Lesson 2 (Collection, Organization, and Interpretation of Data)

DATA COLLECTION

process of gathering information from all the rel;evant sources to find a solution to the research problem. It allows the person to conclude an answer to the relevant question.

• Primary Data

• Secondary Data

CLASSIFICATIONS/TYPES OF DATA

PRIMARY DATA

data that has been generated by the researcher like surveys, interviews, experiments, specially designed for understanding and solving the research problem at hand

SECONDARY DATA

data generated by large government institutions, healthcare facilities, etc. as part of the organizational record keeping. The data is then extracted from more varied datafiles

• QUANTITATIVE DATA COLLECTION METHODS

• QUALITATIVE DATA COLLECTION METHODS

PRIMARY DATA COLLECTION METHODS

• PUBLISHED DATA

• UNPUBLISHED DATA

SECONDARY DATA COLLECTION METHODS

QUALITATIVE DATA COLLECTION METHODS

• Observation Method

• Interview Method

• Questionnaire Method

• Schedules

• Government Publications

• Public Records

• Business Documents

• Technical and Trade Journals

PUBLISHED DATA

• Diaries

• Letters

• Unpublished Biographies

UNPUBLISHED DATA

QUANTITATIVE DATA COLLECTION METHODS

based on mathematical calculations using various formats like close-ended questions, correlations, and regression methods, mean, median, or mode measures. This method is cheaper than qualitative data collection methods and it can be applied in a short duration of time.

QUALITATIVE DATA COLLECTION METHODS

it does not involve any mathematical calculations. This method is closely associated with elements that are not quantifiable. This qualitative data collection method includes interviews, questionnaires, observations, case studies, etc.

OBSERVATION METHOD

used when the study relates to behavioral science. This method is planned systematically. It is subject to many controls and checks.

• Structured and unstructured observation

• Controlled and uncontrolled observation

• Participant, non-participant and disguised observation

Types of Observation

INTERVIEW METHOD

this method of collecting data in terms of verbal responses.

Types of Interview

• Personal interview

• Telephonic interview

QUESTIONNAIRE METHOD

in this method, the set of questions are melted to the respondent. They should read, reply and subsequently return the questionnaire. The questions are printed in the definite order on the form. 

• Short and simple

• Should follow a logical sequence

• Avoid technical terms

• Should have good physical appearance such as color and quality of the paper, to attract the attention of the respondent.

Features of a Good Survey

SCHEDULES

this method is like the questionnaire method with a slight difference. The enumerations are specially appointed for the purpose of filling the schedules. It explains the aims and objects of the investigation and may remove misunderstandings, if any have come up. Enumerators should be trained to perform their job with hard work and patience.

• THE LEVEL OF CONFIDENCE

• ALLOWABLE ERROR

• POPULATION STANDARD DEVIATION

THREE FACTORS IN FINDING THE SAMPLE SIZE

THE LEVEL OF CONFIDENCE

the 95% and the 99% levels of confidence are the most used, but any value between 0 to 100% is possible. The 95% level of confidence corresponds to z value of 1.96, and a 99% level of confidence corresponds to a z value of 2.58. The higher the level of confidence selected, the larger the size of the corresponding sample.

ALLOWABLE ERROR

the maximum allowable error, designated as E, is the amount that is added and subtracted to the sample mean (or sample proportion) to determine the end points of the confidence interval. It is the amount of error those conducting the study are willing to tolerate. It is also ½ the width of the corresponding confidence interval. A small allowable error will require a larger sample. A large sample error will permit a smaller sample.

POPULATION STANDARD DEVIATION

if the population is widely dispersed, a large sample is required. On the other hand, if the population is concentrated (homogenous), the required sample size will be smaller. However, it may be necessary to use an estimate for the population standard deviation.

• Use a comparable study

• Use a range-based approach

• Conduct a pilot study

SUGGESTIONS FOR FINDING THE ESTIMATE (STANDARD DEVIATION)

Use a comparable study.

Use this approach when there is an estimate of the dispersion available from another study.

Use a range-based approach.

To use this approach it needs to know or have an estimate of the largest and smallest values in the population.

Conduct a pilot study

This is the most common method for finding estimate (standard dev)

• To contact the whole population would be time consuming.

• The cost of studying all the items in a population may be prohibitive.

• The physical impossibility of checking all items in the population.

• The destructive nature of some tests.

• The sample results are adequate.

REASONS TO SAMPLE

• SIMPLE RANDOM SAMPLING

• SYSTEMATIC RANDOM SAMPLING

• STRATIFIED RANDOM SAMPLING

• CLUSTER SAMPLING

KINDS OF RANDOM SAMPLING

SIMPLE RANDOM SAMPLING

a sample selected so that each item or person in the population has the same chance of being included.

Fishbowl

write the number on paper and randomly pick.

Table of Random Numbers 

• Start anywhere at the table. I started at 31381. Then put a separator for the three (3) digits since the population is 365 and has three (3) digits.

• Continue putting the separator to the right until eight values with less than 365 will attain.

• Then, we have 313, 199, 040, 261, 144, 155, 101.

Random Numbers on Calculator 

• Shift>Mode>2:Line10

• 365xShift>Ran#>=

Random Sampling using Excel

• Type “Numbers” and number 1-365 in one column.

• Drag all the numbers including “Numbers”.

• Click “Data Analysis” then “Random Sampling”.

SYSTEMATIC RANDOM SAMPLING

a random starting point is selected, and then every kth member of the population is selected.

k

calculated as the population size ÷ sample size.

STRATIFIED RANDOM SAMPLING

a population is divided into subgroups, called strata, and a sample is randomly selected from each stratum. Examples of stratum: fulltime or part time, male or female, traditional or nontraditional, etc

CLUSTER SAMPLING

in this technique, entire groups or clusters are randomly selected instead of individual members. It is particularly useful when dealing with large or geographically dispersed populations, as it can significantly reduce travel time and costs. However, because only certain clusters are chosen, there is a higher chance that the sample may not fully reflect the diversity of the overall population, leading to potential bias

NON-PROBABILITY SAMPLING

Selects individuals using non-random criteria, such as convenience or researcher judgment. It is often used in exploratory and qualitative research, focusing on gaining insights into small or Selects individuals using non-random criteria, such as convenience or researcher judgment. It is often used in exploratory and qualitative research, focusing on gaining insights into small or

• CONVENIENCE SAMPLING

• QUOTA SAMPLING

• PURPOSIVE (JUDGMENT) SAMPLING

• SNOWBALL SAMPLING

KINDS OF NON-PROBABILITY SAMPLING

CONVENIENCE SAMPLING

this method selects participants who are easiest to access, such as surveying people nearby or available at a given time. It is quick, inexpensive, and simple to execute but often fails to represent the broader population, making it prone to selection bias.

QUOTA SAMPLING

involves non-randomly selecting a fixed number of participants from various subgroups to reflect certain demographic characteristics of the target population. It helps ensure representation of key groups but can still suffer from selection and non-coverage bias

PURPOSIVE (JUDGMENT) SAMPLING

relies on the researcher’s expertise to choose participants most likely to provide valuable and relevant information. It is especially useful when focusing on specific experts or individuals with unique knowledge related to the study

SNOWBALL SAMPLING

ideal for reaching rare or hard-to-access populations, this method starts with a few initial participants who refer others in their network. The process continues as more participants are recruited through referrals, forming a “snowball effect”.

• TEXTUAL PRESENTATION

• TABULAR DATA

• VISUAL PRESENTATION

KINDS OF DATA PRESENTATION

TEXTUAL PRESENTATION

use concise bullet points or short paragraphs to explain findings clearly, making complex data more accessible and providing a coherent narrative

TABULAR DATA

displays data in rows and columns for easy organization, side-by-side comparisons, and compact information delivery

VISUAL PRESENTATION

includes charts, diagrams, and infographics to simplify complex data, reveal patterns, and enhance understanding. Requires careful design to avoid oversimplification, misinterpretation, or accessibility barriers.

frequency distribution

a way of organizing and summarizing data to show how often each value or range of values occurs. Instead of dealing with raw, unorganized numbers, a frequency distribution groups data into categories or intervals, making patterns and trends easier to see

• CLASS INTERVALS

• FREQUENCY (f)

• CUMULATIVE FREQUENCY (CF)

• RELATIVE FREQUENCY

typical frequency distribution table

CLASS INTERVALS

the ranges into which data is grouped (e.g. 10-19, 20-29)

FREQUENCY (f)

the number of observations in each class interval.

CUMULATIVE FREQUENCY (CF)

running total of frequencies up to a certain point

RELATIVE FREQUENCY

the proportion or percentage of data in each class

• FREQUENCY DISTRIBUTION

• GROUPED FREQUENCY DISTRIBUTION

TYPES OF FREQUENCY DISTRIBUTION

FREQUENCY DISTRIBUTION

presents how often each unique data value occurs without grouping them into intervals. It is ideal for smaller datasets where listing each individual observation is manageable. This method provides exact frequency counts for specific values, making it easy to see which numbers occur most often.

GROUPED FREQUENCY DISTRIBUTION

organizes large datasets into class intervals or ranges, each representing a group of values. This method is particularly useful when the dataset is too

• HISTOGRAM

• FREQUENCY POLYGON

• PIE CHART

• OGIVE (CUMULATIVE FREQUENCY GRAPH)

GRAPHING FREQUENCY DISTRIBUTION

FREQUENCY POLYGON

a line graph created by plotting points at the midpoints of each class interval and connecting them with straight lines. It provides a clear visual representation of the distribution’s shape and is especially useful for overlaying and comparing two or more data sets on the same graph.

OGIVE (CUMULATIVE FREQUENCY GRAPH)

a graph that plots cumulative frequency values against the upper boundaries of class intervals and connects the points with a smooth or straight line. It is helpful for identifying medians, quartiles, percentiles, and other cumulative measures, making it useful in both descriptive and inferential statistic

PIE CHART

displays relative frequencies as slices of a circle, where each slice’s size is proportional to the percentage it represents in the whole dataset. Pie charts are effective for illustrating parts-to-whole relationships and highlighting the contribution of each category to the total.

HISTOGRAM

a type of bar graph where the bars are placed side by side with no gaps between them, representing continuous data. Each bar corresponds to a class interval, and its height reflects the frequency of observations within that interval. Histograms are excellent for showing the shape of the data distribution, such as whether it is symmetrical, skewed, or uniform.