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www.cut-e.com

cut-e GmbH

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20354 Hamburg

Phone: +49-40-3250.3890

Fax: +49-40-3250.3891

E-Mail: [email protected]

www.cut-e.com

V2.1 October 2008

adalloc adaptive allocation of consent

cut-e 2002-2010, all rights reserved. 2/9


adalloc - adaptive allocation of consent

With the adalloc method the company cut-e offers a new and unique technology for adaptive

measurement of different types of concepts. The adalloc method is suitable for measurement of

competencies, personality dimensions, attitudes, interests, and values, as well as for the

assessment of job requirements.

The adalloc method allows for the measurement of psychological constructs for personnel and

marketing decisions faster and more precisely than any other existing psychometric procedure.

The advantages of the adalloc method emerge from its defining features:

The concepts to be assessed are not rated absolutely, but are compared with each other

successively;

The adalloc instrument adapts its sensitivity constantly to the subject that is to be measured

during the measurement process.

Like the well-known static normative and ipsative procedures, a basic requirement for the adalloc

method is a specific number of concepts to be measured. These concepts constitute the scales

which are represented by a number of test items that load onto these scales.

Example block with a block size of 3 (instrument: shapes)



Items are verbal phrases (typically statements; can be more general stimuli as well) which are

associated with exactly one concept. I.e. the answer (or more general: reaction) of one person

“loads” on exactly one concept.

The adalloc method presents items in blocks. The block size is the number of items per block.

Typical block sizes are 3 or 4 items. However, in principle the block size can be any number

greater than or equal to 2. The block size within an instrument is constant.

The graphic (example: instrument shapes) depicts a block consisting of 3 items. The task for the

candidate is to allocate a specific number of points according to the perceived level of agreement

with the items of the block. The number of points to be allocated within a block is a multiple of

the block size (typically twice the size of the block). For a block size of 3 items there are

therefore e.g. 6 points to assign (8 points for a block size of 4 accordingly). In the example, 3

points have been allocated to the second item, 1 point to the third item, and 2 points have not

yet been allocated. The candidate can optionally allocate all points to one item or make any

pattern of distribution (5-1-0, 4-2-1, 4-1-1, 3-3-0 etc.). Hence, it is possible to allocate an equal

number of points to all items. It is not mandatory to allocate all available points. All items within

a block are associated with different concepts.

The adalloc method structures an instrument into sectors. A sector is – according the adalloc

method – the number of blocks which in turn present the underlying concepts with exactly one

item for each concept. So each concept within one sector has to be represented by exactly one

item. 18 underlying concepts and a block size of 3 items, for instance, require 6 blocks in order

to represent each concept with exactly one item. For 18 concepts and a block size of 3 items

exactly 6 blocks form one sector.

The adalloc method requires that:

The number of concepts is a multiple of the block size (with a block size of 3 e.g. 15,

18 or 21 concepts; with a block size of 4 e.g. 16, 20 or 24 concepts);

The number of items per concept is the same for all concepts.

The first sector of an instrument consists of a random sequence of one item per concept in

blocks of the according block size. All scales of the concepts are initially set to 0. The points

which are allocated to the items by a candidate are added to the respective concept scale.

After the first sector the concepts are sorted by their current values (which have emerged by

going through the first sector). For sector 2 the second items representing the concepts are

grouped into blocks of the defined block size according the current sorting, which results – from

sector 2 onwards – in the grouping of items (each representing one concept) that currently have

similar values.

Per sector, each block is given a block weight that results from the concept values of the

corresponding block. A block of items in which concepts currently have relatively high values is

given a higher block weight than a block of items in which concepts currently have relatively low

values.

The points which are allocated to the items by the candidate from sector 2 on incrementally

change the current value of the corresponding scale by the product of the number of allocated

points and the block weight. The effect is that 6 points allocated to an item in a block with high

block weight have a greater impact than 6 points allocated to an item in a block with low block

weight. The number of sectors of an adalloc instrument corresponds to the number of items per

concept.

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The sum of scale values (raw scores) is always a constant if 100% of the points are allocated.

The specific advantages of the adalloc method are evident if compared to the classical normative

and ipsative procedures. The graphic shows an example of the initial measurement process for

normative and ipsative methods, as well as for the adalloc procedure for the measurement of 9

concepts. Each step corresponds to the application of exactly one item per concept to measure

sector (according the adalloc technology).

In a normative process the items are rated independently on a point-5 or -7 point scale. In step

1 all concepts are given a value of 0. The scale values per concept are the result of the summing

up of individual item ratings. After 3 steps it becomes evident – especially under conditions that

bias responses according social desirability – that the lowest value of the concept scales is

relatively high (distance A) and the variance is relatively small (distance B). As the number of

items grows the mean (distance A) is getting higher and the variance (distance B) is getting

lower).

In an ipsative process the items are presented in blocks. The example shows blocks of 3 items.

Within a block a decision has to be made about which item is most accurate and which item is

least true. The decision “most” typically adds 2 points, the decision “least” adds 0 points and the

decision in between the two adds 1 point to the corresponding scale. The grouping of items in

blocks is predefined for the entire instrument. After 3 items per concept one can typically

observe the smallest value of the concept scales to be low (distance A) but a comparably small

variance (distance B) as well. As the number of items grows the variance (distance B) rises very

slowly. This requires a lot of items, and therefore a long administration time, in order to get a

sufficient amount of variance through an ipsative procedure.

The adalloc method provides a faster and more precise intra-individual differentiation between

the concepts due to its adaptability based on subject responding. Since similar concepts are

individually grouped, a high differentiation (distance B) can be obtained with a relatively small

number of sectors, and therefore within very short administration times.

The adalloc technology allows using concepts directly as items. Based on this an instrument

which differentiates the concepts by multiple sectors (repeated the direct concepts) follows. This

is a very useful procedure if the adalloc technology should be used for identification of ideal

profiles.

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normative

ipsative

adalloc



The differences in quality of measurement of normative, ipsative and adalloc are even more

evident when intentional faking of results by candidates can be assumed. The more likely it is

that candidates fake their results intentionally the better the quality of adalloc measurement.

The likelihood of intentional faking is especially high if an instrument is deployed for selection

purposes. The chart shows the impact of intentional faking on the mean (M) and the standard

derivation (SD) for the different methods of measurement. In various studies candidates were

asked to describe themselves truly first, and afterwards in a way based towards the requirement

of a specific job description. The meta-analysis reveals that for normative procedures the

means of the faked descriptions are significantly higher than for the true self-descriptions. This

higher mean could be compensated by comparing the results to a suitable norm group. However,

the extreme loss of variance at the same time (ceiling effect) spoils the practical relevance of the

normative method dramatically.

Impact of intentional faked self-description (* Bank & Ramsey 2001)

For the ipsative method, the mean is not different for true and intentionally faked self-

description since the sum of the raw scale scores for an ipsative administration is always

constant. The slight decline of variance has no significant effect on practical relevance. However,

these results require extensively long administration times, which has a significant negative

effect on the acceptance of the measure, and therefore on the practical relevance of the ipsative

method.

For adalloc instruments the mean rises slightly for intentionally faked compared to true self-

descriptions. Under biased conditions typically all available points are allocated which is not

necessarily the case for true self-descriptions (depending on the concepts). However, this slight

rise of the mean causes a concurrent slight rise of the variance which even improves the

practical relevance of the results.

Within short administration times, an instrument according to the adalloc method always

provides a highly intra-individual differentiated profile of concepts which is robust to intentional

faking under selection conditions.

The results of an adalloc instrument do not just allow precise level assessments on the basis of

comparing profiles to a norm group. Moreover, the results are ideal for person-requirement-

matches on the basis of pattern-matching and critical gap analysis.



Algorithmic description of the adalloc technology

Declared Variables

/* Number of concepts to be measured */ NUMBER_CONCEPTS

/* Number of items per concept */ ITEMS_PER_CONCEPT

/* Number of items displayed together in a block typ. 3 or 4 */

BLOCKSIZE

BLOCKS_PER_SECTOR = NUMBER_CONCEPTS / BLOCKSIZE

The adalloc technology requires that:

The number of concepts is a multiple of the blocksize

The number of items per concept is the same for all concepts

Declared Arrays

/* Array holds the raw score per concept; updated per item response */

concept_scale [NUMBER_CONCEPTS]

/* Array holds the sorted numbers of the concept scales; updated per sector */

sorted_cs [NUMBER_CONCEPTS]

/* Array holds the item strings ordered by concepts */ item[NUMBER_CONCEPTS, ITEMS_PER_CONCEPT]

/* Array holds the adapted blockweights; updated per sector */

blockweight[BLOCKS_PER_SECTOR]



/* display sector 1 */ /* initialize concept_scale */

for X=1 to NUMBER_CONCEPTS; concept_scale[B]=0; next X.

/* sort concepts randomly */ sort concept_scale[] into sorted_cs[] randomly

/* display blocks for sector 1 and track itemresponses */

for X=1 to BLOCKS_PER_SECTOR;

/* display block out of BLOCKSIZE items */

display block(for C=1 to BLOCKSIZE; item[sorted_cs[((X-1)* BLOCKSIZE)+C],1]; next C)

/* increment concept_scale by itemresponse */

for C= 1 to BLOCKSIZE concept_scale[sorted_cs[[((X-1)* BLOCKSIZE)+C] = _

reponse(item[sorted_cs[[((X-1)* BLOCKSIZE)+C],1])

next C

next X

/* display sector 2 to sector ITEMS_PER_CONCEPT */ for X=2 to ITEMS_PER_CONCEPT;

/* sort concepts by current value of concept_scale */

sort concept_scale[] into sorted_cs[] by value of concept_scale

/* calculate blockweights */

BLOCKSUM=0 for Y=1 to NUMBER_CONCEPTS; BLOCKSUM+=concept_scale[Y]; next Y

for Y= 1 to BLOCKS_PER_SECTOR;

BLOCK_HELP=0 for V= 1 to BLOCKSIZE;

BLOCK_HELP+=concept_scale[sorted_cs[((Y-1)* BLOCKSIZE)+V];

next V blockweight[Y]= square((BLOCK_HELP/BLOCKSUM) +1)

next Y

/* display blocks for sector X and track itemresponses */ for B=1 to BLOCKS_PER_SECTOR;

/* display block out of BLOCKSIZE items */

display block( for C=1 to BLOCKSIZE;

item[sorted_cs[((B-1)* BLOCKSIZE)+C],X]; next C)

/* increment concept_scale by blockweighted itemresponses */

for C= 1 to BLOCKSIZE concept_scale[sorted_cs[((B-1)* BLOCKSIZE)+C] +=

reponse(item[sorted_cs[((B-1)* BLOCKSIZE)+C,X]) * blockweight(B) next C

next B next X



Returned data

An adalloc instrument returns the following data:

Raw score of concept scales The raw scores of the concept scales are put together in a string separated by colons. The

concepts are in numerical order.

Example: “23.45:12.56:17:43………………23.12”

Item responses

The item responses are put together in a string separated by a colon. The item responses are ordered by concept and item of concept.

The data per item response is as follows:

concept_conceptposition,blockposition_block_blockweight_blockreactiontime;response:

Example per item:

1_1,2_7_3.75_32.3;3:

Example of full item response string:

“1_1,2_7_3.75_32.1;3:1_2,3_13_2.51_43.1;1: 1_3,1_21_4.35_12.5;6:………

:18_6,3_36_4.12_14.8;2”

Metascales

Metascales (like match-scores) are returned like raw scores of concept scales, if corresponding equations are defined.

XML-Structure

Response

<player_data> <cand_id> cs12345678 </cand_id>

<client_id> XYS21351231asdf </client_id> <instrument_id> scales_v1.1 </instrument_id>

<time> 19/04/2002 12:33 </time> <lang> DEU </lang>

<scales> 23.45:12.56:17:43………………23.12 </scales> <resp>

1_1,2_7_3.75_32.1;3:1_2,3_13_2.51_43.1;1: 1_3,1_21_4.35_12.5;6:………

:18_6,3_36_4.12_14.8;2 </resp>

</player_data>

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