Study with Mr K: GEL Topic 3

Summary

This video, hosted by Mr. K, focuses on key geographical investigation concepts, with a specific emphasis on topic 3.1. It explores the differences between research questions and hypotheses, various types of data (primary vs. secondary, quantitative vs. qualitative), and data collection methods. Additionally, Mr. K highlights important concepts such as sampling techniques, questionnaire design, data analysis (quantitative and qualitative), and presenting findings. He also discusses limitations, risks, and possible exam questions related to geographical investigations, providing students with essential tips for success.

Takeaways

• 📝 The key difference between a research question and a hypothesis: a research question outlines the scope of investigation, while a hypothesis predicts the relationship between variables.
• 📊 Data can be classified into two types: primary (collected firsthand by the investigator) and secondary (data already collected and available from other sources).
• 🔢 Quantitative data refers to measurable data (numbers), while qualitative data is more subjective, focusing on emotions and reasoning.
• 🎯 Understanding sampling methods is crucial: probability sampling is random, while non-probability sampling is non-random, such as convenience sampling.
• 📋 Questionnaire surveys use predefined responses or different rating scales like Likert scale and frequency scale for collecting data.
• 🧠 Mental maps are useful tools for data collection, where participants visualize important places or routes, helping analyze geographical investigations.
• 📈 Scatter graphs are used to show relationships between two variables, which can be positive, negative, or show no relationship.
• ⚠️ Limitations and risks in geographical investigations are essential to address, such as sample size, timing, and safety concerns.
• 📊 Measures of central tendency (mean, median, mode) and frequency (count, percentage) are important for analyzing quantitative data.
• 🌐 Different presentation methods for findings include bar graphs, pie charts, line graphs, photographs, and word clouds to visualize results.

Q & A

What is the difference between a research question and a hypothesis?

A research question outlines a specific scope for investigation without implying a known relationship between variables. A hypothesis, on the other hand, predicts the relationship between two variables and is more definitive, aiming to confirm this relationship.

What are the two types of data classifications mentioned in the video?

The two classifications of data mentioned are primary vs. secondary data and quantitative vs. qualitative data.

What is primary data, and how is it different from secondary data?

Primary data is collected firsthand by the investigator, while secondary data is information that has already been collected by others and is available through sources like books, articles, and online platforms.

How is quantitative data different from qualitative data?

Quantitative data deals with numbers and can be measured and analyzed mathematically. Qualitative data, on the other hand, is subjective and usually revolves around understanding emotions, reasoning, and experiences that cannot be easily quantified.

What are the two types of sampling methods discussed?

The two types of sampling methods are probability sampling (random sampling) and non-probability sampling (non-random sampling).

What is convenience sampling, and when is it used?

Convenience sampling is a type of non-probability sampling where samples are selected based on their availability and ease of access. It is often used when random sampling is not feasible, such as when surveying people at a busy location like an MRT station.

What is stratified random sampling?

Stratified random sampling is a method where the population is divided into subgroups (strata) based on certain characteristics like age or gender, and random samples are taken from each subgroup to ensure representation.

What types of questions should be used in a questionnaire survey?

In a questionnaire survey, questions should be close-ended, and there should be a variety of question types, such as predefined responses (yes/no), rating scales (e.g., the Likert scale), and ranking scales.

What are mental maps, and how are they used in data collection?

Mental maps are tools that allow participants to visually represent their perception of a location. They can be used to gather data on how participants view routes, important places, or memorable locations in a specific area.

What are some key concepts related to the processing and analyzing of data?

Key concepts include understanding measures of frequency (count, percentage), measures of central tendency (mean, median, mode), and the importance of analyzing qualitative data through centering, bordering, and other features on mental maps.

Outlines

00:00

Introduction and Overview of Geographical Investigation

05:00

Types of Data: Primary vs. Secondary, Quantitative vs. Qualitative

10:01

Sequencing Data Collection and Introduction to Limitations and Risks

15:02

Risk Assessment and Practical Considerations

20:02

Sampling Methods: Probability vs. Non-Probability

25:05

Questionnaire Surveys and Rating Scales

Mental Maps and Analyzing Primary Data

Analyzing Quantitative Data: Measures of Central Tendency

Analyzing Qualitative Data and Graph Types

Presenting Findings and Data Visualization

Keywords

💡Research Question

A research question outlines a specific scope for investigation related to the topic. It doesn't predict a relationship between variables but aims to explore and uncover relationships. In the video, it is used as an example of an open-ended inquiry, such as 'How does the temperature in the school field change over the course of a day?'

💡Hypothesis

A hypothesis is a predictive statement about the relationship between two variables. Unlike a research question, it makes an assumption that can be tested, such as 'The higher the temperature, the more cold drinks are consumed.' The video emphasizes the difference between a hypothesis and a research question.

💡Primary Data

Primary data refers to information collected firsthand by the investigator, such as through fieldwork or experiments. In the video, it's described as data collected by the geographer during research, which can include surveys, observations, or measurements taken in the field.

💡Secondary Data

Secondary data refers to information that has already been collected by other researchers or sources. Examples include data from studies, journals, newspapers, or online sources. The video contrasts this with primary data, explaining how it can be used in geographical investigations.

💡Quantitative Data

Quantitative data is numerical information that can be measured and analyzed statistically. The video provides examples, such as test scores or the number of cold drinks consumed, highlighting that this type of data can be counted and quantified.

💡Qualitative Data

Qualitative data is descriptive and often relates to subjective experiences, such as emotions or opinions. Unlike quantitative data, it cannot easily be measured numerically. In the video, it's explained that qualitative data helps to understand the participants' feelings, and often supports quantitative data.

💡Sampling

Sampling is the process of selecting a subset of individuals or data points from a larger population to represent the whole. The video describes two types: probability (random) sampling and non-probability (non-random) sampling, and gives examples like convenience sampling and stratified random sampling.

💡Stratified Random Sampling

Stratified random sampling involves dividing the population into distinct subgroups (strata) and then randomly selecting samples from each subgroup. The video mentions it as a way to ensure proportional representation of different categories, such as age or gender, in the sample.

💡Questionnaire Survey

A questionnaire survey is a method of collecting data by asking respondents a series of structured questions. The video explains different types of response formats, such as predefined responses, Likert scales, and ranking scales, and emphasizes that questions must align with the hypothesis.

💡Limitations

Limitations are the potential weaknesses or constraints in a study's methods or scope that affect the validity of the findings. The video highlights common limitations such as small sample sizes, short data collection periods, or data that isn't representative of the population, which can reduce the reliability of the results.

💡Risk

Risk refers to the potential dangers or hazards that could affect participants or the study during fieldwork. The video discusses various types of risks, such as weather conditions, minor injuries, or traffic accidents, and explains the importance of evaluating these risks during a geographical investigation.

Highlights

Introduction to geographical methods and the importance of differentiating between research questions and hypotheses.

Clear distinction between a research question, which outlines a scope for investigation, and a hypothesis, which involves predictive relationships between variables.

Explanation of primary and secondary data, emphasizing the need for firsthand collection in primary data and reliance on existing sources for secondary data.

Clarification of quantitative data, which focuses on measurable figures, and qualitative data, which is subjective and often related to emotions or opinions.

Discussion of data sequencing, with flexibility in whether quantitative or qualitative data should be collected first depending on the investigation context.

The importance of acknowledging limitations and risks in geographical investigations, especially in relation to the validity and safety of the research process.

Introduction to the concepts of sampling, with a focus on the differences between probability (random) and non-probability (non-random) sampling methods.

Explanation of convenience sampling as a type of non-probability sampling and its usefulness in practical fieldwork.

Overview of stratified sampling, which involves adding a layer of proportionality (such as gender or age group) to random sampling.

Detailed discussion on questionnaires, including the use of predefined responses, rating scales, and ranking scales to gather data effectively.

Description of mental maps as a tool for primary data collection, used to analyze participants' spatial perceptions and experiences.

Analysis of quantitative data using measures of frequency, central tendency (mean, median, mode), and their respective advantages and disadvantages.

Qualitative data analysis through mental maps, focusing on features like centering, labeling, scale, and perspective.

Explanation of scatter graphs to identify relationships between variables, highlighting positive, negative, and no relationship patterns.

Summary of different data presentation methods, including bar graphs, pie charts, line graphs, and word clouds, used to effectively present findings.

Transcripts

hi welcome to another episode of

starting with Mr K this is topic

Three

G right so for topic three uh I'll be

going through the geographical methods

and

investigation all right and uh perhaps

to also this is a good revision for

those who are still confused about uh

some components of the f work okay so uh

basically I think uh I'll be

highlighting some key things um every

students need to know okay first of all

okay let's start off with topic 1.3

sorry topic um

3.1 right for three topic

3.1 okay firstly you need to know the

difference between a research question

versus a

hypothesis right so what's the

difference between the two basically uh

recent question

is outlines a specific scope for

investigation related to the topic on

the other hand the hypothesis it is very

clearcut it consists of one or two

variables usually two variables uh in

our syllabus so a research question will

sound like how does the temperature in

the school field change over the course

of a day so you can see that in the

research question there will there will

not be um there isn't any

uh conclusion yet there isn't any known

relationship here you're trying to find

out about the relationship between the

variables and the relationships are

uncertain but on the other hand for

hypothesis uh it is used when uh

relationship between the variables are

pretty uh clear you just want to really

confirm the hypo the hypothesis and it

is a predictive in nature anual sound

like uh the higher the temperature is

the the more cold drinks I consume

something like that or the more cold

drinks students consume something like

along this nature so that is the

difference between research question and

hypothesis right and for next we're

going to talk about

data okay the data they actually uh two

types of classification okay the first

type of classification is between prime

army versus

secondary okay next will be

quantitative versus

qualitative all right so these are the

uh how data can be classified right and

what's the difference between the

primary and secondary data basically for

primary data they are actually collected

firsthand and that is basic basically

whereby you as the investigator or

geographer will go out to collect your

data right for the secondary okay it

will be from perhaps a uh uh a study

that's been conducted a study that has

been conducted and the data is actually

available it can be online it can be uh

on books newspaper articles journals so

on and so forth right so these are

actually secondhand not say secondhand

um these are actually

um data that's already been produced but

just not by yourself right so this is

where you do your research right okay

this is where you do your research so

for quantitative and qualitative data uh

if we talk about quantitative data we

are looking at numbers definitely okay

as literal as it sounds we are looking

at this numbers we are looking that

about add data that can be quantifiable

for example your marks for geography

paper that's how I analyze them right so

for qualitative data uh that is data

they're not easily me measurable they're

actually usually subjective and most of

the time for qualitative data the the

the objective is to understand how a

participant feel so most of time it's

about emotions feelings and their

reasoning some of which cannot that

cannot be

um take

some of which data that cannot be taken

away from quantitative data so the twool

actually help to support each other all

right and then of course uh there is a

small part small portion on sequencing

data collection right how do we sequence

the data collection basically um there

is no hard and fast R about that okay

you can actually look at quantitative

data followed by followed by um

followed by qualitative on all the

others or on the other hand you could be

qualitative data followed by

quantitative data so it really depends

on the context of the

GI question that you are working on

right so that is that K I'll be touching

on some form of

limitations and

risk okay this uh component is usually

missed by quite a number of students

okay you do not you did not even touch

on this okay you do not need to spend

more than 5 minutes on this page but you

need to know the limitations and the

risk that's involved in a GI because

that is what uh every geographers will

do they will ascertain whether they able

to safely conduct the the the FI work or

there or are there any limitations to

their Fu or not because limitation is a

very big thing okay in fact it is is

actually your AO3 question okay uh in my

previous video you realize that uh AO3

questions there are two types and these

limitations we are talking about methods

right so there are two types basically

there's for A3 there's the hypothesis

question and there is one there is on

methods where where most of the time

they'll ask about the validity of the

investigation and when we answer about

the validity of a question of of the uh

sorry validity of the research we often

looks at how limited is is the method

right for example we are looking at

sample

size we are looking at for example uh

the timing okay the time of

duration right we're going to look at

how many times the investigation is

being uh carried out in months in weeks

or is it in even day odd day or is it

just a week or is it just one day of

collection so that it can actually limit

the

reliability of a uh of the data that's

being collected right and for sample

size if the sample size is not if the

sample size is not uh big enough it will

be unreliable as well so these are just

examples other uh examples would also be

uh whether there is uh uh whether is it

like for example uh let me thing other

validity it could be

whether

the data collector is useful or not

right useful to what useful always to

the hypothesis okay so always remember

there's always limitations okay and last

but not least risk for 3.1 this the last

uh last uh key Concepts over here for

risk uh we are talking in your books uh

the risk that's being indicated of false

Cuts me minor injuries traffic accidents

all these are injuries right uh sorry

all these are injuries right and uh of

course you can also talk about weather

conditions like for example if you were

to go to a open place all right um it

will also mean that you need to be wary

of the weather conditions because there

will be risk uh taking your interview in

the tunder

showery day right you'll get yeah

there'll be uh there'll be risk involved

of course and then you also have to look

at uh for example are there any risk if

you're are working at for example near

volcano you need to look at the

uh the uh seison graph of the day you

need to look at the uh

seismo data of the day so to predict

whether will there be any volcano

eruption on the day itself or not yeah

so these are things that you can

consider and that's is 3.1 and in this

3.1 you will uh these are all I will

consider this pref work okay so what the

things that could be tested uh in terms

of here okay I've actually numbered them

out already so they can test you

research question versus hypothesis they

can test you uh how uh to to to come out

with a

hypotheses right they can test you to um

maybe uh come up with a research

question right or they may ask you

what's the difference between a research

question and hypothesis right so do look

at a syllabus document do look at some

sample answers from uh papers and yeah

and get your practices secondly for data

right uh it is a very brief uh summary

of the the data that's being involved so

there is primary and secondary and

therefore uh they may test you uh what

are the example of primary data that can

be collected uh in accordance to the

context of the questions that's been

given all right so that could be one

kind of reason and they may for

quantitative and qualitative data they

may ask you uh for that context would

qualitative be better than quantitative

or vice versa so for especially for Co

job paper it is really unpredictable for

G GI question because you have 20 marks

that's allocated to this topic only so

it's going to be uh it's going to be

tough but trust me just look at the

question carefully look at the GI

context very carefully you will most of

you will get it right and the last but

not least uh uh possible question for

limitation and risk okay uh limitations

as I mentioned is a AO3 six marks

question uh for risk they may ask uh

perhaps suggest possible risk for

certain context that is being given okay

if if let's say for example uh it could

be going to the beach to

collect um data right so what you what

kind of risk are you exposed to okay you

may you can even say okay uh wear at

least sandals or wear at least covered

shoes to go to the beach to conduct

because uh it can be

dangerous yeah if you get injured or

Cuts or yeah any Shar objects on the

beach all right so that's for topic 3.1

okay moving on let's go to topic

3.2 okay for topic

3.2 uh it is about the collection of

primary data okay we we we zoom into

this data right we zoom into primary

data okay for topic 3.2 uh I just want

to explain a little bit about uh this

concept of uh first first thing first is

about

sampling okay sampling has been the most

most confusing thing in this book I mean

in this uh topic right and a lot of you

um are confused about uh the way things

are being sampled and what are the

different types of sampling basically

okay you for sampling you need to know

that there are two types of

sampling okay one is probability

sampling okay let me write it down the

other is

non probability

sampling right

and and what exactly do these two um con

geographical terms mean okay for

probability

sampling okay just remember it is

random okay

random okay sorry yeah it is random and

on the other hand for non probability it

is

non-random right so these uh what is the

how come there's a need to do a random

or non-random so when you're doing

random sampling okay uh you are able to

remove biases that may come from a

choice made by yourself for example if

you were to go to a MRT Station you're

going to do random sampling how are you

going to do it you have to assign

random numbers to all the passengers

that what pass you or that is at the MRT

station and that is not doable at all

that is not a viable method so what

would you have done you would have used

non probable sampling and one of the non

probable sampling method is actually

called convenience sampling right that's

what you have learned so far okay so

convenience

sampling okay uh it is about your own

convenience how you are selected because

I mean uh you have selected your your

your sample because they conveniently

being collected like just now that

example right so when there is Con so

for non probable there is convenience

sampling right on top of that once you

conduct your convenience sampling okay

you may also have another layer whereby

you uh select a sample that has the

proportional makeup to the population

it can be based on age can be based on

gender okay so on and so forth and this

becomes C sampling so your textbook uh

explained in a quite a clear manner so

so yeah all right so this is non

probability sampling whereby it is non

random you don't use a random generator

you just do it at your own convenience

so the first layer is convenience

sampling right and after you I I mean

let's use back the example just at MRT

right uh you conveniently ask people to

do your survey or whatnot and after and

and and that is one type of sampling

that you can use right then after which

then you realize a actually uh based on

my population size uh based on my the uh

the town has a 6 to four 6 is to four uh

gender ratio six uh every six 60 to 40

genter ratio and that is uh 60 male and

to 40 females right and then you decided

he actually I want to add another layer

to my to my sampling and that is your

second layer and once you add the second

layer so Kota sampling has the component

of convenience sampling okay so just to

make sure you understand this for the on

the other hand for random sampling

is also quite uh simple random sampling

okay which is

uh whereby you use a random generator

right uh random number generator right

and that is random sampling

however however if you were

to if you want to really uh make sure

there is a proportional makeup to the

population you add in another layer

whereby

using your number random number

generator you add in another layer of

rules whereby you want certain um number

of males and females or you want certain

number of people from this age group

okay and that will become that will

become

stratify all right so let's be clear

about this uh sampling methods okay it

will become stratified random

sampling okay

so random sampling is a subset of

stratified random sampling is it is just

a layer of age and gender being added

added all right so uh what type of

question can come out from this

component okay first of all you need to

know why

random why nonrandom this is usually a

question being ask okay why is random

sampling used in that in that context so

for GI question most of the questions

are your ao2 questions whereby you need

to use the context right and why is it

why do I use non-random sampling is

there a way that can do random sampling

if there is no way then it will be

nonrandom okay just for everyone to take

note then they may ask you questions

like for example what are some

limitations okay of using random or what

are some limitation or using nonrandom

right so these can be used uh these uh

these are possible questions okay let's

move on to uh another portion okay which

are

questionnaire questionnaire survey okay

for questionnaire survey

uh you need to understand that um there

are various ways that you can do a

questionnaire survey okay you can use

predefined responses such as yes no

somewhat two to three times okay so that

is predefined

responses okay that's the first type and

I don't think I need to explain that the

second type of responses we are looking

at okay is about uh using different

types of rating skills okay rating

skills right and uh what are the

different types there is the lied scale

the lied scale looks the likeed scales

uh as usually uh there are two extremes

and usually there is um five about five

options excellent above average average

below average ex extremely poor that's

the like scale okay and then you have

the frequency

scale okay whereby uh it talks about the

number of occurrences and then you have

your ranking

skill okay whereby it talks about

uh how the participant would like to

rank the certain uh issues that you're

looking at so in the possible questions

that comes up from this segment over

here it's about being able to develop

that questions so in your answers when

they you are asked to to uh to come up

with

questions okay with uh question as

surveys right just remember firstly it

is all

close-ended okay never come up with any

open Ender unless you're doing a semi

structured interview second okay all

your question should be uh there should

be the different between questions that

means let's say the question is a four

Mar question do not just keep on using

lighted skill do not just keep on doing

predefined responses there should be a

range of questions that's being used in

this in answering the question okay in

the paper right okay so that's that and

how else can they test you maybe perhaps

they can test you they can give you a

certain question and you're asked to

change the question improve that

question okay so that's improvement

improvement to certain question okay

basically and the rule of time is all

questions must answer your hypothesis

right that is the rule of time if it

doesn't answer the hypothesis there is

no point in that question all right so

that's that okay so uh that's for

questionnaire survey another type of

data that we are looking at uh will be

mental Maps Okay these mental map is

pretty new okay um are you required to

draw a mental map have you had practices

I hope you have had all right because uh

for mental Maps uh I think it is

important for you to note

that mental Maps can be a very good tool

for data all right and this data

um you can give your participants a

either a uh either a base map or a blank

piece of paper all right and it really

depends on how you want to scope your

geographical investigation it really

depends on uh what do you want to see is

it the route that used to travel to

school or what is the most memorable

place in that certain neighborhood so on

and so forth okay so that's that okay

let's move on so for topic 3.2 we just

talked about the primary data okay

remember these are primary data things

that you collect okay things that you

actually went on to collect right and

then next we go on

to

the uh I will say the I will say

difficult part okay which is the

processing and

analyzing of

data okay the process the processing and

analyzing of data which is topic uh 3.3

all right uh there are few ways of doing

that just now we talk about quantitative

data okay so please make sure you know

your

measures of frequency okay if you don't

know this word please go to your guide

book and look it up what are measures of

frequency it is nothing spectacular it

is just your count your

percentage okay and then next you have

the

measures of central tend

okay trying to find out what is the uh

what is

theh the average for example so you have

your mean

medium and

mode right so know all these method make

sure uh you know how to use them and how

can they come out please know the

advantage versus the disadvantage

okay and make sure that's the first

thing and another thing you can you need

to know is actually your

uh how to count calculate right so yeah

what kind of Advantage are we talking

about for example when do you use mode

when do you use mean when do you use

medium why why why is mod prefer over a

medium right so there are advantages

disadvantages in your guide book please

go and look at them right next we talk

about uh qualitative data the next queue

qualitative

data okay for qualitative data uh mostly

it comes from mental

Maps Okay for mental Maps they're being

analyzed in various forms okay such as

centering and bordering okay whether

what are the features centered at the

center uh what what kind of features CAU

attention so usually in a in a base map

okay whatever that's in the center okay

it is usually the center of attention

right and why is that so and then you

have the scale of map you have the

labeling you have the

colors and legends okay so these are

parts of the map that you will need to

analyze okay

perspective okay uh and

orientation and other

features okay please go and read up on

this okay it is very difficult to teach

you on a YouTube video but uh perhaps I

will concentrate on this uh in my

next in my last few videos for this year

right so yeah okay for the next

component okay we talk about scatter

graph

okay for scatter

graphs okay always remember we are

looking at relationship and there's only

three types of relationship in the world

positive you good I good

negative you bet are good or you just

don't want to have any relationship no

relationship all right so what is

positive this is how it looks like

negative this is how sorry it's a

straight lineer sorry and then last but

not least no

relationship okay so that's for SC graph

right so the scet graph best fet line is

only for relationship and the X and

sorry the Y and the X okay are your two

variables as mentioned in your

hypothesis right

yes okay so uh please note your

outliers okay oh sorry uh this Le not

out outliers here outliers could be

somewhere out of the best fit line okay

uh make sure you know that okay last but

not least let's go on to presenting

findings okay presenting findings I

think for find uh for so that is topic

3.3 for topic 3

3.4 just be very clear um you need to

know how to construct your graphs all

right your different graphs okay there

is no need for me to go through the

different graphs but basically you are

you need to be able to fill up for

example bar graph bar graph Pi charts

and your line graphs okay these are the

different type of graph that we have

ever constructed okay and we all usually

present our data in terms of photograph

text or even color

coded quotations okay do take note of

this and how to do this okay please make

sure that uh there will be there may be

surprises for this okay so know that and

last but not least work Cloud all right

so work cloud is just a simple mixture

of uh words all right I got a go a

lesson to catch

goodbye hope you enjoy this uh starting

with Mr K

okay