A deductive hypothesis is derived from a general theory and then tested with specific observations.
In Action Research, the researcher is actively involved in the setting or problem being studied, often to bring about practical change.
Studying the "impact" of CAI on achievement implies a cause-and-effect relationship, which is the primary goal of Experimental Research.
A correlation coefficient, such as Pearson's r, ranges from -1.00 to +1.00. Positive values indicate a direct relationship, while negative values indicate an inverse relationship.
A correlation coefficient near +1.00 signifies a strong positive linear relationship. This means as one variable increases, the other variable also tends to increase proportionally.
The independent variable is the factor manipulated or changed by the researcher. It is the 'cause' (C) that produces an effect, the 'treatment' (B) applied in an experiment, and it 'antecedes' (A) the outcome. Therefore, all listed options are correct synonyms for an independent variable.
The dependent variable is the outcome measured, which is presumed to be 'effected' (C) by the independent variable. While 'Consequent' (A) is also a synonym, 'Effect' is a more direct and widely used term in research. 'Behavioural' (B) is too specific, as dependent variables can be non-behavioral.
For correlational research, a sample size of 30 subjects (A) is generally considered the minimum acceptable. This size helps ensure statistical power and representativeness, allowing for reliable correlation analysis. Smaller samples risk unreliable results, while larger samples (B, C, D) are not the *minimal* requirement.
In a Cross-sectional survey (B), data is collected from a sample at a single point in time, providing a snapshot of a population's characteristics or attitudes. A Longitudinal Survey (A) collects data over an extended period, making it distinct from the single-point-in-time approach.
A Longitudinal Survey (A) involves collecting data from the same subjects repeatedly over an extended period. This method is specifically designed to measure growth, change, or development over time. A Cross-sectional survey (B) collects data at one point in time, which is not suitable for measuring change.
Instrumentation refers to inconsistencies or changes in the measuring tools or procedures during an experiment, which can compromise the reliability of the collected data.
Pre-experimental designs are characterized by a lack of random assignment and often no control group, offering minimal control over extraneous variables.
Quasi-experimental designs utilize pre-existing or naturally formed groups, lacking random assignment, which results in a less satisfactory degree of experimental control.
Quasi-experimental designs are employed when random assignment to treatment groups is not possible or practical, making them suitable for real-world settings.
True experimental designs utilize randomization to ensure equivalence between groups and control exposure to treatments, establishing cause-and-effect relationships effectively.
Action research typically has the least precision and control as it is conducted in real-world settings to solve immediate problems, often with limited rigor.
Basic research aims to expand fundamental knowledge. Animal models allow for highly controlled experiments and precise manipulation of variables, which is often ethically or practically impossible with human subjects, thus facilitating foundational scientific discoveries. Applied research focuses on practical problems.
Control (C) refers to the researcher's efforts to minimize or eliminate the influence of extraneous variables, ensuring that observed effects are solely due to the independent variable. Manipulation (A) is changing the independent variable, Replication (B) is repeating studies, and Randomization (D) is for group assignment.
Deductive reasoning involves starting with a general principle or theory and applying it to specific cases to reach a logical conclusion. Conversely, inductive reasoning moves from specific observations to broader generalizations. Data analysis is a process, and a hypothesis is a testable statement.
Manipulation (A) is the process of intentionally varying or introducing the independent variable to observe its effect on the dependent variable. This is a core component of experimental research. Control (C), Replication (B), and Randomization (D) are distinct research techniques, not the act of using an independent variable.
Rationality emphasizes that knowledge is primarily acquired through reason and logical thought, rather than solely through sensory experience or empirical observation. The scientific method combines both reason and experience, while tradition and sense perception rely on inherited beliefs and direct experience, respectively.
Replication (B) involves repeating a study, often through a number of smaller experiments, to confirm findings and enhance their reliability and generalizability across different contexts or samples. Manipulation (A), Control (C), and Randomization (D) are different experimental procedures.
The scientific method is a systematic, controlled, and critical process for investigating hypothetical propositions. It involves observation, hypothesis formation, experimentation, and analysis to test and refine understanding. Tradition, sense perception, and rationality are components or approaches to knowledge, not the full investigative process.
Randomization (D) is the process in which every member of a population has an equal chance of being selected for a sample or assigned to a group. This minimizes bias and helps ensure that groups are comparable, increasing the study's validity. The other options are different research concepts.
Secondary sources interpret, analyze, or summarize information from primary sources. Textbooks and encyclopedias synthesize existing knowledge from various original works, making them classic examples of secondary sources. Primary sources are original materials or direct evidence.