Exploring the Relationship Between Academic Satisfaction and Self-Perceptions of Learning

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Discussing the interplay between academic satisfaction and self-perceptions of learning, this study by Steve Graunke from Indiana University-Purdue University Indianapolis delves into models and statistical analyses using the IUPUI Continuing Student Survey. Examining how these constructs relate, the study investigates the implications of reciprocal causation, comparing models where satisfaction predicts perceptions or vice versa.


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  1. SATISFACTION AND SELF-PERCEPTIONS: HOW ARE THE RELATED? STEVE GRAUNKE INDIANA UNIVERSITY-PURDUE UNIVERSITY INDIANAPOLIS

  2. PRESENTATION Discuss the relationship between academic satisfaction and self- perceptions of learning Explore a model of academic satisfaction and self-perceptions of learning using the IUPUI Continuing Student Survey Demonstrate statistical model features in Mplus (that SPSS and SAS can t do as well) Discuss implications

  3. BACKGROUND Academic Satisfaction Indirect assessment

  4. ACADEMIC SATISFACTION Edwards and Waters (1982) Satisfaction and GPA predict persistence Schreiner (2009) Comprehensive study of satisfaction and retention Pullins (2011) Sophomore students Satisfaction with Campus Climate Advising

  5. INDIRECT ASSESSMENT Direct Assessment demonstrations of skills Indirect Assessment Feelings about learning Perceptions of abilities

  6. HOW THESE CONSTRUCTS RELATED? Student- Faculty interaction Grades Academic Persistence

  7. RECIPROCAL CAUSATION BETWEEN SATISFACTION AND PERFORMANCE Work organization literature Suggests satisfied employees perform better (and vise versa) Bean & Bradley (1986) Based on organizational literature Satisfaction stronger Pike (1991) Reciprocal causation model between grades and satisfaction fits well Satisfaction wins

  8. CURRENT STUDY Does reciprocal causation better describe the relationship between academic satisfaction and self-perceptions of learning than models in which academic satisfaction predicts self-perceptions (or self-perceptions predicts academic satisfaction)?

  9. IUPUI CONTINUING STUDENT SURVEY Multipurpose survey Overall Academic Satisfaction = 0.813 Overall, how satisfied are you with your academic experiences at IUPUI? How satisfied are you with the quality of the academic programs at IUPUI? Principles of Undergraduate Learning Indirect Assessment Communication Skills (4 items) Quantitative Skills (4 items) Exponent transformation

  10. LATENT VARIABLES Variables that are not observed directly Example: Socio-economic status Indicators Accounts for measurement error Advantages More consistent parameter estimates Large samples Disadvantages May need more complicated software to do this

  11. THE LATENT VARIABLE PART Formally communicate ideas and information (oral, visual, aural, etc.) (n802) Write a final report on a project or other work assignment (n803) Communicate with a team to solve problems (n804) Read and understand books, articles, and instruction manuals (n801) Solve mathematical problems (n805) Support an argument using quantitative data (n808) Use mathematics in everyday life (n806) Understand a statistical report (n807)

  12. STUDY 521 Senior respondents 3 Exogenous observed variables Gender (65% female) IU Cumulative GPA STEM major Structural Equation model

  13. MODEL 1/ SATISFACTION PREDICTS SELF- PERCEPTIONS n801 IU Cumulative GPA n802 Academic Satisfaction Comm. skills Gender (Flag for female) n803 n804 STEM major n805 Quant. skills n806 n807 n808

  14. MODEL 2/ SELF- PERCEPTIONS PREDICT SATISFACTION n801 IU Cumulative GPA n802 Academic Satisfaction Comm. skills n803 Gender (Flag for female) n804 STEM major n805 Quant. skills n806 n807 n808

  15. MODEL 3/ RECIPROCAL CAUSATION n801 IU Cumulative GPA n802 Academic Satisfaction Comm. skills n803 Gender (Flag for female) n804 STEM major n805 Quant. skills n806 n807 n808

  16. MODEL ESTIMATION Maximum Likelihood estimation (ml) Enables clearer comparisons between models Chi-square difference test 2 1 2 = improvement in model fit 0

  17. DESCRIPTIVE STATISTICS FOR OBSERVED VARIABLES Variables N Mean 4.01 1.43 1.42 1.42 1.42 1.37 1.37 1.34 1.38 0.65 3.18 0.29 Variance 0.586 0.008 0.008 0.009 0.007 0.013 0.013 0.013 0.012 0.227 0.315 0.207 521 521 520 521 521 521 521 521 521 521 521 521 Satisfaction with overall academic experiences a Read and understand books, articles, and instruction manuals b Formally communicate ideas and information (oral, visual, aural, etc.) b Write a final report on a project or other work assignment b Communicate with a team to solve problems b Solve mathematical problems b Use mathematics in everyday life b Understand a statistical report b Support an argument using quantitative data b Gender (Flag for Female) GPA STEM major a Scale for included items: 1 = Very Dissatisfied, 2= Dissatisfied, 3= Neutral, 4= Satisfied, 5= Very Satisfied b Scale: 1 = Not at all Effective, 2= Somewhat Effective, 3= Effective, 4= Very Effective. Exponent transformation used on these items. Means and variance account for transformation.

  18. MODEL FIT STATISTICS Null model 2180.2 63 -- -- -- Model 1 Model 2 Model 3 Chi-Square Dof RMSEA CLI TLI 92.3 46 0.044 0.978 0.970 94.5 46 0.045 0.977 0.969 89.2 44 0.044 0.979 0.969

  19. RESULTS All models fit data Satisfaction does impact self-perceptions Self-perceptions do impact satisfaction Not statistically significant improvement Significant effects

  20. SO WHAT AM I GOING TO DO WITH THIS? What interventions effect both satisfaction and self-perceptions? Four page Research Brief Other factors?

  21. FOR MORE INFORMATION ON SEM AND MPLUS Byrne, B. M. (2012). Structural equation modeling with Mplus. New York, NY: Routledge. Any Questions?

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