Plan for Analysis

1
Plan for Analysis

• Strategies for writing quantitative plan for data
  analysis in method section
• Overview of three part process

2
Plan for Quantitative Analysis

• Purpose of Passage
• To articulate a plan that describes how you will
  answer your research questions.

3
Stage 1

• 1) Overview of Sample
• Describe the process you will use in identifying
  the unique and common characteristics of the
  sample regarding demographic characteristics.
• It is important to describe the sample in a
  quantitative study so that the findings may be
  placed in an appropriate context.
• Descriptive statistics are used to describe what
  is typical in the sample and any unique
  characteristics of the sample.

4
Stage 2

• 2) Addressing the Research Questions/ Hypothesis
• Identify the specific tests that will be used to
  answer each research question/hypothesis.
• Describe the critical elements of each test (what
  the test does).
• Explain the logical behind selecting the specific
  test(s).
• Give examples of what the test will allow you to
  do.

5
Stage 2 Continued

• 2) Addressing the Research Questions/ Hypothesis
• In selecting your statistical test, use the
  decision tree.
• Make sure that your questions match the purpose
  of the test.
• Start with parametric tests unless you have a
  small sample size or unless you are working with
  nominal and ordinal data.

6
Stage 3

• 3) Inspection of the Items that comprise the
  Dependent Variable(s)
• In addition to answering your research questions,
  part of your process will involve delving into
  how participants responded to the items that make
  up your dependent variables (survey tools).
• Provide a description of the process you will use
  to draw the big picture or how the sample as a
  whole responded to the questions/items that
  comprise your tool.
• Again, descriptive statistics (frequency
  distributions and measures of central tendencies)
  are helpful in providing insight into the various
  items that comprise the tool.

7
Parametric vs. Nonparametic Tests

• Parametric Tests
• A normal distribution (dependent variable).
• Level of measurement (interval/ratio).
• Large sample size.

• Nonparametric Tests
• A non-normal distribution.
• Level of measurement not at the interval/ratio
  level.
• Smaller sample size.

8
Bivariate Tests

• Parametric
  Non-parametric
• (Relationships)
• Pearsons R ? Spearmans Rho
• (Differences)
• Independent T-Test ? Mann Whitney U
• Paired Samples T-Test ? Wilcoxon Matched
• Oneway ANOVA ? Kruskal Wallis
• 
  Chi-square

9
Univariate Analysis

• Nominal Level Variables FD, mode
• Ordinal Level Variables FD, mode, range/minimum
  and maximum
• Interval Ratio Level Variables FD, mean,
  median, mode, Standard Deviation