partwhole: Part-Whole Correlation
In multicon: Multivariate Constructs

Description Usage Arguments Details Value Author(s) See Also Examples

Returns the part-whole correlations between an item or the mean of all possible groups of nitems and the composite of the full set of items.

1	partwhole(x, nitems = 1, nomiss = 0.8)

`x`	A matrix or data.frame containing the variables (in columns) thought to form a composite.
`nitems`	A numeric element indicating the number of items desired for each possible group of items.
`nomiss`	A numeric between .00 and 1.00 indicating the proportion of scores that must be non-missing for each composite before a score of NA is returned.

The purpose of this function is to determine which subset of items, when formed into a unit-weighted composite, most strongly correlate with both a unit-weighted and a components weighted composite of the full set of items. For example, if one has an 8 item scale and wants to reduce it to a 4 item scale, it might be interest to know which 4 items can be composited and correlate most highly with the composite from the full set of 8 items. It turns out there are 70 ways to form 4-item composites from 8 total items. This function creates all 70 of those composites and correlates each with both a unit weighted composite from the original 8 items and a components scored (1 principal component) composite of the original 8 items. One can then look at the output to determine which 4-item composite best correlated with the full scale composite.

A matrix with 2 rows and K columns where K is the number of possible subset combinations. The column names indicate which items (separated by an underline) make up the subset combination. The first row (UnitWgt) is the result using a unit weighted composite for the total set of items and the second row (Component) is the result using principle component scores for the total set of items.

Ryne A. Sherman

composite

data(bfi.set)
  # Imagine we want to find the best two-item composite that correlates
  # highest with the full 8 items available to measure extraversion. 
  # Three (of the extraversion) items need to be reverse scored
sBFI6r <- 6 - bfi.set$sBFI6
sBFI21r <- 6 - bfi.set$sBFI21
sBFI31r <- 6 - bfi.set$sBFI31
  # Now put them all into one data.frame
ext.vars <- data.frame(bfi.set$sBFI1, sBFI6r, bfi.set$sBFI11, 
	bfi.set$sBFI16, sBFI21r, bfi.set$sBFI26, sBFI31r, bfi.set$sBFI36)
head(ext.vars) # Looks good
  # Now compute the parwhole correlation for all possible 2-item composites
partwhole(ext.vars, 2)

Loading required package: psych
Loading required package: abind
Loading required package: foreach
  bfi.set.sBFI1 sBFI6r bfi.set.sBFI11 bfi.set.sBFI16 sBFI21r bfi.set.sBFI26
1             5      5              4              4       5              4
2             4      3              5              5       2              3
3             3      2              2              3       2              3
4             4      3              4              4       3              3
5             3      4              4              4       3              4
6             5      3              4              4       4              4
  sBFI31r bfi.set.sBFI36
1       5              5
2       1              4
3       2              2
4       2              3
5       2              4
6       4              5
                1_2       1_3       1_4       1_5       1_6       1_7       1_8
UnitWgt   0.8442703 0.8220590 0.8345734 0.8707309 0.7998720 0.8458420 0.8538019
Component 0.8375887 0.8381451 0.8495031 0.8688132 0.7984325 0.8384747 0.8666528
                2_3       2_4       2_5       2_6       2_7       2_8       3_4
UnitWgt   0.8514361 0.8540545 0.8355492 0.8008026 0.8037861 0.8767012 0.7409587
Component 0.8501461 0.8518743 0.8202961 0.7818108 0.7822599 0.8735861 0.7615846
                3_5       3_6       3_7       3_8       4_5       4_6       4_7
UnitWgt   0.9051586 0.7423189 0.8468963 0.8407350 0.8978696 0.7462679 0.8524176
Component 0.9086982 0.7474811 0.8449537 0.8607046 0.9004398 0.7502767 0.8495837
                4_8       5_6       5_7       5_8       6_7       6_8       7_8
UnitWgt   0.8303802 0.8603569 0.8394935 0.9104896 0.7904131 0.7961346 0.8517101
Component 0.8486490 0.8474014 0.8234883 0.9119080 0.7707201 0.7987832 0.8480722