically valid replication
  • inefficient replication
  • randomization
  • no randomization
  • statistically invalid randomization
  • number of statistical units v. number of variables

10
  • the three most common types of important design
    problems seem to be
  • replication
  • no replication
  • no statistically valid replication
  • inefficient replication
  • randomization
  • no randomization
  • statistically invalid randomization
  • number of statistical units v. number of variables

11
  • replication
  • An example of invalid, valid but inefficient, and
    more efficient designs
  • 3 habitat types
  • open cluster along
  • savanna of woody plants a drainage

for simplicity, assume we can only make
measurements on 36 plants
12
replication
valid but inefficient design 2 plots
per habitat type, 6 plants per plot
statistically invalid design only 1 plot per
habitat type
13
replication
alternate approach no plots, 12 plants per
habitat type stratified random plant
locations
statistically more efficient design 4 plots
per habitat type, 3 plants per plot
14
  • the three most common types of important design
    problems seem to be
  • replication
  • no replication
  • no statistically valid replication
  • inefficient replication
  • randomization
  • no randomization
  • statistically invalid randomization
  • number of statistical units v. number of variables

15
randomization
  • some really bad ideas
  • ignore the whole issue and hope it goes away
  • throw hoops with your eyes shut
  • sample whenever there is a place to pull off the
    highway
  • sample every 10 m
  • wander around and sample at what feels like
    random intervals to you

16
randomization
  • Humans dont seem to be able to generate random
    numbers.
  • Solutions
  • random number tables correctly used
  • random number generators in computer packages

17
  • the three most common types of important design
    problems seem to be
  • replication
  • no replication
  • no statistically valid replication
  • inefficient replication
  • randomization
  • no randomization
  • statistically invalid randomization
  • number of statistical units v. number of variables

18
number of statistical units v. number of variables
  • Too many characters on too few statistical units
    ( plots or plants, depending on the design).
    Why?
  • Big plants are big all over usually little is
    gained by measuring gt1 size-related variable.
  • Environmental variables are often so highly
    correlated with each other that little is gained
    by measuring some of them.
  • If plot is the statistical unit of replication,
    more plants per plot usually add little to the
    power of the analysis.

19
  • An example of a statistically valid, relatively
    efficient design the effects of deer browsing on
    Streptanthus bracteatus

2 treatments fenced exclosure control
1 cluster of plants 1 plot
6 plots per treatment in yr 1, 9 plots per
treatment in yr 2
images from http//www.wildflower2.org/NPIN/Galler
y/ and http//www.thenorthview.org/recreation/anim
als/deer.html
20
  • preliminary study of 11 size-related measures of
    Streptanthus bracteatus
  • stem basal diameter predicted dry biomass very
    well (R2 0.95),
  • the other 10 variables did not have
    to be measured in the experiment,
  • and....
  • using this estimate of initial biomass greatly
    increased the power of the analysis

image from http//www.tpwd.state.tx.us/news/tv/vnr
/archive/0307
21
(No Transcript)
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