Quantitative Research Analysis I Module 3 1 This

Quantitative Research & Analysis I Module 3. 1

This Module Discusses… Quantitative Research Numerical Analysis Descriptive Statistics

Quantitative Research A systematic approach to investigation during which numerical data is collected and/or the researcher transforms what is collected or observed into numerical data.

Why Use Quantitative Measures? • To delineate fine differences between people, organizations, or any other unit of analysis • Study differences in people who love their jobs & people who hate their jobs • To provide a consistent device for gauging distinctions (yardstick). • A measure that is not be influenced by timing of its administration nor by the person who administers it. • To produce precise estimates of the degree of the relationship between concepts (using correlation analysis). • If we want to measure both job satisfaction and the things with which it might be related, such as stress-related illness.

Main Preoccupation of Quantitative Researchers • Measurement • Causality • Generalization • Replication

Measurement Preoccupation of Quantitative Researchers • Reliability • Consistency of measures • Validity • Precise measurement of constructs

Causality Preoccupation of Quantitative Researchers • Explanation • How things are and why things are that way? • Direction of Causal Influence • Relationship between dependent & independent variables. • Confidence • Legitimacy of causal inferences; easier achieved with experimental and longitudinal research designs.

Generalization Preoccupation of Quantitative Researchers • Extending findings of a study beyond the particular context. • Focusing on law like principles, that can be used to predict what people will do in certain situations. • A measure of a study’s external validity. • Can be attained by accessing a representative sample. • Ensuring that results are not specific to the group sampled for the study.

Replication Preoccupation of Quantitative Researchers • Minimize researcher biases so the study generates similar results when repeated in differing contexts • Explicit description of procedures • Control of conditions of study • Replication is not always possible in business research, due to changes in work conditions.

Criticisms of Quantitative Research • Static view of social life • cannot provide “deeper” understanding of social phenomena. • Very little or no contact with the “field” or people. • Artificial and spurious sense of precision and accuracy • Lack of ecological validity • reliance on instruments and measurements • the extent to which the conditions simulated in the laboratory reflect real life conditions

Numerical Analysis Dealing with Quantitative Data

Quantitative Data Analysis • Once you have collected your data, you need to make sense of the responses. • The most basic techniques in quantitative data analysis enable us to make sense of data by: • organizing them; • summarizing them; • conducting exploratory analysis; and • communicating meaning to others by presenting data as tables, graphical displays, and summary statistics.

Quantitative Data Analysis • We can also use the more advanced data analysis tools to see: • where responses are similar, for example, we might find that the majority of students all go to the university library twice a week • if there are differences between the things we have studied, for example, 1 st year students might go once a week to the library, 2 nd year students twice a week and 3 rd. year students three times a week • if there is a relationship between the things we have studied, for example, is there a relationship between the number of times a student goes to the library and their year of study?

Quantitative Data Analysis • At the very beginning of your research you must think about what you are trying to find out. So, ask yourself: • 'Am I trying to describe what happens with the participants in my sample? ‘ OR • 'Do I want to be able to generalize my results to the wider population? ' • If you want to describe what happens with your sample of participants then you will most likely use descriptive statistics. • If you want to be able to generalize your results to a wider population you will need to use inferential statistics.

Descriptive Statistics Summarizing Quantitative Data

The Nature of Descriptive Analysis • The elementary transformation of raw data in a way that describes the basic characteristics of the data. • Common techniques used for descriptive analysis: • Tabulation • Graphs & Charts Organizing Data • Measures of Central Tendency • Measures of Variance Summarizing Data

Measurement Levels & Descriptive Statistics

• Frequency tables Tabulation • number of people or cases in each category Descriptive Statistics • often expressed as percentages of sample Sample Demographics Male Female Organization Private 32. 83% 22. 39% Public 20. 90% 23. 88% Total 53. 73% 46. 27% Managerial Level Lower 1. 49% 16. 42% Middle 14. 93% 5. 97% Senior 38. 81% 22. 39% Total 55. 22% 44. 78%

• Contingency Table Tabulation • a display format used to analyze and record the Descriptive Statistics relationship between two or more categorical variables. Starting Salary Gender Less than 20 20 up to 25 25 and more Total Female 12 84 24 120 Male 20 48 12 80 Total 32 132 36 200

Graphs & Charts Descriptive Statistics Number of Grocery Stores 25 • A graphical way of showing a frequency distribution, where columns are positioned over a label that represents a categorical variable. 20 15 10 5 0 • Bar Graph Town Center Al Manaseer Al Sanaiyaa Residential Block

• Histogram Graphs & Charts Descriptive Statistics 12 [SERIES NAME] Frequency 8 6 4 0 distribution, where columns are positioned over a label that represents a quantitative variable. 10 2 • A graphical way of showing a frequency [SERIES NAME] Annual Salary (Thousand AED)

Graphs & Charts Descriptive Statistics Lower Management 18% Senior Management 61% Middle Management 21% • Pie Chart • a circular graphic, which is divided into slices to illustrate numerical proportion. • commonly used to illustrate relative sizes of data that has been organized into categories.

Measures of Central Tendency Descriptive Statistics • Statistics that attempt to describe typical scores that reflect how the data is similar. • Mean - sum all values in distribution, then divide by total number of values • Median - middle point within entire range of values • Mode - most frequently occurring value

Measures of Variance Descriptive Statistics • Statistics that show data differs (its variation, spread, or dispersion). • Standard deviation - the average amount of variation around the mean, reducing the impact of extreme values (outliers) • Range - the difference between the minimum and maximum values in a sample

Quantitative Research - Basics • delineate fine differences in observed data • provide a consistent device for measuring constructs • produce precise estimates of hypothesized relationships • Quantitative studies emphasize • precise measurement, evidence of causality, and support for generalizing and replicating findings. • Analysis of numerical data may take two forms: • Descriptive Statistics: organizing and summarizing data • Inferential Statistics: estimating population parameters using sample results In Summary • Quantitative research involves numerical data, that helps