SURVEY PROPOSAL

John Kuri

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Contents 1. CONTACT SUMMARY ............................................................................................................................ 4

2. OVERVIEW ............................................................................................................................................. 5

3. STRATEGIES ........................................................................................................................................... 6

3.1 Engagement and training of enumerators .................................................................................. 6

3.1.1 Establishing /accessing networks for engaging enumerators ............................................ 6

3.1.2 Contracting and arranging allowances for enumerators .................................................... 6

3.1.3 Training of enumerators ...................................................................................................... 6

3.1.4 Recommendations for improvement in LJSS training manual ........................................... 7

3.1.5 Implementation Schedule .................................................................................................... 7

3.2 Quality control and security of enumerators .............................................................................. 7

3.2.1 Quality Control of live surveys ............................................................................................. 7

3.2.2 Security ................................................................................................................................. 8

3.2.3 Gender and age ratios .......................................................................................................... 8

3.2.4 Field report ........................................................................................................................... 8

3.3 Collation and verification of survey ............................................................................................. 8

3.3.1 Sorting of Survey ................................................................................................................. 8

3.3.2 Inventory .............................................................................................................................. 8

3.4 Data entry and stratification ........................................................................................................ 8

3.4.1 Data entry ............................................................................................................................. 8

3.4.2 Quality control of data entry ............................................................................................... 9

3.4.3 Provision of data files ........................................................................................................... 9

3.4.4 Report ................................................................................................................................... 9

4. ADDITIONAL COMPONENTS ................................................................................................................. 9

4.1 Data Analysis ................................................................................................................................ 9

4.1.1 Disaggregation and interpretation into EXCEL and SPSS of data ....................................... 9

4.1.2 Data analysis and chart development ................................................................................. 9

4.1.3 Comparative analysis ......................................................................................................... 10

4.2 Reporting of data analysis results ............................................................................................. 10

4.2.1 Report draft ........................................................................................................................ 10

4.2.2 Editing by LJSS .................................................................................................................... 10

4.2.3 Submission of finalized report ........................................................................................... 10

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5. APPENDICES ........................................................................................................................................ 10

Appendix 1 – Professional Attachments ................................................................................................. 10

5.1 Resume of Lead Researcher – John Kuri .................................................................................... 11

5.2 References .................................................................................................................................. 14

A .............................................................................................................................................................. 14

B .............................................................................................................................................................. 15

5.3 Resume of Assistant Researcher – James Kuande .................................................................... 16

5.4 IPA Registration Certificate ........................................................................................................ 20

5.5 Previous Work Samples.............................................................................................................. 21

5.5.1 School Based Diet Survey .................................................................................................. 22

5.5.2 School Based Substance Abuse Survey .............................................................................. 23

5.5.3 Teacher Assessment Survey ............................................................................................... 26

5.5.4 Statistical Analysis of Grade 10 Exams (2005) ................................................................... 27

5.5.5 Statistical Analysis of Grade 12 Mock Exams (2005) ......................................................... 27

5.5.6 Quality Teaching Methods Training (2006) ....................................................................... 27

5.5.7 Trend analysis of Grade 12 HSC Exam marks ( 1990 – 2006) ............................................ 27

5.5.8 Trend analysis of Grade 12 HSC Exam marks (1990 – 2008) ............................................. 27

5.5.9 Data bank ............................................................................................................................ 27

5.5.10 Analysis of Port Moresby’s Rainfall Data .......................................................................... 27

5.5.11 Initial Selection Criteria for Enumerators .......................................................................... 36

5.5.12 Quality Control Check List .................................................................................................. 37

Appendix 2 .............................................................................................................................................. 39

6.5 Implementation Schedule ................................................................................................................ 39

Appendix 3 .................................................................................................................................................. 41

6.6 Estimated Budget ....................................................................................................................... 41

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1. CONTACT SUMMARY

Lead Researcher OPTIMAX - Principal Consultant Name: John Kuri P. O. Box 3657 Boroko NCD Ph: 340 2457 Mobile: 728 11265 E- mail: johnkuri@ymail.com Assistant Researcher Name: James Kuande P. O. Box 3657 Boroko NCD Ph: 340 2457

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2. OVERVIEW

Optimax Ltd. is a research consulting firm specializing in a range of activities. Our prime focus is to identify systems in Papua New Guinea that need to be researched with critical data analysis for both quantitative and qualitative data. We provide an insight into inherent data generating mechanisms that might not be observable. We specialize in data collation, analysis, forecasting and. In addition we are able to conduct polls and design data management systems with capability to merge and migrate from one platform to another. By operating as an independent consultancy, the information we provide is accurate and unbiased and will be useful to the organization requesting it. We place a high priority on the use of technology for the efficient processing and reporting of information gathered.

2.1 Lead Researcher John Kuri is a graduate statistician from University of Papua New Guinea (UPNG). He also has a Post Graduate Diploma in Education from Divine Word University (DWU) and a Certificate IV in Assessment and Training from Southbank TAFE, Queensland. Mr. Kuri is currently working on his research thesis on “Economic Convergence of South Pacific Countries”. While being the Head of Department of Mathematics at Port Moresby Grammar School, he compiled and analyzed grade 10 and 12 HSC marks1 of the national exams. Specific skills are listed in Table 1.1 of Appendix 5. He is currently one of the directors of OPTIMAX Ltd.

2.2 Assistant Researcher James Kuande Boyd is a graduate statistician from UPNG (2004). He has vast experience with data compilation and analysis since working with the Department of Agriculture and Livestock (DAL). He has experience in data gathering missions.

1 See Appendix 1 – 6.5

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3. STRATEGIES

3.1 Engagement and training of enumerators

3.1.1 Establishing /accessing networks for engaging enumerators We intend to establish a network that will enable the engaging of personnel who will be reasonably qualified to carry out the task of enumeration. Our focus to access enumerators will be on organizations such as Non – governmental organizations (NGOs), Community Based Organizations (CBOs), tertiary institutions, Churches and Sporting affiliations that exist within the bounds of the urban community of concern. This does not exclude provincial / urban authorities who may have access to data collecting teams. In addition individuals who are educated but waiting for employment will also be considered. The logical step will be to network provincial/urban authorities and seek their advice on where to recruit. Networking will be done in conjunction with relevant authorities. Candidates will undergo an initial screening process2 where they will have to satisfy certain criteria as part of quality control. The chosen twenty will be notified, but at least three will be kept on stand – by.

3.1.2 Contracting and arranging allowances for enumerators Once enumerators are notified of their engagement, a Memorandum of Understanding (MoU) will be drafted. The MoU will outline the

i. time frame of their engagement ii. hourly rate at which their allowance will be calculated3

iii. terms and conditions of receiving their allowances iv. protocols they are to follow when raising queries and issues v. code of ethics needed to be up – held when conducting such surveys.

Each and every enumerator will sign to signify their participation in the survey. Supervisors will

be appointed from the batch. These two will also conduct the enumeration. The supervisors will assist the lead researcher during field work.

3.1.3 Training of enumerators The enumerators will be trained in the initial stages of the survey. Hopefully we intend to complete the training in a week. The training will include practical sessions where enumerators will have to demonstrate competency in

i. Code of ethics ii. Quality control

iii. those expressed in the LJSS training manual iv. Fundamental elements of survey techniques

Basically, a training needs analysis (TNA) will be conducted to garner the general level of the enumerators in the above mentioned items. This is to avoid repetition of points that the

2 See Appendix 1 – 6.5.11 3 To follow rate set by the Minimum Wages Board

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enumerators might already know. However, key areas will be re-enforced to ensure the accepted procedures are adhered to.

3.1.4 Recommendations for improvement training manual There are various methods of running a survey. It is a professional field of its own; as such methods are reviewed and modified constantly. The basic principal though, still remains. With regard to the task at hand. After the completion of the training, different forms of assessment will be submitted via a report. There will be an assessment from the enumerators and one from the lead researcher (who will conduct the training). The assessment forms will evaluate the training manual, competency of the trainer and the content being delivered. The trainer will also submit an evaluation of the manual. From these evaluations, recommendations will be made if there is a need to modify the training manual. 3.1.5 Implementation Schedule See Appendix 2 – 6.5

3.2 Quality control and security of enumerators 3.2.1 Quality Control of live surveys Maintaining quality control of live surveys is an important part of the task. We intend to ensure quality control in the following manner

i. Media release – radio stations will be notified to broadcast the time and place where the survey will take place. Provincial and local authorities will be informed including the police.

ii. Mobility - Keep a tight schedule with transport of enumerators to and from the different clusters. Transport to be provided by reliable company

iii. Selective engagement – prior knowledge of clusters is important as enumerators can be allocated households to survey. This will reduce the possibility of bias( enumerators interviewing people they already know) and reduce security risks( we will not send female enumerators into hot spots)

iv. Daily briefing - enumerators to be briefed before being deployed to the various clusters v. Uniforms – enumerators to wear t – shirts promoting the survey

vi. Back – up enumerators – three individuals will be kept on standby incase one of the enumerators is unable to make it on the day. This will be mentioned in the report.

vii. Competency – all enumerators will have successfully completed the training. viii. Daily quota – enumerators to keep to daily quota of interviews. Survey questionnaires to be

in adequate numbers for distribution during morning briefing and to meet gender and age ratios

ix. Communication – all enumerators must have a mobile phone x. Submission of response sheets- enumerators to return all at the end of day

xi. End of day briefing – enumerators to report back on their day. xii. Quality control check list - A daily quality control check list4 to be submitted for the final

report submission.

4 See sample in Appendix 2 – 6.5.12

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3.2.2 Security Security of enumerators is vital due to the nature of the survey being conducted. As part of our security measures, we will

i. Insist that each enumerator has a mobile phone ii. Top up credits of K5.00 per week will be distributed to each enumerator

iii. The provincial police commander will be notified of the purpose of the survey and, where and when it will be taking place. The survey schedule to be provided.

iv. Lead researcher to constantly monitor their movements v. Lead researcher to have 2 – radio with police frequency

vi. Field supervisor to have 2 – way radio with police frequency vii. Selective engagement – Refer to point (iii) of 3.2.1 These issues to be reported on a daily basis.

3.2.3 Gender and age ratios Gender and age ratios will form part of the quality control checklist as stated in point (viii) of 3.2.1. 3.2.4 Field report A field report will be submitted to stakeholders at the conclusion of the field exercise. This will be a comprehensive document describing what happened in a logical framework.

3.3 Collation and verification of survey 3.3.1 Sorting of Survey All completed survey forms will be sorted and ordered by location and household ID, straight after the completion of the survey. 3.3.2 Inventory An inventory of household IDs to be completed and submitted in the form of a report.

3.4 Data entry and stratification 3.4.1 Data entry Before the survey is carried out the lead researcher will code the data. Once the survey has ended, all data will be decoded and

i. Entered onto EXCEL and SPSS ii. The entry will then be stratified according to

a) Cluster (location) b) Household ID c) Age

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d) Gender e) Crime type f) Other possible stratifications indicated by the survey questionnaire

3.4.2 Quality control of data entry Check and balances will be in place to ensure the right figures are entered into the appropriate stratifications. SPSS and EXCEL offer easy commands to count according the required delineation. These commands will be executed and verified with the manual count. In addition, there will be a second checker to confirm the entries.

3.4.3 Provision of data files Once the data has been entered, stratified and double checked for data integrity, they will be filed according to specifications set by LJSS and presented to LJSS 3.4.4 Report The report will describe the handling of raw data, from coding to decoding, stratification and filing. Basically the processes described from points 3.3 to 3.4 will be discussed.

4. ADDITIONAL COMPONENTS

4.1 Data Analysis Data analysis and interpretation is a critical element of any data gathering activity. It is important to keep in mind that the statistics in this survey represent some phenomena relating to people. We try to keep that in mind when we do these analytical exercises. In the best interests of data integrity, those responsible for gathering the data should analyze and interpret them. Thus, OPTIMAX Ltd will complete the survey by engaging in the data analysis and interpretation. In addition, our staff are competent researchers and skilled in that area. 4.1.1 Disaggregation and interpretation into EXCEL and SPSS of data The disaggregated data will be entered both into EXCEL and SPPS files. The formats of entry into the two software will be homogeneous to maintain consistency. However, different test will be conducted. Thus increasing efficiency for the promulgation of results and reporting. 4.1.2 Data analysis and chart development We will engage in two types of data analysis on the stratified data. The first will be descriptive. This is where we will display the various strata by graphs and charts. Percentages and ratios can be determined from these initial analyses. We can also determine basic statistics such as the average, mode, median, variance, range etc… The second part of the data analysis will involve inferential statistics, where different tests will be carried out. This will determine

I. Trends – linear regression to determine the strength and direction of trends

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II. Association/correlation– association analysis to determine the relationship between different variables( Contingency tables)

III. F – test – to determine the nature of the distribution of the variable in question IV. Time series analysis – to find out if certain occurrences are random or not

Other tests can be conducted upon investigation of the survey questionnaire. All these tests will broaden the understanding of the phenomena under study. This investigation should bring into focus the matter at hand.

4.1.3 Comparative analysis A comparative analysis of variables can be done if data entry stratifications are consistent. Thus previous results will be studied to observe the strata and classifications. And also the methods of determining key statistics. In addition, the same software will be used when doing the comparative analysis. The comparative analysis can be done for both basic and inferential statistics. We will tabulate the variables from previous studies against the current survey. Useful comments can be made, especially when running tests of association and correlation. This can be done to see whether changes can be attributed to specific variables.

4.2 Reporting of data analysis results 4.2.1 Report draft A draft of the report will be submitted to the stakeholder. The format will follow formats of previous report and will contain the complete results of the comparative analyses, including a time series report of the findings. 4.2.2 Editing by Stakeholder The first draft of the report will be submitted to the stakeholder for comment and quality control. Changes will be discussed with the stakeholders to ensure purpose of survey is fulfilled. 4.2.3 Submission of finalized report Once draft is finalized, OPTIMAX researchers will complete the final report with recommendations. This will be submitted before the agreed date.

5. APPENDICES

Appendix 1 – Professional Attachments

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5.1 Resume of Lead Researcher – John Kuri

KURI John

Personal Details

Experience

2006- 2009 Port Moresby Grammar school

Head of Department (Mathematics)/ School Statistician

2007 Port Moresby Grammar School - conducted training to 200 participants on Outcomes Based Education - presented an analysis of grade 12 performance in the 2006 examinations to the Board of Management. 2006 Port Moresby Grammar School

Acting Head of Department ( Mathematics)

2004 – ,Port Moresby Grammar School, Port Moresby NCD,

Coordinator for Grade 12 Mathematics A.

Extra- curricular activities include

- Conducted in – service to the whole school on the use of Quality Control Techniques in monitoring student performance levels

- Statistical analysis of student performance in exam classes.

- In – house surveys, collecting and compiling of data (both quantitative and qualitative) for the

P.O.Box 3657 Boroko NCD Papua New Guinea

Ph: 340 2457

E-mail : johnkuri@ymail.com Digicel :72811265

Name: John Surname: Kuri Date of Birth: 3rd May 1975 Sex: Male Height: 169 cm Weight: 78 Kg Marital Status: Married Religion: Christianity (Catholic) Province: Simbu Province Nationality: Papua New Guinea

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purpose of analysis.

2002 – 2003 , University of Papua New Guinea/ Maths Discipline, NCD

Tutor (Part – Time)

Tutoring

Applied Finite Maths, Foundation Maths, Network Programming, Statistical Methods and Computer Applications Software. Each during different periods of the academic year.

1997, North Simbu Rural Development Project (Ausaid) , Kundiawa, Simbu Province

Administration /Procurement Officer

Ensured companies on contract were paid.

Kept track of supplies for all funded projects.

Made sure all vehicles were roadworthy.

Education

& Training

2008 – Port Moresby Grammar School

Post Graduate Diploma in Education (DWU)

2006 , Port Moresby Grammar School, Port Moresby , NCD

Certificate IV in Assessment and Workplace Training (TAFE, South bank, Queensland, Aus.)

Trained to train trainers in all fields.

2004 , Port Moresby Grammar School, Port Moresby , NCD

Certificate of Participation

HIV/AIDS TEACHER TRAINING WORKSHOP

2003 , University of Papua New Guinea , Port Moresby , NCD

Post Graduate Diploma in Science (in Applied Statistics )

Research Topic : The Theory of Economic Convergence in the South Pacific Region

Reading Course : Data Analysis

Course work : Econometrics , Numerical Methods I and II

2003 , University of Papua New Guinea

Certificate in Introduction To University Teaching

2001 University of Papua New Guinea , Port Moresby , NCD

Bachelor of Science ( Major : Statistics , Minor : Computer Science)

Courses taken in 3rd & 4

th Year.

Statistics Strand : Statistical Methods , Probability Distributions , Statistical Inference , Regression Analysis , Quality Control , Design & Analysis of Experiments , Stochastic Processes, Sampling and Survey Methods, Non – Parametric Statistics.

Computer Science Strand : Introduction to computing , Computer Applications Software, Structured Programming I, II &III, Network Programming, Introduction to Database, Database Management, Systems Analysis, Special Topics in Computer Science (Project) , Information Technology.

Mathematics Strand : Foundation Maths , Algebra and Geometry, Linear Algebra , Abstract Algebra , Group Theory , Calculus I,II,III,IV,V & VI, Ordinary Differential Equations I.

Hobbies Music programming, sound engineering, and watching movies.

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Language

Awards

Interests

Ambition

References

Languages I speak fluently are English, Pidgin and my own vernacular, Kuman.

1989, Grade Eight, Rosary High School , Kondiu , Simbu Province

Australian WesPac Mathematics Competition (Junior Division)

Certificate of Credit

1991 , Grade Ten , Rosary High School , Kondiu, Simbu Province

Australian WesPac Mathematics Competitions (Junior Division)

Certificate of Distinction

1992, Grade Eleven, Aiyura National High School, EHP

Australian WesPac Mathematics Competitions (Senior Division)

Certificate of Credit

1993 , Grade Twelve , Aiyura National High School, EHP

Australian WesPac Mathematics Competitions (Senior Division)

Certificate of Credit

Australian National Chemistry Quiz (Senior Division)

Certificate of Distinction

Applied statistics in research, quality control and data mining in Information Technology.

I hope to be recognized as a data analyst one day.

Referees

Mrs. M. Olley Principal Port Moresby Grammar School Phone: 323 6577

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Email : principal@pomgrammar.ac.pg Mr. Michael Luff Deputy principal Administration Port Moresby Grammar school Phone : 323 6577 principal@pomgrammar.ac.pg Dr. Shafiqur Rahman University of Papua New Guinea School of Natural & Physical Sciences Mathematics Discipline Senior Lecturer Phone : 326 7639 Email : Shafi.Rahman.@upng.ac.pg Mr. Stanis Sesega Port Moresby Grammar School Deputy Principal – Academic Phone : 323 6577 Email : deputyacademic@pomgrammar.ac,pg

5.2 References A

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B

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5.3 Resume of Assistant Researcher – James Kuande

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Profile Well experienced in conducting Surveys- produced technical Statistical analysis

and reports.

Designing Survey methodologies-designing questionnaire Developing coding and decoded the coding system for surveys. Mastered Sampling Surveys and Sampling Techniques.

Quantified qualitative data and developed Mathematical/Statistical models-Did projections/Forecasting.

Conducted public opinion poll.

Conducted participatory Rural Appraisal

Key Skills

Better interpersonal communication skills and have the ability to adapt to new environment in short span of time.

In dept knowledge in Microsoft Excel and master tool pack data analysis

and other in built functions, Competency in Statistical modelling-Forecasting/prediction and developing trend lines.

Competency in Sample surveys and sampling techniques, appropriate

calculation of the standard errors of the sample data that can infer about the population.

Master Probability Distribution and Estimation and Testing Hypothesis

.Master the Developing information system-PNGINFO (statistical data base software developed by United Nation for PNG development indicators toward millennium development goals (MDG) and MTDS (by GoPNG)

Have the ability to do research and can quantify qualitative variables and can do numerical analysis to explain the relationships.

In dept knowledge in statistical quality control in industrial products as

well as services orientated. Tabulation, Developing Graphs/Charts-using Excel and Minitab. Strength in design and analysis of Experiments-ANOVA (Analysis of

Variances). Strength in analysing multivariate data. Strength in trend analysis.

Strength in Market research. Strength in analysing Time Series Data. Strength in numerical analysis

Employment History

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2008-2010: Acting Chief Statistician- Policy, Planning & Economic Research Division- Department of Agriculture& Live

Stock (DAL). 2004-2007: Assistant Statistician- Policy, Planning & Economic

Research Division- Department of Agriculture& Live

Stock (DAL).

Duties Performed Routinely collecting, collating and compiling Agricultural Statistics and updating

the data-Base.

Producing Annual Agriculture Statistical Hand Book Dissemination of statistical information to a number of regulars or ad hoc

publications to the top management and other line agencies and on request to

Food and Agriculture Organization of the United Nation (FAO). Provided technical assistance to provincial agriculture in designing and

conducting Agriculture Surveys, data storage system, quality control of the

surveys, Sample Adequacy and data validity checks etc. Assisting the Chief Economic advisor in writing up the Department’s Corporate

Plan, writing up the ministerial brief and ministerial statement to the parliament

on the agriculture sector performance. Provided Technical and General Statistical Services to clients and relevant line

agencies when requested. Developed the Strategic work plan for the Statistic Section. Did Economic Projections/Forecasting (Estimates) for the Agriculture sector

performance in the economy Produced graphs-charts; tabulations, developed the trend line of all the

Agricultural export commodities and basing on the time series data made

available, did projection to foresee the future trend and advised the superiors. Produced several detailed statistical report to the DAL top management team Evaluated and measured the progress of Agriculture sector performance through

statistical analysis against the existing agriculture policies. Wrote letters to the commodity boards and other relevant line agencies and

collected agricultural statistics.

Compilation and collating of agricultural and economic statistics. Analysing the trend of all Agricultural Export Commodities.

Additional Experiences while in the Public Sector

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Being the Member of the User Advisory Committee (UAC) for 2006 Demographic and Health Survey (NSO).

Attended Service Improvement Program (SIP) workshop for the public sector-ran by the Prime Minister’s Department.

Attached to Marketing Unit and attended Several WTO and other trade related

meetings and reported to the Trade Advisor. Initiated DAL to be the key member of the implementing partners in filling up

the data gaps that has left out when evaluating the progress of Millennium

Development Goal (MGD). Current member of Technical Working Committee for Millennium Development

Goals (MDGs)

August 2008-2009- Part-time attachment to World Bank Project-Productive Partnership in Agriculture Project (PPAP) within DAL.

Qualified trainer for PNGInfo-(Statistical database software –Socio – Economic

Indicators-developed by UN)

Education Background.

1999-2002: Bachelors Degree in Science UPNG- Specialty- Applied Statistics and Mathematics Minored Economics

1996-1998: Rockhampton Grammar (QLD): Higher School Certificate

1992-1995: Kerowagi H/School (Simbu) High school Certificate

1986-1991: Guruma Primary School: Primary School

Certificate

Voluntary Community Service

April 2005- Peer Education Training on HIV/AIDS/STI, Violence against women and

Substance abuse.

1996-1998: Charity collections- Blue Nursing, Red Cross, Kidney &Heart

Foundation (all in QLD Australia)

1998- (Aitape Tsunami) Lead in raising funds and cloths for the survivor (at school Rocky Grammar QLD)

Personal Particulars DOB - 11-12- 1979 Home province - Simbu

Marital Status - Married District - Yongumugl Religion - Christianity

Nationality - PNGean

Objective

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Work to reflect and drive forward the ethos that has established by the organisation.

Referees Mr. Harry Godfrid Boto Gaupo Team Leader (World Bank Agriculture Project) Chief Economic Advisor Policy Division Policy Division P O Box 2033 P O Box 2033 POM-NCD POM- NCD Ph: 3202869/8/7 Ph: 3202869 Fax: 3202866 Fax: 3202866 Professor Allan Easton Dr. Shafiqur Rahman Pro-VC Academic Senior Lecturer in Statistics Senior Lecturer in Applied Mathematic P O Box 320 UNI PO P O Box 320 Uni PO NCD NCD Ph: 3267639 Ph: 3267639 Fax: 3267187 Fax: 3267187

5.4 IPA Registration Certificate

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5.5 Previous Work Samples

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5.5.1 School Based Diet Survey

Research Survey by John Kuri Part 1: Points to consider

1. What is the question that you want answered

2. What is the population of interest?

3. Determine the number of people that will be surveyed 4. What is the error that will be allowed? 95%

5. How are the subjects going to be selected?

6. What is the medium through which the responses will be collected? Part 2: Designing the experiment

1. What will the sample size be? The use of samples mean that inferential statistics will be used. That means determining the measures of central tendency and dispersion

2. What type of sampling structure is to be used .( It has to be quick and inexpensive) - Random sampling - Stratified sampling - Systematic sampling - Cluster sampling - Convenience sampling

3. Write the questionnaire 4. Collect the data

5. Compile the data

6. Analyze the data ; by graphs and calculations

7. Reporting

DIET SURVEY QUESTIONNAIRE

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By John Kuri Grade _______ Age ________ Male /Female This is a research project intended to find out what students eat when in Port Moresby Grammar School. Please answer the questions as honestly as possible. Circle the best option. Q1. Are your lunch/recess meals prepared at home? (a) Yes (b) No If you answer YES then answer Questions 2 – 3. If you answered NO then answer Questions 4 – 7. Q2. What type of food do you have during lunch/recess? (You can select more than one option.) (a) Sandwich (b) Biscuits (c) Fruits (d) Pastries (e) Juice / Carbohydrates (f) Water Q3. How often are your lunch /recess meals prepared at home? (a) Often (c) Depends (c) Once in a while Q4. Do you purchase your lunch/recess meals at school? (a) Yes (b) No Q5. If you do then what type of food do you buy? (You can select more than one option.) (a) Sandwich (b) Biscuits (c) Fruits (d) Pastries (e) Juice (d) Water Q6.What do you think about on the variety of food offered by the canteen with respect to nutritional value.

(a) There is definitely a variety in nutrition offered (b) The canteen needs to offer more nutritional foods.

Q7. What do you think about the price of food at the canteen? (a) It’s okay (b) Could be cheaper (c) Way too high

5.5.2 School Based Substance Abuse Survey

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Research Project Questionnaire

This is a research project survey questionnaire on Drug and Alcohol abuse in secondary schools. The information you provide is confidential so please be as honest as possible in all your answers.

Research Project Title: Alcohol and drug abuse in secondary schools. Population: Port Moresby Grammar School. Grade: ________Sex: __________

Part A: Socio – economic status Choose the letter that describes your situation, Q1. Where do you live in? (a). Private Home (b) Flat (b) Settlement (d) Other Q2. Who do you live with? (a) Parents (b) Relatives (c) Guardians (d) Friends (e) Single Parent (Father/Mother) Q3. Use the key below to describe the economic status of the people who you live with. (a) Low income earners (b) Middle income earners (c) High income earners (d) Very high income earners.

KEY

Low income: 00.000,20K , 00.000,21K middle income 00.000,35K , 00.000,36K high income

00.000,50K , very high income 00.000,50K

Q4. Select the choice which best describes the amount of money you spend on a daily basis covering, transport and lunch.

(a) 00.5K (b) 00.10K (c) 00.20K (d) 00.20K

Part B: Drugs and Alcohol Q1. Please tick the box beside the activity that you have engaged in (You may tick more than 1 box) Smoking tobacco Smoking marijuana Drinking alcohol Chewing betel nut Q2. The activities in Q1 are again listed here, this time with measures of how frequent you do these activities. (a) Smoking tobacco (b) Smoking marijuana (c) Alcohol consumption Not often Not often Not often Moderate Moderate Moderate Regular Regular Regular Heavy Heavy Heavy Q3. Please circle the option that best represents who or what influenced you to engage in these activities (a) Smoking tobacco (b) Smoking marijuana (c) Alcohol consumption

(i.) Peer pressure (i) Peer pressure (i) Peer pressure (ii.) Family issues (ii) Family issues (ii) Family issues

(iii.) Imitating celebrities (iii) Imitating celebrities (iii) Imitating celebrities

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(iv.) Other (iv) Other (iv) Other

Q4. If you selected option (i) in any of the activities in Q3; which of the following statements describe you. (a) Every body in your peer group does so, and you feel left out if do not do what they do therefore you

take part. (b) People in your peer group who are actively involved in these activities encourage you to take part so

you do. (c) People in your peer group who are actively involved discourage you but you take but you still take

part. (d) Without taking part in these activities, you will not have a peer group.

Q5. If you selected option (ii) in any of the activities in Q3; select the statement that best describes you.

(a) Family members are actively involved in these activities, as such your part – taking in these activities is easily accepted.

(b) Family members are not actively involved but are not taking any strict notice of my involvement (c) Family members are not actively involved and discourage me, but I still continue.

Q6. If you selected option (iii) in any of the activities in Q3; which of the following statements bests describes you.

(a) People you idolize engage in such activities as such you feel you can relate to them by engaging in these activities.

(b) Impressing someone (girl friend/boy friend / peers / older siblings etc…) is the motivating factor behind engaging in such activities.

(c) Engaging in such activities makes you feel mature. (d) You need attention from someone but do know how to get them to notice you.

Q7. If you selected option (iv) in Q3; circle the letter that bests suites you; You engage in these activities because you are

(a) Facing family problems (Broken homes, separation, etc…) (b) Financial problems (School fees, accommodation, etc…) (c) Been doing it for quite a while and have become addicted. (d) These activities are the only form of enjoyment available to you.

Q8. This question refers to marijuana and alcohol. Have you ever been violent under the influence of these two substances? (a) Yes (b) No Q9. What do you know about the harmful effects of drug and alcohol abuse? (a) Nothing (b) A little (c) Enough (d) A lot Q10. Describe the government’s effort to control drug and alcohol abuse in secondary schools (a) No effort (b) Need to put more effort (b) Fair amount of effort (d) Doing an excellent job Q11. The government has been doing awareness campaigns against drug and alcohol abuse, how well do you think, these campaigns are achieving their goals, (a) Not achieving their goals at all (b) It’s hard to tell whether anything has been achieved (c) Gradual changes seem to be happening (d) Awareness campaigns are achieving very well their aims and goals. Q12. Briefly explain in your own words how you would conduct an awareness campaign against drug and alcohol abuse.

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________________________________________________________________________

5.5.3 Teacher Assessment Survey

Port Moresby Grammar School TAG

Survey

The questionnaire will be treated with utmost confidentiality and will not be used in any way against you. PLEASE answer HONESTLY… Part 1: Details Sex: M F Department: _____________ Number of contact hours: ___________ Administrative Position (i.e., HOD, Dean etc…): Yes No Teaching one subject: Yes No Teaching one grade: Yes No Part 2: Questions Q1. You are not in a rush to prepare for the next assessment task. (a) Always (b) Sometimes (c) Never Q2. You did not cover enough content before the next scheduled test. (a) Always (b) Sometimes (c) Never Q3. You need more than 1 week before you can return marked scripts (a) Always (b) Sometimes (c) Never Q4. You assist students who are struggling in areas of concern (a) Always (b) Sometimes (c) Never Q5. You notify appropriate personnel of students who are constantly underperforming. (a) Always (b) Sometimes (c) Never Q6. You record and use the measures of dispersion of student performance in each assessment task to evaluate their performance.

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(a) Always (b) Sometimes (c) Never A research survey by John Kuri

5.5.4 Statistical Analysis of Grade 10 Exams (2005) ..\..\..\Desktop\LJSSSubmission\Statistical Analysis of The Grade 10 (2005.pptx

5.5.5 Statistical Analysis of Grade 12 Mock Exams (2005) ..\..\..\Desktop\LJSSSubmission\Statistical Analysis of Grade 12 Mock Exams(2005).pptx 5.5.6 Quality Teaching Methods Training (2006) ..\..\..\Desktop\LJSSSubmission\High Academic Performance Through Quality Teaching(2006).pptx

5.5.7 Trend analysis of Grade 12 HSC Exam marks ( 1990 – 2006) ..\..\..\Desktop\LJSSSubmission\GRADE 12 HSC EXAM TREND(1990- 2006).pptx 5.5.8 Trend analysis of Grade 12 HSC Exam marks (1990 – 2008) ..\..\..\Desktop\LJSSSubmission\GRADE 12 HSC EXAM TRENDS (1990 - 2008).pptx 5.5.9 Data bank ..\..\..\Desktop\LJSSSubmission\HSC -1999-2008.xlsx

5.5.10 Analysis of Port Moresby’s Rainfall Data

READING COURSE

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TIME SERIES ANALYSIS OF RAINFALL DATA

A REPORT OF PORT MORESBY ‘S RAINFALL

THE UNIVERSITY OF PAPUA NEW GUINEA

SCHOOL OF NATUARL & PHYSICAL SCIENCES

SUBMITTITED TO THE DISCIPLNE OF MATHEMATICS

TO FULFILL PARTIALLY THE REQUIREMENTS OF POST-GRADUATE DIPLOMA IN SCIENCE (APPLIED STATISTICS)

2003

BY: JOHN KURI BONGERE 94020043 READING COURSE SUPERVISOR: Dr. SHAFIQUR RAHMAN

1.0 Analysis of Rainfall Data of Port Moresby The purpose of this report is to

1. Test the normality of the data 2. Test the randomness of the data and hence identifying whether there exists a trend

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3. Determine the periodicity of rainfall Rainfall data is an important aspect of many activities in life. Knowledge about when to expect an increase or decrease in rainfall is crucial to agriculture, bodies governing hydro electricity, water supplying towns and cities etc…Papua New Guinea, being a developing country needs to monitor the patterns in rainfall so that its activities are parallel to rainfall. An example of what could be done with rainfall data is reported here. When a series of observations are indexed by time, we call this a time series. Usually there are three

components to a time series. The trend, tT , seasonal component tS and irregular variation t . Some will

include a cyclic component to a time series as well. Fluctuations generated by a certain time of the year or month are referred to as seasonal variation and are easily noticeable in a time series. The long-term movement of the series is known as the trend. This indicates whether the overall direction of the series is to increase, decrease or show none at all. Cyclic effects are recurring patterns that take place over many years. A time series that has a cyclic component is said to be periodic. The irregular component exists, as there is always the unexplained variation in a time series. We can decompose a time series with an additive model

tttt STX (1.01)

or a multiplicative model,

tttt STX (1.02)

We consider the multiplicative model for our data. The trend is mostly modeled by low order polynomials (e.g. linear or quadratic) whilst trigonometric functions or seasonal indicators usually model the seasonal

component. We assume that the irregular components t , are uncorrelated.

A better understanding of the series may be gained by decomposing the series into individual components. Say the series shows some seasonality, then the option here would be to deseasonalize the data to remove the seasonal variation, thus leaving the data with the trend and irregular component. The trend is then the difference between the deseasonalized series and the irregular component. There are many methods to test whether a trend exists. We use a non-parametric test to identify if the series is random or not. Seasonal variation occurs as a result of many variables. The elements of nature play a huge role in many activities in life; like consumer demand and prices. And thus removing the seasonal variation helps to bring out the trend of the series, if there is any. We follow the ratio to moving average method; more precisely the Census Method II-X11 variant program. The following steps were followed to deseasonalize the data. Step 1: We take a 12- point moving average Step 2: A 2- point moving average was taken again to center the data points with the moving averages This ultimately results in loss of 6 months data at the beginning and end of the data. Removal of the seasonality is not affected by this as will be seen later. Step 3: Now we compute the normalized seasonal indexes of each month

Step 4: From step 3, the observations, tx are divided by the corresponding seasonal indexes to give the set of

deseasonalized observations. With the normalized seasonal indexes we can now deseasonalize the data points at the beginning and end of the series, accordingly.

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We show a sample from the calculations done. Table 1 contains the rainfall of 2 years 1971 and 1972. In the third column we show the 12-step centered moving average. Table 1

Month Rainfall (mm) 12-CMA Specific Seasonal Index

Normalizer Deseasonalized Series

1973

January 246.0 2.2400 109.817

February 109.0 1.8291 59.5894

March 233.0 2.2637 102.92

April 41.0 1.2402 33.058

May 162.0 0.9140 177.23

June 57.0 0.4001 142.45

July 21.0 121.5 0.1727 0.2998 70.04

August 15.0 134.1 0.1118 0.3161 47.44

September 5.0 135.8 0.0368 0.2357 21.20

October 9.0 131.0 0.0687 0.3879 23.197

November 283.0 130.1 2.1755 0.5354 528.49

December 240.0 125.0 1.9206 1.3374 179.45

1974

January 321.0 123.7 2.5956 2.2400 143.29

February 335.0 123.3 2.7162 1.8291 183.14

March 49.0 125.0 0.3921 2.2637 21.6

April 108.0 127.1 0.8498 1.2402 87.07

May 74.0 115.5 0.6404 0.9140 80.957

June 22.0 96.2 0.2286 0.4001 54.98

July 25.0 81.4 0.3070 0.2998 83.38

August 3.0 66.3 0.0452 0.3161 9.4894

September 56.0 67.9 0.8250 0.2357 237.5

October 9.0 78.2 0.1150 0.3879 23.19

November 6.0 81.6 0.0735 0.5354 11.20

December 53.0 94.3 0.5623 1.3374 39.6

The specific Seasonal indexes are found by dividing the series by their appropriate 12-soe moving averages. So for July 1974, 25/81.4 = 0.307. Then the normalized seasonal indexes are in column 5. The deseasonalized entry is taken as the series divided by the normalizer.

1.1 Testing for Normality Knowing the distribution of a set of observations is important when making inferences or knowing the distribution followed by a set of observations is crucial. We may approximate a set of data to some known distribution. Nearly all natural events follow the normal distribution. As such it is fitting that we test the

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normality of the rainfall data. We begin by measuring the data’s symmetry. This is called skewness and is given by the coefficient,

2/3

2

3

1

where 2 and 3 are the 2nd and 3rd central moments. The normal distribution is symmetric and as such has

a value of 01. Another measure of the characteristic of a distribution its density around the mean. This is

called kurtosis and is given as,

32

2

42

where 4 is the 4th central moment. The normal distribution has a value 33 . We need to know the

moments of the data set. Raw moments and central moments are essential in determining the characteristics of the data. This also will identify the distribution of the set of observations. The first four moments are sufficient to determine any distribution and as such we determine the first four moments of our rainfall observations. Listed below is the relationship between raw moments and central moments.

1st raw moment = N

xN

t

t

1'

1

2nd raw moment = N

xN

t

t

1

2

'

2

3rd raw moment = N

xN

t

t

1

3

'

3

4th raw moment = N

xN

t

t

1

4

'

4

1st central moment = 0)(1 xE

2nd central moment = 22'

1

'

22

3rd central moment = 2'

1

'

1

'

2

'

33 23

4th central moment = 4,

1

'

1

'

2

'

1

'

3

'

44 364

Using the above relationships the normality was tested. The initial results are listed in Table 1. Table 2

Central moments

Raw moments '

1 = 94.78

'

2 = 17902.006 9.89462 =

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226.5171872'

3 91.994583

1964426400'

4 7.7264546034

The coefficients of skewness and kurtosis are then,

117.02/3

2

3

1 and

129.332

2

42

These results are approximately similar to the coefficients expected of a normal distribution. More specifically, the rainfall distribution is very weakly skewed to the right and only slightly denser around the mean than the normal distribution. Hence we can say that the rainfall data of Port Moresby from 1973 to 1999 follows a normal distribution.

1.2 Test For Randomness We can identify whether there is a trend associated with the monthly rainfall. We focus our attention on non-parametric tests to test the randomness of the series. We use the runs above and below the mean test. The average rainfall from the series is about 94.34mm per month that will serve as our focal point. The observations above and below the focal point are denoted 1,0 respectively. Let m be the number of observations above the focal point and n is the number below the focal point. We assign U to be the number of runs. We dichotomize the series by assigning the focal point to be the mean rainfall. We can then note whether each observation exceeds or is exceeded by this value. The null hypothesis is that an ordered sequence of two types of symbols can be considered a random arrangement or that the process generating the series is a random process. The hypothesis is then Ho: The series is random Hi: The series is not random The test statistic U is defined as the number of runs exhibited by either of the elements in the series. For sample sizes larger than 12, U is standardized and a continuity correction factor is introduced (since U can take only integer values) The asymptotic sampling distribution of a standardized U is the normal probability function. Thus the z statistics are defined as

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)1(

)(2

/215.0

2 NN

Nmnmn

NmnUzL

,

)1(

)(2

/215.0

2 NN

Nmnmn

NmnUzR

We consider Lzz if U<1+2mn/N and Rzz if U >1+2mn/N

Using the symmetry of the normal distribution, the large sample, asymptotic approximate P-values can be conveniently found. The results are ; m = 114 n = 199 U = 135 ZL = -0.0041 ZR = -0.0044 According to the stated alternative statement, we use right tail probability for z. The z statistic to be considered is – ZL since U<1+2mn/N -ZL=-(0.0041)=0.0041 P=2*0.4801 = 0.9602 The result suggests that the series is a random process, which implies that the series does not possess a trend.

1.3 Periodogram Analysis A pertinent question in the mind of an analyst is whether a series has a cyclic component associated with the series. She may want to know whether or not a series is periodic and if it is, the length of time it will take to recur. Periodogram analysis measures the correlation of a known wavelength with that exhibited by the series. A strong correlation results in an increase in the intensity. The highest intensity indicates the periodicity of the series. Suppose the series was generated by the trigonometric model,

tj

q

j

jjjt ttX ))cos()sin((1

210 (1.20)

where N

j2 are Fourier Frequencies and represents the j th harmonic of the fundamental frequency

1/N. Some useful results can be derived from this consideration. Prominently, the estimates of the

coefficients of j10 , and j2 can be computed using OLS. They can easily shown to be

x0 , )sin(2

1

1

N

t

jtj txN

, )cos(2

1

2

N

t

jtj txN

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If we define the periodogram as a function of the variation decomposed into the contributions at various harmonic frequencies,

)()(1 1

2

jN

N

j

N

j

t Ixx

then the intensity of correlation at various frequencies is defined as,

2

2

2

1)( jjjNI

The sample spectrum shows the various intensities at the corresponding wavelengths when j to takes

different values between given limits,. The Periodogram is used to detect and estimate the amplitude of a sine component of known frequency and to test for randomness of a series. The definition of a Periodogram assumes that the frequencies are harmonics of the fundamental frequency. This has far reaching implications since it means that the variance of the series can be decomposed into contributions tied to a set of distinct frequencies. The values at each frequency is called a power spectrum and give us an alternative way of looking at the series. It is advisable that a series should be demeaned before testing for periodicity. The periodogram for the demeaned rainfall series is shown in Figure 1. We have a peak at 0.602. This suggests that the periodicity for

the rainfall data is 212 years. However, it is unsure whether the peak is a real peak given that the

periodogram is an irregular function. A descriptive procedure is given in Janeck and Swift (1991) if the reader wants to pursue the concept of testing the significance of the peak. We however will not elaborate further into this. The periodogram is shown in Figure 1. Figure 1

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From the analysis, the rainfall data of Port Moresby recorded at latitude 55006 and at an elevation of 35 m may be approximated to the normal distribution shows no trend to increase or decrease and has a periodicity

of about 212 years.

0

50000

100000

150000

200000

250000

300000

350000

0 1 2 3 4 5 6

Inte

ns

ity

Frequency

Periodogram of Rainfall Data of Port Moresby

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References

Abraham and Ledolter (1983) “Statistical Methods For Forecasting” John Wiley & Sons

Janeck and Swift (1991) ‘Time Series: Forecasting Formulation, Application’ Ellis Horwood Ltd.

5.5.11 Initial Selection Criteria for Enumerators

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Enumerator Selection Criteria

For the purpose of the LJSS Urban Crime Survey , interested persons who want to be engaged as enumerators will have to satisfy the following criteria in order to be eligible to be selected to become an enumerator in this survey. Enumerators must

1. Be Literate and numerate ( At least completed Grade 10) 2. Have no criminal record 3. Be residing in a community within the city of concern 4.

Contractual Obligations which they must understand. Enumerators must be willing to

1. Work on 6 days a week if need be 2. Understand the purpose of the survey

5.5.12 Quality Control Check List

Hohola 1 Bombex Street P.O. Box 3657 Boroko Phone: 340 2457 Fax: E -mail:johnkuri@ymail.com

g

Optimum Solutions, Maximum Gains.

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Survey Quality Control Daily Checklist

Date : __/__/2010

Items Comments Morning Briefing Daily Quota Security Transport Attendance Team allocation Afternoon Briefing

The morning briefing will retract on the previous day’s activities and outline the day’s activities. The afternoon briefing will be for enumerators to report their live survey.

Hohola 1 Bombex Street P.O. Box 3657 Boroko Phone: 340 2457 Fax: E -mail:johnkuri@ymail.com

g

Optimum Solutions, Maximum Gains.

Appendix 2

6.5 Implementation Schedule URBAN CRIME SURVEY Action Area Task Action Officer Supervised

by Dates

1. Engagement and training of enumerators

Week 1 26/4/10

Week 2 3/5/10

Week 3 10/5/10

Week 4 17/5/10

Week 5 24/5/10

Week 6 31/5/10

Week 7 7/6/10

Week 8 13/6/10

Week 9 20/6/10

Week 10 27/6/10

1.1 Establishing/accessing networks for enumerators

Lead researcher Lead researcher, LJSS

X X 1.2 Contracting and

arranging wages for enumerators

Lead researcher Lead researcher, LJSS

X 1.3 Training of enumerators Lead researcher Lead

researcher X 1.4 Practical training and

assessment Lead researcher Lead

researcher X 1.5 Recommendations to

improve training manual Lead researcher Lead

researcher X 1.6 Implementation plan Lead researcher Lead

researcher X 2. Management of

enumerators

2.1 Quality control of live

surveys Lead researcher/supervisors

Lead researcher X X X

2.2 Daily reports of enumerators

Lead researcher Lead researcher X X X

2.3 Security of enumerators Lead researcher/ supervisors/ police

Lead researcher X X X

2.4 Monitoring of age and gender quota ratios

Lead researcher Lead researcher X X X

2.5 Field report at conclusion of field work

Lead researcher Lead researcher

3. Collation and verification of surveys

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3.1 Surveys sorted and ordered by location and household ID

Lead researcher , assistant researcher

Lead researcher X

3.2 Inventory list of number of surveys against household IDs

Lead researcher, assistant researcher

Lead researcher X

4. Managing data entry into SPSS

X

4.1 Ensuring data entry of all surveys

Lead researcher , assistant researcher, data entry operator

Lead researcher, LJSS

X 4.2 Maintaining quality

control of data entry Assistant researcher, data entry operators

Lead researcher X

4.3 Provision of data files Lead researcher, assistant researcher

Lead researcher X

4.4 Provision of completion report

Lead researcher, assistant researcher

Lead researcher, LJSS

X 5. Data analysis in SPSS

5.1 Disaggregated data

analysis into EXCEL and SPSS

Lead researcher, assistant researcher

Lead researcher X

5.2 Data analysis and chart development

Lead researcher , assistant researcher

Lead researcher X

5.3 Data analysis against previous surveys

Lead researcher , assistant researcher

Lead researcher, LJSS

X 6. Report write up

6.1 Quality drafting of report Lead researcher ,

assistant researcher Lead researcher, LJSS

X 6.2 Draft report to LJSS M&E

unit for commentary Lead researcher , assistant researcher

Lead researcher, LJSS

X 6.3 Final draft of report with

recommendations Lead researcher , assistant researcher

Lead researcher, LJSS

X

Appendix 3

6.6 Estimated Budget Task Reference Unit cost K’000 Quantity Time (days) Total Cost

K’000 Per day

INPUTS

Personnel

Supervisor 2.1, 2.3 0.06 2 18 2.16

Data Entry Operator 4.1, 4.2 0.07 3 10 2.1

Enumerators 2.1 0.045 18 18 14.58

Sub-total Personnel 18.84

Materials

Training Venue Hire 1.3,1.4 0.4 1 5 2

Training 1.3, 1.4 0.04 20 5 4

Office Administration 1.1 – 6.3 0.03 1 50 1.5

Sub – total Materials 7.5

Communication

Phone 1.1 – 6.3 0.04 1 50 2

Internet 2.2 0.03 1 16 0.48

Radios/Frequency 2.1 1 3 0

Sub – total Communication 2.48

Travel

Vehicle Hire 2.1 0.8 1 21 16.8

Air fare( return trip) 1.1 2 1 2

Boarding & Lodging 1.2 – 2.5 0.5 1 21 10.5

Sub – total Travel 29.3

Management Costs

Lead Researcher 1.1 – 6.3 0.45 1 50 22.5

Assistant Researcher 3.1 – 6.3 0.25 1 25 6.25

Sub – total Management Costs 28.75

Miscellaneous 3.31

TOTAL COST 90

10% GST 9

Grand Total 99