﻿ Table 27.1 Item subtotal summaries on one line

# Table 27.1 Item subtotal summaries on one line

(controlled by ISUBTOT=, UDECIMALS=, REALSE=)

These summarize the measures from the main analysis for all items selected by ISUBTOT= (Table 27), including extreme scores.

Table

27.2 Measure sub-totals bar charts, controlled by ISUBTOT=

27.3 Measure sub-totals summary statistics, controlled by ISUBTOT=

Subtotal specification is: ISUBTOTAL=\$S1W1

ALL ITEM SCORES ARE NON-EXTREME

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|   ITEM    MEAN    S.E.                            MODEL      MODEL          |

|  COUNT  MEASURE   MEAN    P.SD    S.SD  MEDIAN  SEPARATION RELIABILITY CODE |

|-----------------------------------------------------------------------------|

|     25     .00     .29    1.41    1.43     .16     5.86        .97     *    |

|      4     .31     .73    1.27    1.47    -.11     5.89        .97     F    |

|      4   -2.24     .39     .67     .78   -2.26     1.62        .72     G    |

|      5     .32     .51    1.02    1.14     .42     4.81        .96     L    |

|      1     .60       -     .00       -     .60      .00        .00     M    |

|      3    -.26     .34     .49     .60    -.48     2.14        .82     R    |

|      1     .53       -     .00       -     .53      .00        .00     T    |

|      7     .82     .45    1.10    1.19    1.10     5.29        .97     W    |

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SUBTOTAL RELIABILITY: inestimable

UMEAN=0 USCALE=1

 Subtotal specification is: ISUBTOTAL=\$S1W1 identifies the columns in the item label to be used for classifying the item by \$S1W1 or whatever, using the column selection rules. EXTREME AND NON-EXTREME KID SCORES ALL SCORES ARE NON-EXTREME NON-EXTREME SCORES ONLY The items included in this summary table. Items with non-extreme scores (omits items with 0% and 100% success rates) ITEM COUNT count of items. "ITEM" is the name assigned with ITEM= MEAN MEASURE average measure of items S.E. MEAN standard error of the average measure of items P.SD population standard deviation of the item measures. S.SD sample standard deviation of the item measures. MEDIAN the measure of the middle item REAL/MODEL SEPARATION the separation coefficient: the "true" adjusted standard deviation / root-mean-square measurement error of the items (REALSE= inflated for misfit). REAL/MODEL RELIABILITY the item measure reproducibility = ("True" item measure variance / Observed variance) = Separation ² / (1 + Separation ²) ITEM CODE the classification code in the item label. The first line, "*", is the total for all items. The remaining codes are those in the item columns specified by \$S1W1 or whatever, using the column selection rules. SUBTOTAL RELIABILITY the reliability (reproducibility) of the means of the subtotals = true variance / observed variance = (observed variance - error variance) / observed variance. Observed variance = variance of MEAN MEASURES Error variance = mean-square of the S.E. MEAN inestimable = some subtotal counts are too small to estimate Reliability UMEAN=0 USCALE=1 Current user-scaling

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|    ITEM   MEAN DIFFERENCE        Welch-2sided |

| CODE CODE MEASURE   S.E.    t    d.f.  Prob. |

|----------------------------------------------|

| 0    1      -9.06    .57 -15.95   10    .000 |

| 0    2      -9.72    .87 -11.14   10    .000 |

| 0    4      -6.29    .94  -6.71   11    .000 |

| 1    2       -.66    .66  -1.00    2    .423 |

| 1    4       2.77    .75   3.71    3    .034 |

| 2    4       3.43   1.00   3.44    3    .041 |

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 ITEM CODE the classification code in the item label for subtotal "1" CODE the classification code in the item label for subtotal "2" MEAN DIFFERENCE difference between the mean measures of the two CODE subtotals, "1" and "2" MEASURE size of the difference between "1" and "2" S.E. standard error of the difference = sqrt ( (S.E. Mean "1")² + (S.E. Mean "2")² ) t Student's t = MEASURE / S.E. Welch2-sided 2-sided t-test using Welch's adaptation of Student's t-test. d.f. Welch's degrees of freedom Prob. two-sided probability of Student's t. See t-statistics.

One-way ANOVA of subtotal means and variances

This reports a one-way analysis of variance for the subtotal means. Are they the same (statistically) as the overall mean?

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| ANOVA -    KID                                              |

| Source  Sum-of-Squares   d.f.  Mean-Squares  F-test  Prob>F |

|-------------------------------------------------------------|

| @TOPIC            1.70    1.00         1.70    1.89   .1761 |

| Error            26.91   30.00          .90                 |

| Total            28.61   31.00          .92                 |

|-------------------------------------------------------------|

| Fixed-Effects Chi-squared: 1.7026 with 1 d.f., prob. .1919  |

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 Source the variance component. @TYPE (the specified ISUBTOTAL= classification) the variation of the subtotal mean measures around the grand mean. Error Error is the part of the total variation of the measures around their grand mean not explained by the @TYPE Total total variation of the measures around their grand mean Sum-of-Squares the variation around the relevant mean d.f. the degrees of freedom corresponding to the variation (= number of measures - 1) Mean-Squares Sum-of-Squares divided by d.f. F-test @TYPE Mean-Square / Error Mean-Square Prob>F the right-tail probability of the F-test value with (@TYPE, Error) d.f. A probability less than .05 indicates statistically significant differences between the means. Fixed-Effects Chi-Square (of Homogeneity) a test of the hypothesis that all the subtotal means are the same, except for sampling error d.f. degrees of freedom of chi-square = number of sub-totals - 1 prob. probability of observing this value of the chi-square or larger if the hypothesis is true. A probability less than .05 indicates statistically significant differences between the means. inestimable some item counts are too small and/or some variances are zero.

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