RES 6023 Quantitative Research Designs sample papers, module by module

Reviewed by Hollis Fairweather, PhD · Quantitative Research Designs · American College of Education · Free custom samples in 24–48h

RES6023 covers quantitative research designs in ACE's doctoral sequence. The papers that score well choose a design that can answer a specific question, name the threats to its validity and plan measurement and sampling that a committee would accept.

How this shelf works

Send the exact assignment or rubric from your classroom and a custom sample written to it lands in 24 to 48 hours, the first one free. RES6023 is ACE’s Quantitative Research Designs course. It centers on quantitative research designs and their application, including experimental, quasi-experimental, correlational, causal-comparative and survey designs, threats to internal and external validity, measurement and instrument quality and sampling and statistical power. Searches like "res 6023 module 3 assignment example", "RES6023 sample paper", and "RES6023 module samples" land on this page.

What RES6023 is really about

Quantitative designs differ mainly in how much they can say about cause. A randomized experiment can support causal claims; a correlational study cannot, no matter how large. RES6023 teaches students to see those limits, using the classic threats to validity described by Shadish, Cook and Campbell, and to choose designs that fit both the question and the realities of schools, health agencies and companies, where random assignment is often impossible.

Measurement gets equal attention: choosing instruments with evidence of reliability and validity, deciding between existing scales and new ones and planning a sample large enough to detect a meaningful effect, often with a power analysis in G*Power. ACE graders reward design papers that acknowledge weaknesses honestly and explain how the design reduces them.

What RES6023’s assessments ask for

Comparing quantitative designs often opens the course. Threats to internal and external validity commonly follow. Instrument selection with reliability and validity evidence is a frequent third task. Sampling and power analysis tend to fill the fourth module. A full quantitative design proposal usually closes the course.

Where students lose points in RES6023

Points drop when correlational designs are written up as causal, when instruments are chosen without validity evidence and when sample sizes are guessed. Cite the source for every instrument.

The RES6023 drawers

Module 1

RES 6023 Module 1 assignment example

Module 1 typically compares quantitative research designs. On request, free, 24-48h.

Request it free →
Module 2

RES 6023 Module 2 assignment example

Module 2 often examines threats to internal and external validity. On request, free, 24-48h.

Request it free →
Module 3

RES 6023 Module 3 assignment example

Module 3 usually selects instruments using reliability and validity evidence. On request, free, 24-48h.

Request it free →
Module 4

RES 6023 Module 4 assignment example

Module 4 in many sections sizes the sample with a power analysis. On request, free, 24-48h.

Request it free →
Module 5

RES 6023 Module 5 assignment example

Module 5 frequently closes with a quantitative design proposal. On request, free, 24-48h.

Request it free →
Different?

Your classroom shows something else?

American College of Education revises courses; module counts and deliverables shift between terms. Send what your classroom shows and the desk matches it exactly.

Send it over →

Using a RES6023 sample the right way

A RES6023 sample shows how a design is chosen and defended. Build yours around your own question.

How these samples are written

Method, in one line: rubric first, structure from the rubric, application real, format exact. Module counts vary by course; the catch-all row absorbs the difference. Your free request matches what your classroom actually shows.

RES6023 questions, answered

What is a quasi-experimental design?

A design that compares groups or times without random assignment, such as a nonequivalent control group design, which limits causal claims.

How do I justify a sample size?

Run a power analysis with your planned test, expected effect size, alpha level and desired power, and report the inputs.

Can I create my own survey?

You can, but it needs evidence of validity and reliability; an established instrument is usually easier to defend.