How to Tally, Encode, and Analyze your Data using Microsoft Excel (Chapter 4: Quantitative Research)

Summary

This lesson introduces quantitative data analysis, explaining how to prepare, organize, and analyze data using statistical tools. It covers key concepts such as coding data, using software like SPSS and Excel, and presenting data through graphs and charts. The video also explores different statistical techniques, including descriptive statistics (mean, median, mode, and standard deviation) and advanced methods like correlation, regression, and ANOVA. Practical examples show how to apply these methods to research data, interpret results, and determine significant relationships or differences between variables, making complex data analysis accessible for beginners.

Takeaways

• 📊 Quantitative data analysis simplifies the interpretation of data through statistical tools like SPSS or Excel.
• 📋 The first step in quantitative data analysis is data preparation using a coding system to convert words, images, or pictures into numerical values.
• 🖥️ Microsoft Excel or SPSS is used to input and process data from surveys, with Excel being appropriate for smaller datasets.
• 📉 Different types of graphs are used based on the data type: bar graphs and pie charts for nominal data, histograms or frequency polygons for interval or ratio data.
• 🔢 Data tabulation involves organizing data into categories and calculating percentages, such as the gender distribution in a survey.
• 📈 Descriptive statistical techniques include frequency distribution, measures of central tendency (mean, median, mode), and standard deviation.
• 🔬 Advanced analytical methods include correlation, ANOVA, and regression to determine relationships and predictions between variables.
• 📊 Coding data into Excel involves assigning numerical values to variables (e.g., 1 for male, 2 for female) and analyzing responses using Likert scales or other metrics.
• 📑 Descriptive statistics can help rank and interpret items based on their mean, median, and standard deviation, providing insights for research papers.
• 🧪 Statistical tests like t-tests are used to determine if there is a significant difference between variables, with interpretation based on the t-stat value compared to a standard alpha value.

Q & A

What is the first step in quantitative data analysis?

The first step in quantitative data analysis is preparing your data using a coding system, where words, images, or pictures are converted into numbers to make them suitable for analytical procedures.

What tools are commonly used for data entry in large and small studies?

For large, complex studies, SPSS (Statistical Package for the Social Sciences) is commonly used, while for smaller datasets, Microsoft Excel is often sufficient.

What types of graphs are appropriate for different data types?

For nominal data, bar graphs and pie charts are used. Ordinal data also uses bar graphs, while interval and ratio data are often represented with histograms or frequency polygons.

What are the basic methods of analyzing quantitative data?

Quantitative data can be analyzed using descriptive statistical techniques such as frequency distribution, measures of central tendency (mean, median, mode), and standard deviation. Advanced methods include correlation, ANOVA (analysis of variance), and regression analysis.

What is the role of standard deviation in quantitative analysis?

Standard deviation measures the extent of variation or dispersion in a set of data values from the mean, helping to understand how spread out the data points are.

What is the difference between correlation and regression in data analysis?

Correlation measures the strength and direction of a relationship between two variables, while regression goes further to describe the nature of that relationship and whether one variable can predict another.

When would you use parametric vs. non-parametric tests?

Parametric tests are used for interval and ratio data, where assumptions about the data's distribution are made. Non-parametric tests are used for nominal or ordinal data that do not meet these assumptions.

What is the Pearson’s r coefficient used for?

Pearson’s r coefficient is used to measure the strength and direction of the relationship between two variables, where values closer to 1 or -1 indicate a stronger relationship.

How do you interpret a correlation coefficient value of 0.57?

A correlation coefficient of 0.57 suggests a moderate positive relationship between the two variables, as it is closer to 1 than to 0.

How can you determine if there is a significant difference between two variables using a t-test?

To determine if there is a significant difference, compare the t-test result (t-stat) to the alpha value (0.05). If the t-stat is greater than 0.05, there is a significant difference. Otherwise, there is no significant difference.

Outlines

00:00

📊 Introduction to Quantitative Data Analysis

This paragraph introduces the topic of quantitative data analysis, emphasizing its importance after data collection. It reassures the audience that with the help of statistical tools, the analysis process becomes easier. The objectives of the lesson include explaining procedures for preparing data, describing methods for organizing and presenting quantitative data, and using statistical techniques for data analysis.

05:02

🔢 Steps in Quantitative Data Analysis

This paragraph discusses the initial steps in quantitative data analysis, starting with preparing data using a coding system. It explains the conversion of words, images, or pictures into numbers for analytical purposes, and covers data entry and recording. The paragraph mentions tools like SPSS for large studies and Microsoft Excel for smaller datasets. It also details the types of graphs and charts suitable for different data types, including nominal, ordinal, interval, and ratio scales.

10:02

📈 Data Tabulation and Analysis Techniques

This paragraph focuses on data tabulation, including frequency and percentage distribution, and analyzing data using descriptive statistical techniques. It explains frequency distribution, measures of central tendency (mean, median, mode), and standard deviation. The paragraph also introduces advanced quantitative analytical methods like correlation, analysis of variance (ANOVA), and regression, which help describe relationships and determine differences between data categories.

15:03

🔍 Coding and Analyzing Data in Microsoft Excel

This paragraph provides a step-by-step guide on coding and analyzing data using Microsoft Excel. It covers the process of converting words into numbers, coding variables, and transferring data from survey questionnaires to Excel. The paragraph explains how to use color coding for categories, input data, and use Excel's data analysis tools to perform statistical treatments like correlation and descriptive statistics. It also describes how to interpret correlation coefficients and rank items based on descriptive statistics.

📑 Presenting Data Results in Research

This paragraph explains how to present data results in a research paper. It details the process of finding means, ranks, and verbal interpretations for survey items, and describes how to calculate and interpret average weighted means. The paragraph also discusses using t-tests to determine significant differences between variables, including how to interpret t-test results based on standard alpha values. The goal is to help researchers present their data analysis clearly and effectively in their reports.

Keywords

💡Quantitative Data Analysis

Quantitative data analysis refers to the process of analyzing data that can be quantified, or represented in numerical form. In the video, this involves converting collected data into numbers, allowing for statistical analysis using tools like SPSS or Microsoft Excel. The aim is to uncover patterns or relationships in the data.

💡Coding System

A coding system is used to convert non-numerical data (like words or images) into numerical form to make it suitable for analysis. In the video, the instructor explains how to code data by assigning numerical values to categories such as gender, where male is coded as '1' and female as '2'. This step is essential for quantitative analysis.

💡Descriptive Statistics

Descriptive statistics summarize and describe the features of a dataset, typically using measures like the mean, median, mode, and standard deviation. The video highlights how descriptive statistics are used to provide an overview of the data before performing more complex analyses, such as identifying which items in a survey have the highest averages.

💡SPSS (Statistical Package for the Social Sciences)

SPSS is a widely-used software tool for statistical analysis, particularly for large and complex datasets. In the video, SPSS is mentioned as an advanced tool for processing data, though smaller datasets can be analyzed using Microsoft Excel. SPSS simplifies data coding, entry, and analysis.

💡Correlation

Correlation measures the relationship between two variables and how they change together. In the video, Pearson's correlation coefficient is used to determine the strength of this relationship. A value closer to 1 or -1 indicates a strong relationship, while a value near 0 indicates little or no correlation.

💡T-Test

A T-test is a statistical method used to compare the means of two groups to determine if there is a significant difference between them. In the video, the T-test is applied to check if the data from two variables (Part A and Part B) show significant differences, with the result determining whether to accept or reject the null hypothesis.

💡ANOVA (Analysis of Variance)

ANOVA is a statistical test used to analyze the differences among group means and determine if they are statistically significant. The video briefly mentions ANOVA as a tool for comparing the means of different data categories to see if any of the differences between them are significant.

💡Frequency Distribution

Frequency distribution refers to the organization of data based on the frequency of occurrences of various responses. In the video, it is explained as the first step in descriptive statistical analysis, where one can determine how often certain responses or answers appear in a dataset.

💡Standard Deviation

Standard deviation is a measure of the dispersion or spread of a set of data points relative to the mean. In the video, the instructor explains that standard deviation is used to assess how much individual data points differ from the average, helping researchers understand the variability in their dataset.

💡Regression Analysis

Regression analysis explores the relationship between a dependent variable and one or more independent variables. It is used to determine if one variable can predict another. In the video, regression is explained as a more extensive method than correlation, helping researchers understand and predict relationships between variables.

Highlights

Introduction to quantitative data analysis, emphasizing the importance of statistical tools for easier data analysis.

Explanation of the three key objectives of the lesson: preparing data, organizing and presenting quantitative data, and using statistical techniques for analysis.

Introduction to different scales of measurement: nominal, ordinal, interval, and ratio, with corresponding chart types like bar graphs, pie charts, and histograms.

Explanation of coding system: converting qualitative data such as words, images, or pictures into numerical form for analytical procedures.

Demonstration of how to enter data into Microsoft Excel for analysis, using a coding system for variables like gender and age.

Introduction to data tabulation, with an example of calculating percentage distribution based on variables like gender and course.

Explanation of two methods for data analysis: descriptive statistical techniques and advanced quantitative methods, depending on the research design.

Detailed description of descriptive statistical techniques: frequency distribution, measure of central tendency (mean, median, mode), and standard deviation.

Introduction to advanced quantitative methods, including correlation, ANOVA (analysis of variance), and regression analysis to determine relationships between variables.

Steps to calculate correlation in Excel and interpret the results based on the correlation coefficient, with values closer to 1 indicating a stronger relationship.

Explanation of how to rank items based on their mean values using descriptive statistics and determining the item with the highest rank.

Explanation of verbal interpretation for mean values, with a scale ranging from 'strongly agree' to 'strongly disagree' for interpreting Likert scale results.

Instructions for performing a T-test in Excel, along with interpretation based on the T-stat value and alpha value (0.05).

Explanation of the interpretation of the T-test results: if the T-stat value is greater than 0.05, there is a significant difference between the two variables.

Final recap on analyzing and interpreting relationships between variables, using both descriptive and advanced statistical techniques to support research findings.

Transcripts

for today we are going to learn about

quantitative data analysis now that

you're

done collecting your data it is time to

analyze

them i hope you keep on watching as we

start okay to start with

let me show you these figures what comes

to your mind when you see

this symbols maybe there are some

difficulty

in your mind you see it hard

but let me assure you that for

quantitative data analysis

with the help of our statistical tool

your analysis will be easier the

objectives of our lesson

are the following explain the different

procedures

involved in preparing data for

quantitative analysis

describe the different ways of

organizing and presenting quantitative

data

and use statistical techniques to

analyze

data when these data

appears that in words images or pictures

but in numerical forms such as fractions

numbers and percentages

they become quantitative data

now we are going to learn steps in

quantitative data analysis the first

step is you prepare your data using

coding system

when we say coding system we convert the

words

images or pictures into numbers that

they become

fitted for any analytical procedures

data entry or data recording that means

to say

you transfer information from

questionnaires

or code sheets to computer files

for processing for large

complex studies we use the aid of

statistical package for the social

sciences we call it as spss

for smaller data microsoft excel

can also be used

graphs and charts one criterion for

deciding on the most appropriate

type of graph or chart to use

depends on the type of data that you

have

collected we have different types of

data

such as nominal ordinal interval in

eurasia's scale

that we have already discussed for you

and here are some examples for you to

review upon those

types of data for a nominal

you could use bar graph and pi chart

for ordinal you use bar graph

and then for interval and ratio you

could either use

histogram or frequency

polygon the second step is data

tabulation

when there are frequency and percentage

distribution

we use a data tabulation here's an

example

gender core school as your variables and

then you could find the percentage

of male respondent who participated

in your survey and you do the same for

the rest

step two is analyzing the data

there are two different ways to analyze

your data depending

on the purpose of your research

if it is descriptive then we use

descriptive statistical technique if it

is correlational research design

experimental research design comparative

research design

and so on and so forth then we use

advanced quantitative analytical methods

now let's go first to the descriptive

statistical technique

we have frequency distribution measure

of central tendency

and then standard deviation when we say

frequency distribution this is where you

determine

the number of responses given repeatedly

for one question okay

measure of central tendency that's

where you determine the mean which is

the average

of all the item

median which is the score in the middle

of a sep mode which means

the item who has most repeated

appearance

in the set when we say standard

deviation

this is the extent of the difference of

the data

from the mean moving forward

we have advanced quantitative analytical

methods

correlation uses statistical analysis to

yield results that describe the

relationship

of two variables for analysis of

variance anova

used to determine if the difference in

the means or averages

of two categories of data are

statistically

significant regression shows the nature

of relationship

of variables and gives more extensive

results

than that of correlation it also

determines whether a variable is capable

of predicting

the strength of the relation between

independent variable

and the dependent variable

so to find whether there is a

significant difference between

samples we have here period sample

thetas by the way

when we say parametric this is usually

used to test data

that are in the interval and ratio

levels of

measurement and for non-parametric you

test data that are original

or nominal categorical data

and then here are the following analysis

tools

okay that's it

for finding significant relationship

between

variables when you only find the

relationship between two variables we

use

pearson's r coefficient of correlation

but when we find significant

relationship more than

two variables we usually use multiple

regression

right now i'm going to show you how to

code and analyze

your data i'm going to teach you

how you could transfer data from

survey questionnaire to a computer file

using microsoft excel so the first step

is to code your data when we see coding

you convert

words into numbers let's say your

nominal categorical data is a gender

so the male stands for

one as you numerical conversion

f stands for 2 as your

code for each maybe you have 5h

bracket so let's say your population

is from ages 16 to 30

so your first each bracket is 16

and the next thing is you have to code

each

age bracket

so you already have a code for your

items

so you are now ready to start

transferring the information

to this microsoft excel

first respondent of course you stand it

wanted one

okay r stands for respondent and how

many respondents do you have

let's say you have 90 respondents

and then next you determine now the

gender of each respondent

one stands for male and two stands for

female and then

you start coding the age bracket of

your respondents in order for you to

easily

distinguish your categories you use

color code

you are now ready to code their

responses to your

items so let's say you have two

parts or two variables first variable

part a second variable part b

for the part a you use the code a for

the part b you use the code b

statement you use the code s and then

item number of course the number itself

let's say you use four point likert

scale so you have 4.0 you strongly agree

to strongly disagree without using the

neutral

and then you start encoding their

responses

you do the same way to the part b

variable

after that you total the sum

just drag it down

usually microsoft excel doesn't have

data analysis

over here therefore you go to the file

and click options to provide

add-ins

[Music]

and click check box of analysis tool

pack

once you have your data analysis you now

have your analysis

tools to use for a statistical treatment

the next thing that you have to do

is to click the data analysis in which

we start

analyzing the two variables click

correlation

it says here input range

so in this case we are going to

determine

part a and part b relation click

part b click your shift

button part a

you still hold the shift you start

clicking the

control button and then click

the arrow so you select all the

responses

where do you want to see the result

output range

beside the data we just click somewhere

here

and then we could see the data over

there

click ok so it says there

you have 0.57

40 25 so here is the interpretation

the closer your correlation coefficient

is to positive one or

negative one then the more

closely the two variables are related

but if your correlation coefficient is

closer to 0 then it means that there is

no correlation if this is your result

0.57 is it closer to 0

or closer to one that is closer to one

right so that means to say that there is

a correlation between your part a and

part b but if your result is

closer to zero that means there is no

correlation between them the usual

results

are zero point zero one

zero point two

and so on and so forth so here is a

strength of relationship between

two tests point zero

zero two point

twenty the strength of relationship is

poor

point 21 to point 40

the strength of relationship is fair

point 41

to point 60 the strength of relationship

is moderate point 61 2.80

the strength of the relationship is

substantial point

81 to 1.00

the strength of relationship is almost

perfect there you go i hope you

understand how to find

significant relationship between two

variables okay right now

we will determine which item

has the highest rank let's say

your part a has seven items

you determine now which among the items

is the highest we use descriptive

statistics click descriptive statistics

and then your input range

don't forget to click labels in first

row

where do you want to see your output you

click

output range click this one beside the

data

click any any cell don't forget to click

summary statistics

it's easier to know the median mode

standard deviation and then the mean

for each item of the part a

so the first one that you have to do is

you transfer

the label aligning it to the result

okay it's easier to see right so here's

the mean of the first

so right now you're going to determine

which

item ranks the highest okay take a look

on that

and as i can see this is the highest

put the rank label here

okay there you go you have your mean

your median

mode and then the standard deviation

you just get all the results and

transfer it

to your thesis file

okay so this is how you present the data

result

in your research paper here are the

following

statements by which you find out the

mean

and then its rank and then its verbal

interpretation

how can you determine the verbal

interpretation for each item

here's the scale for each verbal

interpretation

3.26 to 4 that interprets strongly agree

2.51 to 3.25 that is agree

1.76 to 2.50

disagree and then 1.1 to 1.75

is strongly disagree in the given

instance

your first item has 3

weighted mean and therefore it's at the

middle of

2.51 to 3.25 that has its verbal

interpretation of

agree remember to also find out the

average

weighted mean and then its verbal

interpretation

after finding out which one is the

highest you know interpret this way

looking closely to the table item per

item it has

observed that the students use

powerpoint presentation through laptop

and

smart television and it has the highest

average

weighted mean among the 10 items and

interpreted as strongly agree

after you've written a report for its

analysis

you know provide a supporting literature

on the said result

okay to find a significant difference

between the two

whether there is a significant

difference or

no significant difference at all between

the two variables

you use t-test

let's go to data analysis and find

peta's paired two sample for means

your variable one is part a

click the whole responses

so how are we going to interpret the

following numbers

to determine whether there is a

significant difference

you take a look at the t-test that

the result is 4.34

your standard alpha is 0.05

if the result is

less than 0.05

we accept our null hypothesis which is

that there is no significant difference

but if your t stat value is greater than

0.05 then we

reject our null hypothesis that there is

no significant difference

and therefore the interpretation is

there is a significant

difference between two variables

let's see if the t-step value is

greater than 0.05

your t-step value is 4.34

is it greater than 0.05 or

less than 0.05 it is

greater than 0.05

so if it is greater than we reject

our null hypothesis and therefore

the interpretation is there is a

significant difference

between our part a and part b

that's it i hope you learned something

from our discussion today