Literacy and numeracy at work

Publication Details

This report looks at the use of literacy and numeracy skills at work, and how this relates to the skills and education of employees. It uses data from the Adult Literacy and Lifeskills (ALL) survey to look at how well employees’ skills match the literacy and numeracy practices that they undertake at work. It looks at how skills and education relate to different sets of practices, such as financial literacy and numeracy. It also identifies which groups of employees are more likely to have a skills shortfall or skills excess, and some of the barriers to further training for those with a skills shortfall.

Author(s): David Earle, Tertiary Sector Performance Analysis and Reporting, Ministry of Education.

Date Published: February 2011

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Appendix C: Regression models

The regression results shown in chapter 4 are from ordinary least squares regressions that use the job practices variable as the dependent variable and age, qualifications and document literacy as the independent variables. First language was also tested in each model and was only significant in one of them.

The models were run using PROC SURVEYREG in SAS. The procedures were run for each plausible value of document literacy, using the jack-knife option and the 30 replicate weights in the ALL data set. The estimates are the average of the estimates across the results for the five plausible values. The standard error of the estimate was calculated as the square root of the sample variance and imputation variation. The sample variance is the mean of the variances across the five plausible values. The imputation variance is the variance of the estimates. The standard errors were then used to calculate the p-values using the t-test.

Table 8: Regression model for financial literacy and numeracy job practices
Parameter Estimate p-value
Intercept 1.237
Document literacy 0.224 0.000
Age 0.066 0.001
Age squared -0.001 0.001
Qualification = None 0.000
Qualification = School 0.341 0.000
Qualification = Certificate 1-3 0.168 0.026
Qualification = Certificate 4 0.213 0.022
Qualification = Diploma 5-7 0.259 0.005
Qualification = Bachelors degree 0.354 0.000
Qualification = Postgraduate 0.363 0.000
Gender = Male 0.151 0.001
Gender = Female 0.000
Document literacy * Male 0.152 0.006
Document literacy * Female 0.000
Table 9: Regression model for intensive literacy job practices
Parameter Estimate p-value
Intercept 1.901
Document literacy 0.260 0.000
Document literacy squared -0.077 0.000
Age 0.053 0.001
Age squared -0.001 0.002
Qualification = None 0.000
Qualification = School 0.351 0.000
Qualification = Certificate 1-3 0.179 0.015
Qualification = Certificate 4 0.419 0.000
Qualification = Diploma 5-7 0.501 0.000
Qualification = Bachelors degree 0.581 0.000
Qualification = Postgraduate 0.665 0.000
Gender = Male 0.102 0.010
Gender = Female 0.000
Table 10: Regression model for practical literacy and numeracy job practices
Parameter Estimate p-value
Intercept 2.307
Document literacy 0.156 0.000
Document literacy squared -0.068 0.000
Age 0.038 0.035
Age squared 0.000 0.034
Qualification = None 0.000
Qualification = School 0.282 0.000
Qualification = Certificate 1-3 0.167 0.047
Qualification = Certificate 4 0.357 0.001
Qualification = Diploma 5-7 0.531 0.000
Qualification = Bachelors degree 0.489 0.000
Qualification = Postgraduate 0.586 0.000
Gender = Male 0.516 0.000
Gender = Female 0.000
Qualification = None * Male 0.000
Qualification = School * Male -0.163 0.033
Qualification = Certificate 1-3 * Male 0.030 0.386
Qualification = Certificate 4 * Male 0.030 0.382
Qualification = Diploma 5-7 * Male -0.325 0.002
Qualification = Bachelors degree * Male -0.321 0.009
Qualification = Postgraduate * Male -0.347 0.016
First language = Other -0.127 0.018
First language = English 0.000