LeBel & Gawronski (2009, EJP)
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Data and SPSS syntax underlying LeBel & Gawronski (2009, EJP) wherein the optimality of 5 different name-letter task (NLT) scoring algorithms were scrutinized based on 18 samples totalling N=2690 ( _ALL_18_LETTER_RESULTS_final_ANALYSIS_forFigshare.sav and _NLEreliabilities_forFigshare.sps). Based on the evidence, we recommend the I-algorithm (NLTi) as most optimal for use in research using the NLT to assess implicit self-esteem.
Also included is an SPSS syntax file (and example input and output files) to automatically calculate NLT scores (and reliability estimates) from raw letter ratings.
Also included are materials for the typical administration of the NLT measure (basic parameters included in materials_NLT_instructions.doc and a MediaLab/DirectRT implementation of it in materials__NLT.zip).