Points to remember
The following are the major points introduced in this chapter. Ensure that you are very confident with their meaning, content, context and significance.
1
Tables and graphs are the most popular methods of data presentation in
qualitative research.
2
Table presentation is expected to adhere to rules of clarity, simplicity,
economy of space, order of variables, appearance, accuracy and objectivity.
3
Graphs present data visually. Most graphs are constructed within the framework
of the coordinate axes; the X-axis (abscissa) and the Y-axis (ordinate).
4
A mean describes the central trend or average of all observations.
5
Mode is the category of a distribution that has the largest number of
observations.
6
Median is the point on a distribution that divides the observations (not their
values) into two equal parts.
7
The variance is the average of the squared deviations from the mean.
8
Standard deviation is the square root of the variance.
9
The range describes the distance between the lowest and the highest score in a
distribution.
10
Correlations display the relationship between two variables.
11
Correlation coefficients demonstrate the presence or absence of correlation, the
direction of correlation and the strength of correlation.
12
A correlation coefficient ranges from +1 to -1.
13
Spearman's rho and Pearson's r are the most commonly used measures of
association.
14
Spearman's rank order correlation coefficient is employed when data are
ordinally measured.
15
Pearson's rank order correlation coefficient is suitable for interval/ratio
measured data.
16
A positive correlation means that when one variable is increased the other will
increase too.
17
A negative correlation indicates that an increase in one variable is associated
with a decrease in the other.
18
A zero correlation means that there is no association between the variables.
19
The coefficient of determination is the square of the coefficient of correlation
and displays the degree of variability shared by the two variables.
20
Tests of significance relate statistics to parameters; the sample to the target
population; the study to the society.
21
Parametric tests assume that the variable in question is normally distributed in
the population; they employ the normal curve.
22
Non-parametric tests do not assume that the variable is normally distributed in
the population.
23
Tests of significance work around the notion of null hypothesis (Ho): if the
significance level of a particular test is below .05, the Ho is rejected; if it
is above .05, the Ho is accepted.
24
The most common tests of significance are chi-square test, t-test and analysis
of variance.
25
Chi-square tests are suitable for nominal data; t-tests and ANOVA are suitable
for interval/ratio level data.
26
The t-test and ANOVA compare means rather than scores. They answer the question
whether the differences between the means are significant or not.
27
Computer programs such as SPSS can assist with computing significance tests,
making relevant computations and conclusions fast, precisely and easily.