Goodness-of-fit　tests　aim　at　discerning　model　misspecification　and　identifying　a　model　which　is　poorly　fitting　a　given　data　set.　They　are　methods　used　to　determine　the　suitability　of　the　fitted　model.　The　subject　of　assessment　of　goodness-of-fit　in　logistic　regression　model　has　attracted　the　attention　of　many　scientists　and　researchers.　Several　methods　for　assessing　how　well　observed　data　can　fit　into　logistic　regression　models　have　been　proposed　and　discussed　where　test　statistics　are　functions　of　the　observed　data　values　and　their　corresponding　estimated　values　after　parameter　estimation.　Considering　a　correctly　specified　panel　data　model　with　balanced　data　set,　the　conditional　maximum　likelihood　estimates　of　the　parameters　are　less　biased　and　the　estimated　response　variable　values　are　actually　in　the　neighborhood　of　the　observed　values.　Relative　to　the　induced　biases　of　the　parameter　estimates　resulting　from　imputation　of　missing　covariates,　the　performances　of　the　goodness-of-fit　tests　may　be　misjudged.　This　study　looks　at　the　susceptibility　of　the　goodness-of-fit　tests　for　logistic　panel　data　models　with　imputed　covariates.　Simulation　results　show　that　Bayesian　imputation　impacts　less　on　the　goodness-of-fit　test　statistics　and　therefore　stands　out　as　the　better　technique　against　other　classical　imputation　methods.　An　increased　proportion　of　missingness　however　appeared　to　reduce　the　confidence　interval　of　the　test　statistics　which　in　turn　reduces　the　chances　of　adopting　the　model　under　study.