The　presence　of　unobserved　factors　in　the　motorcycle　involved　two-vehicle　crashes　(MV)　data　could　lead　to　heterogenous　associations　between　observed　factors　and　injury　severity　sustained　by　motorcyclists.　Capturing　such　heterogeneities　necessitates　distinct　methodological　approaches,　of　which　random　and　scale　heterogeneity　models　are　paramount.　Herein,　we　undertake　an　empirical　evaluation　of　random　and　scale　heterogeneity　models,　exploring　their　efficacy　in　delineating　the　influence　of　external　determinants　on　the　degree　of　injury　severity　in　crashes.　Within　the　effects　of　scale　heterogeneity,　this　study　delves　into　two　dominant　models:　the　scaled　multinomial　logit　model　(S-MNL)　and　its　generalized　counterpart,　the　G-MNL,　which　encompasses　both　the　SMNL　and　the　random　parameters　multinomial　logit　model　(RPL).　While　the　random　heterogeneity　domain　is　represented　by　the　random　parameters　multinomial　logit　and　an　upgraded　variant　-　the　random　parameters　multinomial　logit　model　with　heterogeneity　in　means　and　variances　(RPLHMV).　Motorcycle　involved　two-vehicle　crashes　data　were　extracted　from　the　UK　STATS19　dataset　from　2016　to　2020.　Likelihood　ratio　tests　are　computed　to　assess　the　temporal　variability　of　the　significant　factors.　The　test　result　demonstrates　the　temporal　variations　over　a　five-year　study　period.　Some　very　important　differences　started　to　show　up　across　the　years　based　on　the　model　estimation　results:　that　the　RPLHMV　model　statistically　outperforms　the　G-MNL　model　in　the　2016,　2018,　and　2019　models,　while　the　S-MNL　model　is　statistically　superior　in　the　2017　and　2020　years.　These　important　findings　suggest　that　the　origin　of　heterogeneity　in　explaining　factor　weights　can　be　captured　by　scale　effects,　not　just　random　heterogeneity.　In　addition,　the　model　results　further　show　that　motorcyclists＇　injury　severities　are　significantly　affected　by　motorcycle-related　characteristics;　there　is　the　added　factor　of　external　influences,　such　as　non-motorcycle　drivers　(males,　young　drivers,　and　elderly　drivers)　and　vehicles　(the　moving　status,　age,　and　types　of　vehicles)　that　collide　with　motorcycles.　The　results　of　this　paper　are　anticipated　to　help　policymakers　develop　effective　strategies　to　mitigate　motorcycle　involved　two-vehicle　crashes　by　implementing　appropriate　measures.