Act like you are a lecturer teaching research methodology for marketing research at second year level. 

Could you please set an exam paper with 30 multiple choice questions with 4 possible answers. 

Here are rules you need to follow when setting the paper:

1. The scope of these questions must be loosely based on topics discussed in the slides from your previous lecture.
2. For each question you must indicate the correct option.

Here are the slides from your past lecture:
What is statistics?
• Why is statistics in marketing management decision making important?
• What is some of the language used in statistics?

• data are individual pieces of factual information recorded and used for the purpose of
analysis.
• It is the raw information from which statistics are created. 
• Statistics are the results of data analysis - its interpretation and presentation.
• Often these types of statistics are referred to as &#39;statistical data&#39;.
Look around you, what do you think the average amount of siblings are in this class? Let’s use
statistics to find the actual answer out by referring back to the previous slide
• Business statistics in marketing management
Marketers need to know:
• who their customers are
• what their wants and needs are.

The role of marketing research:

“The systematic (organised, structured, regular) collection, analysis and interpretation of
information about all marketing problems, by means of marketing recognised methods, to
provide information that marketing management can use in decision-making.
• Social scientists attempt to explain and predict human behaviour by taking “educated
guesses” in a precise and structured way.

• Social scientists examine characteristics of human behaviour called variables (examples
please?) or from one point in time to another (examples please?)
• It is important to note that not all human variables vary (give examples of variables that
vary and ones that do not)
• Must also determine the unit of observation (the unit at, or, for which data is collected. E.g.
individual, household, community, or school
• Sometimes focus their research on aggregates (the way measures vary across entire
collections of people)
• The ideas that social scientists have about social reality are called hypotheses. These are
frequently expressed in a statement of the relationship between two or more variables
(dependent and independent variables)

Which gender vapes more and why?

Our everyday common sense observations are generally based on narrow, often biased
preconceptions and personal experiences. These can lead us to accept, without criticism,
invalid assumptions about the characteristics of social phenomena and behaviour.
• The problem to be studied is reduced to a testable hypothesis
• An appropriate set of instruments (tool for gathering the data) is developed
• Data is collected
• Data is analysed for its bearing on the initial hypothesis
• Results of the analysis are interpreted and communicated
Please note that in this module, our focus mainly lies in step 4 (organising the raw data

to test the accuracy of the hypothesis)

• Involves naming and labelling

• Placing data into categories and counting the frequency of occurrence
• Data is mutually exclusive (can only fall into one category)
• Categories must be exhaustive (a place for every bit of data
• The numbers used do not imply quantity (just category) and is thus not ranked or graded
for qualities
• Let’s look at the example that follows…
20 students are asked if they have vaped
1 = yes
2 = no

• Orders the data/cases according to degrees
• It does NOT indicate the magnitude of differences between the categories and numbers
• The intervals between the points are not meaningful (there is no amount or score assigned
between the difference between 1 st and 2 nd and 3 rd etc.
Let’s look at the example that follows…
The 8 students are asked to rank who vapes the most to the least. These are the results:

• Indicate the ordering of categories AND also the exact distance between them (how much
they vary from each other)
• The difference between a ratio and an interval is simply that a ratio has an absolute zero
point and an interval has no zero point

• Some variables are naturally interval/ratio e.g. age (ratio), weight (ratio), temperature
(interval)
• Some variables are interval/ratio because of how we scale them (are not naturally
occurring)
The students were asked to indicate how satisfied they are with 3 different flavours of vape:

The 3 flavours will then be placed in a hierarchy depending on their mean (average) score:
Strawberry = 4.85
Apple = 3.72
Coffee = 1.40

Researchers use numbers to quantify (express the quantity of) their data for description and

decision-making.

Let’s understand this by doing a class case study…(I need data capturers please)

How many hook-ups (kissing a stranger) have you had?

Let’s organise the data to describe it (mean average)
As you can see from the previous exercise, the raw data was meaningless until we used
statistics to organise it.
Thus, we can see how statistics was used to summarise and describe the raw data into
information that is easily communicated
Statistics is not simply to describe but also to make inferences/conclusions (generalising).
These decisions are made on a sample of the larger population and error inevitably results from

sampling.

Statistics is useful for the purpose of generalising findings, with a high degree of confidence,

from small samples to larger populations.

Let’s look at how sampling errors occur by using our ‘hook-up’ data as our entire population and

then drawing samples to generalise these findings

• Let’s draw a sample of 5 to represent the population and work out the average
• Let’s draw a sample of 10 and work out the average and see how different they are from
the ‘hook-up’ population average
With the use of statistics, we can confidently and readily make decisions about relationships
between samples and populations

I will ask you a few questions which we will discuss, then you will do a recap quiz (on next slide
and you have 5 minutes)
These questions are taken out of your textbook on page 18
• A tailor uses a tape measure to determine exactly where to cut a piece of cloth
• The speed of runners in a race is timed in seconds by a judge with a stopwatch
• A zoologist counts the number of tigers, lions, and elephants she sees in a designated
area
• Based on attendance figures, a ranking of the top 10 rock concerts for the year is compiled
• Cheryl counts the number of first year, second year and third year students at NewBridge
• A parent uses a yardstick to measure the growth of his/her child each year
• You are asked to do stock-take of all the textbooks in our storeroom
Collection of raw data is only the beginning of statistical analysis. This is the collection of raw
material that social researchers use to analyse data, obtain results and test hypotheses about

the nature of social reality.

We need to be able to transform raw data into a meaningful and organised set of measures that

can be used to test hypotheses.

The first thing we do is to create a frequency distribution table

I am conducting research on what young adults between the ages of 18 – 23 view “blue ticking”
to represent from the following categories:
• Angry (you’ve done something wrong)
• Disrespectful (they don’t care enough)
• Busy
• Ignoring (just not interested enough)
Please note the sex of each respondent

In groups, come up with a way to represent the data we collected previously as a frequency
distribution table. Yes… you may research how to do this.
Do not include the sex of each respondent yet

7 minutes will be given

Now, I would like to understand if there is a difference between how males and females view
‘blue ticking’. I want to do this so that I can clarify my results and add additional information to
my research

In your groups, draw a frequency table for comparing distributions (hint: it will look very similar
to your previous table but there should now be 3 columns)
7 minutes will be given

We need to compare (make comparisons between groups) or standardise frequency

distributions. We use the following two methods:

• Proportion: compares the number of cases in a given category with the total size of
distribution
• Percentage: the frequency of occurrence of a category of 100 cases.

Social researchers often use visual aids such as pie charts, bar graphs, frequency polygons,
line charts and maps in a effort to increase the readability of their findings.

In your groups, represent the data in the pie chart in the form of a bar graph

You have 7 minutes
• Best used for comparing data quickly and easily
• It uses columns/bars to compare data and to show the total of each item
• The bars do not touch
• The categories are given on the horizontal (x) axis
• The frequencies are given on the vertical (y) axis.

• A histogram is different to a bar chart because in a histogram the bars DO touch due to
it measuring continuous numerical data (the categories are a continuation of each other
e.g. age) and often use interval data to represent continuity along a scale
• A bar chart measures categorical data (which have no relationship or continuation
between them e.g. colours) and often represents the difference in nominal data. Thus, the
bars do NOT touch

A useful way to describe a group as a whole is to find a single number that represents what is

average or typical of that set of data.

In research, such a value is known as a measure of central tendency because it is generally
located toward the middle or centre of a distribution where most of the data tend to be

concentrated.

What is the more common in our classroom as a choice of pet, from the following:
1-Dogs
2-Cats
3-Birds
Work out the mode (of pet)

Important:
The mode is the data that occurs more than any other score in the set it occurs in.
The mode is NOT the frequency of the most correct score (amount of times it occurred) but the
value (what number represented the category) of the most frequent score.
What do I mean by this? Let’s go back and look at our case study to understand this
• When ordinal or interval data is arranged in order of size, it becomes possible to locate the
median
• The median is the middle number of an ordered set of data.
• It divides an ordered set of data values into two equal halves

• i.e. 50% of data values lie below the median and 50% lie above it
• Median lies in the ( ﷐��+1﷮2﷯ ) position of the data (don’t need to do more when n= odd)
e.g. 11, 12, 13, 16, 17, 20, 25 (work out the Mdn)
• When n = even then average the data value in this position with the next consecutive data
value (add the two central values together and /2)
e.g. 11, 12, 13, 16, 17, 20, 25, 26 (work out the Mdn)
• Remember: always first make sure your data is in ascending order
• Mean (average) = ﷐��﷯
• The mean is simply the statistical term for the average
• Thus, it lies at the centre of a set of numeric data values
• The mean IS distorted by data outliers
Let’s work out the mean age of this class by looking at the following slide

• Mean ﷐��﷯ know your symbols
• ﷐��﷯ = the sample mean (average)
• �� = number of data values in the sample
• ﷐��﷮﷯= value of the data value of random variable ��
• ﷐��﷮﷐��﷮﷯﷯= the sum of the data values of the random variable

Formula for mean: ﷐��﷯=﷐����﷮��﷯

• Mode can be used for any set of data at nominal, ordinal or interval level of measurement
(because it is only a frequency count)
• Median can only be obtained from ordinal or interval data (because it requires an ordering
from highest to lowest or lowest to highest)
• Mean is only used for interval data

Although the mode, median and mean can be used to summarize in a single number what the

average of a distribution is.
However,

If used alone, any measure of central tendency provides and incomplete picture of the set of

data and can mislead or distort the data.

Thus,

We need a measure of what is commonly referred to as variability (spread, width, dispersion) to

show how the scores are scattered around the centre of the distribution

• Gives the quickest and roughest measure of variability
• Range is the difference between the highest and lowest data values
• Range = Maximum value – minimum value
��=��−��

• The range is influenced by extreme data values (outliers) and is not widely used.
CA group 1 students: 18,18,19,19,20,20,21,22,22,23
23 – 18 = 5 year age range
CA group 2 students: 18,18,19,19,20,20,21,22,22,43
43 – 18 = 25 year age range

In fact, both classes have a very similar age range, with the exception of only 1 student (outlier
data) which distorts the range

• Firstly, what is variance? Variance is a measure of dispersion that takes into account the
spread of all data points in a data set.
• To overcome the problem of the zero total on our previous slide, we square the actual
deviations from the mean and add them together, like this: ﷐﷮﷮﷐﷐��−﷐��﷯﷯﷮2﷯﷯
• Once we have done this we divide this sum by �� to represent the number of data scores
involved, like this: ﷐﷐﷮﷮﷐﷐��−﷐��﷯﷯﷮2﷯﷯﷮��﷯
• This is the measure of average squared deviation from the central value
• Variance = how far a set of numbers (random) are spread out from their average value
(mean)
• Deviation = a difference of measure between an observed variable value and often the
mean

Formula = ﷐��﷮��﷯= ﷐﷐﷮﷮﷐﷐��−﷐��﷯﷯﷮2﷯﷯﷮��﷯

• Calculate the ﷐��﷯
• Calculate the squared deviation of each data value from the ﷐��﷯ and �� them
• Divide the �� of these squared deviations by N
• Let us do an example from page 76 of your textbook using the following data scores:

9, 8, 6, 4, 2, 1

• The standard deviation (s) is the average amount of variability in your data set. It tells you,
on average, how far each score lies from the mean.

• A quantity expressing by how much the members of a group differ from the mean value for
the group.

Formula ��=﷐﷮﷐﷐﷮﷮﷐﷐��−﷐��﷯ ﷯﷮��﷯﷯﷮��﷯﷯

• The �� is the square root of the variance
• Thus … ��=﷐﷮﷐��﷮2﷯﷯

Please find the �� for the variance example we have just done

The range is a quick but very rough indicator of variability, which is easily determined by taking

the difference between the highest and lowest scores.

A more precise measure of the spread of scores can be found in the variance, the mean of the
squared deviations from the mean. The variance and its square root, known as the standard
deviation are two reliable, interval-level measure of variability which can be employed for more

advanced descriptive and decision-making operations.