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Research Methods for Business & Managers

Requires not just identification of chosen techniques but a

meaningful discussion as to their suitability and, if possible, a

small discussion of the procedure involved in applying your

techniques. This section is heavily weighted in the overall

scheme of things so please pay due diligence thereto.

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Explanation through numbers

Objective

Deductive reasoning

Predefined variables and measurement

Data collection before analysis

Cause and effect relationships

Explanation through words

Subjective

Inductive reasoning

Creativity, extraneous variables

Data collection and analysis intertwined

Description, meaning

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Quantitative measures are typically referred to as variables.

A variable is anything that has different values eg numbers or

names

Any variable that is affected by or whose value is changed by

the occurrence of another variable is known as a dependent

(y) variable eg. If when pay is adjusted, performance changes

the performance is the dependent variable. Performance

can also be called the outcome.

Variables which are viewed as impacting upon the outcome,

are often referred to as independent (x) variables. So pay is

the independent variable.

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A nominal variable relates to a set of categories (such as ethnic groups,

political parties, gender )-which is not ordered or which cannot be

ranked/rated

An ordinal variable relates to a set of categories in which the categories

are ordered, (such as levels of educational qualification, organizational

rank, Likert scales)

An interval-level variable relates to a scale measure, (such as age or

income), that can be subjected to mathematical operations such as

averaging

Univariate analysis where a single variable is considered eg an analysis of

pay in a particular organization. Also known as simple statistics.

Bivariate analysis - where the relationship between two variables are

considered eg relationship between pay and performance. Also known as

effect or outcome statistics.

Multivariate analysis - where the aim is to explain why two variables are

related to other variable/s eg pay and working conditions impacting

performance and motivation. Also known as (multiple) effect or outcome

statistics.

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Descriptive or Simple Statistics

Summarize data

Effect Statistics:

Associational which measure connections

Inferential - which allows generalizations from samples to populations

Simple (or descriptive) statistics used for nominal and ordinal

variables

Usually displayed and described using frequencies,

proportions or odds

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Frequency Distribution

Counts and Percentages - A simple table showing how many, or what percent, of the cases fall into each variable category.

Central Tendency or Location

The mode is the most common or frequently occurring number.

The median is the middle point and the 50th percentile.

The mean, the arithmetic average, is the most widely used measure of central tendency

Measuring Dispersion (Spread)

You can measure variation in three ways: range, percentile, and standard deviation.

Range consists of the largest and smallest scores

Percentiles tell us the score at a specific place within the distribution.

Standard deviation = a widely used measure of the variability of a variable that indicates the

average distance of cases from the mean value.

Z-scores = a standardized measure that allows comparisons of groups that differ in their means

and standard deviations.

Charts and graphs are suitable for presenting and

summarizing frequency data

Type of Charts

Bar Chart, Pie Chart

Histogram

Frequency Polygon

Type of Data Bar Chart Pie Chart Histogram Frequency Polygon

Nominal X X

Ordinal X

Interval X X

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Do you want to know how many individuals checked each answer? Frequency

Do you want the proportion of people who answered in a certain way? Percentage

Do you want the average number or average score? Mean

Do you want the middle value in a range of values or scores? Median

Do you want to show the range in answers or scores? Range

Do you want to compare one group to another? Cross tab

Do you want to show the degree to which a response varies from the mean?

Standard deviation

Depend on the type of y and x variables. Main ones:

Y X Test Shows

numeric numeric linear regression slope, intercept, correlation

numeric nominal t-test ;ANOVA difference in mean

nominal nominal chi-square; contingency table

differences in frequency of ratio

nominal numeric categorical modeling relative risk or odds ratio

ordinal

whatever regression; t-test; implies causal direction

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Measures of Association measures the strength of the

association between 2 variables

Covariation or correlation = When two or more variables go together

or are associated with one another.

Statistical Independence = The absence of an association or

covariation between two variables.

Quantitative Analysis Techniques - Examples of associational statistics

Method Purpose Examples of application

Cross-tabulations Frequency distribution A preference for a brand of cereal based on gender

Scatter diagrams Frequency distribution Exploring the link between car mileage and petrol

consumption

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Scattergrams

A graph on which you plot the value of each case or observation. Each

axis of the graph represents the values of one variable, and the graph

can reveal bivarate relations.

Bi-variate cross-tabulation = Placing two variables in a

table at the same time allow you to see how cases that

have values on one variable align with values on a second

variable for those same cases.

Multi-variate cross-tabulation a table with two or

more variables that has been cross-tabulated

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Gender * Promotions Crosstabulation

Promotions

Not Promoted Promoted Total Gender Male Count

812 385 1197

Expected Count 800.8 396.2 1197.0

% within Gender 67.8% 32.2% 100.0%

% within Promotions 95.9% 91.9% 94.5%

Female Count 35 34 69

Expected Count 46.2 22.8 69.0

% within Gender 50.7% 49.3% 100.0%

% within Promotions 4.1% 8.1% 5.5%

Total Count 847 419 1266

Expected Count 847.0 419.0 1266.0

% within Gender 66.9% 33.1% 100.0%

% within Promotions 100.0% 100.0% 100.0%

Contingency table = A table with two or more variables

that have been cross-tabulated.

Department No. of Male Managers

Salary Ranges No. of Female Managers

Salary Ranges

Production 16 $2500-$5500 22 $2000-$5000

Sales 11 $4000-$7000 16 $3500-$6500

Accounting 9 $4500-$7500 8 $4000-$7000

Human Resources 5 $4oo0-$7000 9 $4000-$7000

Marketing 1 $4000-$7000 3 $4000-$7000

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Involves using quantitative data collected from a sample to

draw conclusions about a complete population

Population includes the totality of observations that might

be made

Whereas, a sample comprises a subset of the population

where observations will be or have been made

Hypothesis testing Confidence intervals Time series analysis Pearsons coefficient (P) Spearmans coefficient of rank correlation (NP) Students t-Test Simple regression (P) Multiple regression (P)

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Components Procedures Outcomes

Data Reductions

Data Display

Conclusions &

Verification

Coding

Categorisation

Abstraction

Comparison

Dimensionalisation

Integration

Interpretation

Description

Explanation/

Interpretation

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As the name implies, similar to grounded theory as described

in our look at research strategies

Given this reasoning, 3 key steps normally involved in this

type of analysis:

Open coding the initial attempt to develop categories which

illuminate the data

Axial coding saturation of categories and development of

subcategories

Selective coding - the process of integrating and refining categories

to form a larger theoretical scheme

Appropriate for data that are collected through narrative discourse

Where the data are analyzed by following the sequence of the narrative

to ensure that meaning and context are not lost

Usually follows a pattern:

What is the story about

What happened, to whom, where, and why

What were the consequences of this

What is the significance of these events

What was the final outcome

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Focuses on language as a social practice in its own right

and is concerned with how individuals use language in

specific social contexts

Enables researcher to gain an understanding of how and

why individuals use language to construct themselves

and the world around them

Many different branches most popular critical

discourse analysis

Involves analyzing images that may come from primary or

secondary findings

Used for example:

When you wish to analyze how many magazine ads used

celebrity endorsements

What is the most popular USP of ads

Although less time consuming that other methods, it is

more challenging to interpret data on the basis of visual

images

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Analysis of written documents

Developing categories of words and phrases

Looks at frequency of words, uses word counts

Used for historical trends

e.g. feminism in womens magazines over the last 10 years

e.g. number of centimetres devoted to sport in newspapers

Can be used to analyse interview texts

e.g. counting expressions of conflict