Will I have a random or nonrandom sample and is my sample of sufficient size that I can assume the scores approach that of a normal distribution? What is my anticipated sample size and will I have a sample of sufficient size such that I can conduct the statistical procedures I have planned to run? How you answer these questions will, to a degree, influence the questions you ask on your survey and help establish the response categories for the questions. Most importantly they will influence the kinds of statistical procedures you are able to conduct for your study. Constructing Your Response Categories - Establishing Your Level of Measurement If you are constructing your data collection instrument, you have the opportunity to establish the level of measure for many of your variables. As an example, the variable education can be constructed in such a way that your data may be a nominal, ordinal, or an interval/ratio measure. Education as a nominal measure: Do you have a high school diploma? ____Yes ____No Education as an ordinal measure: What is your current standing? ___Senior ___Junior ___Sophomore ___Freshman Education as an interval/ratio measure: How many years of education do you have? ______Years As you examine the examples above of how you could construct a question about one’s level of education, you should recognize that designing and constructing a survey instrument is both a science and an art, and you should think of a question in terms of its response categories and level of measure. The next section further illustrates the importance of the response categories of your questions. Associating Response Categories of a Question to Statistical Procedures This section presents the relationship between level of measure of the response categories of a question and possible basic statistical procedures you can conduct. As noted earlier and illustrated in sections still to come, you should think in terms of both univariate and bivariate data analysis. The tables below provide a basic guide for the types of univariate and bivariate data analysis you can conduct, based on the measurement level of your variables. In the tables below, measurement level refers to the response categories for a given question on a survey.
Measures of association for 2 nominal variables: Phi - for 2x2 tables Cramer’s V for tables (2x3, 3x3, etc.) Example 1 - Chi square test The contingency table and data analysis examines the relationship between general happiness and marital status of the respondents. Examples of possible survey questions are noted below. 1. What is your current marital status? ___1 Married ___2 Widowed ___3 Divorced ___4 Separated Never married 2. What is your current level of general happiness with life? ___ Very happy ___ Pretty happy ___ Not too happy SPSS Output of a Contingency Table and Test GENERAL HAPPINESS by MARITAL STATUS (N=494) MARITAL STATUS Total GENERAL HAPPINESS MARRIED WIDOWED DIVORCED SEPARATED NEVER MARRIED VERY HAPPY Count % within Marital Status Std. Residual 81 33.5% 3.1 8 16.3% -1.1 12 16.0% -1.4 1 9.1% -1.0 15 12.8% -2.4 117 23.7% PRETTY HAPPY Count % within Marital Status Std. Residual 143 59.1% -1.1 33 67.3% .2 52 69.3% .5 8 72.7% .3 83 70.9% .9 319 64.6% NOT TOO HAPPY Count % within Marital Status Std. Residual 18 7.4% -2.0 8 16.3% .9 11 14.7% .7 2 18.2% .6 19 16.2% 1.4 58 11.7% Total Count % 242 100.0% 49 100.0% 75 100.0% 11 100.0% 117 100.0% 494 100.0% Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 29.537 8 .000 Likelihood Ratio 30.445 8 .000 Linear-by-Linear Association 22.369 1 .000 N of Valid Cases 494 a. 2 cells (13.3%) have expected count less than 5. The minimum expected is 1.29. Symmetric Measures Value Approx. Sig. Nominal by Nominal Phi .245 .000
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