Bell, Bryman, and Harley: Research Project Guide

In the example above, content analysis will involve the design of a coding schedule and a coding manual. The coding schedule will be used to analyse the data into meaningful phrases/words for analysis and the coding manual will be a guide to the researcher for coding the phrases and words.

1. The first task is to break down the company codes into words or phrases which can be used to describe the information across all three cases (Information).

2. The second task is to number the words or phrases chronologically (Number).

3. The third task is to allocate a code (Code) which describes the data.

4. From this, SPSS, or another similar data analysis package, could be used to analyse the data. Students might wish to perform the SPSS analysis themselves to try and content analyse the company codes and look for similarities and differences. In this case, all three companies will be analysed using the same schedule and manual and, although the Codes might differ from company to company, the Number and Information should not. The company codes thus disaggregated, could then be input into SPSS. In this case, there would be three rows (Companies) of data in the SPSS input sheet and twelve columns representing the coded information. The Code numbers would be input into the columns, which would represent the Information. The columns would be headed with abbreviations representing the Information description.

The four stages are exemplified as follows:

The Coding Schedule. In this case the coding schedule is based on words and themes. An example is as follows:

The Coding Manual. In this case the coding manual used could be as follows:

Input into SPSS

The first line of the data sheet in SPSS would be as follows (assuming the above data is for company one only):

a) First explain what an arithmetic mean is as opposed to a geometric mean i.e. the sum of all values in a series and dividing by the total number of items in the series.

This exercise is grouped data rather than ungrouped data, so the exact values of each item are not known. This makes it necessary to approximate somewhat by taking the midpoint of each class interval to represent the whole class. If an assumed mean is chosen it should be the midpoint of the class and any deviations (d) from the assumed mean (A) are measured from the midpoint of the class in question.

In order to answer the question the mid point of the class has to be calculated. This value is then multiplied by the frequency of occurrence to get the correct number of mean values. The mean can then be calculated by summing all these values and dividing by the sum of the frequencies.

b) Features of the mean:

easy to understand and calculate and most common average

uses every value in the distribution, therefore, mathematically exact can be used for further data processing

it can be determined if only the total value of the items and the number of items are known, without knowing individual values

it can be distorted by extreme values in the distribution

for a discrete distribution the mean may be an "impossible" figure i.e. 17.68 when the value in the distribution are whole numbers

a) In order to locate the median it is entered to calculate the cumulative frequency.

The distribution has an odd number of values. The median is therefore the value of:

By looking at the data of cumulative frequencies the 36th item is between 15.5 and 20.5 values:

The median is 20.5 + 2/8 x 5 = 15.88

Assumption: data is evenly spread

The mode for grouped data is the class with the highest frequency provided that all class intervals are equal. Otherwise frequency density must be considered.

The modal class is therefore 10.5 - 15.5.

The modal value is:

L = Lower limit of modal class

Cl = Class Interval

f l, fo. f2 = frequencies of modal class, next lower class and next higher class respectively.

b) Features of the median and mode

Median

• it is a measure of rank or position. Half the items in the series will have a value greater than or equal to the median and half less than or equal to the median
• it is easy to understand
• it is unaffected by the presence of extreme items in the distribution
• if found directly from (ungrouped data) it will be the same as an actual item in the distribution
• it may be found when the values of all the items are known, provided that values of middle items and the total number of items are known
• ranking the items can be tedious
• the median cannot be used for further mathematical processing
• it may not be representative if there are few items

Mode

• for discrete data it is an actual single value
• for continuous data it is the point of highest frequency density
• it is easy to understand
• extreme items do not affect its value
• it can be estimated from incomplete data
• it may not be unique or clearly defined
• it cannot be used for further mathematical processing

To determine what would result from an equal situation use the mean e g. to determine per capita consumption of beer

If position or ranking is involved, use the median, which gives the halfway value e.g. a salesman interested if his monthly sales places him in the upper or lower half of the sales team will need to compare his sales figure with the median sales figure

Where the most typical value is required use the mode e.g. a shoe shop manager might want to know the average size of men' or women's shoes. For sales planning it will be the mode that he requires as it will tell him the most common size of shoe.

Input data into SPSS as below:

Then follow the instructions below.

1. Descriptive statistics.

Assume the data is interval, normally distributed, homogenous and parametric (NB. with this small sample the data, it probably would not be normally distributed, but assume so for the purposes of the exercise.)

The permissible descriptive statistics for interval data are:

Range, Mean, Standard Deviation and Product Moment Correlation

For the Product Moment Correlation, the data is from one sample so use a Pearson's Correlation

NB. to analyse the data in SPSS use the AnalySe button on the toolbar

• AnalySe
  • Descriptive Statistics
  • Descriptives/Descriptives Options

Use the same AnalySe box for the other computations but use the test menus

2. Inferential statistics

Use a t test one sample

To test if there is any significant difference between the means of the 4 variables:

• Set up a number of null hypotheses for the different variables

• Use a Paired t test. Choose a significance level.

Interpretation of the paired T test

1. Compare parameters i.e. mean, standard deviation or standard errors to see if there are obvious differences between the groups
2. Observe if the t value is significant or not. NB it should not be if the 3 measures in 1 are close

If the T value is 0.50 or above it could be significant. Look at the Probability of occurrence. Say the t value was computed and probability was above 60%. The t value would happen 62% of the time so we cannot reject the null hypothesis. There is no significant difference between the two means.

Students should examine the Chapter 22 and suggest an appropriate technique including content analysis, narrative analysis and so on.

One simple 'step like content' analysis technique could as follows:

Step 1 arrange the responses into a matrix

Step 2 look for common/different themes or responses

Step 3 develop hypotheses from the common themes or responses

For example:

Step 1

Step 2

Differences of opinion:

• Divergent to Questions 4 and 5
• Question 1 quality component differences but same issue

Similarities:

• Question 1, 2, and 3 and certain issues in Question 4, e.g. past reputation

Step 3

General conclusions:

• Question 1. Overall agreement on good quality of courses and services; however, private sector concerned about length of courses, fees, and currency of courses. Public sector concerned over declining quality. General agreement on the public sector suggested the quality of courses was declining through lack of competent and knowledgeable trainers
• All agreed the courses were generally very dated, some course fees too high, courses needed better targeting, facilities in the evening were poor, no telephone extensions and beds were too small. Interior did not match exterior image
• Question 2. General agreement on lack of response to course applications, post-course follow-up, courses too theoretical so on the job training growing, lack of Achievement Certification rather than Attendance Certification
• Question 3. General agreement that service quality needs to be good, communications timely, high quality image, provision of Certificate of Achievement, competent, knowledgeable and interesting presenters and better IT facilities
• Question 4. Public sector had a joint Director/prospective participant decision, private sector individual initiative then supervisor persuasion. Reputation, size of training budgets and lack of alternative training providers were other common reasons
• Question 5 Difference of opinion. Public sector would opt for Cornwell but private sector unlikely

From these general conclusions, the researcher may form hypotheses for further testing. Also, these findings could constitute the basis for quantitative research.