Validity SD Logic Homework Question

In summary, validity in SD logic refers to the accuracy and applicability of a study's constructs, measurements, and conclusions. It can be assessed through content, criterion, and construct validity, but there are potential threats to validity such as selection and measurement bias. To improve validity, researchers should carefully design and execute their studies, use multiple methods of data collection and analysis, and control for confounding variables. However, complete certainty in validity is not possible and it is a continuous process that requires careful consideration of potential threats.
  • #1
thefuturism
4
0
Show that the following argument is valid in SD.
(I will use "⊃" to show conditional.)

Code: 
(E v (L v M)) & (E ≡ F)
L ⊃ D
D ⊃ ~ L
----------------------
E v M

I'm only allowed to use the basic derivation rules of SD:
Code: 
Reiteration (R)
Conjunction Intro. (&I) and Conjunction Elim. (&E)
Conditional Intro. (⊃I) and Conditional Elim. (⊃E)
Negation Intro. (~I) and Negation Elim. (~E)
Disjunction Intro. (vI) and Disjunction Elim. (vE)
Biconditional Intro. (≡I) and Biconditional Elim. (≡E)

My attempts at a solution have not been very successful, but this is what I have come up with so far:

Code: 
1. (E v (L v M)) & (E ≡ F) [assumption]
2. L ⊃ D [assumption]
3. D ⊃ ~ L [assumption]
------
4. ~(E v M) [subproof1open: assumption]
---
5. L [subproof2open: assumption]
---
6. L [subproof2close: 5R]

7. M [subproof2open: assumption]
---
8. ~L [subproof3open: assumption]
---
9. L [subproof3: ?]
10. ~ L [subproof3close: 8R]
11. L [subproof2close: 8-10~E]
12. D [subproof1: 3-13⊃E]
13. L [subproof1: 5-6,7-11vE]
14. ~ L [subproof1close: 3-12⊃E]
15. E v M [4-14~E]

Any help at all would be great, thanks in advance.
 
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  • #2
Here is the solution:1. (E v (L v M)) & (E ≡ F) [assumption]2. L ⊃ D [assumption]3. D ⊃ ~ L [assumption]------4. E v M [1,2&E]5. ~L [3-4⊃E]6. E [1,5~E]7. E v M [6vI]
 

1. What is validity in SD logic?

Validity in SD logic refers to the extent to which the constructs, measurements, and conclusions of a study accurately represent the phenomenon being studied. In other words, it is the degree to which the results of a study are meaningful and applicable to the real world.

2. How is validity assessed in SD logic?

There are several ways to assess validity in SD logic, including content validity, criterion validity, and construct validity. Content validity refers to the extent to which the measures used in a study cover all the important aspects of the phenomenon being studied. Criterion validity is the degree to which the results of a study can be compared to an external standard. Construct validity refers to the degree to which the measures used in a study accurately represent the underlying theoretical constructs being studied.

3. What are some common threats to validity in SD logic?

There are several potential threats to validity in SD logic, including selection bias, measurement bias, and confounding variables. Selection bias occurs when the participants in a study are not representative of the larger population. Measurement bias refers to errors in the measurement instruments or procedures used in a study. Confounding variables are other factors that may influence the relationship between the variables being studied.

4. How can validity be improved in SD logic research?

To improve validity in SD logic research, it is important to carefully design and execute the study. This includes using appropriate measurement instruments, ensuring a representative sample, and controlling for potential confounding variables. It is also important to use multiple methods of data collection and analysis to increase the reliability and validity of the results.

5. Can validity ever be achieved with certainty in SD logic?

While researchers strive for high levels of validity in SD logic research, it is important to acknowledge that there is always some degree of uncertainty. Validity is a continuous process and can never be achieved with complete certainty. However, by carefully considering the potential threats to validity and implementing strategies to mitigate them, researchers can increase the validity of their findings.

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