Effects of response format on psychometric properties and fairness of a matrices test: multiple choice versus free response

[ben-domingue]

https://osf.io/5ru9q/

[ben-domingue]

https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2020.00015/full

[Monicaxichen]

DOSP scale.csv

[Monicaxichen]

import spss using "/Users/xichen85/Desktop/FRM_2020 Breuer/Data_Effects of Response Format (1).sav", case(lower) clear drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i drop lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 rename nr id reshape long dosp, i(id) j(resp) rename resp item rename dosp resp save "/Users/xichen85/Desktop/FRM_2020 Breuer/DOSP scale.dta"

[Monicaxichen]

FRM scale.csv

[Monicaxichen]

import spss using "/Users/xichen85/Desktop/FRM_2020 Breuer/Data_Effects of Response Format (1).sav", case(lower) clear drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll drop frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 rename nr id foreach i of numlist 1/25 { rename frm_fri' Qi' } reshape long Q, i(id) j(resp) rename resp item rename Q resp save "/Users/xichen85/Desktop/item .dta" clear import spss using "/Users/xichen85/Desktop/FRM_2020 Breuer/Data_Effects of Response Format (1).sav", case(lower) clear drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do drop date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 rename nr id foreach i of numlist 1/25 { rename secfrmi' Qi' } reshape long Q, i(id) j(resp) rename resp item rename Q time merge 1:1 _n using "/Users/xichen85/Desktop/item .dta" drop _merge rename time rt label values item item label define item 1 "FRM_FR1" 2 "FRM_FR2" 3 "FRM_FR3" 4 "FRM_FR4" 5 "FRM_FR5" 6 "FRM_FR6" 7 "FRM_FR7" 8 "FRM_FR8" 9 "FRM_FR9" 10 "FRM_FR10" 11 "FRM_FR11" 12 "FRM_FR12" 13 "FRM_FR13" 14 "FRM_FR14" 15 "FRM_FR15" 16 "FRM_FR16" 17 "FRM_FR17" 18 "FRM_FR18" 19 "FRM_FR19" 20 "FRM_FR20" 21 "FRM_FR21" 22 "FRM_FR22" 23 "FRM_FR23" 24 "FRM_FR24" 25 "FRM_FR25" save "/Users/xichen85/Desktop/FRM scale.dta"

[Monicaxichen]

FRM_MC scale.csv

[Monicaxichen]

import spss using "/Users/xichen85/Desktop/FRM_2020 Breuer/Data_Effects of Response Format (1).sav", case(lower) drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 drop frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang drop tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 foreach i of numlist 1/25 { rename frm_mci' Qi' } rename nr id reshape long Q, i(id) j(resp) rename resp item rename Q resp label values item item label define item 1 "FRM_MC1" 2 "FRM_MC2" 3 "FRM_MC3" 4 "FRM_MC4" 5 "FRM_MC5" 6 "FRM_MC6" 7 "FRM_MC7" 8 "FRM_MC8" 9 "FRM_MC9" 10 "FRM_MC10" 11 "FRM_MC11" 12 "FRM_MC12" 13 "FRM_MC13" 14 "FRM_MC14" 15 "FRM_MC15" 16 "FRM_MC16" 17 "FRM_MC17" 18 "FRM_MC18" 19 "FRM_MC19" 20 "FRM_MC20" 21 "FRM_MC21" 22 "FRM_MC22" 23 "FRM_MC23" 24 "FRM_MC24" 25 "FRM_MC25" save "/Users/xichen85/Desktop/FRM_2020 Breuer/FRM_MC scale.dta" export delimited using /Users/xichen85/Desktop/Untitled.csv

[Monicaxichen]

TAI scale.csv

[Monicaxichen]

import spss using "/Users/xichen85/Desktop/FRM_2020 Breuer/Data_Effects of Response Format (1).sav", case(lower) clear drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x drop dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 rename nr id foreach i of numlist 1/15 { rename taii' Qi' } reshape long Q, i(id) j(resp) rename resp item rename Q resp label values item item lable label define item 1 "TAI1" 2 "TAI2" 3 "TAI3_i" 4 "TAI4" 5 "TAI5" 6 "TAI6" 7 "TAI7" 8 "TAI8_i" 9 "TAI9" 10 "TAI10" 11 "TAI11" 12 "TAI12" 13 "TAI13" 14 "TAI14" 15 "TAI15_i" label values item item save "/Users/xichen85/Desktop/FRM_2020 Breuer/TAI scale.dta" export delimited using /Users/xichen85/Desktop/Untitled.csv

[KingArthur0205]

In the paper, they mentioned 4 measures being used:

1. Free Response Matrices(FRM): 25 dichotomous(0/1) items. The processed dataset aligns with the paper. The processed data contains 5750 NAs.
2. FRM With MC Format(FRM_MC): 25 dichotomous(0/1) items. The processed dataset aligns with the paper. The processed data contains 5075 NAs.
3. Domain-Specific Risk-Taking Scale in its German version, ethics for pupils(DOSPERT-ES): 9 items on a 5-point Likert scale. The processed dataset aligns with the paper. The processed data contains 513 NAs.
4. Test Anxiety Inventory–German version(TAI-G): 15 items on a 4-point Likert scale. The processed dataset aligns with the paper. The processed data contains 855 NAs.

[KingArthur0205]

@Monicaxichen @ben-domingue Hi Monica. Great job! Could you just do 2 small tweaks for me:

1. There are lots of NAs (stands for missing values) in the response. If we look at the original data, we can spot that many participants simply didn't respond to any items from an entire scale. In this case, we would like to remove that participant from the processed dataset of that scale.

For example, you can see the participant with id 61 didn't give any responses

2. May I bother you to take some time to remove the absolute path in the code above? So change /Users/xichen85/Desktop/FRM_2020 Breuer/TAI scale.dta to ./TAI scale.dta for example. Apologies for the extra work.

Everything else is good. : )

[Monicaxichen]

Sure thing! Will work on these….

Monica

From: Arthur Pan @.> Date: Monday, November 18, 2024 at 5:24 AM To: ben-domingue/irw @.> Cc: Monica (Xi) Chen @.>, Mention @.> Subject: Re: [ben-domingue/irw] Effects of response format on psychometric properties and fairness of a matrices test: multiple choice versus free response (Issue #435)

@Monicaxichenhttps://github.com/Monicaxichen @ben-dominguehttps://github.com/ben-domingue Hi Monica. Great job! Could you just do 2 small tweaks for me:

1. There are lots of NAs (stands for missing values) in the response. If we look at the original data, we can spot that many participants simply didn't respond to any items from an entire scale. In this case, we would like to remove that participant from the processed dataset of that scale.

For example, you can see the participant with id 61 didn't give any responses

1. May I bother you to take some time to remove the absolute path in the code above? So change /Users/xichen85/Desktop/FRM_2020 Breuer/TAI scale.dta to ./TAI scale.dta for example. Apologies for the extra work.

Everything else is good. : )

— Reply to this email directly, view it on GitHubhttps://github.com/ben-domingue/irw/issues/435#issuecomment-2483035466, or unsubscribehttps://github.com/notifications/unsubscribe-auth/BL7B5ZWNQZ5IC2AKMK45ZFD2BHTAZAVCNFSM6AAAAABORGTJNCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDIOBTGAZTKNBWGY. You are receiving this because you were mentioned.Message ID: @.***>

[Monicaxichen]

DOSP.csv

[Monicaxichen]

import spss using "/Data_Effects of Response Format (1).sav", case(lower) clear drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i drop lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 rename nr id reshape long dosp, i(id) j(resp) rename resp item rename dosp resp drop if missing(resp) save "/DOSP scale.dta"

[Monicaxichen]

TAI.csv

[Monicaxichen]

import spss using "/Data_Effects of Response Format (1).sav", case(lower) clear drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x drop dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 rename nr id foreach i of numlist 1/15 { rename taii' Qi' } reshape long Q, i(id) j(resp) rename resp item rename Q resp label values item item lable label define item 1 "TAI1" 2 "TAI2" 3 "TAI3_i" 4 "TAI4" 5 "TAI5" 6 "TAI6" 7 "TAI7" 8 "TAI8_i" 9 "TAI9" 10 "TAI10" 11 "TAI11" 12 "TAI12" 13 "TAI13" 14 "TAI14" 15 "TAI15_i" label values item item

drop if missing(resp) save "/TAI scale.dta"

[Monicaxichen]

FRM.csv

[Monicaxichen]

import spss using "/Data_Effects of Response Format (1).sav", case(lower) drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll drop frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do drop in 204/433 drop date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 drop secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 foreach i of numlist 1/25 { rename frm_fri' Qi' } reshape long Q, i(nr) j(resp) rename resp item rename Q resp save "/frm_fr.dta"

import spss using "/Data_Effects of Response Format (1).sav", case(lower) drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 frm_mc1 frm_mc2 frm_mc3 frm_mc4 frm_mc5 frm_mc6 frm_mc7 frm_mc8 frm_mc9 frm_mc10 frm_mc11 frm_mc12 frm_mc13 frm_mc14 frm_mc15 frm_mc16 frm_mc17 frm_mc18 frm_mc19 frm_mc20 frm_mc21 frm_mc22 frm_mc23 frm_mc24 frm_mc25 frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do drop date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 foreach i of numlist 1/25 { rename secfrmi' Qi' } reshape long Q, i(nr) j(resp) rename resp item rename Q time save "/frm time.dta" use "/frm_fr.dta" use "/frm time.dta" merge 1:m nr item using "/frm_fr.dta" save new_file.dta use "/frm matched.dta" drop _merge rename nr id rename item Q rename time rt label define item 1 "FRM_FR1" 2 "FRM_FR2" 3 "FRM_FR3" 4 "FRM_FR4" 5 "FRM_FR5" 6 "FRM_FR6" 7 "FRM_FR7" 8 "FRM_FR8" 9 "FRM_FR9" 10 "FRM_FR10" 11 "FRM_FR11" 12 "FRM_FR12" 13 "FRM_FR13" 14 "FRM_FR14" 15 "FRM_FR15" 16 "FRM_FR16" 17 "FRM_FR17" 18 "FRM_FR18" 19 "FRM_FR19" 20 "FRM_FR20" 21 "FRM_FR21" 22 "FRM_FR22" 23 "FRM_FR23" 24 "FRM_FR24" 25 "FRM_FR25" label values Q Q label define Q 1 "FRM_FR1" 2 "FRM_FR2" 3 "FRM_FR3" 4 "FRM_FR4" 5 "FRM_FR5" 6 "FRM_FR6" 7 "FRM_FR7" 8 "FRM_FR8" 9 "FRM_FR9" 10 "FRM_FR10" 11 "FRM_FR11" 12 "FRM_FR12" 13 "FRM_FR13" 14 "FRM_FR14" 15 "FRM_FR15" 16 "FRM_FR16" 17 "FRM_FR17" 18 "FRM_FR18" 19 "FRM_FR19" 20 "FRM_FR20" 21 "FRM_FR21" 22 "FRM_FR22" 23 "FRM_FR23" 24 "FRM_FR24" 25 "FRM_FR25" save "/frm matched.dta", replace clear import spss using "/Data_Effects of Response Format (1).sav" clear use "/frm matched.dta" drop if missing(resp) count if !missing(rt) & !missing(resp) save "/frm matched.dta", replace

[Monicaxichen]

FRM_MC scale.csv

[Monicaxichen]

import spss using "/Data_Effects of Response Format (1).sav", case(lower) drop vpnr resp_form frm_total theta_23items frm_x frm_time tai_total dosp_total bart_total lmi_total gender age grade_last4 year method coll frm_fr1 frm_fr2 frm_fr3 frm_fr4 frm_fr5 frm_fr6 frm_fr7 frm_fr8 frm_fr9 frm_fr10 frm_fr11 frm_fr12 frm_fr13 frm_fr14 frm_fr15 frm_fr16 frm_fr17 frm_fr18 frm_fr19 frm_fr20 frm_fr21 frm_fr22 frm_fr23 frm_fr24 frm_fr25 drop frm_all1 frm_all2 frm_all3 frm_all4 frm_all5 frm_all6 frm_all7 frm_all8 frm_all9 frm_all10 frm_all11 frm_all12 frm_all13 frm_all14 frm_all15 frm_all16 frm_all17 frm_all18 frm_all19 frm_all20 frm_all21 frm_all22 frm_all23 frm_all24 frm_all25 frmfr_x frmmc_x tai1 tai2 tai3_i tai4 tai5 tai6 tai7 tai8_i tai9 tai10 tai11 tai12 tai13 tai14 tai15_i dosp1 dosp2 dosp3 dosp4 dosp5 dosp6 dosp7 dosp8 dosp9 lmi1_fx_i lmi2_be lmi3_ls lmi4_in_i lmi5_en lmi6_do lmi7_fx lmi8_be_i lmi9_ls lmi10_in_i lmi11_en lmi12_do lmi13_fx_i lmi14_be_i lmi15_ls lmi16_in_i lmi17_en lmi18_do_i lmi19_fx_i lmi20_be_i lmi21_ls lmi22_in_i lmi23_en lmi24_do_i lmi25_fx_i lmi26_be lmi27_ls lmi28_in_i lmi29_en_i lmi30_do lmi31_fx lmi32_be_i lmi33_ls lmi34_in lmi35_en lmi36_do lmi37_fx lmi38_be_i lmi39_ls lmi40_in_i lmi41_en lmi42_do lmi43_fx lmi44_be lmi45_ls lmi46_in_i lmi47_en lmi48_do lmi49_fx lmi50_be_i lmi51_ls lmi52_in_i lmi53_en lmi54_do lmi55_fx lmi56_be_i lmi57_ls lmi58_in lmi59_en_i lmi60_do secfrm1 secfrm2 secfrm3 secfrm4 secfrm5 secfrm6 secfrm7 secfrm8 secfrm9 secfrm10 secfrm11 secfrm12 secfrm13 secfrm14 secfrm15 secfrm16 secfrm17 secfrm18 secfrm19 secfrm20 secfrm21 secfrm22 secfrm23 secfrm24 secfrm25 date number_pumps_uncorrected number_collect_balloons number_total_pumps_collect pumps1 collect1 pumps2 collect2 pumps3 collect3 pumps4 collect4 pumps5 collect5 pumps6 collect6 pumps7 collect7 pumps8 collect8 pumps9 collect9 pumps10 collect10 pumps11 collect11 pumps12 collect12 pumps13 collect13 pumps14 collect14 pumps15 collect15 tai_beso tai_aufg tai_mang drop tai_inte tai_ges dosp_ges lmi_fx lmi_be lmi_ls lmi_in lmi_en lmi_do lmi_ges school_type school_level tai_beso_mean tai_aufg_mean tai_mang_mean tai_inte_mean lmi_fx_mean lmi_be_mean lmi_ls_mean lmi_in_mean lmi_en_mean lmi_do_mean akze_mg_mean akze_kob_mean akze_auf_mean akze_bef_mean grad prof study sem _v1 foreach i of numlist 1/25 { rename frm_mci' Qi' } rename nr id reshape long Q, i(id) j(resp) rename resp item rename Q resp label values item item label define item 1 "FRM_MC1" 2 "FRM_MC2" 3 "FRM_MC3" 4 "FRM_MC4" 5 "FRM_MC5" 6 "FRM_MC6" 7 "FRM_MC7" 8 "FRM_MC8" 9 "FRM_MC9" 10 "FRM_MC10" 11 "FRM_MC11" 12 "FRM_MC12" 13 "FRM_MC13" 14 "FRM_MC14" 15 "FRM_MC15" 16 "FRM_MC16" 17 "FRM_MC17" 18 "FRM_MC18" 19 "FRM_MC19" 20 "FRM_MC20" 21 "FRM_MC21" 22 "FRM_MC22" 23 "FRM_MC23" 24 "FRM_MC24" 25 "FRM_MC25" drop if missing(resp) save "/FRM_MC scale.dta"

[Monicaxichen]

@KingArthur0205 Hi Arthur, new files and codes just got uploaded here. Please check them when you have time. Thank you SO much for your amazing QC work! :)

[KingArthur0205]

@Monicaxichen Hi Monica! I’ve reviewed your work, and I must say it’s truly fantastic. Great job! I’ll go ahead and send these over to Ben.

Here are a couple of small tweaks I made:

1. In the FRM dataset, the "item" column was labeled "Q," so I renamed it. It’s not a big deal, just a minor adjustment.
2. This isn’t on you—it's more on me for not clarifying earlier. For datasets or measures that assess the same or similar abilities (like FRM and FRM_MC in this case), I’d recommend merging them. Deciding when to lump or split datasets is one of the trickiest parts of processing them, and I’ll need to double-check with Ben on this. If you’re ever unsure about how to handle something like this, feel free to reach out!
3. I've renamed the files to have a prefix "MCVFR_Breuer_2017" to give a more descriptive name of these datasets
4. May I kindly ask you to merge the updated codes into a single file? This might make it more convenient for users who wish to reproduce the results. I apologize in advance for the extra work and truly appreciate your help!

[Monicaxichen]

Thank you for your kind words. Appreciate your great QC work!

Happy Holidays!

Monica

From: Arthur Pan @.> Date: Saturday, November 23, 2024 at 9:32 AM To: ben-domingue/irw @.> Cc: Monica (Xi) Chen @.>, Mention @.> Subject: Re: [ben-domingue/irw] Effects of response format on psychometric properties and fairness of a matrices test: multiple choice versus free response (Issue #435)

@Monicaxichenhttps://github.com/Monicaxichen Hi Monica! I’ve reviewed your work, and I must say it’s truly fantastic. Great job! I’ll go ahead and send these over to Ben.

Here are a couple of small tweaks I made:

1. In the FRM dataset, the "item" column was labeled "Q," so I renamed it. It’s not a big deal, just a minor adjustment.
2. This isn’t on you—it's more on me for not clarifying earlier. For datasets or measures that assess the same or similar abilities (like FRM and FRM_MC in this case), I’d recommend merging them. Deciding when to lump or split datasets is one of the trickiest parts of processing them, and I’ll need to double-check with Ben on this. If you’re ever unsure about how to handle something like this, feel free to reach out!
3. I've renamed the files to have a prefix "MCVFR_Breuer_2017" to give a more descriptive name of these datasets

— Reply to this email directly, view it on GitHubhttps://github.com/ben-domingue/irw/issues/435#issuecomment-2495551514, or unsubscribehttps://github.com/notifications/unsubscribe-auth/BL7B5ZRFGEEVEH4MMQDAM3L2CC33BAVCNFSM6AAAAABORGTJNCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDIOJVGU2TCNJRGQ. You are receiving this because you were mentioned.Message ID: @.***>

[KingArthur0205]

@Monicaxichen Hi Monica, would you mind merging these code scripts into a single file such that users can execute this one file and generate the 4 data files above? Thank you !

[Monicaxichen]

Sure. I can certainly do that. 😊

Monica

From: Arthur Pan @.> Date: Tuesday, November 26, 2024 at 4:39 AM To: ben-domingue/irw @.> Cc: Monica (Xi) Chen @.>, Mention @.> Subject: Re: [ben-domingue/irw] Effects of response format on psychometric properties and fairness of a matrices test: multiple choice versus free response (Issue #435)

@Monicaxichenhttps://github.com/Monicaxichen Hi Monica, would you mind merging these code scripts into a single file such that users can execute this one file and generate the 4 data files above? Thank you !

— Reply to this email directly, view it on GitHubhttps://github.com/ben-domingue/irw/issues/435#issuecomment-2500691182, or unsubscribehttps://github.com/notifications/unsubscribe-auth/BL7B5ZVQAPQLGLKK6WL4MCL2CRTYRAVCNFSM6AAAAABORGTJNCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDKMBQGY4TCMJYGI. You are receiving this because you were mentioned.Message ID: @.***>