Study strategy predictors of performance in introductory psychology.

In this study, the relationship between student study strategies and performance in Introductory Psychology was examined. Eighty-eight students in three sections of Introductory Psychology at a Midwestern university in the United States completed a demographic questionnaire and the Learning and Study Strategies Inventory-2nd edition (LASSI-2) during class time within the first two weeks of the semester. The relationships between LASSI-2 subscales and a variety of achievement variables (high school GPA, college GPA, ACT score) as well as final grade in the course were explored. A discriminant analysis indicated that the Motivation subscale was the most important discriminator between students who were successful in Introductory Psychology (as measured by a grade of A and B) versus those who were unsuccessful (as measured by a grade of D or F). These results highlight the importance of considering motivational factors in introductory courses.

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Considering the challenges many students face during the transition from the high school to the college academic environment, it is important for educators to be able to identify those students who are most likely to struggle during this transitional period. Typically, global measures of academic achievement such as standardized achievement test scores (ACT, SAT) and high school grade point average are used to identify students who may be at-risk during the transition to college academics. Although useful, the identification of other characteristics (study skills, learning styles) that are predictors of difficulties in transition is an important research goal.

In this study, we examined the usefulness of a popular study skills instrument, the Learning and Study Strategies Inventory (LASSI)--2nd Edition (Weinstein & Palmer, 2002), for predicting performance in a common first year course, Introductory Psychology. Only a few studies have examined the LASSI--2nd Edition (Prevatt, Petscher, Proctor, Hurst, & Adams, 2006; Proctor, Prevatt, Adams, Hurst, & Petscher, 2006), and Cano (2006) notes that studies examining the relationships between the LASSI and academic performance are especially needed.

Research on the Relationship between Study Skills and Academic Performance

The relationship between various predictors and academic success in college has been examined in a number of studies. In a recent meta-analysis, Robbins et al. (2004) examined the influence of psychosocial and study skills factors on academic performance (GPA). The best predictors of performance were academic self-efficacy and achievement motivation. In addition, study skills such as time management, utilizing information, and taking notes in class were important for academic success.

Although study skills are obviously an important precursor for academic success, effective studying is actually a complex activity. Plant, Ericsson, Hill, and Asberg (2005) reviewed research indicating that study time alone is a poor predictor of how well students will perform in college courses. Instead of study time, a combination of factors including previous achievement, academic ability, self-regulation, and quality of study appear to be crucial for predicting college course performance. Rather than merely exhorting students to "study more," it seems imperative to help students study more effectively during their actual study time.

Predicting Academic Performance in Introductory Psychology

Researchers have studied a number of variables that may help predict performance in introductory psychology, although none of these studies have used the LASSI as a measure of study skills. Gurung (2005) surveyed students in Introductory Psychology regarding their use of 11 different study techniques. The three most frequently used study techniques were reading the text, reading notes, and using mnemonics. Although these techniques were correlated with exam performance, they were not the strongest predictors. Two less frequently used techniques, self-testing of knowledge and memorizing definitions, were the strongest predictors of exam performance. These results suggest that different study techniques are not equally effective and students may not necessarily use those techniques that are most effective.

Gadzella, Ginther, and Bryant (1997) studied learning styles among students enrolled in Introductory Psychology. Students who earned an A scored significantly higher than students who earned a C on two subscales of the Inventory of Learning Processes. Those subscales reflected use of deep processing while studying (i.e., critically evaluating, organizing, and comparing/contrasting information) and "methodological study" (use of study techniques recognized as effective).

In addition to examining specific study strategies, a number of researchers have investigated motivational and goal-related variables as they relate to performance in Introductory Psychology. For example, Harackiewicz, Barron, Tauer, Carter, and Elliot (2000) found that performance goals were a better predictor of achievement than mastery goals. Mastery goals refer to the desire to learn in order to understand a concept or area of study, whereas performance goals refer to the desire to do well in order to earn a reward (or grade).

Waschull (2005) investigated the relation between performance in an online introductory psychology course and time commitment to the course, self-discipline/motivation, and perceived effectiveness of study skills. Of these factors, self-discipline/motivation was the only variable significantly related to performance. Similar to the self-discipline/motivation variable studied by Waschull (2005), "work drive" (i.e., a student's "drive" to complete academic work and be a successful student) has also been reported to correlate with final course grade in introductory psychology (Ridgell & Lounsbury, 2004).

Other studies have examined variables such as class attendance. Grabe, Christopherson, and Douglas (2004-2005) found that frequency of missing class due to other academic demands (e.g., studying for a test in another class) was negatively related to exam performance in Introductory Psychology. This finding supports the idea that students who more frequently miss class because they are studying for another class may have poorer time-management skills, a skill that is important to academic success in general.

The LASSI 2nd Edition

Learning and study strategies are crucial factors in the success of first year college students and a popular tool for the assessment of college level learning and study strategies has been the Learning and Study Strategies Inventory (LASSI) (Weinstein & Palmer, 2002). The first edition of the LASSI has been used in a number of studies and is an extremely popular instrument among practitioners in academic assistance centers and developmental education (Flowers, 2003; Melancon, 2002; Stevens & Tallent-Runnels, 2004). The LASSI, along with its high school counterpart, the LASSI-HS (Olivarez & Tallent-Runnels, 1994), has been examined as a predictor of academic achievement in a number of recent studies (Everson, 2003; Everson, Weinstein, & Laitusis, 2000; Loomis, 2000; Prus, Hatcher, Hope & Grabiel, 1995). In one large scale study (N = 1600) examining the use of the LASSI as a predictor of performance among college students, Everson (2003) found that the addition of the LASSI improved predictions of academic achievement beyond GPA and PSAT (verbal and math) scores alone. In 2002, a revised version of the LASSI (LASSI-2) was published which included a number of new and improved items. The LASSI-2 consists of the same 10 subscales as the first edition of the LASSI, but each of the subscales include additional items. Although a substantial number of studies have been completed with the original LASSI, very little research has been conducted with the 2nd edition of the LASSI.

In a recent study using the LASSI-2, Proctor et al. (2006) examined differences in study skills between academically successful and academically struggling students. Students with a low GPA, and those who had been clinically referred (some identified as learning disabled, and some not), demonstrated statistically significant weaknesses on the LASSI-2 subscales of Anxiety, Concentration, Motivation, Selecting Main ideas, and Test-taking Strategies. When compared to their normally achieving peers, the "struggling" students displayed difficulties with study skills.

An important question that has remained relatively unexplored in the research literature is the ability of the LASSI-2 to predict college academic performance. Prus et al. (1995) examined how well the first edition of the LASSI predicted freshman year grade point average in a sample of 317 college students. Using multiple regression procedures, the LASSI marginally improved the ability to predict performance beyond traditional measures such as high school grade point average, SAT scores, and high school class rank. However, the addition of the LASSI scales only increased the amount of variance explained from 37% to 42%, an improvement of 5%. Of the ten LASSI scales, the only significant predictor was Motivation.

Research Questions

This study was conducted to examine the relationship between study strategies and academic performance in Introductory Psychology. The study was guided by the following research questions:

1. Are LASSI-2 scores correlated with overall measures of academic achievement (ACT, high school GPA, college GPA)?

2. What is the relationship between study strategies and performance in Introductory Psychology?

3. Which study strategies best discriminate between students who are successful (as measured by a grade of A or B in the course) and unsuccessful (as measured by a grade of D or F in the course) in Introductory Psychology?

Method

Participants

Eighty-eight students (43 male, 44 female, 1 did not report) in three sections of Introductory Psychology at a Midwestern university in the United States participated in the study. All three sections were taught by the same instructor. Assessment in the course consisted of four unit exams and short writing assignments. The sample consisted of 52 freshman, 21 sophomores, 11 juniors, and 4 seniors. Students were primarily white/ Caucasian with regards to ethnicity (89%), with 3.4% Asian, 3.4% Hispanic/Latino, 1.1% African-American, and 2.3% other.

Instruments

Demographic questionnaire. The demographic questionnaire consisted of student gender, age, grade level, ethnicity, and self-reported college grade-point average (college GPA), high school grade-point average (hs GPA), composite ACT score, college major, and disability status.

Learning and Study Strategies Inventory 2nd Edition (Weinstein & Palmer, 2002; Weinstein et al., 2002). The LASSI 2nd edition consists of 80 Likert-type self-report items, composing ten subscales. The subscales and the internal consistency reliability coefficients (Cronbach's alpha) as reported in the LASSI-2 Manual (Weinstein & Palmer, 2002) are: Anxiety ([alpha] = .87),Attitude ([alpha] = .77), Concentration ([alpha] = .86), Information Processing ([alpha] = .84), Motivation ([alpha] = .84), Self-Testing ([alpha] = .84), Selecting Main Ideas ([alpha] = .89), Study Aids ([alpha] = .73), Time Management ([alpha] = .85), and Test Strategies ([alpha] = .80). Each item is a declarative statement such as "I feel panicky when I take an important test" and "I try to find relationships between what I am learning and what I already know." Participants read the questions and indicate on the test form whether the statement is: (a) not at all typical of me, (b) not very typical of me, (c) somewhat typical of me, (d) fairly typical of me, or (e) very much typical of me.

For the current study, the following reliability coefficients (Cronbach's Alpha) were obtained: Anxiety ([alpha] = .86), Attitude ([alpha] = .75), Concentration ([alpha] = .87), Information Processing ([alpha] = .82), Motivation ([alpha] = .82), Self-Testing ([alpha] = .86), Selecting Main Ideas ([alpha] = .85), Study Aids ([alpha] = .67), Time Management ([alpha] = .84), and Test Strategies ([alpha] = .85).

Procedure

Students completed an informed consent document, the demographic questionnaire, and the LASSI-2 during class time within the first two weeks of the semester. Students scored their own instruments and were given a brief presentation on how to interpret the results. In addition, information regarding campus tutoring resources was provided. After completing the LASSI, students tore out a summary of their scores to keep and then handed in the LASSI booklet to the instructor. Composite scores for each of the LASSI subscales were computed by the researcher from the LASSI booklet turned in by the students. At the end of the semester, a variety of course related outcome measures were gathered from the course instructors and matched to student data (demographics, LASSI).

Results

Descriptive statistics are reported in Table 1. Pearson correlation coefficients were computed for ACT, high school GPA, college GPA, LASSI subscales, and final course grade (See Table 2).

Correlational Analysis: There were a number of significant correlations between the background achievement variables (ACT, hs GPA, and college GPA) and the LASSI subscales (See Table 2). With regards to final course grade, Motivation (r = .33, p < .01) was the only subscale that was significantly correlated.

Differences in LASSI study skills between successful and unsuccessful students. In order to determine which study skills differentiated students who were successful (as defined by a grade of A or B; n = 41) and those who weren't successful (as defined by a grade of D or F; n = 22), a discriminant analysis was conducted in order to determine which combination of study strategies best predicted membership in the successful or unsuccessful group. Prior to conducting the analysis, Mahalanobis distance was calculated and two multivariate outliers were identified and removed from the data set. Box's M test was not significant, indicating that the assumption of homogeneity of covariance was met. One discriminant function was generated and was significant, Wilk's lambda = .689, [chi square] (10, N = 63) = 20.85, p = .022, indicating that the function of predictors significantly differentiated between students who were successful (grade of A or B) or unsuccessful (grade of D or U) in Introductory Psychology. The canonical correlation was .557, indicating that the study strategy variables accounted for 31% of the function variance. At the p < .05 level, the only significant difference between successful and unsuccessful students was on Motivation (See Table 3). The discriminant analysis identified Motivation as the variable most closely associated with the function (Function coefficient = 1.18; See Table 4). The discriminant function correctly classified 70.7% of the successful students and 72.7% of the unsuccessful students. Overall, 71.4% were correctly classified.

Discussion

Studies examining how the LASSI relates to various academic outcomes will be helpful to practitioners as they use the LASSI as a tool for identifying students at-risk for academic failure. In support of the general importance of the study skills identified by the LASSI-2 for academic performance, a number of significant correlations were found between global indicators of academic achievement such as ACT score and high school GPA and the various LASSI subscales. In particular, the LASSI subscale of Motivation was significantly correlated with college gpa, high school gpa, and final course grade. This finding is consistent with that of Prus et al. (1995), who found that Motivation was the only LASSI subscale which helped improve predictions of first year academic performance beyond conventional measures such as SAT scores, high school grade point average, and high school class rank. Correlational results indicated that ACT and high school gpa were most closely associated with final course grade (r = .40 and .44), but the Motivation subscale was close in terms of strength of relationship (r = .33). Due to limitations in sample size, regression procedures were not conducted. However, based on these results as well as those of Prus et al. (1995), it appears that the Motivation subscale holds promise for predicting performance.

These results also highlight specific study strategies that may be particularly important for successful performance in Introductory Psychology and other freshman level survey courses. Results of the discriminant analysis indicated that Motivation was the subscale that best discriminated between successful and unsuccessful students in Introductory Psychology. Study strategy instruction certainly needs to address the cognitive and metacognitive factors necessary for success, but the results of this study may indicate that addressing more basic motivational issues is crucial. Before students engage in more complex cognitive learning activities (e.g. cognitive and metacognitive study strategies), motivational issues may need to be addressed.

Hidi and Harackiewicz (2000) note the tendency to dichotomize academic motivation into extrinsic and intrinsic factors. At the college level, it may be particularly appealing to focus on intrinsic motivation as a contributing factor in poor academic performance. Although instrinic motivation certainly affects performance, Hidi and Harackiewicz (2000) suggest that it is more useful to focus on how intrinsic and extrinsic factors interact to promote positive motivation for academic tasks. Instructional strategies that seek to promote situational interest (in addition to individual or personal interest) in the subject of psychology will likely have positive effects on student motivation. Situational interest is stimulated by experiences in the context of the classroom, and it may be enhanced by activities such as relating psychology to everyday life, discussing psychological perspectives on current events, and relating learning to students' personal lives. When coursework is internalized as being personally meaningful, it is more likely that situational interest will endure to become a more longstanding individual interest. These types of instructional strategies may be especially important for those students who begin an introductory course with little individual interest in the topic of the course.

Limitations

A number of limitations should be considered when evaluating the results of this study. First, although identical course objectives and textbooks were used for all three sections of the course, it is possible that students in each of the courses experienced different "treatments." Due to a variety of factors (e.g. class size, time of day the section was offered, local history), it is possible that students experienced a somewhat unique learning environment in each of the separate sections.

Second, the size of the individual sections (which ranged from 25-50 students) may limit the generalizability of the results. At large universities in the United States, it is common to have sections of Introductory Psychology which number in the hundreds of students. The study skills that are important for performance in Introductory Psychology in small sections may be different from those that are important in large sections. For example, there are probably variations in course structure (e.g. lecture vs. discussion format, multiple-choice vs. essay assessments, writing requirements, access to instructor) between small and large sections which place unique demands on student study skills. Future research examining the usefulness of the LASSI-2 for predicting performance in large sections would help answer this question of possible differences.

Third, the small sample size and the limited number of students who were successful (n = 41) or unsuccessful (n = 22) may have affected the ability to detect differences and also the results of the discriminant analysis. Future studies with larger sample sizes would help overcome this limitation.

This study provides limited empirical support for the use of the LASSI as a predictor of performance in a common first year course. The LASSI-2 was able to correctly classify students as either successful or unsuccessful approximately 71% of the time. Future research examining how well the LASSI-2 predicts performance in other courses or overall first year performance would be helpful. This information would likely help inform developmental educators and those responsible for first year programming about the study strategies essential for first year success. All of the study strategies identified by the LASSI are worthy goals for remediation, but the results of this study indicate that Motivation may be especially important.

Author Note

Portions of this research were presented at the annual meeting of the Mid-Western Educational Research Association, Columbus, OH, October, 2006.

References

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Grabe, M., Christopherson, K., & Douglas, J. (2004-2005). Providing introductory psychology students access to online lecture notes: The relationship of note use to performance and class attendance. Journal of Educational Technology Systems, 33, 295-308.

Gurung, R. A. R. (2005). How do students really study (and does it matter)? Teaching of Psychology, 32, 239-241.

Harackiewicz, J. M., Barron, K. E., Tauer, J. M., Carter, S. M., & Elliot, A. J. (2000). Short-term and long-term consequences of achievement goals: Predicting interest and performance over time. Journal of Educational Psychology, 92, 316-330.

Hidi, S.,& Harackiewicz, J.M. (2000). Motivating the academically unmotivated: A critical issue for the 21st century. Review of Educational Research, 70, 151-179.

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Melancon, J. G. (2002). Reliability, structure, and correlates of Learning and Study Strategies Inventory scores. Educational and Psychological Measurement, 62, 1020-1027.

Olivarez, A. & Tallent-Runnels, M.K. (1994). Psychometric properties of the learning and study strategies inventory-high school version. Journal of Experimental Education, 62, 243-258.

Plant, E.A., Ericsson, K.A., Hill, L., & Asberg, K. (2005). Why study time does not predict grade point average across college students: Implications of deliberate practice for academic performance. Contemporary Educational Psychology, 30, 96-116.

Prevatt, F., Petscher, Y., Proctor, B. E., Hurst, A., & Adams, K. (2006). The revised learning and study strategies inventory: An evaluation of competing models. Educational and Psychological Measurement, 66, 448-458.

Proctor, B. E., Prevatt, F., Adams, K., Hurst, A., & Petscher, Y. (2006). Study skills profiles of normal-achieving and academically-struggling college students. Journal of College Student Development, 47, 37-51.

Prus, J., Hatcher, L., Hope, M. & Grabiel, C.(1995). The Learning and Study Strategies Inventory (LASSI) as a predictor of first-year college academic success. Journal of the Freshman Year Experience, 7, 7-26.

Ridgell, S. D. & Lounsbury, J. W. (2004). Predicting academic success: General intelligence, "Big Five" personality traits, and work drive. College Student Journal, 38, 607-618.

Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130, 261-288.

Stevens, T., & Tallent-Runnels, M. K. (2004). The Learning and Study Strategies Inventory-High School Version: Issues of Factorial Invariance Across Gender and Ethnicity. Educational and Psychological Measurement, 64, 332-346.

Waschull, S. B. (2005). Predicting success in online psychology courses: Self-discipline and motivation. Teaching of Psychology, 21, 190-192.

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Heath Marrs, Department of Psychology, Fort Hays State University, Hays, KS. Ellen Sigler, Division of Education, Indiana University Kokomo, Kokomo, IN. Kaira Hayes, Department of Psychology, Fort Hays State University, Hays, KS.

Correspondence concerning this article should be addressed to Heath Marts at [e-mail].

Table 1
Descriptive Statistics for Academic Performance
Variables and LASSI Subscales

           N        M       SD

Colgpa     62     3.14      .67
Hsgpa      85     3.40      .49
ACT        76    22.75     3.96
ANX        87    24.95     7.07
ATT        87    31.36     4.98
CON        87    25.69     6.39
INP        87    26.97     5.29
MOT        86    31.35     5.08
SFT        87    23.62     6.63
SMI        86    28.10     5.66
STA        86    23.77     5.08
TMT        87    23.98     6.50
TST        87    28.78     5.97

Note: ANX = Anxiety; ATT = Attitude; CON = Concentration;
INP = Information Processing; MOT = Motivation; SFT = Self-testing;
SMI = Selecting Main Ideas; STA = Study Aids; TM = Time Management;
TST = Test Strategies.

Table 2
Correlations of Academic Achievement Variables with LASSI Subscales

           Col GPA     HS GPA      ACT     Final Grade

Col GPA                                        .33 *
HS GPA      .62 **                             .44 **
ACT         .34 **      .61 **                 .40 *
ANX         .01         .03        .29 *       .16
ATT        -.04        -.08       -.05         .04
CON         .11         .16        .14        -.04
INP         .29 *       .16        .13         .03
MOT         .51 **      .40 **     .13         .33 **
SFT         .43 **      .28 *      .18         .14
SMI         .01         .16        .21         .06
STA         .14         .09       -.15         .08
TMT         .17         .13       -.03         .13
TST         .07         .11        .21         .16

Note: ACT =ACT Composite score; ANX = Anxiety; ATT = Attitude;
CON = Concentration; INP = Information Processing; MOT =
Motivation; SFT = Self-testing; SMI = Selecting Main Ideas;
STA = Study Aids; TM = Time Management; TST = Test Strategies.

* p < .05

** p < .01

Table 3
Means and Standard Deviations on LASSI-2 Subscales for Successful
and Unsuccessful Students

        Successful (A or B)   Unsuccessful (D or U)
                N=41                  N=22
Scale          M(SD)                  M(SD)           F      p       d

 ANX        26.83(6.61)            23.64(7.28)        3.11    .08
 ATT        32.78(3.54)            31.73(4.66)        1.01    .32
 CON        26.95(6.27)            26.45(6.19)         .09    .76
 INP        28.07(5.02)            27.27(4.82)         .37    .54
 MOT        33.46(4.50)            29.68(4.87)        9.56   <.01   .82
 SFT        25.46(6.56)            22.64(6.64)        2.64    .11
 SMI        29.68(5.58)            27.95(5.21)        1.44    .24
 STA        24.68(5.36)            23.27(5.00)        1.04    .31
  TM        25.56(6.35)            22.50(7.99)        2.77    .10
 TST        30.93(4.89)            28.14(6.05)        3.95    .05

Note: ACT = ACT Composite score; ANX = Anxiety; ATT = Attitude;
CON = Concentration; INP = Information Processing; MOT = Motivation;
SFT = Self-testing; SMI = Selecting Main Ideas; STA = Study Aide
TM = Time Management TCT = Test Strategies.

Table 4
Correlation Coefficients and Standardized Function Coefficients

                         Correlation Coefficients      Standardized
                         with Discriminant Function      Function
                                                       Coefficients

Anxiety                              .336                   .819
Attitude                             .192                  -.732
Concentration                        .057                  -.923
Information Processing               .117                  -.464
Motivation                           .589                  1.180
Self-Testing                         .310                   .552
Selecting Main Ideas                 .229                  -.006
Study Aids                           .194                   .325
Time Management                      .317                  -.022
Test Strategies                      .379                   .135