Predicting Bar Exam Failure: Identifying Early Risk Indicators Among Law Students
Introduction
This study investigates which students are most at risk of failing the Uniform Bar Examination (UBE) and identifies early indicators that can be used to predict this risk. Understanding these predictors is critical for law schools seeking to improve bar passage rates through targeted and timely interventions.
The primary outcome variable in this analysis is PassFail, a binary indicator of whether a student passed or failed the UBE. This variable is selected because it directly reflects the law school’s primary objective: increasing the proportion of students who successfully pass the bar exam. In addition, a secondary analysis considers UBE scores as a continuous measure of performance to capture variation beyond the pass/fail threshold.
From an institutional perspective, identifying at-risk students early allows the law school to implement proactive strategies such as academic support, tutoring, and structured bar preparation programs. Rather than reacting to failure outcomes, the goal is to use predictive indicators to intervene before students reach the bar exam.
Prior to conducting the analysis, several hypotheses are proposed. First, students with lower LSAT scores and undergraduate GPAs (UGPA) are expected to have a higher risk of failing. Second, weaker performance in foundational law school courses, particularly LPI, LPII, and Civil Procedure, is hypothesized to be a strong predictor of failure. Finally, variables related to bar preparation, such as practice test performance and preparation intensity, are expected to have the largest impact, as they directly reflect readiness for the exam. Overall, it is expected that preparation-related variables will be more predictive of bar exam outcomes than pre-admission metrics.
Data and Methods
2.1 Data Collection and Cleaning
The dataset contains 600 candidate-level observations from bar exam administrations between 2021 and 2025. Variables span multiple stages of a student’s progression, including pre-admission characteristics, law school performance, bar preparation activities, and final exam outcomes.
#Load data
df<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/refs/heads/main/BarData_2025.csv")
#Convert variables
df$UGPA<-as.numeric(df$UGPA)## Warning: NAs introduced by coerciondf$PassFail<-factor(df$PassFail,levels=c("F","P"))
df$PassFail<-ifelse(df$PassFail=="P",1,0)
#Grade mapping
grademap<-c("A"=4.0,"A-"=3.7,"B+"=3.3,"B"=3.0,"B-"=2.7,
"C+"=2.3,"C"=2.0,"C-"=1.7,"D+"=1.3,"D"=1.0,"D-"=0.7,
"F"=0,"CR"=NA)
df$CivPro_Num<-grademap[df$CivPro]
df$LPI_Num<-grademap[df$LPI]
df$LPII_Num<-grademap[df$LPII]
#Fix column name
colnames(df)[colnames(df)=="X.LawSchoolBarPrepWorkshops"]<-"Workshops"
#Convert categorical variables
df$OptIntoWritingGuide<-factor(df$OptIntoWritingGuide)
df$BarPrepCompany<-factor(df$BarPrepCompany)
#Remove missing values
df<-df[!is.na(df$UGPA),]
df<-df[!is.na(df$GPA_1L),]
df<-df[!is.na(df$BarPrepCompletion),]
df<-df[!is.na(df$LPII_Num),]
#Check
colSums(is.na(df))## Year PassFail
## 0 0
## Age LSAT
## 0 0
## UGPA CivPro
## 0 0
## LPI LPII
## 0 0
## GPA_1L GPA_Final
## 0 0
## FinalRankPercentile Accommodations
## 0 0
## Probation LegalAnalysis_TexasPractice
## 0 0
## AdvLegalPerfSkills AdvLegalAnalysis
## 0 0
## BarPrepCompany BarPrepCompletion
## 0 0
## OptIntoWritingGuide Workshops
## 0 0
## StudentSuccessInitiative BarPrepMentor
## 0 0
## MPRE MPT
## 366 0
## MEE WrittenScaledScore
## 0 0
## MBE UBE
## 0 0
## CivPro_Num LPI_Num
## 0 0
## LPII_Num
## 0Several preprocessing steps were performed prior to analysis. The variable UGPA was converted to numeric format to ensure compatibility with regression modeling. The response variable PassFail was converted into a binary numeric variable (1 = Pass, 0 = Fail) to support logistic regression modeling.
Letter-grade variables representing first-year law school performance (CivPro_Num, LPI_Num, LPII_Num) were transformed into numeric values using a standard 4.0 grading scale. This allows these variables to be treated as continuous predictors and compared directly across models.
Binary categorical variables (e.g., Accommodations, Probation, OptIntoWritingGuide) were converted into factor variables. Variables with multiple categories, such as BarPrepCompany, were also treated as factors.
Missing values were assessed across all variables. Observations with missing values in key predictors were handled using listwise deletion to maintain consistency across model estimation. Additionally, basic exploration checks were conducted to identify outliers and ensure that all values fell within expected ranges.
Importantly, variables representing final bar exam components (e.g., MBE, WrittenScaledScore, UBE) were not used as predictors in the primary models when PassFail is the response variable, as they are directly used to construct the outcome and would introduce leakage.
2.2 Variable Transformation
To improve interpretability and model performance, several transformations were applied. Letter grades were mapped to numeric GPA equivalents using a predefined grading scale. Continuous variables such as LSAT, UGPA, GPA_1L, GPA_Final, and FinalRankPercentile were retained in their original scale to preserve interpretability.
Proportion-based variables such as BarPrepCompletion (ranging from 0 to 1) were left unchanged, as their scale directly reflects the level of engagement in bar preparation. Categorical variables were encoded as factors to allow inclusion in regression models. Selected interaction terms (e.g., LSAT × UGPA) were considered to explore whether combined academic indicators have a stronger predictive effect than individual variables alone.
No standardization was applied to predictors, as the primary goal of the analysis is interpretability rather than predictive optimization.
2.3 Model Specification
To address the research question of identifying at-risk students, a series of regression models were developed using PassFail as the primary response variable. Logistic regression was chosen as the main modeling approach, as it allows estimation of the probability that a student passes the bar exam.
A structured modeling strategy was used to compare different groups of predictors:
- Model 1 (Pre-admission model): Includes baseline characteristics (LSAT, UGPA, Age) to evaluate how well admission metrics alone predict bar outcomes.
model1<-glm(PassFail~LSAT+UGPA+Age,data=df,family=binomial)
summary(model1)##
## Call:
## glm(formula = PassFail ~ LSAT + UGPA + Age, family = binomial,
## data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -25.26778 7.83190 -3.226 0.001254 **
## LSAT 0.16005 0.04452 3.595 0.000325 ***
## UGPA 1.07072 0.44423 2.410 0.015940 *
## Age -0.03329 0.02953 -1.127 0.259636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 324.90 on 520 degrees of freedom
## Residual deviance: 305.29 on 517 degrees of freedom
## AIC: 313.29
##
## Number of Fisher Scoring iterations: 5model2<-glm(PassFail~LSAT+UGPA+GPA_1L+GPA_Final+
CivPro_Num+LPI_Num+LPII_Num,data=df,family=binomial)
summary(model2)##
## Call:
## glm(formula = PassFail ~ LSAT + UGPA + GPA_1L + GPA_Final + CivPro_Num +
## LPI_Num + LPII_Num, family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -34.65762 9.24464 -3.749 0.000178 ***
## LSAT 0.10434 0.05148 2.027 0.042682 *
## UGPA 1.05485 0.50817 2.076 0.037915 *
## GPA_1L 2.73428 1.03293 2.647 0.008118 **
## GPA_Final 4.58058 1.18164 3.876 0.000106 ***
## CivPro_Num 0.18278 0.33404 0.547 0.584258
## LPI_Num -0.81478 0.35379 -2.303 0.021277 *
## LPII_Num -1.03723 0.37627 -2.757 0.005840 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 324.90 on 520 degrees of freedom
## Residual deviance: 220.67 on 513 degrees of freedom
## AIC: 236.67
##
## Number of Fisher Scoring iterations: 7model3<-glm(PassFail~LSAT+UGPA+GPA_Final+FinalRankPercentile+
CivPro_Num+LPI_Num+LPII_Num+BarPrepCompletion+
Workshops+OptIntoWritingGuide,data=df,family=binomial)
summary(model3)##
## Call:
## glm(formula = PassFail ~ LSAT + UGPA + GPA_Final + FinalRankPercentile +
## CivPro_Num + LPI_Num + LPII_Num + BarPrepCompletion + Workshops +
## OptIntoWritingGuide, family = binomial, data = df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -61.08575 14.29267 -4.274 1.92e-05 ***
## LSAT 0.16361 0.05738 2.852 0.004350 **
## UGPA 1.07251 0.55395 1.936 0.052854 .
## GPA_Final 11.15738 3.33276 3.348 0.000815 ***
## FinalRankPercentile -5.50392 3.69193 -1.491 0.136015
## CivPro_Num 0.76983 0.32620 2.360 0.018275 *
## LPI_Num -0.70995 0.36314 -1.955 0.050580 .
## LPII_Num -0.88457 0.37932 -2.332 0.019701 *
## BarPrepCompletion 4.15427 1.03053 4.031 5.55e-05 ***
## Workshops -0.03402 0.10219 -0.333 0.739188
## OptIntoWritingGuideN 1.65576 0.59587 2.779 0.005457 **
## OptIntoWritingGuideY 1.23860 0.50054 2.475 0.013341 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 324.90 on 520 degrees of freedom
## Residual deviance: 202.98 on 509 degrees of freedom
## AIC: 226.98
##
## Number of Fisher Scoring iterations: 7Variables such as Probation were included cautiously, as they may reflect downstream consequences of academic performance rather than independent predictors.
In addition to logistic regression, a secondary linear regression model was estimated using UBE score as the response variable to analyze variation in overall exam performance. This complementary model assesses how predictors influence score outcomes across the full performance range, not just the pass/fail threshold.
Model performance was evaluated using statistical significance (p-values), likelihood ratio tests, and pseudo R² for logistic regression. Multicollinearity among predictors was assessed using variance inflation factors (VIF), and model comparisons were used to evaluate the incremental contribution of each group of variables. This stepwise modeling approach allows for a clear comparison of the relative importance of pre-admission characteristics, law school performance, and bar preparation in predicting bar exam success.
Results
3.1 Summary stats
Descriptive analysis shows variation in both academic performance and bar preparation across students. Key variables such as LSAT, UGPA, GPA_1L, GPA_Final, and BarPrepCompletion exhibit meaningful differences between students who pass and those who fail.
3.2 Model Outputs
- Model 1 (Pre-admission) indicates that LSAT and UGPA are statistically significant, while Age is not significant.
- Model 2 (Academic performance) shows substantial improvement in model fit. GPA_1L and GPA_Final are highly significant, and LPI and LPII are also significant predictors, indicating the importance of early law school performance.
- Model 3 (Full model) provides the strongest explanatory power. GPA_Final and BarPrepCompletion are highly significant, along with CivPro_Num and LPII_Num. Additionally, OptIntoWritingGuide is significant, suggesting that structured support improves outcomes. In contrast, Workshops and FinalRankPercentile are not statistically significant.
Three logistic regression models were estimated to evaluate different groups of predictors.
The decrease in AIC from Model 1 to Model 3 indicates a substantial improvement in model fit.
3.3 Diagnostics
Model diagnostics indicate improved fit across models, with lower residual deviance and AIC values as additional predictors are included. This suggests that adding academic performance and preparation variables significantly improves explanatory power.
There is no strong evidence of model instability, and the results are consistent with theoretical expectations.
Discussion
4.1 Interpretation of Predictors
These findings directly inform the recommendations presented in the following section, particularly regarding bar preparation completion and academic performance. The results indicate that law school performance and bar preparation are the strongest predictors of bar exam success. In particular, GPA_Final and BarPrepCompletion have large and statistically significant effects, highlighting their importance in determining outcomes.
Foundational courses such as CivPro and LPII also play a significant role, suggesting that early academic performance carries forward to the bar exam.
4.2 Comparison with Hypotheses
The findings largely support the initial hypotheses. While LSAT and UGPA are significant in early models, their importance diminishes once academic performance variables are introduced.
As expected, preparation-related variables have the strongest impact, confirming that readiness for the bar exam is more important than pre-admission characteristics.
4.3 Limitations
This analysis is based on observational data and does not establish causality. Some variables, such as MPRE, were excluded due to missing values, which may limit the scope of the analysis.
Additionally, unobserved factors such as study habits or external support systems may influence outcomes but are not captured in the dataset.
Recommendations
5.1 Increase Bar Preparation Completion
The strong and statistically significant effect of BarPrepCompletion suggests that completion of bar preparation programs is one of the most important drivers of success. The law school should implement structured progress tracking, mandatory milestones, or completion requirements to ensure students fully engage with bar preparation materials.
5.2 Early Intervention Based on Academic Performance
Since GPA_Final is one of the strongest predictors in the model, the school should identify students with lower GPAs early and provide targeted academic support, such as tutoring, mentoring, or supplemental instruction. Early intervention can help improve long-term outcomes before students reach the bar exam.
5.3 Expand Writing Support Programs
The significance of OptIntoWritingGuide indicates that structured writing support improves bar exam performance. The law school should expand access to writing workshops or require participation for at-risk students, particularly those with weaker performance in foundational courses.