Prior to writing your MBA Project or PhD proposal, Kenyan universities require a student to write an academic proposal for evaluation of the feasibility of the research project. Due to the difficulties associated with such an endeavour, many students may opt for the services of a Kenyan company that offers proposal wiring writing services. Some of the factors that students should consider in a Kenyan Proposal writing services company should include the reviews from other clients who have used the proposal writing services of the Kenyan writers. As such the students are able to receive the right proposal writing services at a reasonable cost.

Our academic proposal writing services in Kenya are offered by qualified Kenyan academic proposal writers who are experts in your areas of study and are Knowledgeable about the local conditions. This ensures that the academic proposal meets all the required conditions set by your university.

• Identify the purpose of the analysis or project
• Understand the sample(s) under study
• Understand the instruments being used to collect data
• Be cognizant of data layouts and formats
• Establish a unique identifier if matching or merging is necessary
• Plan your work and work your plan!

Components of a Data Analysis Plan

• Statement of research questions
• Methods used to answer research questions
• Timeline
• Budget
• File restructuring procedures (syntax creation, adding new variables as needed)
• Algorithms for scoring, equating, etc.
• Data cleaning procedures (e.g. removing outliers)
• Quality control procedures at every step in the project

Examples of Analyses

• Frequency Distributions and Cross -Tabulations
• Descriptive Statistics (Means, Std. Deviations, Correlations)
• T-tests and Analysis of Variance (ANOVA)
• Regression
• Principal Components/Factor Analysis (Data Reduction)
• Cluster and Discriminant Analyses (Segmentation)
• Latent Class Analysis (Classification)
• Hierarchical Linear Modeling (HLM)
• Differential Item Functioning (DIF)

More Advanced the Analysis, Greater the Amount of Preparation

• Most analyses can be executed straight from a working data file
• Some analyses may require transformations of the raw data, subsets, or specific input data to comply with statistical software

Data Types & Representation

Variables may require special coding for different data representation

• Numeric
• String
• Date & time
• Monetary

ASCII Text Files

• Usually rectangular in structure
• One record per observation
• Each data variable in same position on each record
• Each record may have multiple instances of data
• Arrays
• Repeating blocks (sets of variables)
• File may have multiple records per observation
• Number of records per observation can be variable
• Most government data files come in this format at a minimum
• Every software package can handle this file type

CSV Files (Comma-Separated Values)

• Individual data elements separated by commas
• Usually rectangular structure
• One record (line) per observation
• Fixed number of elements on each record
• Problems if data elements contain delimiter or blank spaces (i.e. text strings)
• Missing data must be represented by nulls

System Files (SAS®, SPSS®)

• Data stored in binary (machine) format
• Issues of portability across platforms
• Structured as rectangular tables
• SAS files can be indexed for direct access
• Self-contained documentation
• Data variable labels & formats
• Data value labels
• Most analysis packages provide facility for reading (but not writing!) system files from other packages (SPSS more than SAS)
• Using default data formats can yield system files that are much larger than source ASCII files

Overview of Softwares

• SAS and SPSS are most commonly used and tend to focus on the “classic” statistical routines:
• Descriptive statistics and non-parametric (“distribution-free”) tests
• ANOVA / Regression
• Factor analysis
• However, many psychometric procedures (e.g. IRT) and newer statistical models are not as well supported by these programs
• Very specialized programs are used
• Designed to do a specific task or validate a theory
• Specialized programs may have issues
• Interface not very user-friendly
• Additional data types or files required
• Expense

What is Excel?

• Data are organized by worksheets, rows and columns
• Worksheet limits are 256 columns and 65,536 total cells
• Cells contain data or formulas with relative or absolute references to other cells
• Direct manipulation of data and flexibility to move data “around” (e.g. sorting, replacing, merging)
• Opens many file types
• Quite useful in prepping files for use in SPSS, SAS or other programs
• Conditional formatting
• Also features macro capabilities, replicating user actions, allowing simple automation of regular tasks

Data Presentation Options in Excel

• Tables and graphs can be exported to a wide variety of software packages
• Can tweak and perfect example graph or table and then replicate by replacing only the data being used
• Main advantage is ability to combine data from multiple sources – not just what is found in the data file
• “Two-for-One” deal – table creation usually puts data into a format that leads to easy graph creation
• User has control over virtually all aspects of a graph – size, colors, fonts, titles, legends, labels, etc.
• Can combine graphs with tables and use cell layout to produce more complex presentations
• Final graphs can be of “publication” quality

What is SAS?

• A general purpose statistical package with a basic programming capability utilizing scores of statistical and mathematical functions in numerous “modules”
• Can readily access data from a wide variety of sources, perform data management, and present findings in a variety of report and graph formats
• Provides powerful tools for both specialized and enterprise-wide analytical needs

SAS – Strengths

• Versatile data input and output formats
• SAS provides both SQL and DATA steps to manipulate data:
• SQL provides a way of carrying out relational algebra on tables and views
• SAS data sets can be indexed for direct access or processed sequentially, without reading all records into memory, which is sometimes much more efficient

SAS – Weaknesses

• Steep learning curve; volume of functions, options and documentation can be overwhelming for the novice
• Inconsistent syntax across different procedures or modules
• Not a good choice for applications that interact with external systems such as hardware devices or software programs because of its inconvenient interface
• Difficult interaction with other programming languages
• Expensive

What is SPSS?

• A commercially produced statistical software package that is widely used in the fields of Education and Psychology
• Program functionality is broken into over a dozen different modules which are sold individually
• Most commonly used are Base, Regression Models, and Advanced Models
• Other modules can be installed to run more complex analyses
• SPSS data files include both the data and also variable information (variable and value labels, formats and missing values)

SPSS – Strengths

• Easily opens data from other programs such as Excel and SAS
• Variable view screen allows for quick overview of file contents and allows for easy modifications of names, formats, labels, and variable order
• Having all data information in a single file allows sharing files on a project to be very easy
• Point-and-click menus do not require memorizing syntax for majority of procedures
• Many procedures can be expanded beyond the menu options in syntax
• Split-file command allows all output to be replicated for various groups through a single command
• Journal file tracks all commands used for life of program, with good resources to find code accidentally deleted

SPSS – Weaknesses

• Ease of doing data manipulation can sometimes lead to mistakes as the program does not preclude inappropriate modifications to the data
• Matching feature requires exact match
• Duplicate records generate warnings but can be marked in file
• Error logs are hard to interpret at times
• Incompleteness of menus means some options are only available via syntax
• While the majority of output is saved as pivot tables allowing great flexibility in modifying tables
• Output tables and graphs generally not done as well as Excel and are harder to manipulate

LISREL

• Ideal for discrete data types
• Test data, Likert scale item data
• Data can be imported in various types
• ASCII, Access, Excel, SAS, SPSS, etc.
• Variable names have length restrictions
• Data files then stored as system files for later use
• Basic statistics (e.g. means and correlations) are generated in an underlying program called PRELIS
• LISREL itself is used to confirm the structural validity of a measurement model for any assessment
• Requires syntax and input matrices

EQS

• Ideal for continuous data types (test subscales)
• Data can be imported in various types – ASCII, Access, Excel, SAS, SPSS, etc. but variable names have length restrictions
• Data files then stored as system files for later use
• EQS itself is used to confirm the structural validity of a measurement model for any assessment
• Some model syntax can be built through the menus

HLM

• Hierarchical Linear Modeling (HLM) is becoming a more popular type of analysis, namely in cohort trend modeling
• Also allows you to look at variance component estimates and regression models given a nested sample of respondents
• Students within countries within global regions on personality variables
• More tedious to set up analysis with fewer available file types
• Also requires more upfront work as multiple data files are needed

What Program Should I Use?

• Microsoft Excel is the most basic and accessible spread sheet program available today
• It is most ideal for general data exploration, histograms, scatter plots, etc.
• Appearance of tables can be customized to meet APA standards
• Allows for easy transition to other programs to complete analyses and write reports
• However, its heritage is not as a statistical analysis program
• Certain statistical programs are designed for specific analytic tasks
• Balance the results and what will being presented
• Choose wisely in the interests of efficiency and accuracy of results
• Some output is good for looking at the data through basic exploration and to generate basic tables, but not to present the data

Summary

• Be very clear about the analysis objectives
• Be very familiar with all aspects of what defines your data
• Develop and stay true to your data analysis plans and research questions
• Be cognizant of which statistical software programs can best answer your research questions and present your results
• Be thorough in your analyses, express openness to additional investigations, yet be mindful of limitations given the data and the programs you are using

]]> http://www.kenyanmba.com/key-considerations-in-data-analysis/feed/ 0 http://www.kenyanmba.com/spss-data-analysis-in-kenya-techniques-of-data-analysis/ http://www.kenyanmba.com/spss-data-analysis-in-kenya-techniques-of-data-analysis/#comments Wed, 18 Jan 2012 16:11:03 +0000 admin http://www.kenyanmba.com/?p=282 Techniques of Data Analysis

Data analysis – “The Concept

• Approach to de-synthesizing data, informational, and/or factual elements to answer research questions
• Method of putting together facts and figures to solve research problem
• Systematic process of utilizing data to address research questions
• Breaking down research issues through utilizing controlled data and factual information

Categories of data analysis

• Narrative (e.g. laws, arts)
• Descriptive (e.g. social sciences)
• Statistical/mathematical (pure/applied sciences)
• Audio-Optical (e.g. telecommunication)
• Others

Most research analyses, arguably, adopt the first three.

The second and third are, arguably, most popular  in pure, applied, and social sciences

Statistical Methods

• Something to do with “statistics”
• Statistics: “meaningful” quantities about a sample of objects, things, persons, events, phenomena, etc.
• Widely used in social sciences.
• Simple to complex issues. E.g.

1.  correlation
2. anova
3. manova
4. regression
5. econometric modelling

Two main categories:

• Descriptive statistics
• Inferential statistics

Descriptive statistics

Use sample information to explain/make abstraction of population “phenomena”.

Common “phenomena”:

* Association (e.g. σ1,2.3 = 0.75)

* Tendency (left-skew, right-skew)

* Causal relationship (e.g. if X, then, Y)

* Trend, pattern, dispersion, range

Used in non-parametric analysis (e.g. chi-square, t-test, 2-way Anova)

Inferential statistics

Using sample statistics to infer some “phenomena” of population parameters

Common “phenomena”: cause-and-effect   * One-way r/ship

* Multi-directional r/ship

* Recursive

Use parametric analysis

Which one to use?

Nature of research

* Descriptive in nature?

* Attempts to “infer”, “predict”, find “cause-and-effect”,   “influence”, “relationship”?

* Is it both?

Research design (incl. variables involved). E.g.

Outputs/results expected

* research issue

* research questions

* research hypotheses

Principles of analysis

Goal of an analysis:

* To explain cause-and-effect phenomena

* To relate research with real-world event

* To predict/forecast the real-world phenomena based on research

* Finding answers to a particular problem

* Making conclusions about real-world event based on the problem

* Learning a lesson from the problem

• Data can’t “talk”
  • An analysis contains some aspects of scientific reasoning/argument:

* Define

* Interpret

* Evaluate

* Illustrate

* Discuss

* Explain

* Clarify

* Compare

* Contrast

An analysis must have four elements:

* Data/information (what)

* Scientific reasoning/argument (what? who? where? how? what happens?)

* Finding (what results?)

* Lesson/conclusion (so what? so how? therefore…)

Basic guide to data analysis:

* “Analyse” NOT “narrate”

* Go back to research flowchart

* Break down into research objectives and research questions

* Identify phenomena to be investigated

* Visualise the “expected” answers

* Validate the answers with data

* Don’t tell something not supported by data

• When analysing:

* Be objective

* Accurate

* True

• Separate facts and opinion
• Avoid “wrong” reasoning/argument. E.g. mistakes in interpretation.

A proposal is a document that request support-usually money- for work a proposer wants to do. What makes a proposal a proposal is that it asks the audience to approve, fund, or grant permission to do the proposed project.

Types of proposals

Internal proposal: If you write a proposal to someone within your organization, it is an internal proposal. With internal proposals, you may not have to include certain sections (such as qualifications), or you may not have to include as much information in them.

External proposal: is one written from one separate, independent organization or individual to another such entity.

Solicited proposal: If a proposal is solicited, the recipient of the proposal in some way requested the proposal. Typically, a company will send out requests for proposals (public announcements requesting proposals for a specific project) through the mail or publish them in some news source.

Unsolicited proposals: are those in which the recipient has not requested proposals. With unsolicited proposals, you sometimes must convince the recipient that a problem or need exists before you can begin the main part of the proposal.

Things to remember when writing a proposal:

The proposer has a particular interests and goals, and that’s why he/she writes the proposal.

The recipient of the proposal, be it an organization, a person, or a group, has its own interests and goals which may or may not coincide with those of the proposer.

So, the proposal should be convincing to the potential funder, and it should show that the proposed activity will be a good investment.

This is especially important when there is a competition between you and other proposers.

Always make sure that your proposal meets the expectations of the funder.

How to make sure that your proposal meets the expectations of a given funder:

In order to write a proposal that meets the expectations of a given funder, you should try to know the funder`s goals and interests.

If you are writing an unsolicited proposal to a private company, a good source of information might be the company’s published reviews and annual reports.

Requests for proposals are usually the best source of information when you are writing a solicited proposal.

If your proposed activity and the request for proposal (RFP) don’t match, try to look for another funding agency.

Common Sections in Proposals

The general outline of the proposal should be adapted and modified according to the needs of the readers and the demand of the topic proposed. For example, long complicated proposals might contain all the following sections. In contrast, shorter or simpler proposals might contain only some of the sections or the main ones.

Title page

Specific formats for title pages vary from one proposal to another but most include the following:

• The title of the proposal ( as short as informative as possible)
• A reference number for the proposal
• The name of the potential funder ( the recipient of the proposal)
• The proposal’s date of submission
• The signature of the project director and responsible administrator(s ) in the proposer`s institution or company

Abstract

• The Abstract is a very important part of the proposal because it provides a short overview and summary of the entire proposal.
• The Abstract of the proposal is short, often 200 words or less.
• In a short proposal addressed to someone within the writer’s institution, the Abstract may be located on the title page.
• In a long proposal, the Abstract will usually occupy a page by itself following the Title page.
• The Abstract should briefly define the problem and its importance, the objectives of the project, the method of evaluation, and the potential impact of the project

Table of contents

The table of contents lists the sections and subsections of the proposal and their page numbers.

Introduction

Plan the introduction to your proposal carefully. Make sure it does all of the following things (but not necessarily in this order) that apply to your particular proposal:

• Indicate that the document to follow is a proposal.
• Refer to some previous contact with the recipient of the proposal or to your source of information about the project.
• Find one brief motivating statement that will encourage the recipient to read on and to consider doing the project.
• Give an overview of the contents of the proposal.

Background

Often occurring just after the introduction. The background section discusses what has brought about the need for the project – what problem, what opportunity there is for improving things, what the basic situation is.

It’s true that the audience of the proposal may know the problem very well, in which case this section might not be needed. Writing the background section still might be useful, however, in demonstrating your particular view of the problem. And, if the proposal is unsolicited, a background section is almost a requirement—you will probably need to convince the audience that the problem or opportunity exists and that it should be addressed.

Benefits and feasibility of the proposed project

Most proposals discuss the advantages or benefits of doing the proposed project. This acts as an argument in favour of approving the project. Also, some proposals discuss the likelihood of the project’s success. In the unsolicited proposal, this section is particularly important.

Description of the proposed work (results of the project):

Most proposals must describe the finished product of the proposed project. In this course, that means describing the written document you propose to write, its audience and purpose; providing an outline; and discussing such things as its length, graphics, and so on.

Method, procedure, theory

In most proposals, you’ll want to explain how you’ll go about doing the proposed work, if approved to do it. This acts as an additional persuasive element; it shows the audience you have a sound, well-thought-out approach to the project. Also, it serves as the other form of background some proposals need. Remember that the background section (the one discussed above) focused on the problem or need that brings about the proposal. However, in this section, you discuss the technical background relating to the procedures or technology you plan to use in the proposed work.

Schedule

Most proposals contain a section that shows not only the projected completion date but also key milestones for the project. If you are doing a large project spreading over many months, the timeline would also show dates on which you would deliver progress reports. And if you can’t cite specific dates, cite amounts of time or time spans for each phase of the project.

Qualifications

Most proposals contain a summary of the proposing individual’s or organization’s qualifications to do the proposed work. It’s like a mini-resume contained in the proposal. The proposal audience uses it to decide whether you are suited for the project. Therefore, this section lists work experience, similar projects, references, training, and education that show familiarity with the project.

Costs, resources required

Most proposals also contain a section detailing the costs of the project, whether internal or external. With external projects, you may need to list your hourly rates, projected hours, costs of equipment and supplies, and so forth, and then calculate the total cost of the complete project. With internal projects, there probably won’t be a fee, but you should still list the project costs: for example, hours you will need to complete the project, equipment and supplies you’ll be using, assistance from other people in the organization, and so on.

Conclusions

The final paragraph or section of the proposal should bring readers back to a focus on the positive aspects of the project (you’ve just showed them the costs). In the final section, you can end by urging them to get in touch to work out the details of the project, to remind them of the benefits of doing the project, and maybe to put in one last plug for you or your organization as the right choice for the project.

Appendices

Appendices (supplementary material that is collected and appended at the end of a proposal) should be devoted to those aspects of your project that are of secondary interest to the reader. Begin by assuming that the reader will only have a short time to read your proposal and it will only be the main body of your proposal (not the Appendices). Then, assume that you have gotten the attention of the reader who would now like some additional information. This is the purpose of the Appendices.

Here are some possible sections to include in the Appendices:

Dissemination Plan – An important aspect of your proposal will be the plan for disseminating information of/from the project to other audiences. Most funding agencies are interested in seeing how their financial support of your project will extend to other audiences. This may include newsletters, workshops, radio broadcasts, presentations, printed hand-outs, slide shows, training programs, etc. If you have an advisory group involved with your project they can be very helpful in disseminating project information to other audiences.
Time Line – A clear indication of the time frame for the project and the times when each aspect of the project will be implemented. Try creating the time line as a graphic representation (not too many words). If done well, it will help demonstrate the feasibility of the project in a very visible way.
Letters of Support – Funding agencies would like to know that others feel strongly enough about your project that they are willing to write a letter in support of the project. Talk through with the potential letter writers the sort of focus that you think will be important for their letter. (Try and draw on the reputation of the letter writing group.) Do not get pushed into writing the letters for the agencies – they will all sound alike and will probably defeat your purpose of using them. The letters must be substantive. If not, do not use them! Have the letters addressed directly to the funding agency. (Do not use a general “To Whom It May Concern” letter – it makes it appear that you are applying to many different potential funding agencies and are using the same letter for each. This may really be the case, so make sure you personalize each letter to the specific potential funding agency).

Purpose

• Provide an overview of the data analysis process
• Provide an overview of the preparation of Preliminary and Final Reports
• Discuss dissemination of analysed data results

Process for report preparation

Before producing tabulations and writing reports, the following tasks should be completed:

• Carry out basic checks of data quality (non-sampling errors)
• Calculate response rates Response rates lower than 90% suggests results may be biased; include response rates in the reports
• Check for variables with large numbers of missing values Make sure not a problem with data entry or editing; any variable with 10% or more missing values use with caution; if very high do not use at all
• Check for variables with large numbers of “don’t know” or “other” answers                                                                                             Large numbers of DKs can indicate difficulty understanding the questions; High proportions of “other” indicates questionnaire did not account for most common responses
• Check for expected patterns in the data Unexpected patterns may result from faulty sample design, improper implementation of the sample, interviewer errors, or respondents’ inaccurate answers.
• Decide on basic background variables and their groupings Often include geographic areas, level of education, urban/rural residence, male/female, wealth quintiles, etc.
• Decide on minimum sample sizes for displaying results Cells based on very small numbers of cases are not reliable and should not be shown; results based on fewer than 25 unweighted cases should not be shown.

Producing a Preliminary Report

• Objective: To convey the main results of the survey quickly and to stimulate interest in the current situation among government agencies, NGOs, other multilateral donors, the press and the public
• Length: 15-20 pages
• When: Within one month to six weeks following the end of fieldwork

Outline of Preliminary Report

Background and objectives of the survey

Results

Sample and fieldwork methodology

• Sample design
• Response rates
• Questionnaires
• Fieldwork and field staff
• Processing of data

Circulation of Preliminary Report and Dissemination of Findings

• Circulation – Distribute to relevant government institutions, NGOs, donors, and the press
• Meeting – Present results at a meeting with representatives of key agencies and the press
• Press release – Prepare a press release summarizing main results

Outline of Final Report

• Executive summary
• Introduction and background
• Data quality and sample characteristics
• Results (with sections by topic)
• Sample design
• Survey instruments

Circulation of Final Report and Dissemination of Findings

Distribution – to key government agencies, NGOs, donors, researchers and the press

Regional and international circulation – by UNICEF, and at meetings and conferences

Meeting on main findings – All major participants in the survey should be invited to a meeting to present main findings

Specific topics – Meetings and presentations that focus on specific topics can also be arranged and may lead to additional analysis of the data

Further Analysis of the Data

MICS data can be used for statistical analysis beyond that contained in the survey report

The data collected should be exploited as fully as possible

• Potential topics for further analysis:
• Analysis of data quality
• Analysis of trends
• Identification of “most vulnerable” groups
• Examination of health outcomes for orphans versus other children
• Multivariate analyses of determinants of child health or schooling outcomes

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Effective funding proposals should be able to produce a useful funding proposal to request support for your project and your organisation.As such our funding proposal writing services is tailored specifically at those who do not feel confident about writing funding proposals and who may not have much experience in it. It can be used by the individual project manager or by a team committed to increasing the organisation’s capacity to raise money.

Our services come in handy when:

• When you have an organisational financing strategy which includes raising money from donors
• When you have a project idea for which you need donor funding
• When a donor asks you to present a funding proposal for a project

A possible funding proposal should have the following format:

Title page:

• Title of proposal.
• Name of agency to whom proposal is being submitted.
• Name and address of your organisation, if possible with a logo.
• Name of the contact person who will best be able to discuss the proposal.
• Date

Summary page

• Summary of the proposal – about 300 words in length.

Contents page

• List the main headings and page numbers

Proposal – body of the proposal

• Context (about a page)
• Specific/relevant opportunities and/or problems for organisation and for donor (about half a page)
• Objectives (about half a page – includingclarification of the beneficiaries)
• Intended process (about three or four pages)

Conclusions and budgeting

• State briefly what you are requesting from the agency and why. Include a brief budget summary.

Appendices

• Detailed technical description of project, methods, timetable, detailed budget, annual report, audited financial statements, and so on.

Bibliography /references

• List any references you have used, in the order in which you mention them in the text.

A second format can be the following:

1. Project Title/Cover Page
2. Project Overview
3. Background Information/Statement of the Problem
4. Project Detail

a)      Goals and Objectives

b)      Clientele

c)      Methods

d)     Staff/Administration

1. Available Resources

6. Needed Resources

a. Personnel

b. Facilities

c. Equipment/Supplies/Communication

d. Budget

7. Evaluation Plan

8. Appendices

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