Influence of students' background and perceptions on science attitudes and achievement

JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 23, NO. 3, PP. 177-187 (1986)

INFLUENCE OF STUDENTS’ BACKGROUND AND PERCEPTIONS ON SCIENCE ATTITUDES AND ACHIEVEMENT

R.A. SCHIBECI

School of Education, Murdoch University, Murdoch Western Australia 6150

J.P. RILEY, I1

Department of Science Education, University of Georgia, Athens, Georgia 30602

Abstract

The purpose of the study was to investigate the influence of students’ background and perceptions on science attitude and achievement. The data analysed came from Booklet 4 given to 17-year-olds during the 1976-1977 National Assessment of Educational Progress (NAEP) survey. Causal modeling procedures were used to analyze the data. In particular, the LISREL method which underlies the LISREL IV computer program, (Joreskog and Sorbom, 1978) was employed. The influence of five background variables (sex, race, home environment, amount of homework, and parents’ education) on three dependent variables (student perception of science instruction, student attitudes, and student achievement) was examined. Sex, race, and the home environment were shown to have substantial influence on student achievement in science. Further, two different models were tested: a model in which attitudes influence achievement and its converse (achievement influences attitudes). The data supported the first model, that is, attitudes influence achievement.

Influences on student achievement are many.and varied. Both school and non- school variables have been shown to have some influence (e.g., Averch, Carroll, Donaldson, Kiesling, & Pincus, 1974; Bridge, Judd, & Moock, 1979; Centra & Potter, 1980; Glassman & Biniaminov, 1981; Kremer & Walberg, 1981). Student background variables, perceptions of instruction, and student attitudes have all been shown to correlate to some degree with achievement. Previous research has identified a con- sistent relationship between certain background measures such as family size, ethnicity, socioeconomic status, and student learning (Coleman et al., 1966; Walberg & Rasher, 1979; Wiley & Hamischfeger, 1974). Welch, Anderson and Harris (1982), using National Assessment data, found that select background variables explained about 30% of the variance in math achievement of seventeen-year-olds.

0 1986 by the National Association for Research in Science Teaching Published by John Wiley & Sons, Inc. CCC 0022-4308/86/030177-11$04.00

178 SCHIBECI AND RILEY

Student perceptions of the science classroom have been shown to be positively correlated with achievement and attitude (Anderson, 1970; Walberg, 1969; Fraser & Fisher, 1982). Students perceptions of the learning environment account for 13 to 46% of the variance in science achievement and about 30% of the variance in attitudes toward science (Lawrenz, 1976).

In a review of studies examining the relationship between attitudes and general academic achievement, Kahn (1969) found correlations ranging from 0.51 to - 0.23. A quantitive synthesis of forty motivation and achievement studies reported that motivation, on the average, accounts for l l .4% of the variance in achievement (Ugurogla and Walberg, 1979). A meta-analysis of studies K to 12, focussed on attitudes and science achievement, reported a correlation of 0.14 (Willson, 1980). Another meta- analysis (Steinkamp and Maehr, 1983) reported correlations of 0.19 for males and '0.18 for females between affect and science achievement.

Previous research has been successful in identifying and measuring relationships among student background, perceptions of instruction, attitude, and science achievement. However, it has not had the same success in identifying the direction of causality in these relationships (Steinkamp & Maehr, 1983). Evidence for these directional links was sought in this study.

Purpose

This study used causal modeling procedures (see below) to analyze nonexpen- mental data to test causal inferences about hypothesized relationships among student background, perceptions attitude, and achievement. The research hypothesis tested is that student background variables influence student perceptions of science instruction; these in turn influence attitude which, in turn, influences achievement. To test this hypothesis, data from the 1976-1977 survey of 17-year-olds conducted by the National Assessment of Educational Progress were used.

Although NAEP data were not originally collected for establishing causal relationships, recent studies have demonstrated the feasibility and utility of this data source in ex postfucto investigations (Suchner & Barrington, 1980; Walberg, Haertel, Pas- carella, Junker, & Boulanger, 1981). One of the major strengths of using the NAEP data is that both cognitive and affective variables are assessed. Development of the items is well documented (NAEP, 1970) and represents state-of-the-art procedures. A second major strength of the NAEP data was the sampling procedures used which supports the claim that the sample of 17-year-olds was appropriate and representative of all 17-year-olds in the United States.

Causal Modeling

The study reported in this article had two concerns: first, the identification of variables which influence student outcomes in science (attitudes and achievement); second, to test a model in which attitudes influence achievement (and the converse model in which achievement influences attitudes).

The LISREL Model

In a structure equation model, unknown parameters are estimated so that the variances and covariances of the variables in the model match the data. Model parameters cannot be estimated without a computer program because no algebraic solution

INFLUENCE OF STUDENTS’ BACKGROUND 179

is available. Rather, the researcher provides initial estimates (“starting values”) which are refined through interactive procedures, least squares, and maximum likelihood.

The maximum likelihood method is used to estimate the parameters in the computer program LISREL IV (Joreskog and Sorbom, 1978). The model underlying this program can be used to estimate a variety of causal models, including those containing errors in latent variables (errors of measurement). Errors in equations (residuals) also must be allowed for. The LISREL model can be used to investigate path analysis (including recursive) models, and factor analytic models.

The LISREL model thus enables the researcher to analyze causal networks with latent variables and measurement errors. It assumes that there is a causal structure among a set of latent variables and sets of observed variables are manifestations of these latent variables or “hypothetical constructs. ” The LISREL model is described by (a) the specification of the structural relationships among the latent variables (the structural equation model); and the specification of the relationships among the latent and observed variables (the measurement model).

In the LISREL model, there may be any number of measured and unmeasured variables and these are expressed in the parameter matrices. The structural equation and measurement models in LISREL can be summarized succinctly in terms of these parameter matrices. Thus, the structural equation model is

where B is the coefficient matrix of the latent dependent variables (q); and r is the coefficient matrix of the latent independent variables (6); and, 5 is a random vector of residuals. The measurement model is

y = Ayq + E

x = A& + 6 (3)

where the parameter matrices A, and A, are the regression matrices of y (observed dependent variables) on q and x (observed independent variables) on g, respectively. The covariance matrices of g, 6, E, and S are, respectively, @, Y, 8, and 8.

Several assumptions underlie the LISREL model, and these have been highlighted by Munck (1979) as follows: the residuals are uncorrelated with the latent dependent variables; the errors of measurement are uncorrelated with the latent variables and with the residuals; the means for the latent variables and for the residuals are zero (that is, the variables are measured as deviations from their means); and the coefficient matrix Beta is nonsingular. The three equations given above, together with the five assumptions above, constitute the LISREL model.

Goodness-of-Fit

The goodness of fit of the model to the data is indicated by x2 values indicate a model which fits the data well, while relatively large x2 values indicate poorly fitting models. This is, of course, precisely the opposite of the usual situation. Normally, the researcher seeks a large value of x2 to indicate that a model which specifies a particular relationship differs from the null model (no relationship). In the present case, two variance-covariance matrices are compared, the matrix implied by the the-


oretical model and the observed matrix. Small x2 values indicate good fit of model and data (Carmines and McIver, 1981).

A large value of chi-square, compared to the number of degrees of freedom, reveals that the model does not fit well and suggests a relaxation of the model is required. That is, paths which are fixed may be relaxed, provided this is substantively meaningful. The results of the initial analysis will suggest ways in which this can be achieved. Large first-order derivatives of the fitting function with respect to fixed parameters reveal those fixed parameters which can be relaxed, with greatest pain in the estimated likelihood. The overriding consideration must at all times be that such relaxations of the model are substantively meaningful.

Relaxation of the model results in a new model with a smaller value for chi- square. “A large drop in the value of chi-square, compared to the difference in degrees of freedom, represents a real improvement” (Joreskog and Sorbom, 1978, p. 15).

Because the x2 statistic is known to be sensitive to sample size, alternative indices of fit have been proposed (Bentler & Bonett, 1980; Hoelter, 1983; Joreskog & Sorbom, 1984) for assessing the fit of a particular model to the data. However, as Gallini and Mandeville (1984) point out, it is more fruitful and convincing to make comparisons among models, rather than relying on the fit of one model to the data. That is, one can compare Model A (which tests hypothesis A) with Model B (which tests an alternative hypothesis, B). This provides a powerful test of the validity of the model in question, and was the method used in this study.

Procedures

NAEP Data

The data used to investigate the influence of student background on attitudes and achievement came from Booklet 4 given to 17-year-olds during the 1976-1977 Na- tional Assessment of Educational Progress (NAEP) survey. Booklet 4 was selected for use in this study because it contained items related to attitudes towards science/ science teaching and perceptions about science/science teaching as well as cognitive items related to science achievement.

Subjects

A total of 3135 individual 17-year-olds, who responded to the items in Booklet 4, were used as the available population in this study. These 3135 respondents rep- resented approximately 9% of all the 17-year-olds involved in the 1976-1977 NAEP survey. The other 91% took one of the other 10 booklets used in the survey. For the purposes of this study, two random samples of 11th graders were drawn from the available population. The first random sample comprised 350 students; the second sample, 323 students.

Instruments

The affective items in Booklet 4 consisted of statements to which respondents indicated agreement, or how often a behavior occurred. Essentially, the affective items used a Likert scale. The cognitive items required respondents to indicate “Yes”/


“No”/“I don’t know” or choose the correct answer from a group of alternatives; thus, the cognitive items were true/false or multiple choice in nature.

Using factor analysis and multiple regression, Napier and Riley (1985) identified several variables from data in Booklet 4 which correlated with an achievement scale derived from the same booklet. These variables were used in this study and are provided in Table I. The correlation matrix for these variables is given in Table 11.

Data Analysis

A causal model was generated and tested on a randomly selected subsample of the grade 11 population. The hypothesis guiding the generation of the causal models was that student background variables would influence students’ perceptions of their science instruction, which in turn would influence their attitude and achievement.

The conventions used in the LISREL IV manual (Joreskog and Sorbom, 1978) are followed in the presentation of the path analysis diagram. Observed variables are enclosed in rectangles: unobserved (latent) variables are enclosed in circles. The values of the parameter estimates are given above arrows for free paths. The standard error for each parameter provides an indication of the importance of the parameter. The ratio of the parameter estimate to the standard error has an approximate z distribution. Thus, the normal curve provides a means for deciding between large and small values (Bentler, 1980). The LISREL IV program calculates the ratio of the parameter estimate to the standard error, and labels the ratio a “T value.” In the path diagram, only paths for which I T I > 2 are included.

Variables designated as independent variables were presumed to influence directly three hypothesized latent variables: perception of science instruction (designated q J, attitude (q2), and achievement (q3). In path analysis terms, this meant that direct paths were allowed from each independent variable to each of these three latent dependent variables, q,, q2, and q3; further the causal chain ql + q2 + q3 was postulated to be part of the model.

The initial model generated using the basic assumption was relaxed after inspec- tion of the LISREL IV output from the initial analysis. Specifically, a path was freed if the first-order derivative of the fitting function with respect to the matrix element corresponding to that path was substantially different from zero, and freeing this path was substantially meaningful. The result of this model-building process was, finally, a model which appeared to fit the data best, as indicated by a large drop in the value for chi-square, compared with the drop in the number of degrees of freedom.

Results

Attitudes and Achievement

The initial model, derived from the research hypothesis, was refined until a final model was generated which best fit the data: x256 = 78.95 (p=O.O2) Bentler (1980) urged cross-validation as a way of establishing the validity of a causal model. He wrote: “Cross-validation provides an appropriate way of establishing whether empir- ically based model modifications represent genuinely valuable information about a model” (p.429). For this reason, the model shown in Figure 1 was tested on a different subsample of the original population; this resulted in x256 = 64.02 (p=O.21). This


3 Usefulness

4 Motivation

5 Enjoyment

TABLE I Variables Included in This Study: Description and Characteristics

Variable Variable Description and Characteristics

1 Teacher Support 6 Items Reliability = 0.76 “Have teachers encouraged you to be creative” Weighted scoring: 1 = never;

2 = seldom; 3 = sometimes; 4 = often; 5 = always.

2 Teacher Enthusiasm 5 Items Reliability = 0.75 “Most recent teacher makes science exciting” Weighted scoring: 1 = strongly disagree;

2 = disagree; 3 = nooption; 4 = agree; 5 = strongly agree.

Perceived usefulness of class (5 items) Reliability = 0.82 “Science classes are useful” Weighted scoring: 1 = strongly disagree;

2 = disagree; 3 = no option; 4 = agree; 5 = strongly agree.

8 items on science activities Reliability = 0.83 “When not required, how often have you read science articles in magazines” Weighted scoring: 1 = never;

2 = seldom; 3 = sometimes; 4 = often.

4 Items Reliability = 0.82 “How often are science classes fun” Weighted scoring: 1 = never;


6 Self Confidence 6 Items Reliability = 0.74 “Have science classes made you feel confident” Weighted scoring: 1 = never;



TABLE I (Continuedfrom previous page)

Variable Variable Description and Characteristics 7

8 9

10

11

12

13

14

15

Achievement

Sex Collrace

Home Environment

Material

Homework

Pared

TV Watched

Schools Attended

42 Items on science achievement Reliability = 0.82 Example “Which one of the following animals probably appeared on earth before the others? (a) Dinosaur; (b) Fish; (c) Horse; (d) Man; (e) Snake; (f) I don’t know Scored correct = 1; incorrect = 0 Coding: 0 = Male; 1 = Female. 1 = White 2 = Other Home Environment (4 items) Example Is there an encyclopedia in your home? Scoring: No = 0; Yes = 1 Composite variable indicating the number of material possessions in the home (e.g., tape recorder) Time spent on homework yesterday 1 = no homework; 2 = didn’t do it; 3 = < 1 hour; 4 = between 1 and 2 hours; 5 = more than 2 hours Parental education Highest level of parental education reported by respondent. Time spent watching TV yesterday “ 1 ” = none; “ 8 9 2 = 6 hours or more Number of schools attended.

cross-validation procedure lends confidence to the model, which shows a strong causal chain as follows: perception of science instruction (q,W attitudes (qJ+ achievement +(q3). In order to confirm the direction of the causal chain (among q,, q2 and q3), an alternative model was tested. In this model, the causal chain was postulated to be in the direction: ql (perceptions) +q3 (achievement+q, (attitudes); that is, achievement influenced attitudes, rather than the reverse. This model yielded x256 = 220.33 (p=O.O), indicating a poorly fitting model. There is thus some support for the prop- osition that attitudes influence achievement, rather than the reverse.

Injluence of Background Variables

The causal model which best fit the data revealed the influence of the five background variables (sex, race, home environment, amount of homework, and parents’ education) on the three dependent variables (student perceptions of science instruction, science attitudes, and science achievement). Three background variables appeared to have no substantial influence, and are therefore excluded from the path diagram. They were: the material background of the student, the amount of television watched, and the number of schools the student had attended.

Gender was an influence on attitudes and achievement, with females scoring lower in attitudes and lower in achievement. Racial background was an influence on achievement, with whites scoring higher. Home environment, homework, and parent’s educational background also had substantial influence as shown in Figure 1. (It will be remembered from the earlier discussion that only those parameters which gave values


TABLE I1 Intercorrelation Matrix for the Variables in Table I

1 2 3 4 5 6

1 2 3 4 5 6 7 8 9

10 11 12 13 14 15

1 .oo 0.54 0.44 0.35 0.51 0.43 0.11

- 0.08 -0.07

0.10 0.01 0.13 0.10

-0.01 0.04

7

1 .oo 0.40 0.31 0.46 0.22 0.10

- 0.02 -0.04

0.15 0.00 0.13 0.08 0.02 0.03

8

1 .oo 0.59 0.62 0.38 0.25

-0.07 -0.03

0.16 -0.03

0.21 0.10 0.02 0.04

9

1 .oo 0.58 0.38 0.31

-0.20 0.02 0.17

-0.03 0.22 0.15

-0.01 -0.03

10

1 .oo 0.48 0.22

-0.14 - 0.09

0.13 -0.05

0.17 0.13

-0.02 0.00

11

0.16 -0.22 -0.13

0.12 0.03 0.15 0.17

-0.04 0.01

12

7 8 9

10 11 12 13 14 15

1 .oo -0.25

0.30 0.30 0.10 0.20 0.38

-0.12 -0.01

13

1 .oo 0.08

-0.01 0.03

-0.01 -0.09 -0.03

0.09

14

1 .oo 0.15 1.00 0.17 0.13 1 .oo 0.02 0.16 0.13 0.16 0.35 0.17

-0.08 -0.06 - 0.04 - - 0.08 -0.00 0.02

15 13 1 .oo 14 -0.11 1 .oo 15 0.09 -0.03 1 .oo

1 .oo 0.13

-0.11 0.03

greater than 2.0 for the ratio, parameter estimate: standard error, were considered substantial.)

Discussion

The results of this study support previous research indicating relationships between such background variables as sex, ethnicity, parental education, and student achievement. The present findings extend previous research by highlighting the causal inference that perceptions of instruction influence student attitudes and that these attitudes in turn influence achievement.

Peterson and Carlson (1979), in their review of science education research for 1977, concluded that, “a much stronger argument can now be made for saying that achievement creates positive attitudes and probably not the reverse, as many of us have thought” (p. 499). This conclusion was based on a cross-lagged, panel, correlational study involving 70,000 students (Eisenhardt, 1977). The findings of the present study are not in agreement with those of Eisenhardt (1977). The present findings indicate evidence of a substantial causal link from attitude toward achievement. The

INELUENCE OF STUDENTS’ BACKGROUND 185

ACHIEVEMENT

Fig. 1. Causal Model for 17-year-old NAEP data (1976-1977).

results also indicate that students’ perceptions of instruction influence these attitudes.

The results of this study must, however be interpreted with caution. The NAEP data were not gathered to test specific research hypotheses. Thus, it is reported in the present study that attitudes influence achievement, rather than the reverse. This sug- gestion would be more persuasive if we were able to impose a chronological order on the variables of the study. It is not possible, however, to do this because of the nature of the NAEP data.

In generating the causal model of Figure 1, the independent variables are shown on the left-hand side of the path diagram. This implies a chronological sequence, which is based on the assumption that these background variables are reasonably stable over time. This assumption is certainly true for some variables (such as sex and race) and probably true for others (such as home environment and material possessions). No unequivocal claim (as indicated above) may be made for the chronology of the three latent variables, student perceptions of science instruction, student attitudes, and student achievement. The direction of the causal chain, however, suggests the chronology: perceptions + attitudes + achievement.

The results of this study support the view that what science teachers do in the classroom does make a difference in student attitude and achievement. The implication of these results for teachers is that they cannot afford to overlook student attitudes. The science teacher who teaches the subject and lets attitudes fall where they may, or is satisfied to let attitudes follow achievement may be doing a disservice to students by making instruction less effective than it could be.

Assuming that student perceptions of their instruction are valid indicators of teaching behavior, then teachers who exhibit such instructional behaviors as encour- aging students to be creative and trying to make science more exciting are more likely to have a positive influence on student attitudes. These attitudes, in turn, can have a positive influence on student achievement.


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Manuscript received April 2, 1985

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Influence of students' background and perceptions on science attitudes and achievement