What is Data?

Collection of facts, figures, and statistics that are used to inform, analyze, and make decisions is called Data.

Types of Data:

It can be in the form of numbers, words, images, sounds, or videos.

Data can be quantitative (numerical) or qualitative (non-numerical).

Examples of data include: –

Numbers: sales figures, temperatures, stock   prices

Text: customer feedback, social media posts, survey responses

Images: photos, videos, medical scans

Audio: voice recordings, podcasts

Other: sensor/ satellite readings, GPS locations, website traffic

Data can be used to: –

-Identify patterns and trends

-Make predictions and forecasts

-Inform business decisions

-Evaluate performance and progress

Answer questions and solve problems In today’s digital age, data is generated and collected at an unprecedented scale, and its effective use has become a key driver of innovation, competitiveness, and success in various fields.

Scientific Data

Scientifically, data refers to a collection of quantitative (numerical) or qualitative (non-numerical) values that are obtained through observation, measurement, or experimentation.

These values are typically numerical or categorical in nature and are used to describe phenomena, patterns, or relationships.

In a scientific context, data is usually considered to be:

1.Empirical:

Data is based on observation or measurement, rather than on intuition or assumption.

2.Systematic:

Data is collected in a structured and organized way, using a predetermined methodology.

3.Quantifiable:

Data can be represented by numbers or categories, allowing for statistical analysis and mathematical manipulation.

Important to Remember:

Data is intended to be free from personal biases and subjective interpretations.

Verifiable:

Data can be checked and validated through repetition or cross-validation.

Examples of scientific data include:

Sensor readings from a laboratory instrument

Survey responses from a population sample

Gene expression levels from a biological experiment

Climate patterns from satellite imagery

Data Analysis and Interpretation

Major Steps for Data analysis:

Involves three major steps,

1.Cleaning and organizing the data for analysis (Data Preparation / Compilling)

2.Describing the data

3.Testing Hypotheses and Models

Statistics

A set of mathematical procedures for describing, synthesizing, analyzing, and interpreting quantitative data.

The selection of an appropriate statistical technique is determined by the research design, Hypothesis, and the data collected.

Compiling Data for Analysis

Data must be accurately recorded and systematically organized to facilitate data analysis:

Tabulating: organizing the data in a systematic manner

Coding: assigning numerals (e.g., ID) to data

Important To Remember:

Data analysis is the process of finding the right data to answer your question,

Understanding the processes underlying the data,

Discovering the important patterns in the data,

and then communicating your results to have the biggest possible impact

Parameters

indices (keywords/hints) calculated by the researcher for an entire population

Statistics

indices calculated by the researcher for a sample drawn from a population

Descriptive statistics

permit the researcher to describe many pieces of data with a few indices (tables/graphs/etc, T-test, T-test)

Types of descriptive statistics

1. Graphs

representations of data enabling the researcher to see what the distribution of scores look like

2. Measures of central tendency

indices enabling the researcher to determine the typical or average score of a group of scores

3. Measures of variability

Mode / Median, Mean

Mode – the  value that occurs the most times

Median – the middle of the distribution, the number where half of the values are above and half are below

 Mean – average of all values of this variable in the data set

Range of values – from minimum value to maximum value

Data Analysis Procedure

Coding – process of translating information gathered from questionnaires or other sources into something that can be analyzed

Involves assigning a value to the information given—often value is given a label

Coding can make data more consistent:

Example: Question = Sex

Answers = Male, Female, M, or F

Coding will avoid such inconsistencies

Coding Systems

Common coding systems (code and label) for dichotomous variables:

             0=No  1=Yes  

  (1 = value assigned, Yes= label of value)

OR:  1=No  2=Yes

When you assign a value you must also make it clear what that value means

In first example above, 1=Yes but in second example 1=No

As long as it is clear how the data are coded, either is fine

You can make it clear by creating a data dictionary to accompany the dataset

Few frequently used Software for Data Analysis

Statistics 8.1

State X

SPSS

R

MS Excel

Also read: Sampling and Sampling Methods By Dr. Ghazala Ali Khan

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The post DATA Collection And DATA Analysis Methods Lecture By Dr. Ghazala Ali Khan appeared first on IM Group Of Researchers - An International Research Organization.