{"id":24415,"date":"2026-02-28T23:19:54","date_gmt":"2026-02-28T17:49:54","guid":{"rendered":"https:\/\/www.sachdevajk.in\/?p=24415"},"modified":"2026-02-28T23:19:54","modified_gmt":"2026-02-28T17:49:54","slug":"identifying-key-academic-and-career-related-challenges-faced-by-mba-students-an-exploratory-factor-and-cluster-analysis","status":"publish","type":"post","link":"http:\/\/www.sachdevajk.in\/?p=24415","title":{"rendered":"Identifying Key Academic and Career-Related Challenges Faced by MBA Students: An Exploratory Factor and Cluster Analysis\u201d"},"content":{"rendered":"

Title:<\/span><\/b><\/p>\n

\u201cIdentifying Key Academic and Career-Related Challenges Faced by MBA Students: An Exploratory Factor and Cluster Analysis\u201d<\/span><\/b><\/p>\n

Authors:<\/span><\/b><\/p>\n

Yash Deshmukh
Siddhi Zokande
Yuvraj Jain<\/span><\/p>\n

Introduction <\/span><\/b><\/p>\n

MBA students face multiple challenges during their academic journey, including academic pressure, skill gaps, and career uncertainty. These challenges impact their performance, confidence, and placement outcomes. Understanding the underlying factors behind these issues can help institutions design better support systems. This study uses Exploratory Factor Analysis (EFA) and Cluster Analysis to identify hidden variables and segment MBA students based on their problems.<\/span><\/p>\n

Objectives<\/span><\/b><\/p>\n

    \n
  1. To understand hidden variables by EFA (Exploratory Factor Analysis).<\/span><\/li>\n
  2. To cluster the respondents based on identified challenges.<\/span><\/li>\n<\/ol>\n

    Problem Statement<\/span><\/b><\/h1>\n

    MBA students face difficulties in balancing academics, developing industry-relevant skills, and preparing for placements. However, these issues may stem from underlying hidden factors such as stress, lack of confidence, or poor time management. The study aims to identify these latent variables and group students based on similar problem patterns.<\/p>\n

    Data Collection<\/span><\/b><\/p>\n

      \n
    • Method:<\/span><\/b> Structured questionnaire<\/span><\/li>\n
    • Scale:<\/span><\/b> 5-point Likert Scale <\/span><\/li>\n
    • Sample:<\/span><\/b> 50 MBA students<\/span><\/li>\n
    • Sampling Technique:<\/span><\/b> Convenience Sampling<\/span><\/li>\n<\/ul>\n

      \u00a0<\/span><\/p>\n

      \u00a0<\/span><\/p>\n

      Data Analysis<\/span><\/b><\/p>\n

      KMO and Bartlett\u2019s Test<\/span><\/b><\/p>\n\n\n\n\n\n\n\n\n
      \n

      Measure<\/b><\/p>\n<\/td>\n

      		\n

      Value<\/b><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

      \n

      Kaiser-Meyer-Olkin Measure of Sampling Adequacy<\/b><\/p>\n<\/td>\n

      		\n

      .513<\/b><\/p>\n<\/td>\n<\/tr>\n

      \n

      Bartlett’s Test of Sphericity<\/b><\/p>\n<\/td>\n

      		\n

      Approx. Chi-Square: 268.850<\/b><\/p>\n<\/td>\n<\/tr>\n

      \u00a0<\/td>\n\n

      df: 66<\/b><\/p>\n<\/td>\n<\/tr>\n

      \u00a0<\/td>\n\n

      Sig.: .000<\/b><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/p>\n

      Interpretation:<\/span><\/b><\/h3>\n

      \u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>The KMO value is 0.513<\/span><\/strong>, which is slightly above 0.5.
      This indicates that the sample is moderately adequate<\/span><\/strong> for factor analysis.<\/p>\n

      \u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 <\/span><\/span><\/span>Bartlett\u2019s Test is significant (p = 0.000 < 0.05<\/span><\/strong>).
      This means the variables are correlated enough to apply factor analysis.<\/p>\n

      Communalities<\/span><\/b><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      \n

      Variable<\/b><\/p>\n<\/td>\n

      		\n

      Initial<\/b><\/p>\n<\/td>\n

      		\n

      Extraction<\/b><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

      \n

      Manage Assignments<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .957<\/p>\n<\/td>\n<\/tr>\n

      \n

      Workload Stress<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .664<\/p>\n<\/td>\n<\/tr>\n

      \n

      Life Balance<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .891<\/p>\n<\/td>\n<\/tr>\n

      \n

      Exam Pressure<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .148<\/p>\n<\/td>\n<\/tr>\n

      \n

      Industry Exposure<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .212<\/p>\n<\/td>\n<\/tr>\n

      \n

      Presentation Skills<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .981<\/p>\n<\/td>\n<\/tr>\n

      \n

      Communication Skills<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .352<\/p>\n<\/td>\n<\/tr>\n

      \n

      Theoretical Curriculum<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .801<\/p>\n<\/td>\n<\/tr>\n

      \n

      Placement Worry<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .833<\/p>\n<\/td>\n<\/tr>\n

      \n

      Specialization Confusion<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .510<\/p>\n<\/td>\n<\/tr>\n

      \n

      Interview Nervousness<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .456<\/p>\n<\/td>\n<\/tr>\n

      \n

      High Competition<\/p>\n<\/td>\n

      		\n

      1.000<\/p>\n<\/td>\n

      		\n

      .248<\/p>\n<\/td>\n<\/tr>\n

      \n

      Extraction Method: Principal Component Analysis.<\/i><\/p>\n<\/td>\n

      		\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/p>\n

      Interpretation:<\/span><\/b><\/p>\n

        \n
      • Extraction values above 0.50<\/b> are considered good.<\/span><\/li>\n
      • Variables like:<\/span>\n
          \n
        • Manage Assignments (0.957)<\/span><\/li>\n
        • Presentation Skills (0.981)<\/span><\/li>\n
        • Life Balance (0.891)<\/span><\/li>\n
        • Placement Worry (0.833)<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

          are strongly explained by the extracted factors.<\/span><\/p>\n

            \n
          • High Competition (0.248) and Industry Exposure (0.212) are weakly explained.<\/span><\/li>\n<\/ul>\n

            \u00a0<\/span><\/p>\n

            \u00a0<\/span><\/p>\n

            \u00a0<\/span><\/p>\n

            \u00a0<\/span><\/p>\n

            Total Variance Explained<\/span><\/b><\/p>\n\n\n\n\n\n\n\n\n\n
            \n

            Component<\/b><\/p>\n<\/td>\n

            		\n

            Initial Eigenvalues Total<\/b><\/p>\n<\/td>\n

            		\n

            % of Variance<\/b><\/p>\n<\/td>\n

            		\n

            Cumulative %<\/b><\/p>\n<\/td>\n

            		\n

            Extraction Sums of Squared Loadings<\/b><\/p>\n<\/td>\n<\/tr>\n<\/thead>\n

            \n

            1<\/b><\/p>\n<\/td>\n

            		\n

            3.145<\/p>\n<\/td>\n

            		\n

            26.21<\/p>\n<\/td>\n

            		\n

            26.21<\/p>\n<\/td>\n

            		\n

            1.841<\/p>\n<\/td>\n<\/tr>\n

            \n

            2<\/b><\/p>\n<\/td>\n

            		\n

            1.959<\/p>\n<\/td>\n

            		\n

            16.33<\/p>\n<\/td>\n

            		\n

            42.54<\/p>\n<\/td>\n

            		\n

            1.758<\/p>\n<\/td>\n<\/tr>\n

            \n

            3<\/b><\/p>\n<\/td>\n

            		\n

            1.587<\/p>\n<\/td>\n

            		\n

            13.23<\/p>\n<\/td>\n

            		\n

            55.77<\/p>\n<\/td>\n

            		\n

            1.321<\/p>\n<\/td>\n<\/tr>\n

            \n

            4<\/b><\/p>\n<\/td>\n

            		\n

            1.306<\/p>\n<\/td>\n

            		\n

            10.88<\/p>\n<\/td>\n

            		\n

            66.65<\/p>\n<\/td>\n

            		\n

            1.282<\/p>\n<\/td>\n<\/tr>\n

            \n

            5<\/b><\/p>\n<\/td>\n

            		\n

            1.264<\/p>\n<\/td>\n

            		\n

            10.53<\/p>\n<\/td>\n

            		\n

            77.18<\/p>\n<\/td>\n

            		\n

            1.203<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            \u00a0<\/p>\n

            Interpretation:<\/span><\/b><\/p>\n