Qualities and Characteristics of Good Reports.

 Qualities and Characteristics of Good Reports

A lot of reports are written daily. Some of them are intended to document the progress of some activities, feasibility reports, investigation reports, some of the reports are for monitoring purposes, some are evaluation reports but it is clear that all the reports have some objective and purpose behind it. That objective and purpose can only be achieved if a report has the following qualities and characteristics:

1.     It should be factual: Every report should be based on facts, verified information and valid proofs.

2.     Clear and Easily understandable: Explained below

3.     Free from errors and duplication

4.     Should facilitate the decision makers in making the right decision:

5.     Result focused and result oriented

6.     Well organized and structured

7.     Ethical reporting style

Reader-Friendly Readers are various stakeholders who receive reports generated by M&E. If reports are reader-friendly, they are likely to be read, remembered and acted upon. Following decisions need to be made by CSOs to make their reports reader-friendly:

·        What do they need to know?

·        When do they need to know?

·        How do they like to know?

Easy, Simple Language M&E reports are meant to inform not impress. Using easy, simple language, be it Urdu or English makes the report friendly on reader. To do this, here are some useful tips:

·        Write only what is necessary

·        Avoid repetition and redundancy

·        Give interesting and relevant information

·        Avoid preaching or lecturing

·        Compose short and correct sentences

Purposeful Presentation Each report has some objective(s) to meet. The “objective” comes from analyzing the needs of the reader. A CSO is working for a project that has several donors, and is channeled through an agency that needs to be informed about some specific things going on in the field. CSOs reports are the main pathways or channels of information to the people who decide to fund this and other such projects. Similarly, field reports are the amin vehicles for the management of the CSOs to make decision regarding the project itself. A good report presents facts and arguments in a manner that supports the purpose of the report.

Organized and Well-Structured Each CSO comes up with a format of internal reporting to suit its requirements. Reporting to donors is done on their prescribed formats. The M&E system should be able to generate information that can be organized using different formats. In the annex, this manual provides some useful formats that can be customized by a CSO.

Result-Focused In general, all readers are interested in the RESULTS. Therefore, one over-riding principle that CSOs should aim for in all report writing is to report on the results of their activities. This requires some analysis on their part that goes beyond a mere description of their activities. Result-focused means that description of activities is liked with the project objectives. This aspect must be addressed especially in the project progress reports. According to Phil Bartle, “A good progress report is not merely a descriptive activity report, but must analyze the results of those reported activities. The analysis should answer the question, "How far have the project objectives been reached?"

Timely Prepared and Dispatched M&E generate “Information Products”, a customized set of information according to needs to a defined group of users. M&E’s information products are time-bound for both internal and external stakeholders. Reports, in suitable formats, need to be timely produced and made available to the readers. It is useful to develop an Information Product Matrix (IPM) like the one described below:

Straightforward A good report is straight forward, honest description. It contains no lies, no deception, no fluff. It is neat, readable and to-the-point. It is well spaced, has titles and subtitles and is free of language errors.

Interpret the data precisely.

 

Interpret the data precisely

It is of paramount importance that the data you have gathered is meticulously and carefully interpreted. It’s extremely vital that our company has access to experts who can give you the correct results.

For instance, perhaps your business needs to interpret data from social media such as Twitter and Instagram. An untrained person will not be able to correctly analyze the significance of all the communication regarding your product that happens on these sites. It is for this reason that most businesses nowadays have a social media manager to deal with such information. These managers know how the social platforms function, the demographic that uses them, and they know how to portray your company in a good light on them as well as extract data from the users.

For every company to be successful, it needs people who can analyze incoming data correctly. The amount of information available today is bigger than it has ever been, so companies need to employ professionals to help stay on top of it all. This is particularly true if the founders of a company don’t have much knowledge of data. It would then be a great idea to bring an analyst into the team early. There is so much strategic information to be found in the data that a company accumulates. An analyst can help you decide what parts of the information to focus on, show you where you are losing customers, or suggest how to improve your product. They will be able to suggest to management which parts of the data need to be looked at for decisions to be made.

For instance, a trained data analyst will be able to see that a customer initially “liked” your product on Facebook. He then Googled your product and found out more about it. He then ordered it online and gave a positive review on your website. The analyst can trace this pattern and see how many other customers do the same. This information can then perhaps help your business with advertising, or with expansion into other markets. For instance, the analyst can collect data regarding whether putting graphics with “tweets” increases interest, and can tell what age group it appeals to more. They’ll be able to tell you what marketing techniques work best on the different platforms.

It is hoped that from this you can see how vital data collection and analysis are for the well-being of your company, and how it can help in all departments of your business, from customer care, to employee relations, to product manufacture and marketing.

The steps involved in analysis and interpretation of data.

 

The steps involved in analysis and interpretation of data.

Business intelligence requirements may be different for every business, but the majority of the underlined steps are similar for most:

Step 1: Setting of goals  This is the first step in the data modeling procedure. It’s vital that understandable, simple, short, and measurable goals are defined before any data collection begins. These objectives might be set out in question format, for example, if your business is struggling to sell its products, some relevant questions may be, “Are we overpricing our goods?” and “How is the competition’s product different to ours?”

Asking these kinds of questions at the outset is vital because your collection of data will depend on the type of questions you have. So, to answer your question, “How is the competition’s product different to ours?” you will need to gather information from customers regarding what it is they prefer about the other company’s product and also launch an investigation into their product’s specs. To answer your question, “Are we overpricing our goods?” you will have to gather data regarding your production costs, as well as details about the price of similar goods on the market.

Step 2: Setting priorities for measurement   Once your goals have been defined, your next step is to decide what it is you’re going to be measuring, and what methods you’ll use to measure it.

Determine what you’re going to be measuring At this point, you’ll need to determine exactly what type of data you’ll be needing to answer your questions. Let’s say you want to answer the question, “How can we cut down on the number of people we employ without a reduction in the quality of our product?” The data you’ll need will be along these lines: the number of people the business is currently employing; how much the business pays these employees each month; other benefits the employees receive that are a cost to the company, such as meals or transport;

All the data that’s gathered to answer the main questions and these secondary questions can be converted into useful information that will assist your company in its decision making. For instance, you may in the light of what is found decided to cut a few posts and replace some workers with machines.

Choose a measurement It’s vital that you choose the criteria that’ll be utilized in the measurement of the data you’re going to collect. The reason is that the way in which the data is collected will determine how it gets analyzed later.

You need to be asking how much time you want to take for the analysis of the project. You also need to know the units of measurement you’ll be using. For example, if you market your company’s product overseas, will your money measurements be in dollars or yen?  Regarding the employee question we discussed earlier, you would, for example, need to decide if you’re going to take the employees’s bonuses or their safety equipment costs into the picture or not.

Step 3: Data Gathering The next phase of the data modeling procedure is the actual gathering of data. Now that you know your priorities and what it is that you’re going to be measuring, it’ll be much simpler to collect the information in an organized way.

There are a few things to bear in mind before gathering the data: Check if there already is any data available regarding the questions you have asked. There’s no point in duplicating work if there already is a record of, say, the number of employees the company has. You will also need to find a way of combining all the information you have.

Perhaps you’ve decided to gather employee information by using a survey. Think very carefully about what questions you put onto the survey before sending it out. It’s preferable not to send out lots of different surveys to your employees, but to gather all the necessary details the first time around. Also, decide if you’re going to offer incentives for filling out the questionnaires to ensure you get the maximum amount of cooperation.

Remember to screen the information for accuracy as soon as it comes in, before logging it. You may need to go back to some of the employees for clarification. For instance, some of the replies on the questionnaires may not be legible, or some may not be complete.

If you’ve gathered data to analyze if your product is overpriced, for instance, check that the dates have been included, as prices and spending habits tend to fluctuate seasonally.

Step 4: Data Scrubbing Data scrubbing, or cleansing, is the process where you’ll find, then amend or remove any incorrect or superfluous data. Some of the information that you’ve gathered may have been duplicated, it may be incomplete, or it may be redundant.

Because computers cannot reason as humans can, the data input needs to be of a high quality. For instance, a human will pick up that a zip code on a customer survey is incorrect by one digit, but a computer will not.

It helps to know the main sources of so called “dirty data.” Poor data capture such as typos are one, lack of company-wide standards, missing data, different departments within the company each having their separate databases, and old systems containing obsolete data, are a few others.The process involves identifying which data sources are not authoritative, measuring the quality of the data, checking for incompleteness or inconsistency, and cleaning up and formatting the data. The final stage in the process will be loading the cleaned information into the log or “data warehouse” as it’s sometimes called.

It’s vital that this process is done, as “junk data” will affect your decision making in the end. For instance, if half of your employees didn’t respond to your survey, these figures need to be taken into account.

Finally, remember the data scrubbing is no substitute for getting good quality data in the first place.

Step 5: Analysis of data Now that you have collected the data you need, it is time to analyze it. There are several methods you can use for this, for instance, data mining, business intelligence, data visualization, or exploratory data analysis. The latter is a way in which sets of information are analyzed to determine their distinct characteristics. In this way, the data can finally be used to test your original hypothesis.

Descriptive statistics is another method of analyzing your information. The data is examined to find what the major features are. An attempt is made to summarize the information that has been gathered. Under descriptive statistics, analysts will use some basic tools to help them make sense of what sometimes amounts to mountains of information. The mean or average of a set of numbers can be used. This helps to determine the overall trend and is easy and quick to calculate. It won’t provide you with much accuracy when gauging the overall picture, though, so other tools are also used. Sample size determination, for instance. When you measure information that has been gathered from a large workforce, for example, you may not need to use the information from every single member to get an accurate idea.

Data visualization is when the information is presented in visual form, such as graphs, charts, and tables or pictures. The main reason for this is to communicate the information in an easily understandable manner. Even very complicated data can be simplified and understood by most people when represented visually. It also becomes easier to compare the data when it’s in this format. For example, if you need to see how your product is performing compared to your competitor’s product

The data analysis part of the overall process is very labor intensive. Statistics need to be compared and contrasted, looking for similarities and differences. Different researchers prefer different methods. Some prefer to use software as the main way of analyzing the data, while others use software merely as a tool to organize and manage the information.

Step 6: Result interpretation Once the data has been sorted and analyzed, it can be interpreted. You will now be able to see if what has been collected is helpful in answering your original question. Does it help you with any objections that may have been raised initially? Are any of the results limiting, or inconclusive? If this is the case, you may have to conduct further research. Have any new questions been revealed that weren’t obvious before? If all your questions are dealt with by the data currently available, then your research can be considered complete and the data final. It may no

What is data analysis?

 

What is data analysis?

You have to know exactly what data analysis is before you can understand the process. Analysis of data is the procedure of first of all setting goals as to what data you need and what questions you’re hoping it will answer, then collecting the information, then inspecting and interpreting the data, with the aim of sorting out the bits that are useful, in order to suggest conclusions and help with decision making by various users.

It focuses on knowledge discovery for predictive and descriptive purposes, sometimes discovering new trends, and sometimes to confirm or disprove existing ideas.                             

Concepts Pertaining to Sampling:-

1. Universe/Population: From a statistical point of view, the term ‘universe’ refers to the total of the items or units in any field of enquiry, whereas the term ‘popu­lation’ refers to the total of items about which information is desired. The attributes that are the object of study are referred to as charac­teristics and the units possessing them are called as elementary units.

The aggregate of such units is generally described as population. Thus, all units in any field of enquiry constitute universe and all ele­mentary units (on the basis of one characteristic or more) constitute population. Quite often, we do not find any difference between popu­lation and universe, and as such the two terms are taken as inter­changeable. However, a researcher must necessarily define these terms precisely.

The population or universe may be finite or infinite. The popula­tion is said to be finite if it consists of a fixed number of elements so that it is possible to enumerate it in its totality. For example, the population of a city, the number of households in a village, the num­ber of workers in a factory, and the number of students in a university are the examples of finite population. The symbol ‘N’ is generally used to indicate how many elements (or items) are there in case of a finite population.

An infinite population is that population in which it is theoretically impossible to observe all the elements. Thus, in an in­finite population, the number of items is infinite, i.e., we cannot have any idea about the total number of items.

For example, the number of stars in the sky, sand particles at a sea beach, and pebbles in a river­bed. From a practical consideration, the term ‘infinite population’ is used for a population that cannot be enumerated in a reasonable pe­riod of time. This way we use the theoretical concept of infinite population as an approximation of a very large finite population.

2. Sampling Frame: The elementary units or the group of cluster of such units may form the basis of sampling process in which case they are called as sam­pling units. A list containing all such sampling units is known as sampling frame. The sampling frame consists of a list of items from which the sample is to be drawn. For instance, one can use telephone directory as a frame for conducting opinion survey in a city. What­ever the frame may be it should be a good representative of the popu­lation.

3. Sampling Design:

A sample design is a definite plan for obtaining a sample from the sampling frame. It refers to the technique or the procedure the re­searcher would adopt in selecting some sampling units from which inferences from the population are drawn. Sampling design is deter­mined before any data is collected.

4. Statistic(s) and Parameter(s):

A statistic is a characteristic of a sample, whereas a parameter is a characteristic of a population. Thus, when we work out certain meas­ures such as mean, median, mode, etc., from samples, they are called statistics for they describe the characteristics of a sample. But when such measures describe the characteristics of a population, they are known as parameters. For example, the population means (μ) is a pa­rameter, whereas the sample means (X) is a statistic. To obtain the es­timate of a parameter from a statistic constitutes the prime objective of sampling analysis.

5. Sampling Error:

Sampling survey does imply the study of a small portion of popula­tion and as such there would naturally be a certain amount of inaccu­racy in the information collected. This inaccuracy may be termed as sampling error or error variance. In other words, sampling errors are those errors which arise on account of sampling and they generally happen to be random variations (in case of random sampling) in the sample estimates around the true population values. It can be numeri­cally described as under:

Sampling error = Frame error + chance error + response error.

6. Precision:

Precision is a range within which the population average (or other parameters) will lie in accordance with reliability specified in the confidence level as a percentage of the estimate ± or as a numerical quantity. For example, if the estimate is Rs. 4000 and the precision desired is ± 4 per cent, then the true value will be not less than Rs. 3840 and not more than Rs. 4160. This is the range (Rs. 3840 to Rs. 4160) within which the true answer should lie. But if we desire that the estimate should not deviate from the actual value by more than Rs. 200 in either direction, in that case the range would be Rs. 3800 to Rs. 4200.

7. Confidence Level and Significance Level:

The confidence level or reliability is expected percentage of times that the actual value will fall within the stated precision limit. Thus, if we take a confidence level of 95 per cent, then we mean that there are 95 chances in 100 (or .95 in 1) that the sample results represent the true condition of the population within a specified precision range against five chances in 100 (or .05 in 1) that it does not.

 

Disadvantages of sampling.

 

Disadvantages of sampling:-

The reliability of the sample depends upon the appropriateness of the sampling method used. The purpose of sampling theory is to make sampling more efficient. But the real difficulties lie in selection, estimation and administration of samples.

Disadvantages of sampling may be discussed under the heads:

• Chances of bias
• Difficulties in selecting truly a representative sample
• Need for subject specific knowledge
• changeability of sampling units
• impossibility of sampling.

1. Chances of bias-The serious limitation of the sampling method is that it involves biased selection and thereby leads us to draw erroneous conclusions. Bias arises when the method of selection of sample employed is faulty. Relative small samples properly selected may be much more reliable than large samples poorly selected.

2. Difficulties in selecting a truly representative sample-Difficulties in selecting a truly representative sample produces reliable and accurate results only when they are representative of the whole group. Selection of a truly representative sample is difficult when the phenomena under study are of a complex nature. Selecting good samples is difficult.

3. In adequate knowledge in the subject-Use of sampling method requires adequate subject specific knowledge in sampling technique. Sampling involves statistical analysis and calculation of probable error. When the researcher lacks specialized knowledge in sampling, he may commit serious mistakes. Consequently, the results of the study will be misleading.

4. Changeability of units-When the units of the population are not in homogeneous, the sampling technique will be unscientific. In sampling, though the number of cases is small, it is not always easy to stick to the, selected cases. The units of sample may be widely dispersed.

Some of the cases of sample may not cooperate with the researcher and some others may be inaccessible. Because of these problems, all the cases may not be taken up. The selected cases may have to be replaced by other cases. Changeability of units stands in the way of results of the study.

5. Impossibility of sampling-Deriving a representative sample is di6icult, when the universe is too small or too heterogeneous. In this case, census study is the only alternative. Moreover, in studies requiring a very high standard of accuracy, the sampling method may be unsuitable. There will be chances of errors even if samples are drawn most carefully.