Work Immersion / Capstone

TIOC Framework for CHAPER I - THE PROBLEM AND ITS BACKGROUND - Introduction (For Crafting or Graphic Organizer)
Trends, Issues, Objectives, Contributions

Trends: Current state or background of the topic. Issues: Problems or gaps in current knowledge.

Objectives: What the research aims to achieve. Contributions: The significance or impact of the study.

SAMPLE RESUME OR CV

SAMPLE CERTIFICATE FORMAT

Sample Application Letter

Personal Data Sheet

CERTIFICATE OF EMPLOYMENT (SAMPLE)

WORK IMMERSION APPLICATION AND REQUIREMENTS

2025 - 2026
(FULL PAPER REFERENCES -
HUMSS SHS)

AMORSOLO (25 - 26) - FINAL MANUSCRIPT

NAKPIL (25 - 26) - FINAL MANUSCRIPT

VENERACION (25 - 26) - FINAL MANUSCRIPT

2025 - 2026
(OTHER PAPER REFERENCES - JHS)

The Performance of Grade 9 in Science Examination Quarter 1 in Disiplina Village Bignay NHS S.Y 25 – 262

SCISPACE

SCI HUB

REQUEST LETTER (INCINERATION)

INTERVIEW RUBRICS

Nominal Data

a type of categorical data that labels variables into distinct, non-overlapping groups without any inherent numerical value or ranking. Examples include different categories for gender, eye color, or favorite color, where the options are simply names or labels that cannot be ordered or measured mathematically.

Characteristics of nominal data

Categorical: It places data into categories or groups.
Qualitative: It describes attributes rather than quantities.
Non-ordered: The categories have no logical or intrinsic order. For instance, you cannot say that blue is "higher" or "lower" than red.
Mutually exclusive: Each data point can only belong to one category.
Labeling: It is essentially "named" data, hence the name, and is often used to gather demographic information.

Examples of nominal data

Marital status: Single, married, divorced
Nationality: American, Japanese, French
Blood type: A, B, AB, O
Favorite color: Red, blue, green
Yes/No answers: A binary variable where the options are just two labels

How to use and analyze nominal data

Surveys: It's commonly collected through surveys to understand customer demographics or preferences.
Frequency tables: You can count the number of responses in each category to see how they are distributed.
Bar charts: These are excellent for visualizing nominal data, as each bar represents a category and its height corresponds to the count or frequency.
Pie charts: Another common way to show the proportion of each category within the whole dataset.
Dummy variables: Nominal data can be converted into dummy variables (e.g., coded as 1 for one category and 0 for all others) to be used in statistical analysis.

Mediating variables are primarily utilized in causal mediation analysis, a statistical approach that can be applied across various research designs, including experimental, quasi-experimental, and observational (e.g., cross-sectional and longitudinal) designs.

The goal of research involving a mediator variable (M) is to explain how or why an independent variable (X) influences a dependent variable (Y), by examining the causal sequence X → M → Y.

Example: Gender Affecting the Relationship Between Stress and Performance
1. Independent Variable (IV): Stress - This is the predictor variable. You want to see how stress influences performance.
2. Dependent Variable (DV): Performance: This is the outcome variable affected by stress.
3. Moderating Variable (MV): Gender changes or influences the strength or direction of the relationship between stress and performance. Example: Stress may affect males and females differently.

Mediating Variable in a Conceptual Framework

Explanation:

Stress directly influences performance.
Gender moderates the relationship—meaning the effect of stress on performance may be stronger, weaker, or different depending on whether the participant is male or female.

How to use Mediating Variables in different Research Designs:

Experimental Designs: True experiments, where the independent variable (X) is randomly assigned, provide the strongest evidence for causality in the X → M and X → Y relationships.

Some experimental approaches use "double randomization", where participants are randomized to levels of X in one study, and the mediator (M) is experimentally manipulated and randomized in a second study to provide robust evidence for the M → Y relationship.
Other complex experimental designs, such as concurrent double randomization or parallel designs, manipulate both X and M simultaneously to gather stronger causal evidence regarding the mediating process.

Longitudinal Designs: Collecting data over multiple time points helps establish the necessary temporal precedence (X happens before M, which happens before Y), which is a key requirement for inferring causation in mediation. These designs often use advanced statistical models like autoregressive models or latent growth curve modeling.

Observational Designs (Correlational): Mediation analysis is frequently used in non-experimental studies (e.g., in psychology, sociology, and epidemiology) where variables are measured as they naturally occur.
Cross-sectional studies (data collected at a single point in time) can use mediation analysis, but drawing definitive causal conclusions is difficult due to the lack of temporal ordering and the potential for unmeasured confounding variables.

In the absence of a true experiment, careful theoretical grounding and statistical controls for potential confounding variables are essential for making preliminary causal inferences in observational mediation studies.