Package 'SDT' reference manual

Title:	Self-Determination Theory Measures
Description:	Functions for self-determination motivation theory (SDT) to compute measures of motivation internalization, motivation simplex structure, and of the original and adjusted self-determination or relative autonomy index. SDT was introduced by Deci and Ryan (1985) <doi:10.1007/978-1-4899-2271-7>. See package?SDT for an overview.
Authors:	Ali Uenlue [aut, cre]
Maintainer:	Ali Uenlue <[email protected]>
License:	GPL (>= 2)
Version:	1.0.0
Built:	2024-12-11 06:43:59 UTC
Source:	CRAN

Self-Determination Theory Measures: The R Package SDT

Description

Self-determination theory (SDT) is a theory of human motivation. The package SDT provides functions and an example dataset for computing measures of motivation internalization and of motivation simplex structure, and the original and adjusted self-determination or relative autonomy index in R.

Details

Package:	SDT
Type:	Package
Version:	1.0.0
Date:	2018-01-12
License:	GPL (>= 2)

SDT was proposed by Deci and Ryan (1985, 2000, 2002) and is a popular theory of motivation. This theory is useful for understanding the motivational basis of human behaviors. The general aim is to investigate the interplay between the extrinsic forces or factors acting on people (e.g., grades or payment) and the intrinsic motives or needs inherent in humans (e.g., interests or enjoyment).

Applications are numerous and are extensively referenced, with comprehensive additional materials on the theory and the available questionnaires, on the website http://www.selfdeterminationtheory.org.

In particular, SDT postulated different types of motivation. As to the introjected and identified regulation of extrinsic motivation, their internalizations were described as “somewhat external” and “somewhat internal” and remained undetermined in the theory. The function internalization implements the constrained regression analysis approach by Uenlue and Dettweiler (2015) that allows these vaguely expressed intermediate motivations to be estimated from questionnaire data. The approach can also be generalized and applied for simplex structure analysis in SDT, where the simplex structure of SDT means that motivation regulation types theoretically closer to one another are more strongly interrelated/correlated. Simplex structure analysis in R is provided with the function simplex. Finally, the third main function sdi of the package SDT implements the popular self-determination or relative autonomy index (SDI or RAI), which is a scoring protocol or summary statistic aggregating individual test or subscale scores to yield an overall informative measure. As discussed in Uenlue (2016), the original SDI or RAI index is confounded (i.e., generally not accommodating biasing effects on the overall index value that may result from a mixture of internal and external motivation), therefore the function sdi also implements an adjusted scoring protocol variant of this measure.

The package SDT is implemented based on the S3 system. It comes with a namespace, and consists of three main functions: internalization, sdi, and simplex. It also contains five functions, which are plot, print, and summary methods for objects of the class sdi, and plot and print methods for objects of the class share: plot.sdi, print.sdi, and summary.sdi, and plot.share and print.share. The features of the package SDT are illustrated with an accompanying dataset: learning_motivation.

Author(s)

Maintainer: Ali Uenlue <[email protected]>

References

Deci, E. L. and Ryan, R. M. (1985) Intrinsic Motivation and Self-Determination in Human Behavior. New York, NY: Plenum. URL https://doi.org/10.1007/978-1-4899-2271-7.

Deci, E. L. and Ryan, R. M. (2000) The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. URL https://doi.org/10.1207/S15327965PLI1104_01.

Deci, E. L. and Ryan, R. M. (Eds.) (2002) Handbook of Self-Determination Research. Rochester, NY: University of Rochester Press.

Uenlue, A. (2016) Adjusting potentially confounded scoring protocols for motivation aggregation in organismic integration theory: An exemplification with the relative autonomy or self-determination index. Frontiers in Educational Psychology, 7(272), 1–4. URL https://doi.org/10.3389/fpsyg.2016.00272.

Uenlue, A. and Dettweiler, U. (2015) Motivation internalization and simplex structure in self-determination theory. Psychological Reports, 117(3), 675–691. URL https://doi.org/10.2466/14.PR0.117c25z1.

Motivation Internalization or Externalization Shares

Description

internalization computes the internalization or externalization shares of an intermediate motivation regulation type with respect to the poles of intrinsic regulation and external regulation as the reference system.

Usage

internalization(intermediate_regulation, intrinsic_regulation,
  external_regulation)
internalization(intermediate_regulation, intrinsic_regulation,
  external_regulation)

Arguments

`intermediate_regulation`	A required numeric vector of intermediate, either identified or introjected, regulation subscale motivation scores. No `NA`, `NaN`, `Inf`, or `-Inf` values are allowed.
`intrinsic_regulation`, `external_regulation`	Required numeric vectors of intrinsic regulation and external regulation subscale motivation scores, respectively. No `NA`, `NaN`, `Inf`, or `-Inf` values are allowed.

Details

This function computes the shares of motivation internalization or externalization pertaining to the notion of “somewhat internal” and “somewhat external” on the self-determination theory subscales of identified regulation and introjected regulation, as the intermediate motivation regulation types—with respect to intrinsic regulation and external regulation, the completely internal and completely external motivation poles of the theory, respectively, as the reference system spanned by these two base elements.

The argument intermediate_regulation, the target variable of the constrained regression analysis, can be either identified regulation or introjected regulation aggregate subscale motivation scores, and the arguments intrinsic_regulation and external_regulation, the predictor variables of the constrained regression analysis, do represent aggregate motivation scores for the intrinsic regulation and external regulation subscales, respectively. The function solve.QP of the package quadprog is applied in internalization to solve the self-determination theory related (convex) quadratic program. For details, see Uenlue and Dettweiler (2015).

Value

If the arguments intermediate_regulation, intrinsic_regulation, and external_regulation are of required types, internalization returns a numeric vector containing the $2$ named components internal share and external share of the intermediate_regulation type with respect to the extreme poles intrinsic_regulation and external_regulation of the theory. The returned object is of the class share and has the attribute analysis set to have the value internalization.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (to use variable names)
attach(learning_motivation)

## internal share and external share of identified regulation
(idr <- internalization(identified, intrinsic, external))
## attribute value and class
attr(idr, "analysis")
class(idr)

## internal share and external share of introjected regulation
(ijr <- internalization(introjected, intrinsic, external))
## all attributes
attributes(ijr)
## attach dataset to search path (to use variable names)
attach(learning_motivation)

## internal share and external share of identified regulation
(idr <- internalization(identified, intrinsic, external))
## attribute value and class
attr(idr, "analysis")
class(idr)

## internal share and external share of introjected regulation
(ijr <- internalization(introjected, intrinsic, external))
## all attributes
attributes(ijr)

Learning Motivation Data

Description

A dataset containing the aggregate learning motivation scores for the subscales of intrinsic regulation, identified regulation, introjected regulation, and external regulation of a total number of $1,150$ students.

Usage

learning_motivation
learning_motivation

Format

A data frame with $1,150$ rows and $6$ variables:

sex: integer vector, female ( $= 1$ ) and male ( $= 2$ )
age: integer vector, years
intrinsic: numeric vector, aggregate intrinsic regulation subscale motivation scores
identified: numeric vector, aggregate identified regulation subscale motivation scores
introjected: numeric vector, aggregate introjected regulation subscale motivation scores
external: numeric vector, aggregate external regulation subscale motivation scores

Details

The variables intrinsic, identified, introjected, and external of the data frame learning_motivation contain aggregate subscale scores in the sense that the scores are the means taken over all raw-data test items that make up a respective subscale.

Source

Mueller, F. H. and Hanfstingl, B. and Andreitz, I. (2007) Skalen zur motivationalen Regulation beim Lernen von Schuelerinnen und Schuelern: adaptierte und ergaenzte Version des Academic Self-Regulation Questionnaire (SRQ-A) nach Ryan & Connell [Scales of motivational regulation for student learning: adapted and supplemented version of the Academic Self-Regulation Questionnaire (SRQ-A) by Ryan & Connell] (Transl. A. Uenlue). In Institut fuer Unterrichts- und Schulentwicklung (Ed.), Wissenschaftliche Beitraege [Scientific Contributions] (Transl. A. Uenlue) (pp. 1–17). Klagenfurt, Austria: Alpen-Adria-Universitaet.

Plot Method for Objects of Class sdi

Description

S3 method to plot objects of the class sdi.

Usage

## S3 method for class 'sdi'
plot(x, minscore = 1, maxscore = 5, ...)
## S3 method for class 'sdi'
plot(x, minscore = 1, maxscore = 5, ...)

Arguments

`x`	A required object of the class `sdi`, obtained from a call to the function `sdi`.
`minscore`, `maxscore`	Optional numerics, integer-valued, giving the minimum score (typically 1) or maximum score (typically 4, 5, or 7) used in the scale procedure, respectively. See also ‘Details’.
`...`	Further arguments to be passed are ignored in this function.

Details

This plot method graphs the results obtained from calculating the original or adjusted SDI or RAI index. It produces a scatterplot of the confounded or adjusted external locus component values ( $y$ -axis) versus the confounded or adjusted internal locus component values ( $x$ -axis), respectively. In addition, for comparison, the reference line $y = x$ (red full line) for visual inspection of the deviation of the two types of values and the admissible range (gray dashed lines) for the original or adjusted SDI or RAI index component values are plotted. Points with higher original or adjusted SDI or RAI overall index values are shown in darker gray tone.

To define and plot the admissible range, we do need the minscore and maxscore arguments, where minscore is the minimum score used in the scale procedure (typically 1), and maxscore is the maximum score used in the scale procedure (typically 4, 5, or 7). Note that translation with ‘ $-$ minscore’ and averaging are applied in the definitions of the components of the adjusted SDI or RAI index. The admissible range in the adjusted measure case is given by $[0, maxscore - minscore]$ , which yields $[0, 4]$ for the default values. The admissible range in the original index case is the interval $[(2 * minscore) + minscore, (2 * maxscore) + maxscore]$ , that is, $[3, 15]$ for the default values.

The function gray of the package grDevices is used to plot the points in the scatterplot at different gray levels determined by their respective original or adjusted SDI or RAI overall index values.

Value

If the arguments x, minscore, and maxscore are of required types, plot.sdi produces the plot and invisibly returns NULL.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (so a variable can be accessed by name)
attach(learning_motivation)

## adjusted index plot
plot(sdi(intrinsic, identified, introjected, external))

## original index plot
plot(sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE))
## attach dataset to search path (so a variable can be accessed by name)
attach(learning_motivation)

## adjusted index plot
plot(sdi(intrinsic, identified, introjected, external))

## original index plot
plot(sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE))

Plot Method for Objects of Class share

Description

S3 method to plot objects of the class share.

Usage

## S3 method for class 'share'
plot(x, target = NULL, reference = NULL, ...)
## S3 method for class 'share'
plot(x, target = NULL, reference = NULL, ...)

Arguments

`x`	A required object of the class `share`, obtained from calls to the functions `internalization` and `simplex`.
`target`	An optional character string giving the label of the target regulation that is used. The default value `NULL` corresponds to generic labeling. See ‘Details’.
`reference`	An optional character vector giving the labels of the reference system base regulations that are used. The default value `NULL` corresponds to generic labeling. See ‘Details’.
`...`	Further arguments to be passed are ignored in this function.

Details

This plot method produces stacked bar plots of the internalization, externalization, or simplex structure shares of a target regulation with respect to a reference system. Generic or user-specified convenient labeling of the plot axes are provided.

The default value target = NULL corresponds to generic labeling, which is “intermediate regulation” in the case of the attribute value analysis = internalization, or “target regulation” in the case of analysis = simplex. If a character string is explicitly specified instead, this is used to label the $x$ -axis of the bar plot. See also ‘Examples’.

The default value reference = NULL corresponds to generic labeling, which is “internalization share (dark)” and “externalization share (bright)” in the case of the attribute value analysis = internalization, or “base regulation 1 share (dark)”, “base regulation 2 share (medium)”, and “base regulation 3 share (bright)” in the case of analysis = simplex. If a character vector is explicitly specified instead, which must be of length $2$ for analysis = internalization or of length $3$ for analysis = simplex, this is used to label the $y$ -axis of the bar plot. See also ‘Examples’.

Value

If the arguments x, target, and reference are of required types, plot.share produces the plot and invisibly returns NULL.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (to use variable names)
attach(learning_motivation)

## internalization plot
(ijr <- internalization(introjected, intrinsic, external))
## with generic labels
plot(ijr)
## with user-specified convenient labels
plot(ijr, target = "introjected regulation",
     reference = c("intrinsic regulation", "external regulation"))

## simplex structure plot 1
(simstr2 <- simplex(target_regulation = external, base_regulation_1 = intrinsic,
                    base_regulation_2 = identified, base_regulation_3 = introjected))
## with generic labels
plot(simstr2)
## with user-specified convenient labels
plot(simstr2, target = "external regulation",
     reference = c("intrinsic regulation", "identified regulation", "introjected regulation"))

## simplex structure plot 2
## different target variable and reference system, conveniently labeled
plot(simplex(identified, intrinsic, introjected, external), target = "identified regulation",
     reference = c("intrinsic regulation", "introjected regulation", "external regulation"))
## attach dataset to search path (to use variable names)
attach(learning_motivation)

## internalization plot
(ijr <- internalization(introjected, intrinsic, external))
## with generic labels
plot(ijr)
## with user-specified convenient labels
plot(ijr, target = "introjected regulation",
     reference = c("intrinsic regulation", "external regulation"))

## simplex structure plot 1
(simstr2 <- simplex(target_regulation = external, base_regulation_1 = intrinsic,
                    base_regulation_2 = identified, base_regulation_3 = introjected))
## with generic labels
plot(simstr2)
## with user-specified convenient labels
plot(simstr2, target = "external regulation",
     reference = c("intrinsic regulation", "identified regulation", "introjected regulation"))

## simplex structure plot 2
## different target variable and reference system, conveniently labeled
plot(simplex(identified, intrinsic, introjected, external), target = "identified regulation",
     reference = c("intrinsic regulation", "introjected regulation", "external regulation"))

Print Method for Objects of Class sdi

Description

S3 method to print objects of the class sdi.

Usage

## S3 method for class 'sdi'
print(x, ...)
## S3 method for class 'sdi'
print(x, ...)

Arguments

`x`	A required object of the class `sdi`, obtained from a call to the function `sdi`.
`...`	Further arguments to be passed are ignored in this function.

Details

This print method prints the main results obtained from calculating the original or adjusted SDI or RAI index, which are the overall index values or SDI or RAI scores. The output can be long, and thus, entries may be omitted.

Value

If the argument x is of required type, print.sdi prints the original or adjusted SDI or RAI overall index values and returns an invisible copy of these scores.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (to use variable names)
attach(learning_motivation)

## adjusted index print
padj <- print(sdi(intrinsic, identified, introjected, external))
## compactly displayed structure of the object padj
str(padj)

## original index print
sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE)
## attach dataset to search path (to use variable names)
attach(learning_motivation)

## adjusted index print
padj <- print(sdi(intrinsic, identified, introjected, external))
## compactly displayed structure of the object padj
str(padj)

## original index print
sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE)

Print Method for Objects of Class share

Description

S3 method to print objects of the class share.

Usage

## S3 method for class 'share'
print(x, ...)
## S3 method for class 'share'
print(x, ...)

Arguments

`x`	A required object of the class `share`, obtained from calls to the functions `internalization` and `simplex`.
`...`	Further arguments to be passed are ignored in this function.

Details

This print method prints the internalization, externalization, and simplex structure shares of a target regulation with respect to a reference system.

Value

If the argument x is of required type, print.share prints the internalization, externalization, or simplex structure shares, and invisibly returns x, stripped off the analysis ( $=$ internalization or simplex) and class ( $=$ share) attributes.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (to use variable names)
attach(learning_motivation)

## internalization print
pidr <- print(internalization(identified, intrinsic, external))
## compactly displayed structure of the object pidr
str(pidr)

## simplex structure print
psimstr <- print(simplex(intrinsic, identified, introjected, external))
## compactly displayed structure of the object psimstr
str(psimstr)
## attach dataset to search path (to use variable names)
attach(learning_motivation)

## internalization print
pidr <- print(internalization(identified, intrinsic, external))
## compactly displayed structure of the object pidr
str(pidr)

## simplex structure print
psimstr <- print(simplex(intrinsic, identified, introjected, external))
## compactly displayed structure of the object psimstr
str(psimstr)

Original and Adjusted SDI or RAI Index

Description

sdi computes the original SDI or RAI scoring protocol and an adjusted variant of it.

Usage

sdi(intrinsic_regulation, identified_regulation, introjected_regulation,
  external_regulation, compute.adjusted = TRUE, minscore = 1)
sdi(intrinsic_regulation, identified_regulation, introjected_regulation,
  external_regulation, compute.adjusted = TRUE, minscore = 1)

Arguments

`intrinsic_regulation`, `identified_regulation`, `introjected_regulation`, `external_regulation`	Required numeric vectors of intrinsic regulation, identified regulation, introjected regulation, and external regulation subscale motivation scores, respectively. No `NA`, `NaN`, `Inf`, or `-Inf` values are allowed.
`compute.adjusted`	An optional logical. The default value `TRUE` corresponds to computing the adjusted variant of the SDI or RAI index, and also allows for setting the argument `minscore`. If set to `FALSE`, the original index value is computed, and the argument `minscore` is irrelevant and ignored in this case.
`minscore`	An optional numeric, integer-valued, giving the minimum score used in the scale procedure (typically 1). See also ‘Details’.

Details

This function provides the popular and original scoring protocol called the self-determination index (SDI), also known as the relative autonomy index (RAI). The version of the used index is for instruments assessing extrinsic motivation (excluding integrated regulation) and intrinsic motivation. With the SDI or RAI, the inventory scores are weighted and combined to give a descriptive overall measure of the behavioral self-regulatory style. The formula is, in respective regulation types:

$SDI = RAI = (2 * intrinsic + identified) - (2 * external + introjected).$

The original SDI or RAI index does not allow one to account for the extent to which the identified and introjected regulation types are internal and external motivation. In particular, in the process of weighting the subscale scores, the same weights are used (1 or -1, respectively).

Correcting adaptations are proposed to accommodate for mixed or confounded internal and external motivation, implemented in the function sdi. This function also computes an adjusted variant of the original SDI or RAI index, which is weighted according to the extent to which the intermediate identified and introjected regulation types are internal and external motivation. For details, including the mathematical formula for the adjusted measure, see Uenlue (2016).

To compute the adjusted variant measure, sdi calls the function internalization. The latter, in turn, uses the function solve.QP of the package quadprog to solve the corresponding constrained regression optimization problem.

The arguments intrinsic_regulation, identified_regulation, introjected_regulation, and external_regulation do represent aggregate subscale scores calculated by averaging the raw-data test items associated with each of the four subscales (i.e., mean over the items that make up a respective subscale). The four subscales are intrinsic regulation, identified regulation, introjected regulation, and external regulation.

The argument minscore only needs to be specified for the adjusted index variant. Translation with ‘ $-$ minscore’ and averaging are applied in the adjusted variant to ensure that the instrument variables and the component and index values all range in the same interval (from 0 to, e.g., 4).

Value

If the arguments intrinsic_regulation, identified_regulation, introjected_regulation, external_regulation, compute.adjusted, and minscore are of required types, sdi returns a named list, of the class sdi and with the attribute variant, which consists of $3$ components, independent of whether the original index computation (variant is then set to original) or the adjusted index computation (variant is then set to adjusted) was performed.

The original index computation list contains the following first $3$ components, the adjusted index computation list the subsequent $3$ components:

`confounded_internal_locus`	A numeric vector of the confounded internal locus original SDI or RAI component values.
`confounded_external_locus`	A numeric vector of the confounded external locus original SDI or RAI component values.
`sdi_original`	A numeric vector of the original SDI or RAI overall index values.
`adjusted_internal_locus`	A numeric vector of the adjusted internal locus adjusted SDI or RAI component values.
`adjusted_external_locus`	A numeric vector of the adjusted external locus adjusted SDI or RAI component values.
`sdi_adjusted`	A numeric vector of the adjusted SDI or RAI overall index values.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (so a variable can be accessed by name)
attach(learning_motivation)

## adjusted index computation
adj <- sdi(intrinsic, identified, introjected, external)
## first six elements of each list component vector and attributes
lapply(adj, head)
attributes(adj)

## original index computation
orig <- sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE)
lapply(orig, head)
attributes(orig)
## attach dataset to search path (so a variable can be accessed by name)
attach(learning_motivation)

## adjusted index computation
adj <- sdi(intrinsic, identified, introjected, external)
## first six elements of each list component vector and attributes
lapply(adj, head)
attributes(adj)

## original index computation
orig <- sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE)
lapply(orig, head)
attributes(orig)

Motivation Simplex Structure Shares

Description

simplex computes the shares of a target regulation type in a reference system consisting of three base regulation types to illustrate the self-determination theory postulated simplex structure of motivation.

Usage

simplex(target_regulation, base_regulation_1, base_regulation_2,
  base_regulation_3)
simplex(target_regulation, base_regulation_1, base_regulation_2,
  base_regulation_3)

Arguments

`target_regulation`	A required numeric vector of either intrinsic, identified, introjected, or external regulation subscale motivation scores. No `NA`, `NaN`, `Inf`, or `-Inf` values are allowed.
`base_regulation_1`, `base_regulation_2`, `base_regulation_3`	Required numeric vectors of, depending on the regulation type specified in `target_regulation`, the remaining three regulation subscale motivation scores. No `NA`, `NaN`, `Inf`, or `-Inf` values are allowed. For an example, see ‘Details’.

Details

This function computes the simplex structure shares of a target motivation (e.g., intrinsic regulation) in a reference system consisting of three base regulation types (e.g., identified regulation, introjected regulation, and external regulation). With the function simplex, the self-determination theory postulated simplex structure can be illustrated, where the simplex structure of the theory means that motivation regulation types theoretically closer to one another are more strongly interrelated/correlated. From a theoretical viewpoint, the simplex structure analysis can be viewed as a generalization of the problem of internalization.

The argument target_regulation, the dependent variable of the constrained regression analysis, can be any regulation type, and the arguments base_regulation_1, base_regulation_2, and base_regulation_3, the independent variables of the constrained regression analysis, do represent the remaining regulation types, with respect to which the optimal shares of the target regulation are computed. The function solve.QP of the package quadprog is applied in simplex to solve the self-determination theory related (convex) quadratic program. For details, see Uenlue and Dettweiler (2015).

Value

If the arguments target_regulation, base_regulation_1, base_regulation_2, and base_regulation_3 are of required types, simplex returns a numeric vector containing the following $3$ named components base_regulation_1 share, base_regulation_2 share, and base_regulation_3 share of the target_regulation with respect to the remaining base_regulation_1, base_regulation_2, and base_regulation_3 of the theory. The returned object is of the class share and has the attribute analysis set to have the value simplex.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (to use variable names)
attach(learning_motivation)

## simplex structure analysis with intrinsic regulation as target variable
## and identified, introjected, and external regulation as reference system
(simstr <- simplex(intrinsic, identified, introjected, external))

## numeric vector, attribute value, and class
mode(simstr)
attr(simstr, "analysis")
class(simstr)
## attach dataset to search path (to use variable names)
attach(learning_motivation)

## simplex structure analysis with intrinsic regulation as target variable
## and identified, introjected, and external regulation as reference system
(simstr <- simplex(intrinsic, identified, introjected, external))

## numeric vector, attribute value, and class
mode(simstr)
attr(simstr, "analysis")
class(simstr)

Summary Method for Objects of Class sdi

Description

S3 method to summarize objects of the class sdi.

Usage

## S3 method for class 'sdi'
summary(object, ...)
## S3 method for class 'sdi'
summary(object, ...)

Arguments

`object`	A required object of the class `sdi`, obtained from a call to the function `sdi`.
`...`	Further arguments to be passed are ignored in this function.

Details

This summary method outlines the results obtained from the original or adjusted SDI or RAI index computation by printing simple summary statistics of the values obtained for the confounded or adjusted internal locus, confounded or adjusted external locus, and for the original or adjusted SDI or RAI overall index.

Value

If the argument object is of required type, summary.sdi prints simple summary statistics of the list components values, and invisibly returns object.

Author(s)

Ali Uenlue <[email protected]>

References

Examples

## attach dataset to search path (for using variable names)
attach(learning_motivation)

## original and adjusted index summary
summary(sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE))
summary(sdi(intrinsic, identified, introjected, external))
## attach dataset to search path (for using variable names)
attach(learning_motivation)

## original and adjusted index summary
summary(sdi(intrinsic, identified, introjected, external, compute.adjusted = FALSE))
summary(sdi(intrinsic, identified, introjected, external))

Package 'SDT'

Help Index

Self-Determination Theory Measures: The R Package SDT

Description

Details

Author(s)

References

Motivation Internalization or Externalization Shares

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Learning Motivation Data

Description

Usage

Format

Details

Source

See Also

Plot Method for Objects of Class sdi

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Plot Method for Objects of Class share

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Print Method for Objects of Class sdi

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Print Method for Objects of Class share

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Original and Adjusted SDI or RAI Index

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Motivation Simplex Structure Shares

Description

Usage

Arguments

Details

Value