PISA 2012 Results

Achievement and test scales
Scale creation

A partial credit IRT model was used to create the achievement scales. New scales were standardized with a mean score of 500 and standard deviation of 100 among OECD countries. Existing scales were scaled independently, and equated to previous scales using linear transformations.

PISA uses the imputation methodology usually referred to as plausible values (PVs). For each scale and subscale, five plausible values per student were included in the international database. Plausible values were imputed using a multi-dimensional model.


List of achievement scales



Mathematics content subscales

  • Change and relationships
  • Quantity
  • Space and shape
  • Uncertainty and data


Mathematics process subscales

  • Formulate
  • Employ
  • Interpret


Computer-based mathematics



Digital reading

Problem solving

Financial literacy

Questionnaire and background scales
Scale Creation

Simple indices were constructed by arithmetical transformation or recoding of one or more items.

Scale indices were constructed by the scaling of items; typically, scale scores for these indices were estimates of latent traits derived through item response theory (IRT) scaling of dichotomous or Likert-type items.

Only scale indices are listed below.

List of background scales

Student scale indices

  • Household possessions
    • Family wealth possessions
    • Cultural possessions
    • Home educational resources
    • Home possessions
  • Attitudes towards mathematics
    • Mathematics Interest
    • Instrumental Motivation for Mathematics
    • Subjective Norms in Mathematics
    • Mathematics Self-Efficacy
    • Mathematics Anxiety
    • Mathematics Self-Concept
    • Attribution to Failure in Mathematics
    • Mathematics Work Ethic
    • Mathematics Intentions
    • Mathematics Behaviour
  • Opportunity to learn (OTL)
    • OTL -  Content
      • Experience with Applied Mathematics Tasks at School
      • Experience with Pure Mathematics Tasks at School
      • Familiarity with Mathematical Concepts
      • Familiarity with Mathematical Concepts (Signal Detection Adjusted)
    • OTL – Teaching Practices
      • Teacher Behaviour: Teacher directed Instruction
      • Teacher Behaviour: Formative Assessment
      • Teacher Behaviour: Student Orientation
    • OTL – Teaching Quality
      • Math Teaching
      • Cognitive Activation
      • Disciplinary Climate
      • Teacher Support
      • Classroom Management
  • School climate
    • Teacher- Student Relation
    • Sense of Belonging to School
  • Attitudes towards school
    • Attitude towards School: Learning Outcomes
    • Attitude towards School: Learning Activities
  • Problem solving
    • Perseverance
    • Openness for Problem Solving
  • ICT familiarity
    • ICT Availability at Home
    • ICT Availability at School 
    • ICT Entertainment Use
    • ICT Use at Home for School-related Tasks
    • Use of ICT at School
    • Use of ICT in Mathematics Lessons
    • Attitudes Towards Computers: Computer as a Tool for School Learning
    • Attitudes Towards Computers: Limitations of the Computer as a Tool for School Learning


School Questionnaire Scale Indices

School leadership

  • Framing and Communicating the School’s Goals and Curricular Development
  • Instructional Leadership
  • Promoting Instructional Improvement and Professional Development
  • Teacher Participation in Leadership

School autonomy

  • School autonomy
  • Teacher Participation/Autonomy

School resources

  • Shortage of Teaching Staff
  • Quality of School Educational Resources
  • Quality of Physical Infrastructure

School climate

  • Student-related Factors Affecting School Climate
  • Teacher-related Factors Affecting School Climate
  • Teacher Morale
  • Teacher Focus


Parent questionnaire scale indices

  • Parents’ perception of school quality
  • Parental involvement in their child’s school
  • Student support
  • Parent attitudes towards mathematics
  • Mathematics career
Overview of key study results

Mathematics (primary focus)

  • Shanghai-China had the highest scores in mathematics, with a mean score of 613 points – 119 points above the OECD average – the equivalent of nearly 3 years of schooling.
  • Boys performed better than girls in mathematics in only 38 of the 65 countries and economies that participated in PISA 2012, and girls outperformed boys in 5 countries.
  • Out of all the countries and economies with trend data between 2003 and 2012, 25 improved in mathematics performance, 25 showed no change, and 14 deteriorated.
  • Between 2003 and 2012, Italy, Poland, and Portugal increased the share of top performers and simultaneously reduced the share of low performers in mathematics.



  • Shanghai-China, Hong Kong-China, Singapore, Japan, and Korea were the five highest-performing countries and economies in reading.
    • Of the 64 countries and economies with comparable data in reading performance throughout their participation in PISA, 32 improved their reading performance, 22 showed no change, and 10 deteriorated in reading performance.
    • Across OECD countries, 8.4% of students were top performers in reading, meaning that they were proficient at Level 5 or 6. Shanghai-China had the largest proportion of top performers – 25.1% – among all participating countries and economies.
  • Between the 2000 and 2012 PISA assessments, Albania, Israel and Poland increased the share of top performers and simultaneously reduced the share of low performers in reading. 



  • Shanghai-China, Hong Kong-China, Singapore, Japan, and Finland were the top five performers in science in PISA 2012.
  • Across OECD countries, 8.4% of students were top performers in science and scored at a proficiency Level of 5 or 6.
  • Between 2006 and 2012, Italy, Poland, and Qatar, and between 2009 and 2012, Estonia, Israel, and Singapore, increased the share of top performers and simultaneously reduced the share of low performers in science.
  • Boys and girls performed similarly in science; on average, this remained true in 2012.