jaspFrequencies::ContingencyTables(
version = "0.18.3",
formula = cbind(GEND, AGE, EDUC, WORKSTAT, INCSTAT, RESI) ~ PURFRE + MIPBREP + MIPB1T + RFQ,
chiSquaredContinuityCorrection = TRUE,
contingencyCoefficient = TRUE,
countsExpected = TRUE,
likelihoodRatio = TRUE,
phiAndCramersV = TRUE,
residualsPearson = TRUE,
residualsUnstandardized = TRUE,
vovkSellke = TRUE)Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PURFRE | |||||||||||||||
| GEND | Once a month | Once every 12 months or less frequently | Once every 6 months | Several times a month | Several times a week | Total | |||||||||
| Female | Count | 181.000 | 56.000 | 176.000 | 135.000 | 27.000 | 575.000 | ||||||||
| Expected count | 171.992 | 54.581 | 178.339 | 138.355 | 31.733 | 575.000 | |||||||||
| Unstandardized residuals | 9.008 | 1.419 | -2.339 | -3.355 | -4.733 | ||||||||||
| Pearson residuals | 0.687 | 0.192 | -0.175 | -0.285 | -0.840 | ||||||||||
| Male | Count | 86.000 | 29.000 | 104.000 | 81.000 | 23.000 | 323.000 | ||||||||
| Expected count | 96.615 | 30.660 | 100.180 | 77.720 | 17.826 | 323.000 | |||||||||
| Unstandardized residuals | -10.615 | -1.660 | 3.820 | 3.280 | 5.174 | ||||||||||
| Pearson residuals | -1.080 | -0.300 | 0.382 | 0.372 | 1.226 | ||||||||||
| Prefer not to disclose | Count | 4.000 | 1.000 | 1.000 | 2.000 | 0.000 | 8.000 | ||||||||
| Expected count | 2.393 | 0.759 | 2.481 | 1.925 | 0.442 | 8.000 | |||||||||
| Unstandardized residuals | 1.607 | 0.241 | -1.481 | 0.075 | -0.442 | ||||||||||
| Pearson residuals | 1.039 | 0.276 | -0.940 | 0.054 | -0.664 | ||||||||||
| Total | Count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | ||||||||
| Expected count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 6.853 | 8 | 0.553 | 1.000 | |||||
| Χ² continuity correction | 6.853 | 8 | 0.553 | 1.000 | |||||
| Likelihood ratio | 7.311 | 8 | 0.503 | 1.000 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.087 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.061 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPBREP | |||||||||||||||
| GEND | Attractive new offers | Other | Satisfaction with products purchased so far | Satisfaction with the delivery of purchased products | Satisfaction with the online shopping process and support | Total | |||||||||
| Female | Count | 23.000 | 10.000 | 411.000 | 62.000 | 69.000 | 575.000 | ||||||||
| Expected count | 28.560 | 12.693 | 392.853 | 67.908 | 72.986 | 575.000 | |||||||||
| Unstandardized residuals | -5.560 | -2.693 | 18.147 | -5.908 | -3.986 | ||||||||||
| Pearson residuals | -1.040 | -0.756 | 0.916 | -0.717 | -0.467 | ||||||||||
| Male | Count | 22.000 | 9.000 | 202.000 | 44.000 | 46.000 | 323.000 | ||||||||
| Expected count | 16.043 | 7.130 | 220.681 | 38.147 | 40.999 | 323.000 | |||||||||
| Unstandardized residuals | 5.957 | 1.870 | -18.681 | 5.853 | 5.001 | ||||||||||
| Pearson residuals | 1.487 | 0.700 | -1.258 | 0.948 | 0.781 | ||||||||||
| Prefer not to disclose | Count | 0.000 | 1.000 | 6.000 | 1.000 | 0.000 | 8.000 | ||||||||
| Expected count | 0.397 | 0.177 | 5.466 | 0.945 | 1.015 | 8.000 | |||||||||
| Unstandardized residuals | -0.397 | 0.823 | 0.534 | 0.055 | -1.015 | ||||||||||
| Pearson residuals | -0.630 | 1.959 | 0.229 | 0.057 | -1.008 | ||||||||||
| Total | Count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | ||||||||
| Expected count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 14.323 | 8 | 0.074 | 1.914 | |||||
| Χ² continuity correction | 14.323 | 8 | 0.074 | 1.914 | |||||
| Likelihood ratio | 13.556 | 8 | 0.094 | 1.654 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.125 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.089 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPB1T | |||||||||||||||||
| GEND | Fast and accurate delivery | Other | To have a secure shopping certificate | To have positive customer reviews | To offer the option of in-store pickup | To offer the payment option I prefer | Total | ||||||||||
| Female | Count | 134.000 | 4.000 | 156.000 | 189.000 | 29.000 | 63.000 | 575.000 | |||||||||
| Expected count | 128.201 | 5.077 | 164.376 | 187.859 | 31.098 | 58.389 | 575.000 | ||||||||||
| Unstandardized residuals | 5.799 | -1.077 | -8.376 | 1.141 | -2.098 | 4.611 | |||||||||||
| Pearson residuals | 0.512 | -0.478 | -0.653 | 0.083 | -0.376 | 0.603 | |||||||||||
| Male | Count | 67.000 | 4.000 | 101.000 | 103.000 | 19.000 | 29.000 | 323.000 | |||||||||
| Expected count | 72.015 | 2.852 | 92.337 | 105.528 | 17.469 | 32.799 | 323.000 | ||||||||||
| Unstandardized residuals | -5.015 | 1.148 | 8.663 | -2.528 | 1.531 | -3.799 | |||||||||||
| Pearson residuals | -0.591 | 0.680 | 0.902 | -0.246 | 0.366 | -0.663 | |||||||||||
| Prefer not to disclose | Count | 1.000 | 0.000 | 2.000 | 4.000 | 1.000 | 0.000 | 8.000 | |||||||||
| Expected count | 1.784 | 0.071 | 2.287 | 2.614 | 0.433 | 0.812 | 8.000 | ||||||||||
| Unstandardized residuals | -0.784 | -0.071 | -0.287 | 1.386 | 0.567 | -0.812 | |||||||||||
| Pearson residuals | -0.587 | -0.266 | -0.190 | 0.858 | 0.862 | -0.901 | |||||||||||
| Total | Count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | |||||||||
| Expected count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | ||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 6.432 | 10 | 0.778 | 1.000 | |||||
| Χ² continuity correction | 6.432 | 10 | 0.778 | 1.000 | |||||
| Likelihood ratio | 7.038 | 10 | 0.722 | 1.000 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.084 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.060 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RFQ | |||||||||||||||||||
| GEND | Because I want to see the product live | Because of negative reviews | Due to inappropriate and hidden information about product delivery and returns | Due to long delivery time | Due to the need for a better price | Due to website design (complicated search, can't find what I'm looking for) | Other | Total | |||||||||||
| Female | Count | 149.000 | 160.000 | 70.000 | 88.000 | 54.000 | 29.000 | 25.000 | 575.000 | ||||||||||
| Expected count | 132.009 | 173.262 | 77.428 | 79.332 | 53.946 | 29.194 | 29.829 | 575.000 | |||||||||||
| Unstandardized residuals | 16.991 | -13.262 | -7.428 | 8.668 | 0.054 | -0.194 | -4.829 | ||||||||||||
| Pearson residuals | 1.479 | -1.007 | -0.844 | 0.973 | 0.007 | -0.036 | -0.884 | ||||||||||||
| Male | Count | 56.000 | 112.000 | 50.000 | 36.000 | 31.000 | 16.000 | 22.000 | 323.000 | ||||||||||
| Expected count | 74.155 | 97.328 | 43.494 | 44.564 | 30.304 | 16.400 | 16.756 | 323.000 | |||||||||||
| Unstandardized residuals | -18.155 | 14.672 | 6.506 | -8.564 | 0.696 | -0.400 | 5.244 | ||||||||||||
| Pearson residuals | -2.108 | 1.487 | 0.986 | -1.283 | 0.127 | -0.099 | 1.281 | ||||||||||||
| Prefer not to disclose | Count | 3.000 | 1.000 | 2.000 | 1.000 | 0.000 | 1.000 | 0.000 | 8.000 | ||||||||||
| Expected count | 1.837 | 2.411 | 1.077 | 1.104 | 0.751 | 0.406 | 0.415 | 8.000 | |||||||||||
| Unstandardized residuals | 1.163 | -1.411 | 0.923 | -0.104 | -0.751 | 0.594 | -0.415 | ||||||||||||
| Pearson residuals | 0.858 | -0.909 | 0.889 | -0.099 | -0.866 | 0.932 | -0.644 | ||||||||||||
| Total | Count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | ||||||||||
| Expected count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | |||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 20.983 | 12 | 0.051 | 2.436 | |||||
| Χ² continuity correction | 20.983 | 12 | 0.051 | 2.436 | |||||
| Likelihood ratio | 22.061 | 12 | 0.037 | 3.025 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.150 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.108 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PURFRE | |||||||||||||||
| AGE | Once a month | Once every 12 months or less frequently | Once every 6 months | Several times a month | Several times a week | Total | |||||||||
| Early Adulthood | Count | 91.000 | 27.000 | 86.000 | 41.000 | 9.000 | 254.000 | ||||||||
| Expected count | 75.976 | 24.110 | 78.779 | 61.117 | 14.018 | 254.000 | |||||||||
| Unstandardized residuals | 15.024 | 2.890 | 7.221 | -20.117 | -5.018 | ||||||||||
| Pearson residuals | 1.724 | 0.588 | 0.814 | -2.573 | -1.340 | ||||||||||
| Early Midlife | Count | 42.000 | 6.000 | 31.000 | 45.000 | 9.000 | 133.000 | ||||||||
| Expected count | 39.783 | 12.625 | 41.251 | 32.002 | 7.340 | 133.000 | |||||||||
| Unstandardized residuals | 2.217 | -6.625 | -10.251 | 12.998 | 1.660 | ||||||||||
| Pearson residuals | 0.352 | -1.864 | -1.596 | 2.298 | 0.613 | ||||||||||
| Late Midlife | Count | 12.000 | 9.000 | 23.000 | 18.000 | 0.000 | 62.000 | ||||||||
| Expected count | 18.545 | 5.885 | 19.230 | 14.918 | 3.422 | 62.000 | |||||||||
| Unstandardized residuals | -6.545 | 3.115 | 3.770 | 3.082 | -3.422 | ||||||||||
| Pearson residuals | -1.520 | 1.284 | 0.860 | 0.798 | -1.850 | ||||||||||
| Midlife | Count | 65.000 | 29.000 | 64.000 | 47.000 | 21.000 | 226.000 | ||||||||
| Expected count | 67.600 | 21.453 | 70.095 | 54.380 | 12.472 | 226.000 | |||||||||
| Unstandardized residuals | -2.600 | 7.547 | -6.095 | -7.380 | 8.528 | ||||||||||
| Pearson residuals | -0.316 | 1.630 | -0.728 | -1.001 | 2.415 | ||||||||||
| Older Adulthood | Count | 1.000 | 1.000 | 3.000 | 2.000 | 0.000 | 7.000 | ||||||||
| Expected count | 2.094 | 0.664 | 2.171 | 1.684 | 0.386 | 7.000 | |||||||||
| Unstandardized residuals | -1.094 | 0.336 | 0.829 | 0.316 | -0.386 | ||||||||||
| Pearson residuals | -0.756 | 0.412 | 0.563 | 0.243 | -0.622 | ||||||||||
| Young Adulthood | Count | 60.000 | 14.000 | 74.000 | 65.000 | 11.000 | 224.000 | ||||||||
| Expected count | 67.002 | 21.263 | 69.475 | 53.898 | 12.362 | 224.000 | |||||||||
| Unstandardized residuals | -7.002 | -7.263 | 4.525 | 11.102 | -1.362 | ||||||||||
| Pearson residuals | -0.855 | -1.575 | 0.543 | 1.512 | -0.387 | ||||||||||
| Total | Count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | ||||||||
| Expected count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 50.519 | 20 | < .001 | 229.606 | |||||
| Χ² continuity correction | 50.519 | 20 | < .001 | 229.606 | |||||
| Likelihood ratio | 54.838 | 20 | < .001 | 843.603 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.230 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.118 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPBREP | |||||||||||||||
| AGE | Attractive new offers | Other | Satisfaction with products purchased so far | Satisfaction with the delivery of purchased products | Satisfaction with the online shopping process and support | Total | |||||||||
| Early Adulthood | Count | 13.000 | 5.000 | 176.000 | 29.000 | 31.000 | 254.000 | ||||||||
| Expected count | 12.616 | 5.607 | 173.539 | 29.998 | 32.241 | 254.000 | |||||||||
| Unstandardized residuals | 0.384 | -0.607 | 2.461 | -0.998 | -1.241 | ||||||||||
| Pearson residuals | 0.108 | -0.256 | 0.187 | -0.182 | -0.218 | ||||||||||
| Early Midlife | Count | 9.000 | 3.000 | 73.000 | 26.000 | 22.000 | 133.000 | ||||||||
| Expected count | 6.606 | 2.936 | 90.869 | 15.708 | 16.882 | 133.000 | |||||||||
| Unstandardized residuals | 2.394 | 0.064 | -17.869 | 10.292 | 5.118 | ||||||||||
| Pearson residuals | 0.931 | 0.037 | -1.874 | 2.597 | 1.246 | ||||||||||
| Late Midlife | Count | 2.000 | 1.000 | 42.000 | 9.000 | 8.000 | 62.000 | ||||||||
| Expected count | 3.079 | 1.369 | 42.360 | 7.322 | 7.870 | 62.000 | |||||||||
| Unstandardized residuals | -1.079 | -0.369 | -0.360 | 1.678 | 0.130 | ||||||||||
| Pearson residuals | -0.615 | -0.315 | -0.055 | 0.620 | 0.046 | ||||||||||
| Midlife | Count | 11.000 | 4.000 | 160.000 | 23.000 | 28.000 | 226.000 | ||||||||
| Expected count | 11.225 | 4.989 | 154.408 | 26.691 | 28.687 | 226.000 | |||||||||
| Unstandardized residuals | -0.225 | -0.989 | 5.592 | -3.691 | -0.687 | ||||||||||
| Pearson residuals | -0.067 | -0.443 | 0.450 | -0.714 | -0.128 | ||||||||||
| Older Adulthood | Count | 1.000 | 0.000 | 4.000 | 1.000 | 1.000 | 7.000 | ||||||||
| Expected count | 0.348 | 0.155 | 4.783 | 0.827 | 0.889 | 7.000 | |||||||||
| Unstandardized residuals | 0.652 | -0.155 | -0.783 | 0.173 | 0.111 | ||||||||||
| Pearson residuals | 1.106 | -0.393 | -0.358 | 0.191 | 0.118 | ||||||||||
| Young Adulthood | Count | 9.000 | 7.000 | 164.000 | 19.000 | 25.000 | 224.000 | ||||||||
| Expected count | 11.126 | 4.945 | 153.042 | 26.455 | 28.433 | 224.000 | |||||||||
| Unstandardized residuals | -2.126 | 2.055 | 10.958 | -7.455 | -3.433 | ||||||||||
| Pearson residuals | -0.637 | 0.924 | 0.886 | -1.449 | -0.644 | ||||||||||
| Total | Count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | ||||||||
| Expected count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 20.786 | 20 | 0.410 | 1.000 | |||||
| Χ² continuity correction | 20.786 | 20 | 0.410 | 1.000 | |||||
| Likelihood ratio | 19.676 | 20 | 0.478 | 1.000 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.150 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.076 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPB1T | |||||||||||||||||
| AGE | Fast and accurate delivery | Other | To have a secure shopping certificate | To have positive customer reviews | To offer the option of in-store pickup | To offer the payment option I prefer | Total | ||||||||||
| Early Adulthood | Count | 55.000 | 1.000 | 73.000 | 85.000 | 16.000 | 24.000 | 254.000 | |||||||||
| Expected count | 56.631 | 2.243 | 72.611 | 82.985 | 13.737 | 25.792 | 254.000 | ||||||||||
| Unstandardized residuals | -1.631 | -1.243 | 0.389 | 2.015 | 2.263 | -1.792 | |||||||||||
| Pearson residuals | -0.217 | -0.830 | 0.046 | 0.221 | 0.610 | -0.353 | |||||||||||
| Early Midlife | Count | 37.000 | 3.000 | 34.000 | 34.000 | 7.000 | 18.000 | 133.000 | |||||||||
| Expected count | 29.653 | 1.174 | 38.021 | 43.453 | 7.193 | 13.506 | 133.000 | ||||||||||
| Unstandardized residuals | 7.347 | 1.826 | -4.021 | -9.453 | -0.193 | 4.494 | |||||||||||
| Pearson residuals | 1.349 | 1.685 | -0.652 | -1.434 | -0.072 | 1.223 | |||||||||||
| Late Midlife | Count | 16.000 | 0.000 | 16.000 | 19.000 | 1.000 | 10.000 | 62.000 | |||||||||
| Expected count | 13.823 | 0.547 | 17.724 | 20.256 | 3.353 | 6.296 | 62.000 | ||||||||||
| Unstandardized residuals | 2.177 | -0.547 | -1.724 | -1.256 | -2.353 | 3.704 | |||||||||||
| Pearson residuals | 0.585 | -0.740 | -0.410 | -0.279 | -1.285 | 1.476 | |||||||||||
| Midlife | Count | 50.000 | 4.000 | 72.000 | 68.000 | 13.000 | 19.000 | 226.000 | |||||||||
| Expected count | 50.389 | 1.996 | 64.607 | 73.837 | 12.223 | 22.949 | 226.000 | ||||||||||
| Unstandardized residuals | -0.389 | 2.004 | 7.393 | -5.837 | 0.777 | -3.949 | |||||||||||
| Pearson residuals | -0.055 | 1.419 | 0.920 | -0.679 | 0.222 | -0.824 | |||||||||||
| Older Adulthood | Count | 1.000 | 0.000 | 0.000 | 2.000 | 2.000 | 2.000 | 7.000 | |||||||||
| Expected count | 1.561 | 0.062 | 2.001 | 2.287 | 0.379 | 0.711 | 7.000 | ||||||||||
| Unstandardized residuals | -0.561 | -0.062 | -2.001 | -0.287 | 1.621 | 1.289 | |||||||||||
| Pearson residuals | -0.449 | -0.249 | -1.415 | -0.190 | 2.635 | 1.529 | |||||||||||
| Young Adulthood | Count | 43.000 | 0.000 | 64.000 | 88.000 | 10.000 | 19.000 | 224.000 | |||||||||
| Expected count | 49.943 | 1.978 | 64.035 | 73.183 | 12.115 | 22.746 | 224.000 | ||||||||||
| Unstandardized residuals | -6.943 | -1.978 | -0.035 | 14.817 | -2.115 | -3.746 | |||||||||||
| Pearson residuals | -0.982 | -1.406 | -0.004 | 1.732 | -0.608 | -0.785 | |||||||||||
| Total | Count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | |||||||||
| Expected count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | ||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 37.455 | 25 | 0.052 | 2.386 | |||||
| Χ² continuity correction | 37.455 | 25 | 0.052 | 2.386 | |||||
| Likelihood ratio | 36.759 | 25 | 0.061 | 2.160 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.199 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.091 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RFQ | |||||||||||||||||||
| AGE | Because I want to see the product live | Because of negative reviews | Due to inappropriate and hidden information about product delivery and returns | Due to long delivery time | Due to the need for a better price | Due to website design (complicated search, can't find what I'm looking for) | Other | Total | |||||||||||
| Early Adulthood | Count | 67.000 | 85.000 | 28.000 | 29.000 | 25.000 | 12.000 | 8.000 | 254.000 | ||||||||||
| Expected count | 58.313 | 76.536 | 34.203 | 35.044 | 23.830 | 12.896 | 13.177 | 254.000 | |||||||||||
| Unstandardized residuals | 8.687 | 8.464 | -6.203 | -6.044 | 1.170 | -0.896 | -5.177 | ||||||||||||
| Pearson residuals | 1.138 | 0.967 | -1.061 | -1.021 | 0.240 | -0.250 | -1.426 | ||||||||||||
| Early Midlife | Count | 28.000 | 29.000 | 18.000 | 23.000 | 14.000 | 7.000 | 14.000 | 133.000 | ||||||||||
| Expected count | 30.534 | 40.076 | 17.909 | 18.350 | 12.478 | 6.753 | 6.900 | 133.000 | |||||||||||
| Unstandardized residuals | -2.534 | -11.076 | 0.091 | 4.650 | 1.522 | 0.247 | 7.100 | ||||||||||||
| Pearson residuals | -0.459 | -1.750 | 0.021 | 1.086 | 0.431 | 0.095 | 2.703 | ||||||||||||
| Late Midlife | Count | 17.000 | 17.000 | 12.000 | 7.000 | 4.000 | 2.000 | 3.000 | 62.000 | ||||||||||
| Expected count | 14.234 | 18.682 | 8.349 | 8.554 | 5.817 | 3.148 | 3.216 | 62.000 | |||||||||||
| Unstandardized residuals | 2.766 | -1.682 | 3.651 | -1.554 | -1.817 | -1.148 | -0.216 | ||||||||||||
| Pearson residuals | 0.733 | -0.389 | 1.264 | -0.531 | -0.753 | -0.647 | -0.121 | ||||||||||||
| Midlife | Count | 37.000 | 66.000 | 40.000 | 34.000 | 21.000 | 17.000 | 11.000 | 226.000 | ||||||||||
| Expected count | 51.885 | 68.099 | 30.433 | 31.181 | 21.203 | 11.475 | 11.724 | 226.000 | |||||||||||
| Unstandardized residuals | -14.885 | -2.099 | 9.567 | 2.819 | -0.203 | 5.525 | -0.724 | ||||||||||||
| Pearson residuals | -2.066 | -0.254 | 1.734 | 0.505 | -0.044 | 1.631 | -0.211 | ||||||||||||
| Older Adulthood | Count | 4.000 | 1.000 | 0.000 | 1.000 | 0.000 | 0.000 | 1.000 | 7.000 | ||||||||||
| Expected count | 1.607 | 2.109 | 0.943 | 0.966 | 0.657 | 0.355 | 0.363 | 7.000 | |||||||||||
| Unstandardized residuals | 2.393 | -1.109 | -0.943 | 0.034 | -0.657 | -0.355 | 0.637 | ||||||||||||
| Pearson residuals | 1.888 | -0.764 | -0.971 | 0.035 | -0.810 | -0.596 | 1.057 | ||||||||||||
| Young Adulthood | Count | 55.000 | 75.000 | 24.000 | 31.000 | 21.000 | 8.000 | 10.000 | 224.000 | ||||||||||
| Expected count | 51.426 | 67.497 | 30.163 | 30.905 | 21.015 | 11.373 | 11.620 | 224.000 | |||||||||||
| Unstandardized residuals | 3.574 | 7.503 | -6.163 | 0.095 | -0.015 | -3.373 | -1.620 | ||||||||||||
| Pearson residuals | 0.498 | 0.913 | -1.122 | 0.017 | -0.003 | -1.000 | -0.475 | ||||||||||||
| Total | Count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | ||||||||||
| Expected count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | |||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 43.165 | 30 | 0.057 | 2.262 | |||||
| Χ² continuity correction | 43.165 | 30 | 0.057 | 2.262 | |||||
| Likelihood ratio | 42.747 | 30 | 0.062 | 2.141 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.213 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.098 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PURFRE | |||||||||||||||
| EDUC | Once a month | Once every 12 months or less frequently | Once every 6 months | Several times a month | Several times a week | Total | |||||||||
| Bachelor | Count | 95.000 | 24.000 | 119.000 | 81.000 | 24.000 | 343.000 | ||||||||
| Expected count | 102.597 | 32.558 | 106.383 | 82.532 | 18.929 | 343.000 | |||||||||
| Unstandardized residuals | -7.597 | -8.558 | 12.617 | -1.532 | 5.071 | ||||||||||
| Pearson residuals | -0.750 | -1.500 | 1.223 | -0.169 | 1.165 | ||||||||||
| Elementary school | Count | 1.000 | 0.000 | 2.000 | 1.000 | 0.000 | 4.000 | ||||||||
| Expected count | 1.196 | 0.380 | 1.241 | 0.962 | 0.221 | 4.000 | |||||||||
| Unstandardized residuals | -0.196 | -0.380 | 0.759 | 0.038 | -0.221 | ||||||||||
| Pearson residuals | -0.180 | -0.616 | 0.682 | 0.038 | -0.470 | ||||||||||
| High school | Count | 127.000 | 54.000 | 129.000 | 82.000 | 15.000 | 407.000 | ||||||||
| Expected count | 121.741 | 38.634 | 126.233 | 97.932 | 22.461 | 407.000 | |||||||||
| Unstandardized residuals | 5.259 | 15.366 | 2.767 | -15.932 | -7.461 | ||||||||||
| Pearson residuals | 0.477 | 2.472 | 0.246 | -1.610 | -1.574 | ||||||||||
| Master of Science | Count | 34.000 | 7.000 | 25.000 | 44.000 | 8.000 | 118.000 | ||||||||
| Expected count | 35.296 | 11.201 | 36.598 | 28.393 | 6.512 | 118.000 | |||||||||
| Unstandardized residuals | -1.296 | -4.201 | -11.598 | 15.607 | 1.488 | ||||||||||
| Pearson residuals | -0.218 | -1.255 | -1.917 | 2.929 | 0.583 | ||||||||||
| PhD | Count | 14.000 | 1.000 | 6.000 | 10.000 | 3.000 | 34.000 | ||||||||
| Expected count | 10.170 | 3.227 | 10.545 | 8.181 | 1.876 | 34.000 | |||||||||
| Unstandardized residuals | 3.830 | -2.227 | -4.545 | 1.819 | 1.124 | ||||||||||
| Pearson residuals | 1.201 | -1.240 | -1.400 | 0.636 | 0.820 | ||||||||||
| Total | Count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | ||||||||
| Expected count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 38.498 | 16 | 0.001 | 43.022 | |||||
| Χ² continuity correction | 38.498 | 16 | 0.001 | 43.022 | |||||
| Likelihood ratio | 39.004 | 16 | 0.001 | 49.631 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.202 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.103 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPBREP | |||||||||||||||
| EDUC | Attractive new offers | Other | Satisfaction with products purchased so far | Satisfaction with the delivery of purchased products | Satisfaction with the online shopping process and support | Total | |||||||||
| Bachelor | Count | 15.000 | 11.000 | 231.000 | 45.000 | 41.000 | 343.000 | ||||||||
| Expected count | 17.036 | 7.572 | 234.345 | 40.509 | 43.538 | 343.000 | |||||||||
| Unstandardized residuals | -2.036 | 3.428 | -3.345 | 4.491 | -2.538 | ||||||||||
| Pearson residuals | -0.493 | 1.246 | -0.219 | 0.706 | -0.385 | ||||||||||
| Elementary school | Count | 1.000 | 0.000 | 3.000 | 0.000 | 0.000 | 4.000 | ||||||||
| Expected count | 0.199 | 0.088 | 2.733 | 0.472 | 0.508 | 4.000 | |||||||||
| Unstandardized residuals | 0.801 | -0.088 | 0.267 | -0.472 | -0.508 | ||||||||||
| Pearson residuals | 1.798 | -0.297 | 0.162 | -0.687 | -0.713 | ||||||||||
| High school | Count | 19.000 | 4.000 | 300.000 | 39.000 | 45.000 | 407.000 | ||||||||
| Expected count | 20.215 | 8.985 | 278.072 | 48.067 | 51.661 | 407.000 | |||||||||
| Unstandardized residuals | -1.215 | -4.985 | 21.928 | -9.067 | -6.661 | ||||||||||
| Pearson residuals | -0.270 | -1.663 | 1.315 | -1.308 | -0.927 | ||||||||||
| Master of Science | Count | 5.000 | 3.000 | 69.000 | 17.000 | 24.000 | 118.000 | ||||||||
| Expected count | 5.861 | 2.605 | 80.620 | 13.936 | 14.978 | 118.000 | |||||||||
| Unstandardized residuals | -0.861 | 0.395 | -11.620 | 3.064 | 9.022 | ||||||||||
| Pearson residuals | -0.356 | 0.245 | -1.294 | 0.821 | 2.331 | ||||||||||
| PhD | Count | 5.000 | 2.000 | 16.000 | 6.000 | 5.000 | 34.000 | ||||||||
| Expected count | 1.689 | 0.751 | 23.230 | 4.015 | 4.316 | 34.000 | |||||||||
| Unstandardized residuals | 3.311 | 1.249 | -7.230 | 1.985 | 0.684 | ||||||||||
| Pearson residuals | 2.548 | 1.442 | -1.500 | 0.990 | 0.329 | ||||||||||
| Total | Count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | ||||||||
| Expected count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 33.834 | 16 | 0.006 | 12.456 | |||||
| Χ² continuity correction | 33.834 | 16 | 0.006 | 12.456 | |||||
| Likelihood ratio | 30.371 | 16 | 0.016 | 5.516 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.190 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.097 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPB1T | |||||||||||||||||
| EDUC | Fast and accurate delivery | Other | To have a secure shopping certificate | To have positive customer reviews | To offer the option of in-store pickup | To offer the payment option I prefer | Total | ||||||||||
| Bachelor | Count | 81.000 | 2.000 | 88.000 | 121.000 | 18.000 | 33.000 | 343.000 | |||||||||
| Expected count | 76.475 | 3.029 | 98.054 | 112.062 | 18.551 | 34.830 | 343.000 | ||||||||||
| Unstandardized residuals | 4.525 | -1.029 | -10.054 | 8.938 | -0.551 | -1.830 | |||||||||||
| Pearson residuals | 0.517 | -0.591 | -1.015 | 0.844 | -0.128 | -0.310 | |||||||||||
| Elementary school | Count | 0.000 | 0.000 | 1.000 | 3.000 | 0.000 | 0.000 | 4.000 | |||||||||
| Expected count | 0.892 | 0.035 | 1.143 | 1.307 | 0.216 | 0.406 | 4.000 | ||||||||||
| Unstandardized residuals | -0.892 | -0.035 | -0.143 | 1.693 | -0.216 | -0.406 | |||||||||||
| Pearson residuals | -0.944 | -0.188 | -0.134 | 1.481 | -0.465 | -0.637 | |||||||||||
| High school | Count | 86.000 | 3.000 | 135.000 | 122.000 | 21.000 | 40.000 | 407.000 | |||||||||
| Expected count | 90.744 | 3.594 | 116.350 | 132.971 | 22.012 | 41.329 | 407.000 | ||||||||||
| Unstandardized residuals | -4.744 | -0.594 | 18.650 | -10.971 | -1.012 | -1.329 | |||||||||||
| Pearson residuals | -0.498 | -0.313 | 1.729 | -0.951 | -0.216 | -0.207 | |||||||||||
| Master of Science | Count | 28.000 | 1.000 | 26.000 | 39.000 | 8.000 | 16.000 | 118.000 | |||||||||
| Expected count | 26.309 | 1.042 | 33.733 | 38.552 | 6.382 | 11.982 | 118.000 | ||||||||||
| Unstandardized residuals | 1.691 | -0.042 | -7.733 | 0.448 | 1.618 | 4.018 | |||||||||||
| Pearson residuals | 0.330 | -0.041 | -1.331 | 0.072 | 0.641 | 1.161 | |||||||||||
| PhD | Count | 7.000 | 2.000 | 9.000 | 11.000 | 2.000 | 3.000 | 34.000 | |||||||||
| Expected count | 7.581 | 0.300 | 9.720 | 11.108 | 1.839 | 3.453 | 34.000 | ||||||||||
| Unstandardized residuals | -0.581 | 1.700 | -0.720 | -0.108 | 0.161 | -0.453 | |||||||||||
| Pearson residuals | -0.211 | 3.102 | -0.231 | -0.032 | 0.119 | -0.244 | |||||||||||
| Total | Count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | |||||||||
| Expected count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | ||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 24.007 | 20 | 0.242 | 1.071 | |||||
| Χ² continuity correction | 24.007 | 20 | 0.242 | 1.071 | |||||
| Likelihood ratio | 19.474 | 20 | 0.491 | 1.000 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.161 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.081 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RFQ | |||||||||||||||||||
| EDUC | Because I want to see the product live | Because of negative reviews | Due to inappropriate and hidden information about product delivery and returns | Due to long delivery time | Due to the need for a better price | Due to website design (complicated search, can't find what I'm looking for) | Other | Total | |||||||||||
| Bachelor | Count | 76.000 | 106.000 | 47.000 | 48.000 | 34.000 | 15.000 | 17.000 | 343.000 | ||||||||||
| Expected count | 78.746 | 103.354 | 46.188 | 47.323 | 32.180 | 17.415 | 17.794 | 343.000 | |||||||||||
| Unstandardized residuals | -2.746 | 2.646 | 0.812 | 0.677 | 1.820 | -2.415 | -0.794 | ||||||||||||
| Pearson residuals | -0.309 | 0.260 | 0.120 | 0.098 | 0.321 | -0.579 | -0.188 | ||||||||||||
| Elementary school | Count | 2.000 | 2.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 4.000 | ||||||||||
| Expected count | 0.918 | 1.205 | 0.539 | 0.552 | 0.375 | 0.203 | 0.208 | 4.000 | |||||||||||
| Unstandardized residuals | 1.082 | 0.795 | -0.539 | -0.552 | -0.375 | -0.203 | -0.208 | ||||||||||||
| Pearson residuals | 1.129 | 0.724 | -0.734 | -0.743 | -0.613 | -0.451 | -0.456 | ||||||||||||
| High school | Count | 94.000 | 122.000 | 58.000 | 54.000 | 41.000 | 22.000 | 16.000 | 407.000 | ||||||||||
| Expected count | 93.439 | 122.639 | 54.806 | 56.153 | 38.184 | 20.664 | 21.114 | 407.000 | |||||||||||
| Unstandardized residuals | 0.561 | -0.639 | 3.194 | -2.153 | 2.816 | 1.336 | -5.114 | ||||||||||||
| Pearson residuals | 0.058 | -0.058 | 0.431 | -0.287 | 0.456 | 0.294 | -1.113 | ||||||||||||
| Master of Science | Count | 30.000 | 32.000 | 10.000 | 19.000 | 8.000 | 6.000 | 13.000 | 118.000 | ||||||||||
| Expected count | 27.091 | 35.556 | 15.890 | 16.280 | 11.071 | 5.991 | 6.121 | 118.000 | |||||||||||
| Unstandardized residuals | 2.909 | -3.556 | -5.890 | 2.720 | -3.071 | 0.009 | 6.879 | ||||||||||||
| Pearson residuals | 0.559 | -0.596 | -1.478 | 0.674 | -0.923 | 0.004 | 2.780 | ||||||||||||
| PhD | Count | 6.000 | 11.000 | 7.000 | 4.000 | 2.000 | 3.000 | 1.000 | 34.000 | ||||||||||
| Expected count | 7.806 | 10.245 | 4.578 | 4.691 | 3.190 | 1.726 | 1.764 | 34.000 | |||||||||||
| Unstandardized residuals | -1.806 | 0.755 | 2.422 | -0.691 | -1.190 | 1.274 | -0.764 | ||||||||||||
| Pearson residuals | -0.646 | 0.236 | 1.132 | -0.319 | -0.666 | 0.969 | -0.575 | ||||||||||||
| Total | Count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | ||||||||||
| Expected count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | |||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 21.600 | 24 | 0.603 | 1.000 | |||||
| Χ² continuity correction | 21.600 | 24 | 0.603 | 1.000 | |||||
| Likelihood ratio | 21.506 | 24 | 0.609 | 1.000 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.153 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.077 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PURFRE | |||||||||||||||
| WORKSTAT | Once a month | Once every 12 months or less frequently | Once every 6 months | Several times a month | Several times a week | Total | |||||||||
| Employed | Count | 141.000 | 49.000 | 140.000 | 158.000 | 38.000 | 526.000 | ||||||||
| Expected count | 157.336 | 49.929 | 163.141 | 126.565 | 29.029 | 526.000 | |||||||||
| Unstandardized residuals | -16.336 | -0.929 | -23.141 | 31.435 | 8.971 | ||||||||||
| Pearson residuals | -1.302 | -0.132 | -1.812 | 2.794 | 1.665 | ||||||||||
| Part-time employed | Count | 29.000 | 7.000 | 30.000 | 13.000 | 2.000 | 81.000 | ||||||||
| Expected count | 24.228 | 7.689 | 25.123 | 19.490 | 4.470 | 81.000 | |||||||||
| Unstandardized residuals | 4.772 | -0.689 | 4.877 | -6.490 | -2.470 | ||||||||||
| Pearson residuals | 0.969 | -0.248 | 0.973 | -1.470 | -1.168 | ||||||||||
| Retiree | Count | 1.000 | 4.000 | 5.000 | 1.000 | 0.000 | 11.000 | ||||||||
| Expected count | 3.290 | 1.044 | 3.412 | 2.647 | 0.607 | 11.000 | |||||||||
| Unstandardized residuals | -2.290 | 2.956 | 1.588 | -1.647 | -0.607 | ||||||||||
| Pearson residuals | -1.263 | 2.893 | 0.860 | -1.012 | -0.779 | ||||||||||
| Student | Count | 72.000 | 19.000 | 86.000 | 36.000 | 8.000 | 221.000 | ||||||||
| Expected count | 66.105 | 20.978 | 68.544 | 53.177 | 12.196 | 221.000 | |||||||||
| Unstandardized residuals | 5.895 | -1.978 | 17.456 | -17.177 | -4.196 | ||||||||||
| Pearson residuals | 0.725 | -0.432 | 2.108 | -2.355 | -1.202 | ||||||||||
| Unemployed | Count | 28.000 | 7.000 | 20.000 | 10.000 | 2.000 | 67.000 | ||||||||
| Expected count | 20.041 | 6.360 | 20.780 | 16.121 | 3.698 | 67.000 | |||||||||
| Unstandardized residuals | 7.959 | 0.640 | -0.780 | -6.121 | -1.698 | ||||||||||
| Pearson residuals | 1.778 | 0.254 | -0.171 | -1.525 | -0.883 | ||||||||||
| Total | Count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | ||||||||
| Expected count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 51.892 | 16 | < .001 | 2834.207 | |||||
| Χ² continuity correction | 51.892 | 16 | < .001 | 2834.207 | |||||
| Likelihood ratio | 50.487 | 16 | < .001 | 1766.807 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.233 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.120 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPBREP | |||||||||||||||
| WORKSTAT | Attractive new offers | Other | Satisfaction with products purchased so far | Satisfaction with the delivery of purchased products | Satisfaction with the online shopping process and support | Total | |||||||||
| Employed | Count | 23.000 | 13.000 | 352.000 | 66.000 | 72.000 | 526.000 | ||||||||
| Expected count | 26.126 | 11.611 | 359.375 | 62.121 | 66.766 | 526.000 | |||||||||
| Unstandardized residuals | -3.126 | 1.389 | -7.375 | 3.879 | 5.234 | ||||||||||
| Pearson residuals | -0.612 | 0.407 | -0.389 | 0.492 | 0.641 | ||||||||||
| Part-time employed | Count | 3.000 | 3.000 | 58.000 | 12.000 | 5.000 | 81.000 | ||||||||
| Expected count | 4.023 | 1.788 | 55.341 | 9.566 | 10.281 | 81.000 | |||||||||
| Unstandardized residuals | -1.023 | 1.212 | 2.659 | 2.434 | -5.281 | ||||||||||
| Pearson residuals | -0.510 | 0.906 | 0.357 | 0.787 | -1.647 | ||||||||||
| Retiree | Count | 3.000 | 0.000 | 5.000 | 1.000 | 2.000 | 11.000 | ||||||||
| Expected count | 0.546 | 0.243 | 7.515 | 1.299 | 1.396 | 11.000 | |||||||||
| Unstandardized residuals | 2.454 | -0.243 | -2.515 | -0.299 | 0.604 | ||||||||||
| Pearson residuals | 3.320 | -0.493 | -0.918 | -0.262 | 0.511 | ||||||||||
| Student | Count | 12.000 | 3.000 | 161.000 | 21.000 | 24.000 | 221.000 | ||||||||
| Expected count | 10.977 | 4.879 | 150.992 | 26.100 | 28.052 | 221.000 | |||||||||
| Unstandardized residuals | 1.023 | -1.879 | 10.008 | -5.100 | -4.052 | ||||||||||
| Pearson residuals | 0.309 | -0.851 | 0.814 | -0.998 | -0.765 | ||||||||||
| Unemployed | Count | 4.000 | 1.000 | 43.000 | 7.000 | 12.000 | 67.000 | ||||||||
| Expected count | 3.328 | 1.479 | 45.776 | 7.913 | 8.504 | 67.000 | |||||||||
| Unstandardized residuals | 0.672 | -0.479 | -2.776 | -0.913 | 3.496 | ||||||||||
| Pearson residuals | 0.368 | -0.394 | -0.410 | -0.324 | 1.199 | ||||||||||
| Total | Count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | ||||||||
| Expected count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 23.385 | 16 | 0.104 | 1.564 | |||||
| Χ² continuity correction | 23.385 | 16 | 0.104 | 1.564 | |||||
| Likelihood ratio | 18.538 | 16 | 0.293 | 1.023 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.159 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.080 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPB1T | |||||||||||||||||
| WORKSTAT | Fast and accurate delivery | Other | To have a secure shopping certificate | To have positive customer reviews | To offer the option of in-store pickup | To offer the payment option I prefer | Total | ||||||||||
| Employed | Count | 121.000 | 7.000 | 150.000 | 161.000 | 27.000 | 60.000 | 526.000 | |||||||||
| Expected count | 117.276 | 4.645 | 150.369 | 171.850 | 28.448 | 53.413 | 526.000 | ||||||||||
| Unstandardized residuals | 3.724 | 2.355 | -0.369 | -10.850 | -1.448 | 6.587 | |||||||||||
| Pearson residuals | 0.344 | 1.093 | -0.030 | -0.828 | -0.272 | 0.901 | |||||||||||
| Part-time employed | Count | 26.000 | 0.000 | 21.000 | 28.000 | 3.000 | 3.000 | 81.000 | |||||||||
| Expected count | 18.060 | 0.715 | 23.156 | 26.464 | 4.381 | 8.225 | 81.000 | ||||||||||
| Unstandardized residuals | 7.940 | -0.715 | -2.156 | 1.536 | -1.381 | -5.225 | |||||||||||
| Pearson residuals | 1.868 | -0.846 | -0.448 | 0.299 | -0.660 | -1.822 | |||||||||||
| Retiree | Count | 3.000 | 0.000 | 2.000 | 2.000 | 2.000 | 2.000 | 11.000 | |||||||||
| Expected count | 2.453 | 0.097 | 3.145 | 3.594 | 0.595 | 1.117 | 11.000 | ||||||||||
| Unstandardized residuals | 0.547 | -0.097 | -1.145 | -1.594 | 1.405 | 0.883 | |||||||||||
| Pearson residuals | 0.350 | -0.312 | -0.645 | -0.841 | 1.822 | 0.835 | |||||||||||
| Student | Count | 39.000 | 1.000 | 65.000 | 80.000 | 14.000 | 22.000 | 221.000 | |||||||||
| Expected count | 49.274 | 1.951 | 63.178 | 72.203 | 11.953 | 22.442 | 221.000 | ||||||||||
| Unstandardized residuals | -10.274 | -0.951 | 1.822 | 7.797 | 2.047 | -0.442 | |||||||||||
| Pearson residuals | -1.464 | -0.681 | 0.229 | 0.918 | 0.592 | -0.093 | |||||||||||
| Unemployed | Count | 13.000 | 0.000 | 21.000 | 25.000 | 3.000 | 5.000 | 67.000 | |||||||||
| Expected count | 14.938 | 0.592 | 19.153 | 21.890 | 3.624 | 6.804 | 67.000 | ||||||||||
| Unstandardized residuals | -1.938 | -0.592 | 1.847 | 3.110 | -0.624 | -1.804 | |||||||||||
| Pearson residuals | -0.501 | -0.769 | 0.422 | 0.665 | -0.328 | -0.691 | |||||||||||
| Total | Count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | |||||||||
| Expected count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | ||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 22.403 | 20 | 0.319 | 1.009 | |||||
| Χ² continuity correction | 22.403 | 20 | 0.319 | 1.009 | |||||
| Likelihood ratio | 23.386 | 20 | 0.270 | 1.040 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.155 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.079 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RFQ | |||||||||||||||||||
| WORKSTAT | Because I want to see the product live | Because of negative reviews | Due to inappropriate and hidden information about product delivery and returns | Due to long delivery time | Due to the need for a better price | Due to website design (complicated search, can't find what I'm looking for) | Other | Total | |||||||||||
| Employed | Count | 108.000 | 149.000 | 77.000 | 85.000 | 44.000 | 32.000 | 31.000 | 526.000 | ||||||||||
| Expected count | 120.759 | 158.497 | 70.830 | 72.572 | 49.349 | 26.706 | 27.287 | 526.000 | |||||||||||
| Unstandardized residuals | -12.759 | -9.497 | 6.170 | 12.428 | -5.349 | 5.294 | 3.713 | ||||||||||||
| Pearson residuals | -1.161 | -0.754 | 0.733 | 1.459 | -0.761 | 1.024 | 0.711 | ||||||||||||
| Part-time employed | Count | 14.000 | 28.000 | 14.000 | 5.000 | 13.000 | 5.000 | 2.000 | 81.000 | ||||||||||
| Expected count | 18.596 | 24.407 | 10.907 | 11.175 | 7.599 | 4.113 | 4.202 | 81.000 | |||||||||||
| Unstandardized residuals | -4.596 | 3.593 | 3.093 | -6.175 | 5.401 | 0.887 | -2.202 | ||||||||||||
| Pearson residuals | -1.066 | 0.727 | 0.936 | -1.847 | 1.959 | 0.438 | -1.074 | ||||||||||||
| Retiree | Count | 7.000 | 0.000 | 0.000 | 2.000 | 0.000 | 0.000 | 2.000 | 11.000 | ||||||||||
| Expected count | 2.525 | 3.315 | 1.481 | 1.518 | 1.032 | 0.558 | 0.571 | 11.000 | |||||||||||
| Unstandardized residuals | 4.475 | -3.315 | -1.481 | 0.482 | -1.032 | -0.558 | 1.429 | ||||||||||||
| Pearson residuals | 2.816 | -1.821 | -1.217 | 0.392 | -1.016 | -0.747 | 1.892 | ||||||||||||
| Student | Count | 62.000 | 77.000 | 24.000 | 21.000 | 23.000 | 7.000 | 7.000 | 221.000 | ||||||||||
| Expected count | 50.737 | 66.593 | 29.759 | 30.491 | 20.734 | 11.221 | 11.465 | 221.000 | |||||||||||
| Unstandardized residuals | 11.263 | 10.407 | -5.759 | -9.491 | 2.266 | -4.221 | -4.465 | ||||||||||||
| Pearson residuals | 1.581 | 1.275 | -1.056 | -1.719 | 0.498 | -1.260 | -1.319 | ||||||||||||
| Unemployed | Count | 17.000 | 19.000 | 7.000 | 12.000 | 5.000 | 2.000 | 5.000 | 67.000 | ||||||||||
| Expected count | 15.382 | 20.189 | 9.022 | 9.244 | 6.286 | 3.402 | 3.476 | 67.000 | |||||||||||
| Unstandardized residuals | 1.618 | -1.189 | -2.022 | 2.756 | -1.286 | -1.402 | 1.524 | ||||||||||||
| Pearson residuals | 0.413 | -0.265 | -0.673 | 0.906 | -0.513 | -0.760 | 0.818 | ||||||||||||
| Total | Count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | ||||||||||
| Expected count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | |||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 50.697 | 24 | 0.001 | 47.147 | |||||
| Χ² continuity correction | 50.697 | 24 | 0.001 | 47.147 | |||||
| Likelihood ratio | 54.134 | 24 | < .001 | 115.292 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.230 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.118 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PURFRE | |||||||||||||||
| INCSTAT | Once a month | Once every 12 months or less frequently | Once every 6 months | Several times a month | Several times a week | Total | |||||||||
| High Income | Count | 31.000 | 6.000 | 26.000 | 47.000 | 16.000 | 126.000 | ||||||||
| Expected count | 37.689 | 11.960 | 39.079 | 30.318 | 6.954 | 126.000 | |||||||||
| Unstandardized residuals | -6.689 | -5.960 | -13.079 | 16.682 | 9.046 | ||||||||||
| Pearson residuals | -1.090 | -1.723 | -2.092 | 3.030 | 3.431 | ||||||||||
| I don't want to say | Count | 70.000 | 28.000 | 71.000 | 46.000 | 13.000 | 228.000 | ||||||||
| Expected count | 68.199 | 21.642 | 70.715 | 54.861 | 12.583 | 228.000 | |||||||||
| Unstandardized residuals | 1.801 | 6.358 | 0.285 | -8.861 | 0.417 | ||||||||||
| Pearson residuals | 0.218 | 1.367 | 0.034 | -1.196 | 0.118 | ||||||||||
| Low Income | Count | 17.000 | 12.000 | 36.000 | 10.000 | 3.000 | 78.000 | ||||||||
| Expected count | 23.331 | 7.404 | 24.192 | 18.768 | 4.305 | 78.000 | |||||||||
| Unstandardized residuals | -6.331 | 4.596 | 11.808 | -8.768 | -1.305 | ||||||||||
| Pearson residuals | -1.311 | 1.689 | 2.401 | -2.024 | -0.629 | ||||||||||
| Mid Income | Count | 65.000 | 17.000 | 64.000 | 54.000 | 7.000 | 207.000 | ||||||||
| Expected count | 61.917 | 19.649 | 64.202 | 49.808 | 11.424 | 207.000 | |||||||||
| Unstandardized residuals | 3.083 | -2.649 | -0.202 | 4.192 | -4.424 | ||||||||||
| Pearson residuals | 0.392 | -0.598 | -0.025 | 0.594 | -1.309 | ||||||||||
| Mid-High Income | Count | 88.000 | 23.000 | 84.000 | 61.000 | 11.000 | 267.000 | ||||||||
| Expected count | 79.864 | 25.344 | 82.811 | 64.245 | 14.735 | 267.000 | |||||||||
| Unstandardized residuals | 8.136 | -2.344 | 1.189 | -3.245 | -3.735 | ||||||||||
| Pearson residuals | 0.910 | -0.466 | 0.131 | -0.405 | -0.973 | ||||||||||
| Total | Count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | ||||||||
| Expected count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 52.421 | 16 | < .001 | 3392.513 | |||||
| Χ² continuity correction | 52.421 | 16 | < .001 | 3392.513 | |||||
| Likelihood ratio | 49.376 | 16 | < .001 | 1221.498 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.234 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.120 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPBREP | |||||||||||||||
| INCSTAT | Attractive new offers | Other | Satisfaction with products purchased so far | Satisfaction with the delivery of purchased products | Satisfaction with the online shopping process and support | Total | |||||||||
| High Income | Count | 5.000 | 3.000 | 83.000 | 16.000 | 19.000 | 126.000 | ||||||||
| Expected count | 6.258 | 2.781 | 86.086 | 14.881 | 15.993 | 126.000 | |||||||||
| Unstandardized residuals | -1.258 | 0.219 | -3.086 | 1.119 | 3.007 | ||||||||||
| Pearson residuals | -0.503 | 0.131 | -0.333 | 0.290 | 0.752 | ||||||||||
| I don't want to say | Count | 10.000 | 5.000 | 160.000 | 20.000 | 33.000 | 228.000 | ||||||||
| Expected count | 11.325 | 5.033 | 155.775 | 26.927 | 28.940 | 228.000 | |||||||||
| Unstandardized residuals | -1.325 | -0.033 | 4.225 | -6.927 | 4.060 | ||||||||||
| Pearson residuals | -0.394 | -0.015 | 0.339 | -1.335 | 0.755 | ||||||||||
| Low Income | Count | 5.000 | 2.000 | 55.000 | 10.000 | 6.000 | 78.000 | ||||||||
| Expected count | 3.874 | 1.722 | 53.291 | 9.212 | 9.901 | 78.000 | |||||||||
| Unstandardized residuals | 1.126 | 0.278 | 1.709 | 0.788 | -3.901 | ||||||||||
| Pearson residuals | 0.572 | 0.212 | 0.234 | 0.260 | -1.240 | ||||||||||
| Mid Income | Count | 11.000 | 4.000 | 140.000 | 26.000 | 26.000 | 207.000 | ||||||||
| Expected count | 10.281 | 4.570 | 141.427 | 24.447 | 26.275 | 207.000 | |||||||||
| Unstandardized residuals | 0.719 | -0.570 | -1.427 | 1.553 | -0.275 | ||||||||||
| Pearson residuals | 0.224 | -0.266 | -0.120 | 0.314 | -0.054 | ||||||||||
| Mid-High Income | Count | 14.000 | 6.000 | 181.000 | 35.000 | 31.000 | 267.000 | ||||||||
| Expected count | 13.262 | 5.894 | 182.421 | 31.533 | 33.891 | 267.000 | |||||||||
| Unstandardized residuals | 0.738 | 0.106 | -1.421 | 3.467 | -2.891 | ||||||||||
| Pearson residuals | 0.203 | 0.044 | -0.105 | 0.617 | -0.497 | ||||||||||
| Total | Count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | ||||||||
| Expected count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 6.601 | 16 | 0.980 | 1.000 | |||||
| Χ² continuity correction | 6.601 | 16 | 0.980 | 1.000 | |||||
| Likelihood ratio | 6.959 | 16 | 0.974 | 1.000 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.085 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.043 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPB1T | |||||||||||||||||
| INCSTAT | Fast and accurate delivery | Other | To have a secure shopping certificate | To have positive customer reviews | To offer the option of in-store pickup | To offer the payment option I prefer | Total | ||||||||||
| High Income | Count | 31.000 | 1.000 | 25.000 | 47.000 | 6.000 | 16.000 | 126.000 | |||||||||
| Expected count | 28.093 | 1.113 | 36.020 | 41.166 | 6.815 | 12.795 | 126.000 | ||||||||||
| Unstandardized residuals | 2.907 | -0.113 | -11.020 | 5.834 | -0.815 | 3.205 | |||||||||||
| Pearson residuals | 0.549 | -0.107 | -1.836 | 0.909 | -0.312 | 0.896 | |||||||||||
| I don't want to say | Count | 49.000 | 2.000 | 76.000 | 68.000 | 11.000 | 22.000 | 228.000 | |||||||||
| Expected count | 50.834 | 2.013 | 65.179 | 74.490 | 12.331 | 23.152 | 228.000 | ||||||||||
| Unstandardized residuals | -1.834 | -0.013 | 10.821 | -6.490 | -1.331 | -1.152 | |||||||||||
| Pearson residuals | -0.257 | -0.009 | 1.340 | -0.752 | -0.379 | -0.239 | |||||||||||
| Low Income | Count | 16.000 | 0.000 | 29.000 | 22.000 | 7.000 | 4.000 | 78.000 | |||||||||
| Expected count | 17.391 | 0.689 | 22.298 | 25.483 | 4.219 | 7.921 | 78.000 | ||||||||||
| Unstandardized residuals | -1.391 | -0.689 | 6.702 | -3.483 | 2.781 | -3.921 | |||||||||||
| Pearson residuals | -0.333 | -0.830 | 1.419 | -0.690 | 1.354 | -1.393 | |||||||||||
| Mid Income | Count | 47.000 | 2.000 | 63.000 | 60.000 | 15.000 | 20.000 | 207.000 | |||||||||
| Expected count | 46.152 | 1.828 | 59.175 | 67.629 | 11.195 | 21.020 | 207.000 | ||||||||||
| Unstandardized residuals | 0.848 | 0.172 | 3.825 | -7.629 | 3.805 | -1.020 | |||||||||||
| Pearson residuals | 0.125 | 0.127 | 0.497 | -0.928 | 1.137 | -0.222 | |||||||||||
| Mid-High Income | Count | 59.000 | 3.000 | 66.000 | 99.000 | 10.000 | 30.000 | 267.000 | |||||||||
| Expected count | 59.530 | 2.358 | 76.328 | 87.232 | 14.440 | 27.113 | 267.000 | ||||||||||
| Unstandardized residuals | -0.530 | 0.642 | -10.328 | 11.768 | -4.440 | 2.887 | |||||||||||
| Pearson residuals | -0.069 | 0.418 | -1.182 | 1.260 | -1.169 | 0.555 | |||||||||||
| Total | Count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | |||||||||
| Expected count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | ||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 22.425 | 20 | 0.318 | 1.010 | |||||
| Χ² continuity correction | 22.425 | 20 | 0.318 | 1.010 | |||||
| Likelihood ratio | 23.378 | 20 | 0.271 | 1.040 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.155 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.079 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RFQ | |||||||||||||||||||
| INCSTAT | Because I want to see the product live | Because of negative reviews | Due to inappropriate and hidden information about product delivery and returns | Due to long delivery time | Due to the need for a better price | Due to website design (complicated search, can't find what I'm looking for) | Other | Total | |||||||||||
| High Income | Count | 21.000 | 48.000 | 11.000 | 25.000 | 8.000 | 10.000 | 3.000 | 126.000 | ||||||||||
| Expected count | 28.927 | 37.967 | 16.967 | 17.384 | 11.821 | 6.397 | 6.536 | 126.000 | |||||||||||
| Unstandardized residuals | -7.927 | 10.033 | -5.967 | 7.616 | -3.821 | 3.603 | -3.536 | ||||||||||||
| Pearson residuals | -1.474 | 1.628 | -1.449 | 1.827 | -1.111 | 1.424 | -1.383 | ||||||||||||
| I don't want to say | Count | 53.000 | 70.000 | 40.000 | 26.000 | 20.000 | 10.000 | 9.000 | 228.000 | ||||||||||
| Expected count | 52.344 | 68.702 | 30.702 | 31.457 | 21.391 | 11.576 | 11.828 | 228.000 | |||||||||||
| Unstandardized residuals | 0.656 | 1.298 | 9.298 | -5.457 | -1.391 | -1.576 | -2.828 | ||||||||||||
| Pearson residuals | 0.091 | 0.157 | 1.678 | -0.973 | -0.301 | -0.463 | -0.822 | ||||||||||||
| Low Income | Count | 23.000 | 23.000 | 9.000 | 6.000 | 11.000 | 4.000 | 2.000 | 78.000 | ||||||||||
| Expected count | 17.907 | 23.503 | 10.503 | 10.762 | 7.318 | 3.960 | 4.046 | 78.000 | |||||||||||
| Unstandardized residuals | 5.093 | -0.503 | -1.503 | -4.762 | 3.682 | 0.040 | -2.046 | ||||||||||||
| Pearson residuals | 1.203 | -0.104 | -0.464 | -1.451 | 1.361 | 0.020 | -1.017 | ||||||||||||
| Mid Income | Count | 50.000 | 50.000 | 28.000 | 27.000 | 29.000 | 8.000 | 15.000 | 207.000 | ||||||||||
| Expected count | 47.523 | 62.374 | 27.874 | 28.560 | 19.421 | 10.510 | 10.738 | 207.000 | |||||||||||
| Unstandardized residuals | 2.477 | -12.374 | 0.126 | -1.560 | 9.579 | -2.510 | 4.262 | ||||||||||||
| Pearson residuals | 0.359 | -1.567 | 0.024 | -0.292 | 2.174 | -0.774 | 1.300 | ||||||||||||
| Mid-High Income | Count | 61.000 | 82.000 | 34.000 | 41.000 | 17.000 | 14.000 | 18.000 | 267.000 | ||||||||||
| Expected count | 61.298 | 80.454 | 35.954 | 36.838 | 25.050 | 13.556 | 13.851 | 267.000 | |||||||||||
| Unstandardized residuals | -0.298 | 1.546 | -1.954 | 4.162 | -8.050 | 0.444 | 4.149 | ||||||||||||
| Pearson residuals | -0.038 | 0.172 | -0.326 | 0.686 | -1.608 | 0.121 | 1.115 | ||||||||||||
| Total | Count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | ||||||||||
| Expected count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | |||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 41.019 | 24 | 0.017 | 5.413 | |||||
| Χ² continuity correction | 41.019 | 24 | 0.017 | 5.413 | |||||
| Likelihood ratio | 41.087 | 24 | 0.016 | 5.484 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.208 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.106 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PURFRE | |||||||||||||||
| RESI | Once a month | Once every 12 months or less frequently | Once every 6 months | Several times a month | Several times a week | Total | |||||||||
| Rural | Count | 37.000 | 16.000 | 45.000 | 21.000 | 2.000 | 121.000 | ||||||||
| Expected count | 36.193 | 11.486 | 37.529 | 29.115 | 6.678 | 121.000 | |||||||||
| Unstandardized residuals | 0.807 | 4.514 | 7.471 | -8.115 | -4.678 | ||||||||||
| Pearson residuals | 0.134 | 1.332 | 1.220 | -1.504 | -1.810 | ||||||||||
| Suburban settlement | Count | 28.000 | 8.000 | 38.000 | 21.000 | 6.000 | 101.000 | ||||||||
| Expected count | 30.211 | 9.587 | 31.326 | 24.302 | 5.574 | 101.000 | |||||||||
| Unstandardized residuals | -2.211 | -1.587 | 6.674 | -3.302 | 0.426 | ||||||||||
| Pearson residuals | -0.402 | -0.513 | 1.193 | -0.670 | 0.180 | ||||||||||
| Town/township | Count | 206.000 | 62.000 | 198.000 | 176.000 | 42.000 | 684.000 | ||||||||
| Expected count | 204.596 | 64.927 | 212.146 | 164.583 | 37.748 | 684.000 | |||||||||
| Unstandardized residuals | 1.404 | -2.927 | -14.146 | 11.417 | 4.252 | ||||||||||
| Pearson residuals | 0.098 | -0.363 | -0.971 | 0.890 | 0.692 | ||||||||||
| Total | Count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | ||||||||
| Expected count | 271.000 | 86.000 | 281.000 | 218.000 | 50.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 13.502 | 8 | 0.096 | 1.638 | |||||
| Χ² continuity correction | 13.502 | 8 | 0.096 | 1.638 | |||||
| Likelihood ratio | 14.657 | 8 | 0.066 | 2.047 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.121 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.086 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPBREP | |||||||||||||||
| RESI | Attractive new offers | Other | Satisfaction with products purchased so far | Satisfaction with the delivery of purchased products | Satisfaction with the online shopping process and support | Total | |||||||||
| Rural | Count | 6.000 | 1.000 | 95.000 | 12.000 | 7.000 | 121.000 | ||||||||
| Expected count | 6.010 | 2.671 | 82.670 | 14.290 | 15.359 | 121.000 | |||||||||
| Unstandardized residuals | -0.010 | -1.671 | 12.330 | -2.290 | -8.359 | ||||||||||
| Pearson residuals | -0.004 | -1.022 | 1.356 | -0.606 | -2.133 | ||||||||||
| Suburban settlement | Count | 6.000 | 2.000 | 73.000 | 7.000 | 13.000 | 101.000 | ||||||||
| Expected count | 5.017 | 2.230 | 69.006 | 11.928 | 12.820 | 101.000 | |||||||||
| Unstandardized residuals | 0.983 | -0.230 | 3.994 | -4.928 | 0.180 | ||||||||||
| Pearson residuals | 0.439 | -0.154 | 0.481 | -1.427 | 0.050 | ||||||||||
| Town/township | Count | 33.000 | 17.000 | 451.000 | 88.000 | 95.000 | 684.000 | ||||||||
| Expected count | 33.974 | 15.099 | 467.325 | 80.781 | 86.821 | 684.000 | |||||||||
| Unstandardized residuals | -0.974 | 1.901 | -16.325 | 7.219 | 8.179 | ||||||||||
| Pearson residuals | -0.167 | 0.489 | -0.755 | 0.803 | 0.878 | ||||||||||
| Total | Count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | ||||||||
| Expected count | 45.000 | 20.000 | 619.000 | 107.000 | 115.000 | 906.000 | |||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 12.540 | 8 | 0.129 | 1.394 | |||||
| Χ² continuity correction | 12.540 | 8 | 0.129 | 1.394 | |||||
| Likelihood ratio | 14.275 | 8 | 0.075 | 1.896 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.117 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.083 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIPB1T | |||||||||||||||||
| RESI | Fast and accurate delivery | Other | To have a secure shopping certificate | To have positive customer reviews | To offer the option of in-store pickup | To offer the payment option I prefer | Total | ||||||||||
| Rural | Count | 24.000 | 0.000 | 39.000 | 42.000 | 3.000 | 13.000 | 121.000 | |||||||||
| Expected count | 26.978 | 1.068 | 34.591 | 39.532 | 6.544 | 12.287 | 121.000 | ||||||||||
| Unstandardized residuals | -2.978 | -1.068 | 4.409 | 2.468 | -3.544 | 0.713 | |||||||||||
| Pearson residuals | -0.573 | -1.034 | 0.750 | 0.393 | -1.385 | 0.203 | |||||||||||
| Suburban settlement | Count | 22.000 | 0.000 | 25.000 | 42.000 | 5.000 | 7.000 | 101.000 | |||||||||
| Expected count | 22.519 | 0.892 | 28.873 | 32.998 | 5.462 | 10.256 | 101.000 | ||||||||||
| Unstandardized residuals | -0.519 | -0.892 | -3.873 | 9.002 | -0.462 | -3.256 | |||||||||||
| Pearson residuals | -0.109 | -0.944 | -0.721 | 1.567 | -0.198 | -1.017 | |||||||||||
| Town/township | Count | 156.000 | 8.000 | 195.000 | 212.000 | 41.000 | 72.000 | 684.000 | |||||||||
| Expected count | 152.503 | 6.040 | 195.536 | 223.470 | 36.993 | 69.457 | 684.000 | ||||||||||
| Unstandardized residuals | 3.497 | 1.960 | -0.536 | -11.470 | 4.007 | 2.543 | |||||||||||
| Pearson residuals | 0.283 | 0.798 | -0.038 | -0.767 | 0.659 | 0.305 | |||||||||||
| Total | Count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | |||||||||
| Expected count | 202.000 | 8.000 | 259.000 | 296.000 | 49.000 | 92.000 | 906.000 | ||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 10.860 | 10 | 0.369 | 1.000 | |||||
| Χ² continuity correction | 10.860 | 10 | 0.369 | 1.000 | |||||
| Likelihood ratio | 13.190 | 10 | 0.213 | 1.116 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.109 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.077 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||
Contingency Tables
|
|||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RFQ | |||||||||||||||||||
| RESI | Because I want to see the product live | Because of negative reviews | Due to inappropriate and hidden information about product delivery and returns | Due to long delivery time | Due to the need for a better price | Due to website design (complicated search, can't find what I'm looking for) | Other | Total | |||||||||||
| Rural | Count | 27.000 | 32.000 | 21.000 | 12.000 | 14.000 | 7.000 | 8.000 | 121.000 | ||||||||||
| Expected count | 27.779 | 36.460 | 16.294 | 16.694 | 11.352 | 6.143 | 6.277 | 121.000 | |||||||||||
| Unstandardized residuals | -0.779 | -4.460 | 4.706 | -4.694 | 2.648 | 0.857 | 1.723 | ||||||||||||
| Pearson residuals | -0.148 | -0.739 | 1.166 | -1.149 | 0.786 | 0.346 | 0.688 | ||||||||||||
| Suburban settlement | Count | 23.000 | 48.000 | 7.000 | 12.000 | 7.000 | 3.000 | 1.000 | 101.000 | ||||||||||
| Expected count | 23.188 | 30.434 | 13.600 | 13.935 | 9.476 | 5.128 | 5.240 | 101.000 | |||||||||||
| Unstandardized residuals | -0.188 | 17.566 | -6.600 | -1.935 | -2.476 | -2.128 | -4.240 | ||||||||||||
| Pearson residuals | -0.039 | 3.184 | -1.790 | -0.518 | -0.804 | -0.940 | -1.852 | ||||||||||||
| Town/township | Count | 158.000 | 193.000 | 94.000 | 101.000 | 64.000 | 36.000 | 38.000 | 684.000 | ||||||||||
| Expected count | 157.033 | 206.106 | 92.106 | 94.371 | 64.172 | 34.728 | 35.483 | 684.000 | |||||||||||
| Unstandardized residuals | 0.967 | -13.106 | 1.894 | 6.629 | -0.172 | 1.272 | 2.517 | ||||||||||||
| Pearson residuals | 0.077 | -0.913 | 0.197 | 0.682 | -0.021 | 0.216 | 0.422 | ||||||||||||
| Total | Count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | ||||||||||
| Expected count | 208.000 | 273.000 | 122.000 | 125.000 | 85.000 | 46.000 | 47.000 | 906.000 | |||||||||||
Chi-Squared Tests
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Value | df | p | VS-MPR* | ||||||
| Χ² | 24.599 | 12 | 0.017 | 5.349 | |||||
| Χ² continuity correction | 24.599 | 12 | 0.017 | 5.349 | |||||
| Likelihood ratio | 25.710 | 12 | 0.012 | 7.025 | |||||
| N | 906 | ||||||||
| * Vovk-Sellke Maximum p -Ratio: Based the p -value, the maximum possible odds in favor of H₁ over H₀ equals 1/(-e p log(p )) for p ≤ .37 (Sellke, Bayarri, & Berger, 2001). | |||||||||
Nominal
|
|||
|---|---|---|---|
| Value | |||
| Contingency coefficient | 0.163 | ||
| Phi-coefficient | NaN | ||
| Cramer's V | 0.117 | ||
| ᵃ Phi coefficient is only available for 2 by 2 contingency Tables | |||