After gathering data through the various sources of data collection, you will be expected to collate the data retrieved and present in an orderly and understandable format; this is where data presentation and analysis come in.

Data presentation is the process of presenting your data with use of tables so that readers can easily assimilate the various data arranged or assembled in rows and columns.

Data analysis is the process of showing the relationships between data sets and variables with the use of various graphical formats and statistical tools. This section helps you to explain how you intend presenting information collected with either your questionnaire, interview or observation or all combined.

You will also be required to tell the statistical model you’ve adopt in testing your hypotheses. The formular should be written as well as reasons behind your choice of adopting the statistical model.

Sample “A”

Data collected from questionnaires, interviews, personal observation, etc. were presented using frequency tables, bar charts, pie charts and percentages. These tools were also used in the description of data. However, the inferential statistical tool used in this study is the X² (Chi) Square. The chi-square is a non-parametric inferential statistical tool used in the analysis of frequencies. It is a two-tailed test that can only indicate whether or not a set of observed frequencies differ significantly from the corresponding set of expected frequencies. The general formula for the computation of chi-square is given as:

X²      =       ∑(o-e)² /e

Where:        o is Observed frequency;

e is Expected frequency and

∑ means Summation of Cells

Sample “B”

The data collected for the study were presented in frequency tables while the analysis was done using percentages and Bar charts.  The hypothesis was analyzed with One-Way ANOVA (Analysis of Variance) in IBM (International Business Machines Corporation) SPSS (Statistical Packages for the Social Science).

The formulas of ANOVA are presented below;

dfwg = (n₁-1) + (n₂-1)+(n₃-1)+(n₄-1)

Dfbg=k-1

SSbg = ((∑x₁)²/n₁+ (∑x₂)²/n₂ + (∑x₃)²/n₃ + (∑x₄)²/n₄) – (∑x₁ +∑x₂ +∑x₃ + ∑x₄)/n₁ +n₂ + n₃ + n₄

SSwg = (∑x₁²+ ∑x₂² + ∑x₃² + ∑x₄²) – ((∑x₁)²/n₁+ (∑x₂)²/n₂ + (∑x₃)²/n₃ + (∑x₄)²/n₄)

 F=MSB/MSE

MS_within = SS_within/df _within

Where:

SS = sum of square

Df = degree of freedom

Ms = mean square

F = f-ratio

SSwg = sum of square within group

SSbg = sum of square between group

Dfwg = degree of freedom within group

Dfbg = degree of freedom between group