The purpose of this research is to investigate the core notion of social networking sites, as well as their relevance, use, and effect on the academic performance of students. The university students from western Odisha are the major source of information for this study. Primary data on demographic information, educational qualifications & achievements are collected through google forms in online and offline methods that were utilized for the study. From the sample of size 277, gender-wise distribution [Female-141 (50.9%) and Male-136 (49.1%)] have been responded to and selected for our study. A five-point Likert Type Rating Scale Questionnaire, titled: Social Media and Academic Performance of Students Questionnaire was used to collect data from the participants. The data so collected are analyzed through IBM SPSS Statistics 26 under MS Excel environment and the results are analyzed. Statistical tools like Graphs, Pie-Charts, Diagrams, Chi-Square tests, and t-tests were used for the statistical data analysis to assess the various effects of social networking sites on the academic performance of students. The results reveal that the students who spend more time on SNSs are likely to demonstrate poor Academic achievement. Students can be able to boost their Academic achievement by collecting data and gathering important information.
Agriculture is the strength of Indian economy which provides employment for approximately 65% of the workforce across the country. Regional inequality due to the unequal level of agricultural developments remains a major problem in India and Odisha in particular. In the present study, an attempt has been made to study the regional inequality in agricultural development among the nine blocks of Sambalpur district. The study utilizes published data obtained from Statistical Abstract of different districts in Western Odisha and 'District Outlines' published annually by the Directorate of Economics and Statistics (DES), Government of Odisha, for the year 2015-2016. Using technique of Principal Component Analysis (PCA), Seven Principal component were extracted which were found to be normally distributed by Kolmogorov Smirnov test. The three Quartiles Q1, Q2 and Q3 of the Normal probability distributions are used to classify the nine blocks into four homogeneous groups namely Meteoric, Progressive, Mediocre and Laggard on the basis of their composite index scores. The analysis finds that three blocks namely Dhankauda, Jamankira and Jujumura have the highest level of agricultural developments and are the Meteoric while two blocks namely Bamra and Naktideul have the lowest level of development and can be categorised as Laggard blocks. Kuchinda and Maneswar blocks are relatively less developed than Dhankauda, Jamankira and Jujumura and can be categorised as Progressive blocks while Rengali and Rairakhol are relatively more developed than the Lagard blockes and less developed than the Progressive blocks in terms of agricultural developments and may be treated as Mediocre blocks.
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