significant difference in Boehm concept attainment based on gender.
To answer research question two, a t -test was used to show the independent
means for the pre-test, posttest and gain scores based on gender. Girls had significantly
82 However, no significant differences (p = .69) were found based on gain scores. This
combination of findings provided partial support to retain the null hypothesis. The data
elements considered to answer research question two were student’s Boehm raw score and gender. The t-test for independent means was the appropriate test because it was used
to compare a dichotomous independent variable (gender) with a continuous dependent
variable (Boehm raw scores) (Ravid, 2010). In addition, the point-biserial correlation was
the Pearson product-moment correlation between the dichotomous independent variable
(gender) with the continuous dependent variable (Boehm raw score). It was used as a
supplemental test to the t-test because it provided a measure of the strength of the
relationship (effect size) between the two variables, gender and Boehm raw scores. These
findings are displayed in Table 8.
Table 8
Gender and Boehm Score ‒Comparison of Scores Based on Student Gender (n = 335) _______________________________________________________________________
Score Gender n M SD rpb t p ________________________________________________________________________
Spring 2010 Raw Score
.12 2.27 .02 Female 157 31.09 4.90
Male 178 29.68 6.28
Winter 2011 Raw Score
.10 1.91 .06 Female 157 40.61 5.22
Male 178 39.44 5.86
Gain Score: Winter minus Spring
.02 0.40 .69 Female 157 9.52 4.51
83 Question 3. Is there a correlation between Boehm posttest raw scores and low
performance on AIMSweb TEL assessments? Null hypothesis three assumed no
significant correlation between Boehm raw scores and low performance on AIMSweb
TEL assessments.
Table 9 displays the relevant Pearson correlations. The Boehm posttest score had
significant positive correlations with all eight AIMSweb TEL assessment measures at the
p < .001 level. This combination of findings provided support to reject the null hypothesis. The rationale for using Pearson correlation was because this test is used to
determine the extent of the linear relationship between two continuous variables (Ravid,
2010), students’ Boehm raw scores and their raw scores on the four AIMSweb TEL assessments.
84 Table 9
Relationship of Boehm to AIMSweb TEL assessments‒Pearson Correlations between Boehm Posttest Scores with AIMSweb TEL assessments (n = 335)
AIMSweb Assessments a Boehm Posttest
LNF Winter .30 LNF Spring .31 LSF Winter .26 LSF Spring .25 PSF Winter .29 PSF Spring .32 NWF Winter .33 NWF Spring .35
Note. All correlations were significant at the p < .001 level. a
Codes: LNF = Letter Name Fluency; LSF = Letter Sound Fluency; PSF = Phoneme Segmentation Fluency; NWF = Nonsense Word Fluency.
Additional findings. Additional analyses included the use of two multiple
regression prediction models, which tested two primary outcome variables, the amount of
gain in students’ scores (Table 10), and students’ winter posttest scores (Table 11). Each of the regression models was focused on the amount of gain on each outcome variable.
The primary independent variable for this research was whether a student
received AIMSweb. Additional analysis, through use of the multiple regression models,
85 and winter score) existed, while controlling for gender, special education, and age. This
was done to rule out other possible explanations for the student’s academic performance. The multiple regression model predicting the student’s gain score was not
significant (p = .80) and accounted for 0.5% of the variance in the dependent variable.
Inspection of the beta weights found no significant predictors.
Table 10
Prediction of Boehm Based on Gender, Special Education Status, & Age‒Prediction of Gain Score Based on Selected Variables (n = 335)
Variable B SE β p
Intercept 10.74 6.94 .12
Gender a 0.15 0.61 .01 .81
Received Special Education Services b 0.39 0.84 .03 .64
Age (in months) -0.02 0.08 -.02 .77
Received AIMSweb b 0.82 0.72 .06 .25
Full Model: F (4, 330) = 0.41, p = .80. R2 = .005. a
Gender: 1 = Female 2 = Male. b
Coding: 0 = No 1 = Yes.
In Table 11, the posttest winter score was significantly predicted (p = .001) and
accounted for 8.2% of the variance in the dependent variable. Inspection of the beta
weights found that the winter posttest score was higher for students who did not receive
86 Table 11
Special Education and Non Special Education Predictions on the Boehm‒
Prediction of Winter Score Based on Selected Variables (n = 335)
Variable B SE β p
Intercept 35.03 6.82 .001
Gender a -0.76 0.60 -.07 .21
Received Special Education Services b -3.84 0.82 -.26 .001
Age (in months) 0.07 0.08 .05 .38
Received AIMSweb b 1.00 0.71 .07 .16
Full Model: F (4, 330) = 7.32, p = .001. R2 = .082. a
Gender: 1 = Female 2 = Male. b
Coding: 0 = No 1 = Yes.
Summary
This study examined the relationship between the universal screening tool of the
Boehm, the progress-monitoring tool of AIMSweb and the impact that those assessments
had on kindergarten student achievement. School records for 335 students in 23
elementary schools were used for this study. Gains in student achievement were not
related to participation in AIMSweb or the student’s gender. However, the posttest
Boehm scores were significantly related to all eight AIMSweb assessment scores. The
student’s gain score could not be predicted based on the combination of gender, special education status, age, or whether they used AIMSweb. However, the student’s posttest
87 CHAPTER 5- SUMMARY, RELEVANCE OF LITERATURE, IMPLICATIONS,