dat.mccurdy2020 {metadat} | R Documentation |
Studies on the Generation Effect
Description
Results from 126 articles that examined the so-called ‘generation effect’.
Usage
dat.mccurdy2020
Format
The data frame contains the following columns:
article | numeric | article identifier |
experiment | character | experiment (within article) identifier |
sample | numeric | sample (within experiment) identifier |
id | numeric | row identifier |
pairing | numeric | identifier to indicate paired conditions within experiments |
yi | numeric | mean recall rate for the condition |
vi | numeric | corresponding sampling variance |
ni | numeric | number of participants for the condition |
stimuli | numeric | number of stimuli for the condition |
condition | factor | condition (‘read’ or ‘generate’) |
gen_difficulty | factor | generation difficulty (‘low’ or ‘high’) |
manip_type | factor | manipulation type of the generate versus read condition (using a ‘within’ or ‘between’ subjects design) |
present_style | factor | presentation style (‘mixed’ or ‘pure’ list presentation) |
word_status | factor | word status (‘words’, ‘non-words’, or ‘numbers’) |
memory_test | factor | memory test (‘recognition’, ‘cued recall’, or ‘free recall’) |
memory_type | factor | memory type (‘item’, ‘source’, ‘font color’, ‘font type’, ‘order’, ‘cue word’, ‘background color’, or ‘location’) |
gen_constraint | factor | generation constraint (‘low’, ‘medium’, or ‘high’) |
learning_type | factor | learning type (‘incidental’ or ‘intentional’) |
stimuli_relation | factor | stimuli relation (‘semantic’, ‘category’, ‘antonym’, ‘synonym’, ‘rhyme’, ‘compound words’, ‘definitions’, or ‘unrelated’) |
gen_mode | factor | generation mode (‘verbal/speaking’, ‘covert/thinking’, or ‘writing/typing’) |
gen_task | factor | generation task (‘anagram’, ‘letter transposition’, ‘word fragment’, ‘sentence completion’, ‘word stem’, ‘calculation’, or ‘cue only’) |
attention | factor | attention (‘divided’ or ‘full’) |
pacing | factor | pacing (‘self-paced’ or ‘timed’) |
filler_task | factor | filler task (‘yes’ or ‘no’) |
age_grp | factor | age group (‘younger’ or ‘older’ adults) |
retention_delay | factor | retention delay (‘immediate’, ‘short’, or ‘long’) |
Details
The generation effect is the memory benefit for self-generated compared with read or experimenter-provided information (Jacoby, 1978; Slamecka & Graf, 1978). In a typical study, participants are presented with a list of stimuli (usually words or word pairs). For half of the stimuli, participants self-generate a target word (e.g., open–cl____), while for the other half, participants simply read an intact target word (e.g., above–below). On a later memory test for the target words, the common finding is that self-generated words are better remembered than read words (i.e., the generation effect).
Although several theories have been proposed to explain the generation effect, there is still some debate on the underlying memory mechanism(s) contributing to this phenomenon. The meta-analysis by McCurdy et al. (2020) translated various theories on the generation effect into hypotheses that could then be tested in moderator analyses based on a dataset containing 126 articles, 310 experiments, and 1653 mean recall estimates collected under various conditions.
Detailed explanations of the various variables coded (and how these can be used to test various hypotheses regarding the generation effect) can be found in the article. The most important variable is condition
, which denotes whether a particular row of the dataset corresponds to the results of a ‘read’ or a ‘generate’ condition.
The data structure is quite complex. Articles may have reported the findings from multiple experiments involving one or multiple samples that were examined under various conditions. The pairing
variable indicates which rows of the dataset represent a pairing of a read condition with one or multiple corresponding generate conditions within an experiment. A pairing may involve the same sample of subjects (when using a within-subjects design for comparing the conditions) or different samples (when using a between-subjects design).
Concepts
psychology, memory, proportions, raw means, multilevel models, cluster-robust inference
Author(s)
Wolfgang Viechtbauer, wvb@metafor-project.org, https://www.metafor-project.org
Source
McCurdy, M. P., Viechtbauer, W., Sklenar, A. M., Frankenstein, A. N., & Leshikar, E. D. (2020). Theories of the generation effect and the impact of generation constraint: A meta-analytic review. Psychonomic Bulletin & Review, 27(6), 1139–1165. https://doi.org/10.3758/s13423-020-01762-3
References
Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning and Memory, 4(6), 592–604. https://doi.org/10.1037/0278-7393.4.6.592
Jacoby, L. L. (1978). On interpreting the effects of repetition: Solving a problem versus remembering a solution. Journal of Verbal Learning and Verbal Behavior, 17(6), 649–668. https://doi.org/10.1016/S0022-5371(78)90393-6
Examples
### copy data into 'dat' and examine data
dat <- dat.mccurdy2020
head(dat)
## Not run:
### load metafor package
library(metafor)
### fit multilevel mixed-effects meta-regression model
res <- rma.mv(yi, vi, mods = ~ condition,
random = list(~ 1 | article/experiment/sample/id, ~ 1 | pairing),
data=dat, sparse=TRUE, digits=3)
res
### proportion of total amount of heterogeneity due to each component
data.frame(source=res$s.names, sigma2=round(res$sigma2, 3),
prop=round(res$sigma2 / sum(res$sigma2), 2))
### apply cluster-robust inference
sav <- robust(res, cluster=article)
sav
### estimated average recall rate in read and generate conditions
predict(sav, newmods = c(0,1), digits=3)
### use methods from clubSandwich package
sav <- robust(res, cluster=article, clubSandwich=TRUE)
sav
## End(Not run)