Quantitative Data Analysis & Interpretation

• Inquiries Quantitative Data Investigations Interpretation & Data and Immersion: Analysis Method

• Verse of the Day is Matthew 6:26

• It states "Look at the birds of the air; they do not sow or reap or store away in barns, and yet your heavenly Father feeds them. Are you not much more valuable than they.”

Percentage and Frequency Use

• Use percentage and frequency for recalling type of questions
• Use percentage and frequency for multiple choice type of questions
• Use percentage and frequency for demographic type of question

Budgeting Behavior in Allowance Allocation

• Budgeting Behavior in Allowance Allocation of Grade 12 Student Senior High School Students in Signal Village National High School Title
• The demographic profile of the respondents in terms of daily allowance; 10php-20php - 50, 21php-30php- 20, 31php- 40php - 30, and 41php above -50
• The total number of respondents: 150

Frequency

• Frequency refers to the number of times a particular value, category, or event occurs within a dataset
• Frequency helps in understanding the distribution of data and how often specific values or categories are repeated

Percentage

• A percentage represents a part of a whole expressed as a fraction of 100

• Percentages is a way to express numbers as a portion of 100 makes comparisons easier when dealing with different quantities

• Check mark (v) in the box corresponds to your response

• Selecting 4 means "Strongly Agree"

• Selecting 3 means "Agree"

• Selecting 2 means "Disagree"

• Selecting 1 means "Strongly Disagree"

Range

• The range is a measure of spread or dispersion in a dataset
• The range is calculated by subtracting the smallest value from the largest value in the dataset
• Range = Maximum value-Minimum value
• For example, a data set of ages: 10, 15, 20, 25, 30, the range is 30-10=20

Chapter 4 Steps:

• Tabulate
• Complete the table
• Compute the total
• Compute the Weighted Mean and Weighted Average Mean
• Interpret the data

Interpretation of Data

• Interpretation of data refers to the implementation of certain procedures through which data results from surveys is reviewed, analyze for the purpose of achieving at valid and evident based conclusion
• Interpretation of data denotes a meaning to the information analyzed and determines its significance and implications to the study

Data Analysis Stage 1: Data Preparation

• The main goal is to transform raw data into something meaningful, significant and user friendly
• Step 1 includes Data Validation

Data Validation

• Data Validation’s goal is to check whether the gathered data was performed according to the set standards

• It is a four-step process, which includes procedures like identifying potential Fraud, Screening, Procedure and ensuring Completeness

• Fraud - To ensure each respondent was actually interviewed

• Screening - To check that respondents were chosen according to the standard research criteria

• Procedure - To make sure that the data collection process was followed

• Completeness - To make sure that the interviewer asked the respondent all the necessary questions versus selecting a few

Step 2: Data Editing

• Many data sets include errors
• Respondents may fill fields incompletely or skip them
• The researcher should conduct the initial data checking and edit of the raw research data to identify and clean out any points, to ensure these errors will not occur to come up with an accurate results

Step 3: Data Coding

• The significant process in data preparation
• Data coding refers to grouping and assigning values/codes to responses from the conducted survey

Quantitative Data Analysis Methods

• Cross-tabulation is most commonly used and the most preferred quantitative data analysis method
• Cross-tabulation uses a basic tabular form to draw inferences between different data-sets of dependent and independent variables, containing data that have some connection with each other

Steps to conduct Quantitative Data Analysis

• Relate measurement scales with variables
• Associate scales of measurement such as Nominal, Ordinal, Interval and Ratio with the variables – dependent and independent variables
• Arrange the data in proper sequence or order by importing into an Excel sheet

Connecting Descriptive Statistics with Data

• Should connect Connect descriptive statistics to contain available data as establishing a pattern in the raw data can be hard

• Commonly using the following Descriptive statistics; Mean, Median, Mode, Frequency

• Mean - An average of values for a specific variable

• Median - A midpoint of the value scale for a variable

• Mode - For a variable, the most common value

• Frequency - Number of times a particular value is observed in the scale

• Minimum and Maximum Values - Lowest & highest values for the scale

• Percentages - Format to express scores and set of values for variables

• Range: the highest and lowest value in a set of values

Mean (Average) Example:

• Add up all the numbers: 4+8+6+5+3+7+8+9+6+8=65
• Divide by the number of values (10 in this case): 65+10=6.5
• Mean = 6.5

Median (Middle value)

• First, arrange the numbers in ascending order: 3, 4, 5, 6, 6, 7, 8, 8, 8, 9
• Median is the average of the two middle numbers (since there are an even number of values):
• Middle Numbers = 6 and 7 Mean = (6+7) ÷2=6.5
• Median = 6.5

Mode (Most frequent value)

• The number 8 appears most frequently (3 times)
• Mode = 8

Measurement Scale

• It is important to decide the measurement scale to conclude a descriptive statistic for the specific variable
• A nominal variable score won't need a Mean or Median

Descriptive Statistics

• Descriptive statistics refers to summarizing or describing the main features of a data set
• Descriptive statistics provides simple summaries and visualizations that help to understand the characteristics of the data

Before data analysis

• Before any data analysis can begin, the scale of measurement must be decided for the data as this will have a long-term impact on data interpretation

• Scales include; Nominal, Ordinal, Interval, and Ratio

• Nominal Scale: non-numeric categories that cannot be ranked or compared quantitatively

• Nominal Scale’s Variables are exclusive and exhaustive; examples; Status, Colors, Gender

• Ordinal Scale: exclusive categories that are exclusive and exhaustive but with a logical order

• Ordinal Scale examples; Quality ratings and agreement ratings; good, very good, fair, etc., or agree, strongly agree, disagree, etc.

• Interval: a measurement scale where data is grouped into categories with orderly and equal distances between the categories

• Interval Scale has always an arbitrary zero point

• Ratio: contains features of all three scales, Normal, Ordinal, Interval

Study Example in El Savador

• Study in El Salvador city Misamis Oriental was conducted to determine the factors affecting customer preferences, among the residence of one barangays of El Salvador City ages 22 to 60 years old

Example - Distribution of Respondents by Age

• 21 – 30 yrs. old represented 45.33% with a frequency of 170 people out of a total of 375
• 31 – 40 yrs .old represented 24.00% with a frequency of 90 people out of a total of 375
• 41 - 50 yrs. old represented 21.33% with a frequency of 80 people out of a total of 375
• 51 – 60 yrs. old represented 9.33% with a frequency of 35 people out of a total of 375

Interpretation of Data (Table 1)

• 45.33 percent of the respondents are in the age bracket of 21- 30 years old, 9.3 percent are ages 51 – 61 years old and above, and 21.33 percent belonged to the 31-40 age range
• Demographic age profile also reflects the current age demographic for the Filipinos according to Philippine Statistics Authority (PSA)
• A younger age cohort has purchasing power of products and services

Example: Distribution of Respondents by Sex

• 61.33% were male a frequency of 230 out of 375
• 38.67% were female a frequency of 145 out of 375

Interpretation of Data (Table 2)

• 61.33 % of the respondents are female, where 38.67% are males
• This represents the current gender distribution of the population in the Philippines
• According to Philippine Statistics Authority (PSA) in 2015 of the total population in the Philippines, 50.40% are males and the rest are females
• Gender distribution is common among most countries where the male population is higher than the female population

Example: Profile of Grade 12 Student-Respondents by Strand

• ABM accounted for f=29, 17.6%
• CAREGIVING accounted for f=13, 7.9%
• EIM accounted for f=7, 4.2%
• GAS accounted for f=11, 6.7%
• HE accounted for f=16, 9.7%
• HUMSS accounted for f=33, 20.0%
• ICT accounted for f=32, 19.4%
• STEM accounted for f=24, 14.5%
• Total was f=165, 100.0%

Analysis

• The number of respondents from the different strand shows a distinct difference
• The HUMSS strand had the highest number of respondents with a 20.0% out of 165 respondents
• Next is ICT strand having 19.4% or 32 respondents while ABM strand has 17.6% or 29 respondents
• 14.5% or 24 out of 165 respondents are from STEM strand, 9.7% or 16 out of 165 respondents are from HE strand, 7.9% or 13 are from Caregiving strand, while GAS strand is 6.7% or 11 out of 165 respondents, and lastly, EIM obtaining the lowest number of respondents which is 4.2% or 7 out of 165 respondents
• Any grade level and strand of students face a lot of difficulty in maintaining and spending according to the budget maintained by them (Abawag, et al., 2019)