Data analysis is an integral part of research process, feasibility study, financial report.

A research is basically a systematic process of moving from the realms of known to unknown. That is the reason why it's important to review relevant literature on the subject matter.

SPSS-Statistical Packages for Social Sciences is a very useful software for data analysis. Learning how to use this software will surely help u .

Now let's get started! As earlier mentioned, SPSS means Statistical Packages for Social Sciences. However, the software use is not limited to field of social sciences. It can be used by almost every researcher.

You can have access to the SPSS software at computer village or elsewhere for between N100-N150. You can see that it is not costly.
After installation, you need to launch the software. Wait a minute, the version purchased usually expire in 14 days. If you're using window 7 you can ignore the warning but with window xp, you'll need to crack d software. Luckily, d crack software & instruction usually come with d software. After installation and sucessfully launching the software, now u are ready for the tutorial. 'you are good to go'. Before I'l begin d tutorial proper, i want to know that people are following. Sincerely speaking, u need to obtain the software and install it if u want to get something out of this tutorial. To know that you are following this tutorial, pls type 'YES' having read this information

Are u following this tutorial?

YES

@obasy09, thanks for addressing this crucial issue....I'm with you .....

Where is the guy?

Sorry for the delay. I'm happy to note that some people have finally shown interest in this tutorial having waited for so long a time. Now let's set the ball rolling.

Data analysis is very broad and can be applied in nearly all human endeavours. For now, I'll limit it to academic research work most especially to the field of social and management sciences, humanities and agriculture.
For real, genuine and effective analysis, it is very vital that you gain some sort of background knowledge (probably through literature review of empirical findings, observation, methodology etc) of what you want to analyse. You must acquaint yourself with the theories surrounding the analysis to be done. Intermediate knowledge of statistics, mathematics, econometrics is very necessary. This is not to scare you. Even if you are not versed in these subjects, you can still do well provided you are ready to do a good review of existing literature!!

GUIDELINES ON THE TUTORIAL
1. INTRODUCTION
-UNDERSTANDING VARIABLES AND VALUES
-SCALES OF MEASUREMENT
2. DATA CODING
- DEFINING VARIABLES
- DATA ENTERING
- DATA CLEANING
- DATA MANIPULATIONS (THIS DOESN'T MEAN INTRODUCTION OF FICTITIOUS DATA)
3. DATA ANALYSIS
A. DESCRIPTIVE STATISTICS
-FREQUENCY DISTRIBUTION TABLES
-MEASURES OF CENTRAL TENDENCIES (E.G. MEAN, MEDIAN, MODE ETC)
-CROSS TABULATION
-GRAPH OR GRAPHING
ETC
B. INFERENTIAL STATISTICS
- ANALYSIS OF VARIANCE (ANOVA)
- CHI-SQUARE (PARAMETRIC AND NON-PARAMETRIC)
- T-TEST
- CORRELATION
- MULTIPLE REGRESSION
ETC
4. INTERPRETATIONS
5. REFERENCING

NOTE: AS WE TREAT EACH OF THESE OUTLINES, PLEASE LET US LIMIT QUESTIONS TO THE TOPIC BEING TREATED SO THAT THE THREAD IS NOT DERAILED. THEN, THIS GUIDE IS STILL SUBJECT TO AMENDMENTS AS SITUATIONS ARISE. THANKS FOR SHOWING INTEREST. PLEASE LET ME KNOW THAT YOU ARE STILL FOLLOWING SO THAT WE CAN BEGIN THE TUTORIAL PROPER. THANKS FOR UR INTEREST ONCE AGAIN.

vikas11233: Thanks for providing such a valuable information about Data analyzing and academic project.Both things are really necessary to complete our degrees and get a good percentage in the finals.
Compound Inequalities

Thanks for showing interest in the tutorial. You can also invite people you think can benefit from this tutorial. Field of research and data analysis is an interesting one! At the end of the tutorial, i believe we would have created a win-win situation for everyone.

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Now let's begin the tutorial.
INTRODUCTION
As earlier stated, data analysis covers broad spectrum of human endeavours. It is the backbone of empirical research. Empirical research, on the other hand, is indispensable to solving myriad of problems facing humanity.In short, without amazing information (both inductive and deductive)derivable from sound data analysis, the dream of technological, economic and social advancements of the so-called advanced countries would have remained a dream. The aforementioned underscores the importance of data analysis and the need for fact-finding individuals, organisations and even nations to embrace it. Anyone that will follow this tutorial should open his/her own mind to the world of possibilities with data analysis. Do not see it as a mere academic exercise but something that can really help you to develop analytical mind. It makes you to be come a better person and above all.you can make a living from it.

UNDERSTANDING VARIABLES AND VALUES
variable is an attribute or property that describes a person or a thing. Age is a variable that describes you. It describes all humans, all living organisms, all objects—anything that exists for some period of time. Surname is a variable, and so are weight in pounds and brand of car. Database jargon often refers to variables as fields, and some Excel tools use that terminology, but in statistics you generally use the term variable (Carlberg, 2010).
PUZZLE: Why is age a variable?
It is a variable because it differs (varies, thus called variable) among observations. For instance, if a study is conducted to determine the age of Nairalanders, we'll discover that age of seun (oga patapata) may differ from that of Fynestboy, obasy09, mukina and hosts of other nairalanders. However, if a study is conducted to determine the' humanliness' (being human) of Nairalanders, this will not vary among nairalanders. Seun is a human, so also mukina and obasy09! This type of obsevation is known as CONSTANT.. You get the drift.
To be continued...
Pls if you're following this tutorial post "yes" on this thread

Continuation...
Variables have values. For instance, "45" is a value of variable "age" if an observation is 45 years old. Likewise,'Toyota' could be a value of variable "brand of cars". "Obasy09" is a value of variable "nairaland users".
PUZZLE: What is the diffrence between 'variable' and 'value'?

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following u obasi....thanks

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Continuation...
Variables can be categorised in various ways and in most cases according to the inherent property of a variable that a researcher is interested in. Variables can be categorised as dependent or independent, discrete or continous variable.
All experiments examine some kind of variable(s). A variable is not only something that we measure, but also something that we can manipulate and something we can control for.
Dependent and Independent Variables
An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable.
For instance, having conducted a Maths test for 100 students of XYZ secondary school, a tutor may be interested in knowing why some students perform better than others. The tutor does not know the answer to this, she thinks that it might be because of two reasons: (1) some students spend more time revising for their test; and (2) some students are naturally more intelligent than others. As such, the tutor decides to investigate the effect of revision time and intelligence on the test performance of the 100 students. The dependent and independent variables for the study are:
Dependent Variable: Test Mark (measured from 0 to 100)
Independent Variables: Revision time (measured in hours) Intelligence (measured using IQ score)
The dependent variable is simply that, a variable that is dependent on an independent variable(s). For example, in our case the test mark that a student achieves is dependent on revision time and intelligence. Whilst revision time and intelligence (the independent variables) may (or may not) cause a change in the test mark (the dependent variable), the reverse is implausible; in other words, whilst the number of hours a student spends revising and the higher a student's IQ score may (or may not) change the test mark that a student achieves, a change in a student's test mark has no bearing on whether a student revises more or is more intelligent (this simply doesn't make sense).
Therefore, the aim of the tutor's investigation is to examine whether these independent variables - revision time and IQ - result in a change in the dependent variable, the students' test scores. However, it is also worth noting that whilst this is the main aim of the experiment, the tutor may also be interested to know if the independent variables - revision time and IQ - are also connected in some way.
Categorical and Continuous Variables
Categorical variables are also known as discrete or qualitative variables. Categorical variables can be further categorized as either nominal, ordinal or dichotomous.
Nominal variables are variables that have two or more categories but which do not have an intrinsic order. For example, a real estate agent could classify their types of property into distinct categories such as houses, condos, co-ops or bungalows. So "type of property" is a nominal variable with 4 categories called houses, condos, co-ops and bungalows. Of note, the different categories of a nominal variable can also be referred to as groups or levels of the nominal variable. Another example of a nominal variable would be classifying where people live in the Nigeria by state. In this case there will be many more levels of the nominal variable (37 including FCT).
Dichotomous variables are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female". This is an example of a dichotomous variable (and also a nominal variable). Another example might be if we asked a person if they owned a mobile phone. Here, we may categorise mobile phone ownership as either "Yes" or "No". In the real estate agent example, if type of property had been classified as either residential or commercial then "type of property" would be a dichotomous variable.
Ordinal variables are variables that have two or more categories just like nominal variables only the categories can also be ordered or ranked. So if you asked someone if they liked the policies of the PDP and they could answer either "Not very much", "They are OK" or "Yes, a lot" then you have an ordinal variable. Why? Because you have 3 categories, namely "Not very much", "They are OK" and "Yes, a lot" and you can rank them from the most positive (Yes, a lot), to the middle response (They are OK), to the least positive (Not very much). However, whilst we can rank the levels, we cannot place a "value" to them; we cannot say that "They are OK" is twice as positive as "Not very much" for example.
Continuous variables are also known as quantitative variables. Continuous variables can be further categorized as either interval or ratio variables.
Interval variables are variables for which their central characteristic is that they can be measured along a continuum and they have a numerical value (for example, temperature measured in degrees Celsius or Fahrenheit). So the difference between 20C and 30C is the same as 30C to 40C. However, temperature measured in degrees Celsius or Fahrenheit is NOT a ratio variable.
Ratio variables are interval variables but with the added condition that 0 (zero) of the measurement indicates that there is none of that variable. So, temperature measured in degrees Celsius or Fahrenheit is not a ratio variable because 0C does not mean there is no temperature. However, temperature measured in Kelvin is a ratio variable as 0 Kelvin (often called absolute zero) indicates that there is no temperature whatsoever. Other examples of ratio variables include height, mass, distance and many more. The name "ratio" reflects the fact that you can use the ratio of measurements. So, for example, a distance of ten metres is twice the distance of 5 metres.Source:https://statistics.laerd.com/statistical-guides/types-of-variable.php

to be continued...

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Following. Pls revive the thread!

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