ESP 178 Applied Research Methods. 2/23: Quantitative Analysis
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1 ESP 178 Applied Research Methods 2/23: Quantitative Analysis Data Preparation Data coding create codebook that defines each variable, its response scale, how it was coded Data entry for mail surveys and in-person surveys; check for accuracy (e.g. enter data twice) Missing values delete surveys with too many missing values or impute missing values Data transformation e.g. convert positive to negative responses; convert ratio variables to nominal Descriptive Statistics: univariate analysis to describe general properties of one variable Frequency distributions and histograms to show central tendency, variation, and skewness; is it a normal distribution, i.e. a bell-shaped curve? Purpose Measure Variable Type Definition Measures of Ratio (sometimes Arithmetic average central tendency Median Ordinal, ratio Middle value (or average of two middle values if even number of cases) Mode Best for nominal Most frequent value Measures of Range Ratio (sometimes Highest value lowest value dispersion Variance Ratio (sometimes Average squared difference of each case from the mean Standard deviation Ratio (sometimes Square root of the variance Note: This table ignores interval variables which are not common in this field. Inferential Statistics: to look for associations between two or more variables Null hypothesis means are not different H 0 : µ 1 = µ 2 Alternative hypothesis means are different H 1 : µ 1 µ 2 p-value: Probability of obtaining an effect at least as extreme as the one in the sample data if the null hypothesis is true. In other words, probability that the association is due to chance. Goal is p-values less than 0.05 (5% significance level) or 0.01 (1% significance level). Independent Variable Nominal or Ordinal Ratio or interval Dependent Variable Nominal or ordinal Crosstabulation with Chi-square test Logistic regression Other forms of modeling Ratio or interval Difference of means with t-test (if 2 categories) Analysis of Variance (ANOVA) with F-test (if multiple categories Correlation coefficient Linear regression Other forms of modeling Chi-square: Compares expected frequencies in cells to observed frequencies in cells F-statistic: Compares variation between groups to variation within groups Multivariate analysis: Allows us to test causal hypothesis with non-experimental data by testing for relationship between independent and dependent variables while controlling for other variables that might cause a spurious relationship.
2 Frequencies - Number of Times Children Played Outside in Last 7 Days Statistics N Valid 389 Missing Median 2.00 Mode 0 Std. Deviation Variance iles Frequency Valid Cumulative Valid Total Missing System Total Histogram Frequency = 2.81 Std. Dev. = N = 389 2
3 Frequencies - Whether or Not Children Played Outside in Last 7 Days Statistics outside_play N Valid 389 Missing Median Mode 1.00 Std. Deviation Variance.204 iles outside_play Frequency Valid Cumulative Valid Total Missing System Total Histogram Frequency = Std. Dev. = N = outside_play 3
4 Crosstabs - Cul-de-sac or Not vs. Played Outside or Not Case Processing Summary outside_play * Cases Valid Missing Total N N N % % % outside_play * Crosstabulation Total outside_pla.00 Count y % within 32.5% 23.7% 29.8% 1.00 Count % within 67.5% 76.3% 70.2% Total Count % within 100.0% 100.0% 100.0% Chi-Square Tests Value df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided) Pearson Chi-Square 3.002(b) Continuity Correction(a) Likelihood Ratio Fisher's Exact Test Linear-by-Linear Association N of Valid Cases 373 a Computed only for a 2x2 table b 0 cells (.0%) have expected count less than 5. The minimum expected count is
5 Oneway ANOVA - Times Playing Outside by Cul-de-Sac or Not Descriptives 95% Confidence Interval for N Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximu m Total Sum of Squares Between df ANOVA Square F Sig Groups Within Groups Total Oneway ANOVA - Times Playing Outside vs. Cul-de-sac score (1 to 4 scale) Descriptives 95% Confidence Interval for N Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximu m Total Sum of Squares Between df ANOVA Square F Sig Groups Within Groups Total
6 Regression - Times Playing Outside as Dependent Variable Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1.361(a) a Predictors: (Constant), Q I. #9 - Nbrs Active Outside, #8 - Current Total Income,, presence of related kids <=5 current, Work or not current, Q I. #9 - Low Traffic, Q I. #9 - Low Crime, Q I. #9 - Nbr Interaction, presence of related kids <=12 current, Q I. #9 - Safe for Kids ANOVA(b) Model Sum of Squares df Square F Sig. 1 Regression (a) Residual Total a Predictors: (Constant), Q I. #9 - Nbrs Active Outside, #8 - Current Total Income,, presence of related kids <=5 current, Work or not current, Q I. #9 - Low Traffic, Q I. #9 - Low Crime, Q I. #9 - Nbr Interaction, presence of related kids <=12 current, Q I. #9 - Safe for Kids b Dependent Variable: Model Coefficients(a) Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) Work or not current #8 - Current Total Income presence of related kids <=5 current presence of related kids <=12 current Q I. #9 - Safe for Kids Q I. #9 - Low Traffic Q I. #9 - Low Crime Q I. #9 - Nbr Interaction Q I. #9 - Nbrs Active Outside a Dependent Variable: 6
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