The Research Proposal

Transcription

1 Describes the: The Research Proposal Researchable question itself Why it's important (i.e., the rationale and significance of your research) Propositions that are known or assumed to be true (i.e., axioms and assumptions) Propositions that will be tested (i.e., hypotheses or postulates) Goals and specific objectives of your research activities Methods you will use to test hypotheses and achieve objectives Expected results and scope of inference

2 Steps in the scientific method Define the researchable question Develop hypotheses, predictions, and objectives Develop materials and methods, including replication Gather data Analyze the data (contingency plans if things go wrong?) Draw conclusions (accept, modify, reject the hypothesis)

3 General definitions A new idea Hypotheses A statement to be tested - an 'educated guess' that needs more study to be confirmed or disproved A proposition that explains some phenomenon Scientific hypothesis - The researchable question restated as a declarative sentence that is assumed to be true for testing purposes Stated as what you believe to be true not what you want to disprove (i.e., not a statistical 'null' hypothesis) Must be testable (e.g., generate predictions) The most valuable hypotheses are simple, consistent with what is already known, and have broad applicability

4 Methods and expected results The materials and methods must describe the: Proposed experiments or investigations Materials and techniques that you will use, including their feasibility Statistical techniques and other methods used to analyze the data Your expected results and interpretations must describe the: Results that will lead you to conclude that the hypotheses are proved or disproved Scope of inference (i.e., to what extent are the results applicable to other locations, times, or situations?) Pitfalls that may be encountered Limitations to the proposed methods

5 Think it through!

6 Typical formats Each hypothesis or objective often has its own set of methods OBJECTIVES My objectives are to: Objective 1 Objective 2 MATERIALS AND METHODS Objective 1 Hypotheses - Rationale Experimental design Measurements Data analysis Expected results Objective 2 Hypotheses - Rationale Experimental design Measurements Data analysis Expected results Pitfalls and limitations (summary) HYPOTHESES I hypothesize that: Hypothesis 1 Hypothesis 2 MATERIALS AND METHODS Hypothesis 1 Objectives - Rationale Experimental design Measurements Data analysis Expected results Hypothesis 2 Objectives - Rationale Experimental design Measurements Data analysis Expected results Pitfalls and limitations (summary)

7 The materials and methods must describe the: Proposed experiments or investigations Materials and techniques that you will use, including their feasibility Statistical techniques and other methods used to analyze the data Approach Connection between methods and conclusions must be clear - why are you doing these things? The strategy connecting hypotheses to conclusions Observational, experimental, modeling? Design Randomization, replication, etc How do you know replication is sufficient? Measurements - Response variables Survey, lab, field? Have you done these before? Are you collaborating with someone who has? Statistical approaches

8 Expected results and interpretations The results you expect to see if your hypotheses are true (i.e., the predictions that flow from your hypotheses) What will conclude if you do not see your expected results (i.e., if your predictions are not observed)?

9 Observations (axioms) Researchable question Expected results and interpretations must describe the: Results that will lead you to conclude that the hypotheses are proved or disproved Hypothesis Prediction deduction Expected results Reject hypoth. (deduction) Accept hypoth. (induction) False Test True Materials and methods

10 Scope of inference The conditions to which the conclusions from the research will apply: Closely linked Scientific Scope of inference Biological Geographical Temporal Statistical Scope of Inference Important to consider when you design your research How broadly do you want to apply your results?

11 Pitfalls and limitations Pitfalls Demonstrates a realistic knowledge of your materials and methods Which procedures are risky? What can go wrong? How will you keep things from going wrong? What will you do if things go wrong - backup plans? What are the consequences if things go wrong? Limitations Scope of inference limitations Describe constraints - i.e., resource, time constraints

12 Evaluation Are the materials and methods adequate to test the hypotheses and achieve the objectives? Is the scope of inference defined, realistic, and adequate? Are issues of representation, replication, and randomization appropriate to the proposal and if so, are they addressed? Is it clear how conclusions will be drawn? Is the proposed study doable and repeatable? Are the pitfalls and limitations understood? Are the experiments novel or creative?

13 Define the question Design the study Analyze the data Carry out the study Draw conclusions

14 The Question of Interest defines responses to measure population to which inference is made groups to compare Does the foliar boron concentration of seedlings differ among the nursery grown Douglas-fir seedlings in western Oregon that receive one of 4 different fertilizer regimes, the standard fertilizer with 0 lb/ac of boron, 1 lb/ac of boron, 2 lb/ac of boron, and 4 lb/ac of boron?

15 Relating the Question of Interest to the Conclusions in the planning stages What outcomes are possible? - Multiple or one? What are the explanations for the outcomes? - a priori decide what you will conclude from potential outcomes Does an outcome lead to more than one explanation? - Not satisfying if an outcome corroborates many explanations

16 Replication Before we accept the existence of an effect, the effect must be observable in replicates that represent the range of variation * over which inference is to be made. -Hurlbert (1983) * The scope of inference!

17 Replication is the repetition of independent applications of a treatment or protocol

18 Experimental Unit - smallest piece of material that receives an independent application of the treatment, a replicate Sampling Unit - smallest piece of material on which a measurement is made, a subsample. Doug-Fir Pine Pine Doug-Fir

19 Boron Fertilizer applied to sections of nursery beds. What gets replicated? How is the fertilizer applied! What gets an independent application? a bed? or a section of a bed? or a or a seedling?

20 Effect of Herbicide on Apple weight Two Orchards, tractor-sprayed herbicide. Assign each set of two rows to either herbicide or water treatment. In each orchard mix up one tank of herbicide and one tank of distilled water and apply each to assigned rows of trees. Herb Herb water

21 Effect of fire severity on re-growth of herbaceous cover Low Severe Medium Low

22 Compare tree regeneration rate after fires in Douglas-fir and Pine Stands. Doug-Fir Pine Pine Doug-Fir Doug-Fir Pine

23 Detecting Differences Accurately Avoid Confounding Confound: To confuse To mingle so that the elements cannot be distinguished Confounding is the state in which 2 or more phenomena occur together in such a way that the study cannot separate the effects of one from the other.

24 Confounding: an example Interest in whether bats forage more along streams then within forest stands. In August, sample nighttime foraging activity of bats along streams in the coastal range. In October, sample nighttime foraging activity of bats in forest stands in McDonald Dunn Forest (near Corvallis). (Note that in the literature it says that nighttime foraging activity of bats increases with increasing nighttime temperature)

25 Confounding: an example In August, sample nighttime foraging activity of bats along streams in the coastal range. In October, sample nighttime foraging activity of bats in forest stands in McDonald Dunn Forest (near Corvallis) To what should you attribute a difference in foraging activity? Forest type Nighttime temperature Other seasonal effects (e.g. day length, seasonally available food, day or night light levels)

26 What we do: Randomization Randomly select pieces of material to sample. randomly select Randomly assign a piece of material to a protocol. randomly assign Order items or protocols randomly. randomly order Physically place items randomly. randomly placed

27 Why do we randomize? Randomization is somewhat analogous to insurance, in that it is a precaution against disturbances that may or may not occur, and that may or may not be serious if they do occur. Cochran and Cox 1957 Randomization ensures that a particular treatment will not be consistently favored or handicapped in successive replications by some extraneous sources of variation, known or unknown. Steele and Torrie 1997 The function of randomization is to ensure that we have a valid or unbiased estimate of experimental error and of treatment means and the differences among the means. Steele and Torrie 1997

28 Randomization What do we mean by randomization? Mixed up the order? Can t repeat a selection or an assignment? See no pattern in a selection or an assignment? Can t explain how we did a selection or assignment?

29 Randomization Each replicate unit has a known chance of being assigned to a treatment. Or Each sample has a known chance of being sampled The process is definable and repeatable. Randomization ensures that the effects we estimate are reasonably believed to be true for the whole set we re interested in, not just for the subset.

30 Randomly selecting a unit to sample or measure can insure no systematic difference between units intended to be replicates

31 Inferences Observational studies can only report associations between responses and groups Because you don t know and can t be sure that something unknown is responsible for the difference you see between your groups Controlled designed experiments allow you to draw cause and effect conclusions Because in theory, all other effects known to affect the response have been controlled Note: natural resource studies are commonly a mix of observational and design studies. It is not easy to have an natural resource study that can make cause and effect conclusions!

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